text: Masaryk University Faculty of Economics and Administration USE OF MODELLING FOR IMPROVEMENT OF A BUSINESS PROCESS Diploma work Thesis Supervisor: Ing. Ondřej ČÁSTEK, Ph.D Author: Fredy A. RODRIGUEZ B. Brno, 2017 2 MASARYK UNIVERSITY Faculty of Economics and Administration MASTER'S THESIS DESCRIPTION Academic year: 2016/2017 Student: Fredy Alberto Rodriguez Bautista Field of Study: Business Management (Eng.) Title of the thesis/dissertation: Use of modelling and simulation for improving a business process Title of the thesis in English: Use of modelling and simulation for improving a business process Thesis objective, procedure and methods used: Aim of the thesis: To improve a chosen business process Procedure and techniques used: The thesis will consist of two parts: Theoretical part will contain research of available methods and their evaluation with respect to the aim of the thesis solved in the second (practical) part of the thesis. The second part will consist of application of chosen method(s) onto a particular business process. This part will include analysis and economical assessment of proposed solutions. There is expected the use of process approach and these methods: observation, description, analysis, modeling, simulation and optimization. The use of the program Witness is an advantage. Extent of graphics-related work: According to thesis supervisor's instructions Extent of thesis without supplements: 60 – 80 pages Literature: LAGUNA, Manuel and Johan MARKLUND. Business Process Mode-ling,SimulationandDesign. : Prentice Hall, 2005. 429 s. JESTON, John and Johan NELIS. Managementbyprocess:aroadmap to sustainable business process management. 1. vyd. Amsterdam: Elsevier/Butterworth-Heinemann, 2008. 303 s. ISBN 0-7506-87614. MADISON, Dan. Processmapping,processimprovementandprocess management. : Paton Press, 2005. 313 s. Chico, California. ISBN 1932828-04-4. GREASLEY, Andrew. SimulationModellingBusiness. : Ashgate Publishing, Ltd., 2004. 226 s. 3 Thesis supervisor: Ing. Ondřej Částek, Ph.D. Thesis supervisor's department: Department of Corporate Economy Thesis assignment date: 2016/04/06 The deadline for the submission of Master's thesis and uploading it into IS can be found in the academic year calendar. In Brno, date: 2017/05/12 4 Abstract The objective of this thesis is to find a solution to the problems that currently occur in the production of coffee in the farm Villa Fran in Colombia. The production is divided into 4 macroprocesses which were analysed and we suggested solutions to improve the current situation in the company. While at the moment the profitability of the company is high, it is losing a lot of money by the bottlenecks, especially those found in the drying part. In the first chapter of this work we will go through the theories that will allow us to carry out the work successfully, having as main focus the study of BPM, the definitions necessary for the analysis and feedback cycle that will be improved every day, in addition we will see the life cycle of this system mentioning how we will apply it to our study. In the second chapter we study the simulations that we are going to carry out, we will understand the software that we are going to use and the way in which this work will be conducted, starting with understanding the problem, making an exhaustive study to collect the necessary data to model, a sketch of what you want to capture in the simulation, build it, verify it and start experimenting to find the necessary solutions. In the third part we will find a small introduction to the world of coffee in order to make the reader understand how the current process is since coffee is planted until it reaches its coffee rate. And finally we find all the above applied, after 6 models developed, we gave 5 solutions which were qualified and weighted to find the one with the greatest benefit to the coffee grower, winning the proposal that called to invest and grow in production. Key Words: Business process, business process management, business process modelling, business process improvement, Witness, coffee production. 5 6 Author's Statement I hereby declare that I worked out the Diploma work, Use of modelling for improvement of a business process, myself, under the supervision of Ing. Ondřej Částek, Ph.D and that I stated in it all the literary resources and other specialist sources used according to legislation, internal regulations of Masaryk University and internal management acts of Masaryk University and the Faculty of Economics and Administration. Brno, 12.04.2017 Fredy A. Rodriguez Bautista 7 8 Acknowledgements First of all, I am grateful to God for the opportunity to study at this prestigious university. I would like to express my sincere gratitude especially to my supervisor Ing. Ondřej Částek, Ph.D for his continuous support, patience, motivation and immense knowledge. His guidance helped me all the time during the analysis and writing of this thesis. I am also thankful to all the teachers who shared their knowledge and advice during lessons, for their assistance, dedicated involvement and willingness during whole 2 years. 9 10 Special Dedication I am very fired up with the Czech government for allowing people from different parts of the world to study at their universities, thus enriching their culture. Also a special mention to my parents and farm workers Villa Fran, who gave me all the support to develop this work. 11 12 Table of Contents INTRODUCTION ..................................................................................................................................15 THEORETICAL BACKGROUND ......................................................................................................19 1. BUSINESS PROCESS MANAGEMENT ..................................................................................21 1.1 BPM FOUNDATION .................................................................................................. 23 1.2 BPM CONTRIBUTION ............................................................................................... 24 1.3 DEFINITION OF TERMS USED IN BPM ...................................................................... 25 1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.3.6 1.3.7 1.3.8 1.4 1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 2. Achievement ........................................................................................................ 25 Organization ....................................................................................................... 26 Objectives ............................................................................................................ 26 Improvement ....................................................................................................... 26 Management ........................................................................................................ 26 Control ................................................................................................................ 27 Essential processes ............................................................................................. 27 Processes ............................................................................................................. 27 BUSINESS PROCESS MANAGEMENT LIFECYCLE ...................................................... 28 Design phase: ...................................................................................................... 28 Modeling phase ................................................................................................... 29 Execution phase .................................................................................................. 29 Monitoring phase ................................................................................................ 29 Optimization phase ............................................................................................. 29 SIMULATIONS PROJECTS ......................................................................................................31 2.1 COMPUTING SIMULATION ....................................................................................... 32 2.2 CONDUCTING A SIMULATION PROJECT ................................................................... 33 2.2.1 Identify the Problem ............................................................................................ 33 126.96.36.199 Establish Objectives ........................................................................................... 33 126.96.36.199 Scope and Level of Model Detail ........................................................................ 34 2.2.2 Design the study .................................................................................................. 34 2.2.3 Design the conceptual Model.............................................................................. 34 2.2.4 Formulate inputs, Assumptions and Process definitions .................................... 35 2.2.5 Build, Verify and Validate the model .................................................................. 37 22.214.171.124 Verification and validation ................................................................................. 37 188.8.131.52 Running the Model .............................................................................................. 38 2.2.6 Experimentation and Optimization ..................................................................... 39 2.2.7 Presentation of Results and Implementation ...................................................... 39 2.3 SIMULATION ............................................................................................................ 40 2.3.1 2.3.2 2.3.3 Visual Interactive Simulation .............................................................................. 40 Discrete Event Simulation ................................................................................... 40 Continuous Simulations ...................................................................................... 40 13 2.3.4 2.3.5 2.3.6 2.3.7 3. Differences Between Continuous and Discrete Simulations. .............................. 41 Hybrid Simulation ............................................................................................... 41 Simulation Software ............................................................................................ 41 Witness simulation software ............................................................................... 42 A BRIEF HISTORY ABOUT COFFEE. ...................................................................................43 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 Coffee Production ............................................................................................... 44 Planting ............................................................................................................... 45 Harvesting the Cherries ...................................................................................... 45 Processing the Cherries ...................................................................................... 46 Drying the Beans ................................................................................................. 47 Milling the Beans ................................................................................................ 47 CASE OF STUDY ..................................................................................................................................49 4. USE OF MODELLING FOR IMPROVEMENT OF A BUSINESS PROCESS IN A COFFEE FARM. ................................................................................................................................................49 4.1 DEFINE THE PROBLEM ............................................................................................. 49 4.1.1 Objectives of the study ........................................................................................ 49 4.1.2 Issues to be addressed ......................................................................................... 49 4.1.3 Domain of the study ............................................................................................ 50 4.1.4 Level of detail ...................................................................................................... 50 4.1.5 The need to model the process ............................................................................ 50 4.2 DESIGN THE STUDY.................................................................................................. 50 4.2.1 Model assumptions.............................................................................................. 50 4.2.2 Number of models Required ................................................................................ 51 4.2.3 Animation, tool and model requested ................................................................. 51 4.2.4 Business process Description ............................................................................. 52 4.2.5 Start-up conditions and model run length .......................................................... 54 4.2.6 Model input data ................................................................................................. 54 4.2.7 Model output ....................................................................................................... 54 4.3 BUILD, VERIFY, VALIDATE AND ANALYSIS THE MODEL .......................................... 55 4.3.1 Model verification and validation ....................................................................... 55 4.3.2 Current situation of the model ............................................................................ 57 4.3.3 Conclusions of the model and the base simulation. ............................................ 58 4.4 EXPERIMENTS WITH THE MODEL AND OPTIMIZATION ............................................. 60 4.4.1 Base system + a new drying machine ................................................................. 60 4.4.2 Base Model + 1 Solar Dryer ............................................................................... 64 4.4.3 Base model + Reduction in production .............................................................. 66 4.4.4 Base Model redistribution ................................................................................... 68 4.5 RESULTS AND CONCLUSIONS ................................................................................... 70 CONCLUSION .......................................................................................................................................71 LIST OF APPENDICES ........................................................................................................................73 APPENDIX A BASIC ELEMENTS ON WITNESS ..................................................................... 75 APPENDIX B PRODUCTION ACCORDING WITH THE MODEL .................................................. 79 14 WORKS CITED .....................................................................................................................................81 TABLES ..................................................................................................................................................85 FIGURES ................................................................................................................................................86 ABBREVIATIONS.................................................................................................................................87 15 16 Introduction It is well known to everyone that one of the most important products of Colombia is coffee, this has been a great source of foreign exchange earnings for the country and source of employment for many Colombians in rural areas. Although it is a profitable business, its level of technology and industrialization is very low, resulting in low yields, loss of product quality due to delays in some crucial processes or production interruptions. On the other hand, we have the Business Process Management, a system focused on improving and refining any process developed to produce a good or provide a service, through data collection and implementation of simulations help find the key points in the production chain and the bottlenecks that are currently generated in it. The objective of this thesis is take and combine this system of improvements and apply them to the production of coffee, will be taken from the grain is harvested until the product is ready for commercialization, finding the bottlenecks that are currently being generated, analyse and give a range of solutions that help improve productivity. All the data is taken from "Villa Fran Farm", located in the municipality of Confines, in the department of Santander. The company employs a workforce close to 30 people, uses 2 solar dryers, a skin remove machine, a washing area, an auxiliary drying machine and a packing machine. Its level of modernization is in the average of the region (medium producers) and the production is close to 40 tons of coffee (dry unroasted) quarterly. The main software we will use is Witness, a powerful simulation tool that fed with all data collection, processing time and drying, among other factors, will allow us to clarify faults that arise, bottlenecks and modify convenience until a possible solution is found. I am very pleasant to work on this topic, because it is a field that I know a lot, although it is not a great manufacturing, I have found many critical processes that can be improved, besides it is a family business that probably assumes in the near future. Another factor that draws attention is that the same simulation can be applied to other local crops with great prospects such as sugarcane (where biofuel is extracted) and cocoa. 17 18 Theoretical Background This chapter is very interesting, here we will choose the theories to be used and how we will apply them to achieve the objectives set to improve the efficiency and quality of coffee production. Although an agro-industrial company is not far from the processes used in large manufactures, we will take the concepts, several examples and take them to the crops, farmers, and their traditional methods. An elaboration of the various theoretical topics involved in our research will be provided in this chapter. The topics we will discuss are business process management, business process modelling, Witness and others simulations systems, Technical languages conceptual modelling, abstraction, design principles. 19 20 1. Business Process Management Business process management (BPM) is a systematic approach towards the definition, execution, management and refinement of business processes. A business process is a collection of activities or tasks that produce a specific service or product, generally involving both human interaction and computer applications. (Panagacos, 2012) For this research, we define Business Process Management as following: Table 1 Business Process Management Definitions Source (Elias, 2015) These concepts are adapting to all the processes that we are going to analyse in this work, takes into account the human factor (very important in coffee production), as well as other sources of information as external factors, for example, the increasing frequency of certain climate and meteorological phenomena, the lack of labour in rural areas for harvesting, and other delays that the artisan processes generate and affect the productivity and quality of the final product. The business process has been marked by a shift from the view of organizations as a collection of departments with separate functions and outputs, to a view of them as systems of interlinked processes that cross functions and link organizational activities (James W. Dean, 1994). 21 Processes are collections of activities that, taken together, produce outputs for customers (Garvin, 1988). Customers include not only external consumers of the organization's products or services, but also a series of internal recipients at linkage points between processes, as outputs from upstream processes become the inputs for subsequent processes. Although programs and awards like TQM, ISO 9000, Six Sigma, the Malcolm Baldrige Award, and Business Process Management differ in scope and approach, they share a core focus on measuring, improving, and rationalizing organizational processes (Harrington, 1997) (Lai, 2013) (Mary J. Benner, 2001) Examples of BPM areas where remarkable progress has been made include: • The syntactic verification of complex business process models before putting them into production, to avoid potentially costly mistakes. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) • The systematic identification of typical process behaviors based on scientific insights provided by the Workflow Patterns initiative. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) • The automatic creation of configurable process models from a collection of process model variants, used to guide analysts in selecting the right configuration. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) • The automatic execution of business process models based on a rigorously- defined semantics, and through a variety of BPM systems. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) • The adaptation of processes on-the-fly and the evaluation of the impact of their changes, in order to react to (unexpected) exceptions. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) • The automatic discovery of process models from raw event data produced by common information systems found in organizations. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) Technological innovation is a central engine of organizational adaptation. As such, to understand how process management techniques affect organizational adaptation (Mary J. Benner, 2001), we first address how it affects exploratory as well as exploitative innovation. A firm's ability to innovate provides its senior team with options to either reinforce or destabilize a technological regime (Burgelman, 1983) (McGrath, 2007). Innovations generated within or absorbed by firms provide variation to proactively shape or reactively respond to technological transitions (Mary J. Benner, 2001) An organization's dynamic capabilities depend on simultaneously exploiting current technologies and resources to secure efficiency benefits and on creating variation through exploratory innovation (Nelis, 2008) As process management techniques focus on continuous improvement in routines and variation reduction their increased utilization in an organization affects the balance between exploratory and exploitative innovation. (Garvin, 1988). 22 1.1 BPM Foundation Business process management (BPM) is rooted in both management science and computer science as a result, it is difficult to pinpoint its starting point. Business Process Management (BPM) has various roots in both computer science and management science. Therefore, it is difficult to pinpoint the starting point of BPM. Since the industrial revolution, productivity has been increasing because of technical innovations, improvements in the organization of work, and the use of information technology. Adam Smith (1723–1790) showed the advantages of the division of labour. Frederick Taylor (1856–1915) introduced the initial principles of scientific management. Henry Ford (1863–1947) introduced the production line for the mass production of "black T-Fords." It is easy to see that these ideas are used in today's BPM systems. (Aalst W. M., 2013). In the last century many process modeling techniques have been proposed. In fact, the wellknown Turing machine described by Alan Turing (1912–1954) can be viewed as a process model. It was instrumental in showing that many questions in computer science are undecidable. Moreover, it added a data component (the tape) to earlier transition systems. Petri nets play an even more prominent role in BPM as they are graphical and able to model concurrency. In fact, most of the contemporary BPM notations and systems use token-based semantics adopted from Petri nets. Petri nets were proposed by Carl Adam Petri (1926–2010) in 1962. This was the first formalism treating concurrency as a first-class citizen. Concurrency is very important as in business processes many things may happen in parallel. Many cases may be handled at the same time and even within a case there may be various enabled or concurrently running activities. Therefore, a BPM system should support concurrency natively. (Aalst W. M., 2013) A good starting point for establishing the road to BPM is from a management concept referred to as business process re-engineering (BPR) In this concept a new way of organizing companies on the basis of business processes was proposed. Following their campaign for the radical redesign of business processes, many companies initiated BPR projects to review and redesign their processes. However, in the late 1990s, the interest in BPR deteriorated and many enterprises terminated their BPR projects and stopped supporting further BPR initiatives. The emergence of BPM can be seen from two key renewed ideas behind BPR (Dumas, 2013). The first is the revelations from the empirical studies, which showed that process-oriented organizations did better than non-process-oriented organizations (Dumas, 2013). As a result of the confirmation of this picture by the follow-up studies, the credibility of the process-oriented concept was renewed. (Elias, 2015) 23 Figure 1 Historic view on information systems' development illustrating that BPM systems can be used to push "process logic" out of the application Source (Aalst W. M., Process Mining Data Science in Action, 2016; Aalst W. M., 2013) The second is the technological development. Following BPR in the late 1990s, enterprise resource planning (ERP) systems and workflow management (WFM) systems gained organizational focus ERP is a business management software, usually a suite of integrated applications, used for managing the business and automating the back office functions related to services, technology and human resources. It integrates all components of an operation, ranging from product planning, development and manufacturing to marketing and sales. On the other hand, WFM systems are systems that distribute work to various actors in a company on the basis of process models (Dumas, 2013) (Elias, 2015) BPM can be perceived as an extension of workflow management (WFM). While the primary focus of WFM is to automate business processes, the scope of BPM is broader – ranging from process automation and analysis to organization of work and operations management. On the one hand, BPM is aimed at improving business processes, perhaps without the use of technologies (Elias, 2015) For example, management may get ideas on how to reduce costs by modelling and analysing a business process using simulation. 1.2 BPM Contribution One promising direction to better link BPM to the concrete improvement of process KPIs lies in exploiting event data present in the organization. For example, Six Sigma (PYZDEK, 2003) has for long applied statistical analysis tools to organizational data in order to measure and reduce the degree of business process variability. The idea is to identify and remove the causes for such variability, e.g. in terms of errors, defects or SLA violations in the output of business processes, and to control that such processes effectively perform within the desired performance targets (e.g. ensuring that there are no more than 10 SLAs per month). However, while Six Sigma is focused on improving business processes by statistically quantifying process performance changes, the data used for such analyses is typically collected manually, e.g. through surveys or observation. This makes the employment of such techniques, when carried 24 out properly, very costly and time consuming. Moreover, Six Sigma rarely looks inside end-toend processes. The focus is on a specific step in the process or on aggregate measures. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) This problem can be obviated through the use of techniques that automatically extract process knowledge from event data logged by common information systems, e.g. ERP or ticketing systems. In this context, process mining (Aalst W. M., Process Mining Data Science in Action, 2016) has emerged as a range of methods and tools for exploiting such data to automatically discover a process model, or check its compliance with existing reference models or norms, or to determine the causes for process deviations or variants. The advantage of relying on logged data as opposed to data that has been collected manually is that any insight extracted from this data is based on evidence, rather than on human confidence, and thus is a more accurate representation of reality. Moreover, the artefacts extracted through process mining, e.g. process models, can be enhanced with (live) process performance information such as statistics on activity duration and resource utilization. This allows organizations to look inside end-to-end processes. For these reasons, process mining methods are now being used across all phases of the BPM lifecycle, from discovery through to monitoring. However, while a wide range of techniques have been developed in this field, the research community has mostly devoted its attention to the quality of the artefacts produced (e.g. the accuracy of the process models extracted from the logs), rather than to improving the actual processes for which such logs are available. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) Therefore, a possible research direction is to bridge the current gap between process mining and Six Sigma. For instance, process mining techniques could be used to extract detailed and accurate process performance measurements (e.g. in the form of process models enhanced with performance statistics) on top of which Six Sigma techniques could be applied to pinpoint causes for variability, and to identify and evaluate the impact of different process changes on the process KPIs. (Aalst W. M., Business Process Management Don't forget to improve the process, 2004) Another avenue to obtain better processes consists in applying techniques from Operations Research to the realm of business processes. Operations Research (OR) is a well-established research area that aims to solve complex decision-making problems by employing a variety of mathematical techniques, such as simulation, queuing theory, optimization and statistics. Many process improvement problems can in fact be traced back into typical problems investigated by OR, since there are typically a number of constraints and options making it hard to find optimal solutions. In a way, the goal is to optimize a process according to given KPIs (typically time and resources usage). 1.3 Definition of terms used in BPM 1.3.1 Achievement Realizing the strategic objectives as outlined in the organization's strategic plan. At a project level, it is about realizing the value or business benefits as outlined in the project business case. (Nelis, 2008) Apply the BPM model in order to reach a mayor for the production of coffee in the analysed farm, and to be precedent to be applied in any other agro-industry process. 25 1.3.2 Organization The organization in this context refers to an enterprise or parts of an enterprise, perhaps a business unit that is discrete in its own right. It is the end-to-end business processes associated with this part of an organization. This end-to-end focus will ensure that a silo approach does not develop. (Nelis, 2008) The organization in this context is the productive unit (Labour force, Machines and crops) that harvests coffee, and transforms the grain to be ready for commercialization and subsequent export. 1.3.3 Objectives The objectives of a BPM implementation range from the strategic goals of the organization through to the individual process goals. It is about achieving the business outcomes or objectives. BPM is not an objective in itself, but rather a means to achieving an objective. It is not 'a solution looking for a problem'. (Nelis, 2008) 1.3.4 Improvement Improvement is about making the business processes more efficient and effective. (Nelis, 2008). Which processes should we improve? We are facing a process that has been doing the same way for many years, the lack of improvements in the processes that go from the collection of coffee and its various traditional techniques have affected productivity and efficiency, here it is important to look to other regions (Brazil or Vietnam, for example) where improvements in their processes have led to their being the first and second largest producer in the world, displacing Colombia to the third place. 1.3.5 Management Management refers to the process and people performance measurement and management. It is about organizing all the essential components and subcomponents for your processes. By this we mean arranging the people, their skills, motivation, performance measures, rewards, the processes themselves and the structure and systems necessary to support a process. (Nelis, 2008) In reference to this process will be measured and managed the ideal performance to be achieved by each person who is harvesting the grain (process that uses most of the labour) and other links in the production chain. This point will be the central axis of the practical part of this work. Proponents of process management have long cited the expected benefits of these practices. Non-value-added activities are removed as processes are streamlined, resulting in reduced costs and efficiency improvements in the form of increased yields and less rework and waste. Tighter intra- organizational linkages increase efficiency by streamlining the handoffs between activities, and speeding development and delivery times (Garvin, 1988) (Dean, 2006). Further, these products are likely to better satisfy customers, leading to increases in revenues. Ultimately, as revenues increase and costs decrease, profits are expected to improve. (Mary J. Benner, 2001) 26 1.3.6 Control BPM is about managing your end-to-end business processes and involves the full cycle of plan– do–check–act (Deming circle, Walton, 1986). An essential component of control is to have the ability to measure correctly. If you cannot measure something, you cannot control and manage it. (Burlton, 2001) The simulation should be done with as many facts as possible to be taken into account in the production process, it will include climatic factors (which directly affects the drying of the product) as well as the labour shortages that are generated in Some seasons, the difficulty of the ground to reach the collection center, usually damages to the machines, etc. 1.3.7 Essential processes Process management is composed of three main practices: mapping processes, improving processes, and adhering to systems of improved processes. Once underlying processes have been recorded through process mapping, process improvement involves developing measures of how well a process meets customer requirements, and using statistical methods to continually eliminate variation in processes and outputs (Wageman, 1995) (Mikel J. Harry, 2006). Process improvement not only involves rationalizing individual work processes, but also streamlining the handoffs between processes (Harrington, 1997) (Garvin, 1988). Organizational participants are trained in effective ways to facilitate cross-functional team meetings and learn standard approaches for identifying and solving problems. These tools, by design and intent, help integrate and coordinate a broad set of activities throughout the organization (Repenning, 1999) (Mary J. Benner, 2001) The last element of process management is adherence to processes that have been mapped and improved. This ensures that processes are repeatable, allowing for ongoing incremental improvement, and the realization of benefits of improvement efforts. As the one responsible for managing resources - funding, facilities, and personnel – the producer makes a significant commitment to the improvement process and wants to ensure that this investment is worthwhile. One way to do this is to focus on improving essential processes - those which are key to the functioning, products, or services of the work unit. Not every process in an organization contributes towards the achievement of the organization's strategic objectives. Essential processes are the ones that do (Burlton, 2001). 1.3.8 Processes What is a process? There are as many definitions of process as there are processes. One we agree with is Roger Burlton's, where he says that 'a true process comprises all the things we do to provide someone who cares with what they expect to receive' (Burlton, 2001). This covers a true end-to-end process, from the original trigger for the process to the ultimate stakeholder satisfaction. Burlton adds that the 'final test of a process's completeness is whether the process delivers a clear product or service to an external stakeholder or another internal process'. (Nelis, 2008) 27 1.4 Business Process Management Lifecycle Like all processes, we have to take into account their development phases, from the beginning until they reach a level of maturity that allows the system to be feedback and improve continuously, with this project starts from zero, we found some studies in the field of the coffee, but there are few and are not extendable to all farms, either by location, availability of labour, level of modernization and a long etcetera. Then we will see each of its stages and how they relate to our work, its importance and the necessary way that has been done to get there. All BPM activities can be attributed to one of the five phases of the BPM lifecycle: 1 Figure 2 BPM lifecycle Source (Dumas, 2013) 1.4.1 Design phase: Capture the business processes at a high level, typical information required for the identification of business processes are tasks, quantifiable deliverables (e.g. documents), responsibilities, computer systems and required resources. 1 The five phases of the BPM life-cycle shown in figure 2 form a never-ending loop, known as the continuous process improvement cycle. 28 Gather just enough detail to understand conceptually how the process works and concentrate on ensuring the high level detail is correct without being distracted by the detail of how it's going to be implemented. 1.4.2 Modeling phase During which the information gathered during the design phase is made explicit in a business process model. These models are usually created with elaborate modelling tools, using a standard for business process modelling, such as the Business Process Modelling Notation (BPMN) and subsequently stored in a so-called modelling repository. (Aguilar-Saven, 2003) 1.4.3 Execution phase During which computer applications are deployed to support the automation of the business process. In traditional organizations, there are many computer applications that each perform a specific task. These applications are not unaware of each other's existence nor are they aware of the context in which they are used. Business process orchestration attempts to connect these applications by a main application, which is aware of the business process [6]. In addition, more recent developments of BPM technology aim to use the business process model itself as a basis for automation. By formalizing the business process model, it can be interpreted and subsequently executed by a so-called business process engine. This approach allows computers to be aware of the actual business process as it is executed, which in turn enables a variety of advantages to the business. (Aguilar-Saven, 2003) 1.4.4 Monitoring phase During which the performance of the implemented business processes is measured. The depth of the analysis depends on what BPM technologies were implemented during the execution phase. When a feature rich BPMS is used, one can measure process performance both at the global level and at the instance level. This information can subsequently be aggregated and displayed in comprehensive monitoring dashboards, which give managers quantifiable real-life data of the performance of their business. When this degree of rigor is employed during the monitoring phase, the act of monitoring becomes a goal by itself, which is also known as business activity monitoring. (Aguilar-Saven, 2003) 1.4.5 Optimization phase During which business processes are optimized based on the findings of the monitoring phase. These optimizations may lead to the redesign of existing business processes or the design of additional business processes. As a result, the optimization phase can be the initiator of a new design phase, thus completing the BPM life-cycle. (Aguilar-Saven, 2003) 29 30 2. Simulations Projects Simulating is to use a model to calculate and provide a certain output variable in order to produce similar evolutions in the presence of identical stimuli. In a simulation it is necessary to know the value of the inputs (manipulability or not), since, what is tried to calculate is the value of the outputs that depend on them. To perform the simulation, a model, an initial situation and the value of the inputs are provided at each subsequent time point (Arahal M., 2006) The industry is forced to build costly pilot plants to simulate and test the behaviour of new processes to later apply the knowledge acquired in the actual plant. This simulation is often complicated and costly, and sometimes imprecise due to changes in scale between the pilot plant and the actual plant; Hence the need to develop computer simulations of industrial plants. Simulation is a working tool that was developed in parallel with the appearance of the computer and that, little by little, has been imposed thanks to the greater speed and capacity that computers have been offering to the industry. Simulation is one method of determining the feasibility and efficiency of the proposed redesigned process options. Simulation can also be used to test the logic and consistency of processes before their implementation. It requires a significant amount of effort, and should not be underestimated or undertaken lightly. There will be a need to gather the necessary metrics and assumptions to run the simulations. Simulation is a good method to test the various scenarios. (Nelis, 2008). Having in our hands a tool like the simulation, we will question all possible scenarios that may arise, such as rains that do not allow the correct drying in the sun, labour shortages, breakdowns in crucial machines and the Impact its repair has on production. This multiplicity of scenarios allows us to anticipate any problem and make better decisions based on simulated results. (Banks, 1998) The simulated 'runs' should then be evaluated and, ultimately, activity based costing and capacity planning estimates completed. This will assist in the determination of performance measurement for the process options. (Nelis, 2008) 31 2.1 Computing Simulation A computer simulation is a program that is run on a computer and that uses step-by-step methods to explore the approximate behaviour of a mathematical model. Usually this is a model of a real-world system (although the system in question might be an imaginary or hypothetical one). Such a computer program is a computer simulation model. One run of the program on the computer is a computer simulation of the system. The algorithm takes as its input a specification of the system's state (the value of all of its variables) at some time t. It then calculates the system's state at time t+1. From the values characterizing that second state, it then calculates the system's state at time t+2, and so on. When run on a computer, the algorithm thus produces a numerical picture of the evolution of the system's state, as it is conceptualized in the model. (Zalta, 2015) This sequence of values for the model variables can be saved as a large collection of "data" and is often viewed on a computer screen using methods of visualization. Often, but certainly not always, the methods of visualization are designed to mimic the output of some scientific instrument—so that the simulation appears to be measuring a system of interest. (Waller, 2012) Two types of computer simulation are often distinguished: equation-based simulations (Equation-based simulations are most commonly used in the physical sciences and other sciences where there is governing theory that can guide the construction of mathematical models based on differential equations (Zalta, 2015)) and agent-based simulations (These are most common in the social and behavioural sciences, though we also find them in such disciplines as artificial life, epidemiology, ecology, and any discipline in which the networked interaction of many individuals is being studied (Zalta, 2015)). Computer Simulations of both types are used for three different general sorts of purposes: prediction (both pointwise and global/qualitative), understanding, and exploratory or heuristic purposes. (Zalta, 2015) 32 2.2 Conducting a Simulation Project Projects that involve simulation have several unique aspects which must be managed particularly carefully to ensure their success. The topics in this chapter outline a typical sequence of events in a project, using a practical methodology: 6Figure 3 Systematic simulation approach. Source. AI-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc. 2.2.1 Identify the Problem Any simulation study should start by defining the problem to be solved and improvement opportunity to be explored. System design challenges, operational problems, and improvement opportunities are the main categories of simulation problems. The simulation problem is defined in terms of study scope, study objectives, and model assumptions. The study scope includes a clear description of the system problem or opportunity, the overall goal of simulation, the challenges, limitations, and issues with the current state. It starts by describing the system structure, logic, and functionality. The problem can be structured using a schematic diagram or process map to make it easy for the analyst to understand different aspects of the problem. The problem statement includes a description of the situation or the system of the study and the problem that needs to be solved. Formulating the problem in terms of an overall goal and a set of constraints provides a better representation of the problem statement. A thorough understanding of the elements and structure of the system under study often helps in developing the problem statement. (Raid Al-Aomar, 2015) 220.127.116.11 Establish Objectives This is the first and most important phase of any simulation project. The aim of any simulation project should be to make a better business decision. You, as simulation modeller, must 33 understand this business decision as it is likely to have important implications for the content of your simulation model. (Lanner Group Ltd, 2009) The current objectives are to improve production, efficiency in the use of resources (especially human capital) and, therefore, the quality of the grain; Through the use of simulations we will find on paper the possible solutions to the problems that affect the production of coffee today. 188.8.131.52 Scope and Level of Model Detail The scope of a simulation model refers to where it begins and where it ends. It is important to limit the scope of the model as far as possible. With regard to the level of detail contained within a model, the golden rule is to model the minimum necessary in order to achieve the model's objective. (Lanner Group Ltd, 2009) Our simulation starts from the time the coffee beans are harvested until the product is packed and ready for transport and subsequent export, are the processes on which we have direct action and which can impact the production, in addition they are the ideal ones to measure and take corrective action if necessary as specified in the results of such simulations. 2.2.2 Design the study Based on the problem formulation, a set of objectives can be assigned to the simulation study. Such objectives represent the criteria through which the overall goal of the study is achieved. Study objectives simply indicate questions that should be answered by the simulation study. Examples include testing design alternatives for plant operations, studying the effects of using trucks rather than rail system in a supply chain and so on. Specifying study objectives serves various purposes. First. we can decide if simulation is the right tool to solve the underlying problem. Do we have enough data to determine metrics for the defined objectives? Can we use analytical methods to answer the questions raised? Is the software tool capable of presenting and analyzing study requirements so that the study objectives are achieved? Such questions can be better answered by unambiguously stating the objectives of the simulation study. (Raid Al-Aomar, 2015) 2.2.3 Design the conceptual Model An important final step before building the simulation model is to structure it. This will identify the most difficult areas for the model building and highlight any additional data requirements that may have been overlooked up to now, such as a transfer time for parts between processes. (Lanner Group Ltd, 2009) Developing a conceptual model is the process through which the modeler abstracts the structure, functionality, and essential features of the real-world system into a structural and logical representation that is transferable into a simulation model. The model concept can be a simple or complex graphical representation such as a block diagram, a flowchart, or a process map that depicts key characteristics of the simulated system such as inputs, elements, parameters, logic, flow, and outputs. The conceptual model should be eventually programmable and transferable into a simulation model using available simulation software tools. Thus, a successful model concept is one that takes into consideration the method of transferring each abstracted 34 characteristics, building each model element, and programming the conceptual logic using the software tool. (Raid Al-Aomar, 2015) The model concept is developed taking into consideration the formulated problem and the objectives of the simulation study. Study objective is the main factor that facilitates the structure of the conceptual model and the level of detail to be used in the model. The art of conceptual modeling also requires system knowledge and model building skills. The modeler starts by establishing a thorough understanding of the simulated system whether it is a new system or an existing one. The modeler studies system inputs, elements, structure, logic, flow, and outputs and abstracts the overall structure and the interrelationships of structure elements into a conceptual model. (Raid Al-Aomar, 2015) 2.2.4 Formulate inputs, Assumptions and Process definitions Simulation models are data-driven computer programs that receive input data, execute a designed logic, and produce certain outputs. Hence, data collection step is a key component of any simulation study. Simulation data can be, however, collected in parallel to building the model using the simulation software. This is recommended since data collection may be timeconsuming in some cases and building the model structure and designing model logic can be independent of model data. Default parameters and generic data can be used initially until the system data is collected. The quality of data used in the model drives the overall model quality and validity. It also impacts the accuracy of the model results and collected statistics. Data elements required for constructing a simulation model are often determined based on the model concept, model structure, and the nature and type of model outcomes and statistics to be collected. For example, in our model the main input is the coffee beans that arrive every day and are unloaded into the tank before being processed by the milling machine. Depending on the nature of the simulation study, model data is collected by reviewing historical data, observing and monitoring system operations, or using benchmark data assumptions. (Raid Al-Aomar, 2015) Figure 4 Collecting Model data Source (Raid Al-Aomar, 2015) 35 Data collection approaches for qualitative research usually involves: • Direct interaction with individuals on a one to one basis • Or direct interaction with individuals in a group setting Qualitative research data collection methods are time consuming, therefore data is usually collected from a smaller sample than would be the case for quantitative approaches - therefore this makes qualitative research more expensive. The benefits of the qualitative approach are that the information is richer and has a deeper insight into the phenomenon under study The main methods for collecting qualitative data are: Individual interviews: Qualitative interviews should be fairly informal and participants feel they are taking part in a conversation or discussion rather than in a formal question and answer situation. There is skill required and involved in successful qualitative research approaches - which requires careful consideration and planning. A good quality qualitative research involves Thought, Preparation, the development of the interview schedule, Conducting and analysing the interview data with care and consideration, (University of Leicester, 2015) Focus groups: The use of focus groups is sometimes used when it is better to obtain information from a group rather than individuals. Group interviews can be used when Limited resources (time, manpower, finances), The phenomena being researched requires a collective discussion in order to understand the circumstances, behaviour or opinions and Greater insights may be developed of the group dynamic - or cause and consequence. (University of Leicester, 2015) Observations: Observation involves may take place in natural settings and involve the researcher taking lengthy and descriptive notes of what is happening. It is argued that there are limits to the situations that can be observed in their 'natural' settings and that the presence of the research may lead to problems with validity. (University of Leicester, 2015) Action Research: doesn't just involve asking about it, it involves doing it. Action Research is a framework that is Collaborative, there is a practical intervention made - i.e. you do something to make a change or intervention in a situation that you research and The researcher will be actively involved in the planned intervention Information for a model is likely to fall into one of three categories: • Available - data is readily available and it is in an appropriate format that the model can use immediately. • Not available but collectable - data is either in an incorrect format or it has not been collated before. You might need to perform a small work study in order to collect this type of data (for example, timing certain processes manually). (Lanner Group Ltd, 2009) • Neither available nor collectable - data is not currently available and it is not easily collectable (for example, for a model of a new factory on a green-field site with new machinery). If the data is neither available nor collectable, you must use estimates. (Lanner Group Ltd, 2009) 36 We will take the majority of data through observation, we will take the average harvesting time, transport, data of the efficiencies of the machines, the average time of drying, among others explained with more precision in the practical part of this work. 2.2.5 Build, Verify and Validate the model It is recommended that you build the model incrementally, and that you test each stage thoroughly before you build the next stage. If you do this, it is easier to find problems in a model than if you have to search through an entire model. This ability to build a model incrementally, testing each section as you go, is a powerful aid to productivity, and generates confidence in the validity of your model. (Lanner Group Ltd, 2009) Data collection and model building often consume the majority of the time required for completing a simulation study. To reduce this time, the modeller should start building the simulation model while data is being collected. The conceptual model can be used to construct the computer model using assumed data until the collected data become available. The overlap between model building and data collection does not impact the logical sequence of the simulation procedure. Constructing model components, flow of entities, and logic depends mostly on the model concept and in most cases is independent of model data. Once the model is ready, model input data and parameter settings can then be inserted into the model. Also, since a large portion of a simulation study is often spent in collecting model data, building the model simultaneously reduces significantly the overall duration of the simulation study and provides more time for model analysis and experimentation. (Raid Al-Aomar, 2015) There is no standard procedure for building a simulation model. The procedure is often based on the modeller's approach and on the simulation software tool used. 188.8.131.52 Verification and validation Model verification is the quality control check that is applied to the built simulation model. Like any other computer program, the simulation model should perform based on the intended logical design used in building the model. Although model logic can be defined using different methods and can be implemented using different programming techniques, the execution of the logic when running the model should reflect the way the programmer or the modeller has initially designed. Different methods are, therefore, used for debugging logical (programming) errors as well as errors in inputting data and setting model parameters. Corrected potential code and data discrepancies should always be verified by carefully observing the changes in model behaviour. (Raid Al-Aomar, 2015) Verification is the task of determining if the implementation of a model has been done correctly, ensures that the content of the model is consistent with your expectations. this means that verification data needs to be generated at various points in the model for comparison with expected values. A good example is the output of mean interarrival times at points matching those from stability analysis. Where differences are substantial, in all likelihood, part of the model implementation has an error. For example, establish that the parts are travelling along the correct routes between elements and that any labour used is attending to the correct elements in the correct priority order. (Lanner Group Ltd, 2009) 37 Model validation is the process of checking the accuracy of the built model representation to the simulated real-world system. It is simply about answering the following question: does the model behave similarly to the simulated system? Since the model will be used to replace the actual system in experimental design and performance analysis, can we rely in its representation of the actual system? (Raid Al-Aomar, 2015) Validation (which usually follows verification) investigates the accuracy of the model compared with the real world. A typical validation exercise might involve providing a typical set of inputs (for example, a part arrival and production schedule) and studying a set of model outputs (for example, the average level of work-in-progress for a part, or part throughput times). (Lanner Group Ltd, 2009) 220.127.116.11 Running the Model After defining, displaying and detailing the elements of your model, you can run it immediately, then modify it by adding, changing or deleting elements. You can then run the model again in order to assess the impact of these changes. 38 2.2.6 Experimentation and Optimization Table 2 Experimentation and optimization Model Validation Method Comparison to Other Models Face Validity Historical Data Validation Parameter Variability – Sensitivity Analysis Predictive Validation Description Various results (e.g., outputs) of the simulation model being validated are compared to results of other (valid) models. For example, (1) simple cases of a simulation model are compared to known results of analytic models and (2) the simulation model is compared to other simulation models that have been validated. Asking individuals knowledgeable about the system whether the model and/or its behaviour are reasonable. For example, is the logic in the conceptual model correct and are the model's input-output relationships reasonable? If historical data exist (e.g., data collected on a system specifically for building and testing a model), part of the data are used to build the model and the remaining data are used to determine (test) whether the model behaves as the system does. This technique consists of changing the values of the input and internal parameters of a model to determine the effect on the model's behaviour of output. The same relations should occur in the model as in the real system. This technique can be used qualitatively— directions only of outputs—and quantitatively—both directions and (precise) magnitudes of outputs. Those parameters that are sensitive (i.e., cause significant changes in the model's behaviour or output) should be made sufficiently accurate prior to using the model. The model is used to predict (forecast) the system's behaviour, and then the system's behaviour and the model's forecast are compared to determine if they are the same. The system's data may come from an operational system or be obtained by conducting experiments on the system, e.g., field tests. Source Author When you are satisfied that the model resembles the behaviour of the real-life situation, you can investigate a number of what-if scenarios. The scenarios should have been defined within the original objectives of the simulation study. Successful experimentation typically involves using a warm-up period or starting conditions, deciding on a suitable run-length, and running the model with more than one random number stream. (Lanner Group Ltd, 2009) 2.2.7 Presentation of Results and Implementation The method of presentation for results depends on the size of the simulation project and the culture of your organization. An animated model provides an effective communication tool to support business decisions, particularly if you have enhanced its graphical display. (Lanner Group Ltd, 2009) 39 2.3 Simulation Simulation has much to offer all organizations, whether they are in manufacturing or in the service industries. The role of simulation is to evaluate practical alternatives available either in support of major strategic initiatives which might involve a large financial outlay, or in support of the continuous search for better performance at operational and tactical levels. Examples of such evaluations include changes to the product mix, increases or decreases in volumes, improvements in throughput, shorter lead times and improved customer response times. Simulation provides the user with a greater breadth and depth of information on which to base decisions: it is not an optimizing tool. It is capable of handling the complexity of large systems, even a whole factory. In addition, the simulation approach supports sensitivity analysis by allowing rapid changes to the model logic and data. (Lanner Group Ltd, 2009) 2.3.1 Visual Interactive Simulation "Visual Interactive Simulation is one which has features for graphical creation of simulation models, dynamic display of the simulated system and user interaction with the running program. Interaction implies that the simulation halts and requests information from the user or the user stops the simulation at will and interacts with the running program." (Lanner Group Ltd, 2009) 2.3.2 Discrete Event Simulation In discrete event simulation, the operation of a system is represented as a chronological sequence of events. Each event occurs at an instant in time and marks a change of state in the system. For example, an event could be "level 6 button pressed", with the resulting system state of "lift moving" and eventually (unless one chooses to simulate the failure of the lift) "lift at level 6". (Waller, 2012) Table 3 Simulation terminology Terminology Element State Event Meaning A building block used to represent a real life 'operation' in the simulation. For example a Call coming into a call center, a Press in a car factory or a Clerk at a bank counter. An element's state is the condition that it is in at a specific time e.g. it may be Busy, Idle or Blocked. There are also other states that an element can take, that are detailed later. An event occurs when an element changes its state. A change of state is instantaneous i.e. it has a zero duration time. Source (Lanner Group Ltd, 2009) 2.3.3 Continuous Simulations Continuous simulators are characterized by the extensive use of mathematical formulae which describe how a simulated component responds when subjected to various conditions. (Cellier, 1991), several equations which describe their respective behaviours. A continuous simulator 40 would apply those equations in the context of the components' environment and connectivity and produce a continuous graph which accurately reflects how the components would react if they were actually hooked up in reality. 2.3.4 Differences Between Continuous and Discrete Simulations. Discrete event simulation is appropriate for systems whose state is discrete and changes at particular time point and then remains in that state for some time. An example of such a system is the number of farmers collecting coffee: The number of farmers is discrete (integer) and the number of farmers only changes when someone enters or finishes its work at the farm. Continuous simulation is appropriate for systems with a continuous state that changes continuously over time. An example of such a systems is the amount of liquid in a tank and or its temperature. Such a system can be described by differential equations. Continuous simulation is a technique to solve these equations numerically. 2.3.5 Hybrid Simulation Hybrid simulation deals with mixing discrete events and continuous variables. These tools combine the features of continuous simulators and discrete simulators (i.e., they solve differential equations, but can superimpose discrete events on the continuously varying system). Very limited applications in the real-world are approached by hybrid simulation. These application target systems with intermittent flow or when a portion of the flow has a delay or wait time. Other examples include the case where it is possible to mix fluids with parts in the discrete machine element. This feature is commonly used when filling vessels with fluid (paint cans with paint, bottles with soft drinks, etc.) It is also commonly used when a fluid of some type is being consumed at a machine that is processing discrete parts. In such situations, there may be a coolant or lubricant being consumed during the machining of a part, or detergent or other cleaning agent being consumed in a part clean-up stage. Such systems can be modelled as either discrete event or continuous based on the level of detail required. Discrete-event models provide much more details about the operation of a system than continuous models. The ability of a commercial simulation tool to allow both continuous and discrete entities provides powerful modelling tool. Here some elements are controlled by differential equations others by events. For example, in a steel mill model there would be an event for heating of steel to start, with temperature rise determined by differential equations according to amount of power applied. (Raid Al-Aomar, 2015) 2.3.6 Simulation Software Simulation software tools are common nowadays among analysts and engineers in various types of applications and for different purposes. The major benefit simulation packages provide to practitioners is the capability of dynamic system modelling under close-to-real-world conditions. Processes of transactional nature in both manufacturing and services often behave in a complex, dynamic, and stochastic manner. Such capability, therefore, allows analysts to better express new concepts and designs for transactional processes, measure process performance with time-based metrics, conduct statistical analyses to capture variability, and improve/optimize new and current systems. 41 Indeed, many simulation software tools were initially built by practitioners to model some particular real-world systems such as manufacturing systems, health-care systems, supply chain systems. They were built from entities and processes that mimic the objects and activities in the real system. To further meet the needs of practitioners and engineers, simulation vendors developed and integrated modules into their software products to simplify model building, facilitate model customization, allow for the creation of impressive animation, and enable analysts to conduct statistical analyses and run optimization searches. 2.3.7 Witness simulation software Figure 5 Witness Simulation Software Source (Lanner Group Ltd, 2009) WITNESS is Lanner Group's simulation software package. Witness is a simulation tool for dynamic process simulation of manufacturing and business processes in 2D or 3D models. With these models real processes can be emulated already within the planning phase and used for experiments. Figure 6 Witness Simulation software tools Source (Lanner Group Ltd, 2009) The WITNESS Manufacturing Performance Edition is the version of WITNESS specially designed for manufacturing applications. It is ideally suited to a variety of production and storage layout and logistical modelling scenarios. (Lanner Group Ltd, 2009). 42 3. A brief history about coffee. Colombia is the 2nd biggest coffee producer and the biggest producer of Arabica coffee, which is considered the highest quality bean. Cultivation, processing, trading, transportation and marketing of coffee provide employment to many people in Colombia. The major coffee importing countries are the United States, Germany, Japan, Italy and other European countries. There are 570,000 producers. The high quality is guaranteed even for the coffee which is cultivated with shade trees. In recent years, Colombia is enthusiastic about organic coffee that does not use chemical fertilizer or pesticides. Still, the unpredictable natural calamity such as El Nino and imbalance on demand and supply in the market hit coffee industry in Colombia. (American University , n.d.) 43 3.1.1 Coffee Production Between the time they're planted, picked and shipped, coffee beans go through a typical series of steps: (Belgium Coffee Bar, 2016) Figure 7Coffee Processing Flowchart Source (Belgium Coffee Bar, 2016) 44 3.1.2 Planting Figure 8 Coffee plant. A coffee bean is actually a seed. When dried, roasted and ground, it's used to brew coffee. If the seed isn't processed, it can be planted and grow into a coffee tree. Coffee seeds are generally planted in large beds in shaded nurseries. The seedlings will be watered frequently and shaded from bright sunlight until they are hearty enough to be permanently planted. Planting often takes place during the wet season, so that the soil remains moist while the roots become firmly established. (National Coffee Association of U.S.A, 2015) Source Author 3.1.3 Harvesting the Cherries Figure 9 Coffee beans Depending on the variety, it will take approximately 3 to 4 years for the newly planted coffee trees to bear fruit. The fruit, called the coffee cherry, turns a bright, deep red when it is ripe and ready to be harvested. There is typically one major harvest a year. In countries like Colombia, where there are two flowerings annually, there is a main and secondary crop. In most countries, the crop is picked by hand in a labourintensive and difficult process, Source Author though in places like Brazil where the landscape is relatively flat and the coffee fields immense, the process has been mechanized. Whether by hand or by machine, all coffee is harvested in one of two ways: • Strip Picked: All of the cherries are stripped off of the branch at one time, either by machine or by hand. • Selectively Picked: Only the ripe cherries are harvested, and they are picked individually by hand. Pickers rotate among the trees every eight to 10 days, choosing only the cherries which are at the peak of ripeness. Because this kind of harvest is labour intensive and more costly, it is used primarily to harvest the finer Arabica beans. 45 A good picker averages approximately 100 to 200 pounds of coffee cherries a day, which will produce 20 to 40 pounds of coffee beans. Each worker's daily haul is carefully weighed, and each picker is paid on the merit of his or her work. The day's harvest is then transported to the processing plant. (National Coffee Association of U.S.A, 2015) 3.1.4 Figure 10 Washing Tank Processing the Cherries Once the coffee has been picked, processing must begin as quickly as possible to prevent fruit spoilage. Depending on location and local resources, coffee is processed in one of two ways: The Dry Method is the age-old method of processing coffee, and still used in many countries where water resources are limited. The freshly picked cherries are simply spread out on huge surfaces to dry in the sun. In order to prevent the cherries from spoiling, they are raked and turned throughout the day, then covered at night or during rain to prevent them from getting wet. Depending on the weather, this process might continue for several weeks for each batch of coffee until the moisture content of the cherries drops to 11%. The Wet Method removes the pulp from the coffee cherry after harvesting so the bean is dried with only the parchment skin left on. First, the freshly harvested cherries are passed through a pulping machine to separate the skin and pulp from the bean. Source Author Then the beans are separated by weight as they pass through water channels. The lighter beans float to the top, while the heavier ripe beans sink to the bottom. They are passed through a series of rotating drums which separate them by size. After separation, the beans are transported to large, water-filled fermentation tanks. Depending on a combination of factors -- such as the condition of the beans, the climate and the altitude - they will remain in these tanks for anywhere from 12 to 48 hours to remove the slick layer of mucilage (called the parenchyma) that is still attached to the parchment. While resting in the tanks, naturally occurring enzymes will cause this layer to dissolve. When fermentation is complete, the beans feel rough to the touch. The beans are rinsed by going through additional water channels, and are ready for drying. (National Coffee Association of U.S.A, 2015) 46 3.1.5 Drying the Beans Figure 11 Solar dryer If the beans have been processed by the wet method, the pulped and fermented beans must now be dried to approximately 11% moisture to properly prepare them for storage. These beans, still inside the parchment envelope (the endocarp), can be sun-dried by spreading them on drying tables or floors, where they are turned regularly, or they can be machine-dried in large tumblers. The dried beans are known as parchment coffee, and are warehoused in jute or sisal bags until they are readied for export. (National Coffee Association of U.S.A, 2015) Source Author Figure 12 Sorting Machine 3.1.6 Milling the Beans Before being exported, parchment coffee is processed in the following manner: Hulling machinery removes the parchment layer (endocarp) from wet processed coffee. Hulling dry processed coffee refers to removing the entire dried husk — the exocarp, mesocarp and endocarp — of the dried cherries. Polishing is an optional process where any silver skin that remains on the beans after hulling is removed by machine. While polished beans are considered superior to unpolished ones, in reality, there is little difference between the two. Source Author Grading and Sorting is done by size and weight, and beans are also reviewed for colour flaws or other imperfections. Beans are sized by being passed through a series of screens. They are also sorted pneumatically by using an air jet to separate heavy from light beans. Typically, the bean size is represented on a scale of 10 to 20. The number represents the size of a round hole's diameter in terms of 1/64's of an inch. A number 10 bean would be the approximate size of a hole in a diameter of 10/64 of an inch, and a number 15 bean, 15/64 of an inch. Finally, defective beans are removed either by hand or by machinery. Beans that are unsatisfactory due to deficiencies (unacceptable size or colour, over-fermented beans, insectdamaged, unhulled) are removed. In many countries, this process is done both by machine and 47 by hand, ensuring that only the finest quality coffee beans are exported. (National Coffee Association of U.S.A, 2015) 220.127.116.11 Figure 13 Coffee Bags Packing and Shipping The milled beans, now referred to as green coffee, are loaded onto ships in either jute or sisal bags loaded in shipping containers, or bulk-shipped inside plastic-lined containers. (National Coffee Association of U.S.A, 2015) Source Author 48 Case of study 4. Use of modelling for improvement of a business process in a Coffee Farm. The case study is a WITNESS simulation project that was conducted in a Coffee farm in Colombia. In this case it will be simulated by the WITNESS software the whole process of coffee production, in addition to explain the data collection that was carried out, how the model was built, the results and possible solutions to the problems currently presented by the production. To take the coffee from the plant to our cups, a complex industrial process is carried out, which requires time and great care in order to have the best export type. This production is only given once a year, for the region where the farm is located production starts in October and ends around January. At the first sight we find several problems, the main bottleneck that delays all previous processes is the drying of coffee, where investing in new infrastructure is not viable (given the size of the company), we have to analyse the better way to find solutions without having a strong impact on the producer's pocket. 4.1 Define the problem 4.1.1 Objectives of the study The project will allow us to: 1. Identify bottlenecks in production and find possible solutions to put into practice 2. Prove that these solutions are economically and efficiently viable and yielding the highest levels of quality in the grain. Being a quarterly production, the simulation will allow us to take action before the other harvest takes place, fix the problems and solve the bottlenecks that are currently generated and that are evidenced by the simulation 4.1.2 Issues to be addressed Knowing the cause before carrying out the simulation which is present in several productions we can assume the main problems consist in: • The coffee drying infrastructure has not evolved proportionally with the increase in production. • The bottleneck generated by drying, delays the process and causes the coffee to lose quality (not being processed fresh, is fermented lowering the quality of the grain) • It depends on the weather (almost 40% of the coffee is being dried in the sun), the situation is aggravated if we encounter periods of rain. • The loss of quality impacts on the price, premium coffee has in price up to 300% higher than the second quality. 49 4.1.3 Domain of the study The information required to develop the study is relevant to the four macro production processes (milling, drying, selecting and packaging) that are affecting us. In addition, we will have a constant flow of raw material with a price that includes its collection, cost of maintenance of the plant, energy costs will be part of the simulation, the cost of capital (land) and their respective deprecations. In other side parts of production that will not be taken into account, given their irrelevance are the collection process of grain, since this is a process based on artisanal labour, with no current opportunity to technify (given the conditions of the land, the sensitivity of the plants, the cost of research), the cost of water, the disposition of the waste (the skin resulting from pulping turned into composting), among others as food and lodging of staff, etc. 4.1.4 Level of detail The level of detail of the process will be very close to reality, it will be a question of addressing all the issues that impact the model, such as climatic factors, the variation of input of raw material to the system, recurrent faults in the system, factors that affect the quality of the grain in the production, the maximum current capacities of the machines and their processing times, among other factors that will be able to be appreciated later during the implementation. 4.1.5 The need to model the process This process has a particularity that is given only once a year, as mentioned above the production is for only 3-4 months per year what gives us a great advantage as well as a disadvantage at the same time. To highlight we have a model that we can fix now without need to stop some production (currently it is not harvest) and we consider as a disadvantage that the data can only be taken for a short period of time, hence the need to simulate, a good model in WITNESS will allow us to see graph, the current behaviour of the system at any period of time and how the proposed improvements will work. 4.2 Design the study 4.2.1 Model assumptions In the development of the simulation model, we will take as assumptions the following criteria that will allow us to perform all kinds of experiments. • Raw material (coffee beans) will always be available. • Every day will arrive an average of 4000 kg with a variation of +/- 1000 Kg. • The arrival time is 7 am and the process will be repeated until the end of the simulation. • The price of coffee took a historical average of the last 3 years, where the premium coffee costs 320,000 / 50kg and the second 80,000 / 50kg (prices 2016 in Colombian pesos). • The product is packaged in sack bags (weight negligible) with 50 kg of dry coffee each. 50 • Workers' hours range from 6 in the morning to 7 at night (with their respective breaks) and the hours of operation of the machines are from 6 in the morning to 6 in the afternoon. The dryer is the only one that works 24 hours a day. • In the pulping process, the result is 70/30, i.e. the removed skin is equal to 70% of the weight and the product that can be used the remaining 30%. • During the drying process, the coffee beans lose a weight of 60% when releasing the moisture gained. • The selection process of the grain has a variable result, if there are no delays in the pulping process, the premium quality will reach 96%, but if this is delayed, the premium coffee can lower at rates of 60% of the total production. • The hours of operation of the solar dryers, will be determined by a climate model, where the shadow hours will be reported as a failure. • The final product is never blocked and is dispatched immediately. • The currency used for calculations are Colombian pesos (COP). • The value of the Kilowatt hour is COP 400 / hour. • The costs of water use are neglected due it is taken freely from a small river. 4.2.2 Number of models Required At the beginning and under the current concepts it is planned to make a total of 3 models apart from the base model for a total of 4 models: • Alternative 1 is intended to expand drying capacity by adding a new dryer machine, eliminating dependence on the sun. • In alternative 2, is intended to expand drying capacity by adding a new solar dryer we intend to change the current model of how the coffee is distributed for drying, pre-drying in the sun and then drying it completely by machine. • In alternative 3, reduce the production. • Alternative 4 and 5 we intend to change the current model of how the coffee is distributed for drying, pre-drying in the sun and then drying it completely by machine. These models are initially thought, if during the implementation of the simulation more ideas are urged, other alternatives will be formulated. 4.2.3 Animation, tool and model requested Initially the model was handled as discrete, but already in the study, it was found that the processes responded better to a continuous model, although the two models are mixed, because at the end of all stages, the coffee is packed and its price is calculated based on a bag of 50 kg. not with respect to its volume. A 2D simulation in WITNESS with both continuous and discrete process will be developed. The scope of the model included four macro process, milling process (which includes the process of arrival of the product, its storage and pulping), the drying process (which includes both solar and fire driven dryer), selection process and finally the packaging process. 51 4.2.4 Business process Description Figure 14 Business Process of coffee Production Source Author 2 The coffee process is separated into 4 large macro processes, which will be detailed below: Milling process: The milling process is taken from the feeding of the system to the peeling of the grain, it covers the unload process, the tank that supplies the mill, the mill, the composting tank and the washing tank, the flow is made by gravity, although support in the unload by the worker is needed. It is composed of the following processes: • Every day the model is fed with an average of 4000 kg of coffee bean. • The coffee is deposited in the tank that supplies the mill, with a capacity of 10000 kg, this process requires a worker to help unload the shipment. • Due to gravity, coffee falls directly to the mill, which has a nominal capacity of 1000 kg / hour and an energy consumption of 1 kW / h, and 100 litters of water / hour • The mill is responsible for separating the coffee bean from its skin, resulting in two products, 70% being the skin removed and 30% the useable grain. • The milling machine has a preventive maintenance on average every 30 cycles, a task that requires a worker and a time of approximately 60 minutes. 2 *60% of the weight is lost due to humidity 52 • Gravity removed skin falls into the compost pile, where it is stored. • The useable grain goes to the washing tank, with a capacity of 2000 kg where the drying process awaits. Drying process: The drying process is taken from the coffee leaving the washing tank, until it is sent to the selected process, the drying machine is part (it works with dried coffee husks), the two solar dryers. • From the washing tank, the coffee is sent by means of piping and by gravity to the different dryers. • Priority number one is given to the drying machine, since it works 24 hours a day, with a nominal capacity of 600 kg and an operation time of 18 hours. • The drying machine requires a cleaning for each process that takes an average of 2 hours, in addition to a preventive maintenance in a range between 5 and 15 processes with a duration of 2 hours, both tasks are done by the worker. • Priority number two and three have solar dryer 1 (600 kg capacity) and solar dryer 2 (400 kg capacity), which do not require maintenance but depend on the daily sunshine hours. The process of drying in the sun requires approximately 24 hours. • Solar dryers require a cleaning that takes the worker approximately 2 hours. • In both systems coffee loses 60% of its weight given its moisture. • The three drying stations are connected to the dryer by piping, where a worker is required to make this flow. Selection process: The process of selection begins with the entrance of the coffee and dry to the selector, process in which separates the coffee of Premium quality of the second quality. • The dryer is a machine that uses a sieve and vibration to select the quality of the coffee. • It has a nominal selection capacity of 1000kg / hour and an energy consumption of 0.5 kW / hour • Requires to be cleaned for each process, which takes approximately 20 minutes. • It has a preventive maintenance that takes place between 10 and 25 processes (depending on the volume of work) that is done by the worker, a process that takes approximately 60 minutes. • It has two output pipes (premium and second quality) that communicate with the packaging machines. Packing Process: It is the last part to be analysed of the coffee production, the two packing machines (one for each quality) are part of it. • Each machine requires a worker to operate, where it is responsible for putting the bag and make the process of filling and closing the bag 53 • This process takes at least 2 minutes with a maximum of 6 minutes • Each bag has a value of $ 2500 pesos. • Energy consumption is 0.15kW / hour • The average quality of premium coffee is 96% of the total production, quality that can be affected by delays 4.2.5 Start-up conditions and model run length The first sack of coffee will be taken as the warmup time and the duration of the harvest as the total time of the simulation, which would estimate the following: • The warmup time will be 5000 minutes, equivalent to three days and 10 hours approximately, time where the first finished product is expected • The total harvest lasts 12 weeks, giving us a simulation value of 125,960 minutes (taking into account the start value). 4.2.6 Model input data The data that feed the model are as follows: • Process times • Climate model of sun hours' daily • Energy prices • Set-up times, cleaning, maintenance. • Work shifts • Hours of operation of the machines • Packaging costs • Fixed costs for the use of machines and land. 4.2.7 Model output The following are the outputs of the model: • Reductions: Moisture lost, skin removed. • Production: Premium quality and second quality coffee • Percentage of quality • Costs, profits and returns 54 4.3 Build, verification, validation and analysis the model Figure 15 Base model in built face Source Witness Software In order to construct the model and its respective variants we already have all the tools, from the identification of the problem, the definition of the processes, the input and output elements that will give life to the simulation, as well as the warmup time and the duration of the simulation. It should also be remembered that a mixed model (a combination of a continuous and discrete model) was chosen as this one will allow us to bring the simulation closer to reality. 4.3.1 Model verification and validation The technique used to measure if our model fit the reality is a comparison with the production of the year 2016, getting to have a simulation that fits up to 95%, for that we had to make the following calibrations. Table 4 Production 2016 vs Simulation Results PerformanceMetric Premium Second CoffeeGrains Compost %Evaporated Quality Actual3 40000 5000 300000 200000 50000 89% Simulation 41812 4690 317405 190644 49013 89.70% %difference 4.53% -6.20% 5.80% -4.68% -1.97% 0.90% Source Author • The model had to be calibrated several times to meet the standards shown in the system description, the changes at the beginning of the simulation from discrete to continuous 3 According to the Producer estimated production 2016 55 (product unload to the milling tank) and at the end of the continuous simulation to Discrete (packing process) are the most attention called when modelling. • Another process with a degree of complexity was to adjust the solar dryers to the climate model (sun/hours), the solution found was to plan the shadow hours, that is to say, by means of a distribution (With variable equal to hours / sun and one Weight equal to annual repetition according to the climatic model of 2016) the dryer will only work in an interval where the most frequent is to have 8 hours of sun daily and 4 of shade, but always varying up or down depending on the variable and the weight Assigned. Figure 14Climate Bright Sunshine Climate: Bright sunshine Sun's Hours per day 0 2 4 6 8 10 12 0 20 40 60 80 100 120 Days per year Source Institute of Hydrology, Meteorology and Environmental Studies (Colombia) • Another difficult issue to address was the loss of quality given the delay in the milling processes (fermentation of the grain), for this purpose a warning was placed on the tank of the mill that is activated when the levels pass 90%, this activates a conditional on the selector that penalise the Premium quality, lowering it from 96% to 60%. • For other scenarios the description of the processes was followed without any inconvenience. 56 4.3.2 Current situation of the model Figure 17 Model base results Source Witness Simulation Software Already running the simulation of the current model, we can see graphically that the bottlenecks actually occur in the first instance to the low capacity or poor distribution of the dryers, as it was seen in figure 23, the operation of the mill is affected, and that almost 60% of its time is in the process of emptying, i.e. in the transfer of the grain to a washing tank that remains full. Figure 18 Milling Machine Statistics in the base Model. Source Witness Simulation Software 57 The consequences are the grain is fermented by staying in wait for the milling machine. In normal conditions the coffee must have a quality near 96%, but at the moment it is located at 89.70% more than 6 percentage points below, this turned money is an approximate loss in the entire harvest of 14 million pesos (15% of the net profit) Figure 19 Model Base results 4 = 96% − 89.7% = 6.3% ∗ 46337 2,919.23 = ∗ 50 50 = 58 ∗ $240,000 $ − $ 5 = $13,920,000 Source Witness Simulation Software Another point where we can appreciate the effect and weight of the dryer delays is in the selector, as shown in figure 25, this machine is underutilized, it waits to be filled 65% of its time and only keeps working 7% Of its time. Figure 20 Selector Statistics in the base Model. Source Witness Simulation Software 4.3.3 Conclusions of the model and the base simulation. The results of the simulated model give us a clearer map of how we are currently working within the production of coffee, this is clear that the main problem is the bottleneck generated in the dryers and their impacts on the quality of the product it represents almost 15% of the total gross profit of the company, but an unexpected result is to see how the other part of the system is being underutilized, since most other machines are used at an average of 20%. This information is key to make decisions, where initially these scenarios will be given: 4 ML=Model Loss, In the equation we took the value of the standard quality and compared with the value thrown by the base model, this result is multiplied by the total production of coffee (before being selected) and finally we convert it into bags of coffee, giving as Result that the inefficiency of the base model is generating 58 bags of second quality coffee more than in normal conditions, this in prices gives us an approximate of 13,920,000 5 58 • Investing in the drying infrastructure (new drying machine) could be cheaper than continuing to work in this way and losing money due to delays and process service could be provided to other farms. • The planting of new plants has now been slowed down in order not to intensify the problem when processing coffee, solving 1 of the 4 macro-processes is believed to have sufficient installed capacity to deal with it without problem. • Another point to consider is the reduction in production, if quality is being affected and prices are reduced up to 300% of the value of premium coffee, it is possible that lowering production will achieve higher profit margins. To clarify all these doubts generated in the current model, a series of tests will be carried out, with the base model + some improvements will be run the simulation evidencing that results throws us. 59 4.4 Experiments with the model and optimization After the analysis and the failures found to the base model, we allow to enter to analyse 4 possibilities with which we will improve the process, one of then includes large investments in infrastructure, others a rearrangement of the drying machines, a combination of the two previous and finally a reduction in production. The experiences will be evaluated with pros and cons in order to make a decision that will be applied in this year's production (2017). 4.4.1 Base system + a new drying machine This is one of the most logical solutions in the first moment, with 2 drying machines the bottleneck is solved and in this way to improve the quality of the product (everything would be freshly grounded). These results must be compared in the same way with the initial investment that would have to be made and see if the company is in a position to do this. Figure 21 Base Model + a new drying machine Source Witness Simulation Software Analysing the results, we have found in this model, that the improvement made would give us excellent results and a quality coffee above the estimated 96%. Another factor that shows us this is a possible solution is the profit, with almost 34 million pesos more than the previous model, which represents around a 30% growth, something reasonable due to the investments that must be made. In contrast, if we take a look at the milling machine, the two dryers and the selector, we realize that the installed capacity is well above what is being worked, i.e. Figure 60 22 plant is in the capacity to receive more coffee since they are kept waiting to be fed at an average of 53%, 25% And 46% respectively. Figure 22 Base Model + a new drying machine Analysis Source Witness Simulation Software As a test in this experiment, it will increase in production from 5% to 25% to see the reaction that has the implementation of this new dryer and that we can both move the production boundary. Table 5 Expansion Base Model + a new drying machine No Dryer % Increase Revenue Cost Profit % 1Dryer % 2 Dryer 1 - COP273,120,000 COP177,447,000 COP95,673,000.00 0% - 2 0% COP322,800,000.00 COP191,720,295.44 COP131,079,704.56 37.01% 0.00% 2 5% COP341,120,000.00 COP200,426,500.00 COP140,693,500.00 47.06% 7.33% 2 15% COP365,760,000.00 COP212,955,739.00 COP152,804,261.00 59.72% 16.57% 2 20% COP356,160,000.00 COP206,504,318.00 COP149,655,682.00 56.42% 14.17% 25% COP357,200,000.00 COP209,172,800.00 COP148,027,200.00 54.72% 12.93% 2 Source Author In table 5 we can see the relative increases accordingly, first the base model with a single drying machine and second with respect to the model of alternative one with two drying machines and a gradual increase of the amount of coffee with which the system is fed. 61 Figure 23 Profit vs % Production Increase Profit vs % Production Increase $155,000,000.00 Profit in COP $150,000,000.00 $145,000,000.00 $140,000,000.00 $135,000,000.00 $130,000,000.00 $125,000,000.00 0% 5% 10% 15% 20% 25% % production Increase Source Author According to the results, we can see that the optimal capacity that we can increase production when added in a new dryer is 15%, what instead of this will generate an additional utility of 60% with respect to the base model (approximately 57 million pesos) A cost that we have to take into account is the new drying machine, valued at approximately 80 million pesos (Cash payment value), in an optimistic result, this will be covered in less than three harvests (without any increase in production) with the additional gains of implementation. Implementation and possible financing Taking advantage of the fact that there is currently no production, there is no need to stop the production line, the implementation would be planned to be carried out in the months before harvest, according to the dryer manufacturer, it takes 15 days to install the new unit of drying in the farm, we must add the current situation of the producer, which does not allow him to make the investment with his own resources, and has to collect money from third parties. We investigated loans focused on agriculture in Colombia and we have found the following form of financing: Table 6 Loan Simulation Source Finangro It should be noted that the credit application was made by the credit line to the promotion of agriculture, having a rate of 22.42% effective annually (the market interest is more than 30%), and the company must pay a value of $ 36,976,999.2 this year, which will be recovered with the substantial improvements made in production. 62 Taking into account the financing, the projected results for the 2017 harvest, when buying a new drying machine, would be those shown in Table 7. Table 7 Profit after Financial costs Scenari o Base Model Base Model + 1 Dryer Base Model + 1 Dryer + 15% Coffee Revenue Cost Finance Cost Profit Profit variation COP 273,120,000 COP177,447,000 0 COP 95,673,000 0% COP 322,800,000 COP 191,720,295 COP 36,976,999 COP 94,102,705 -1.64% COP 365,760,000 COP 212,955,739 COP 36,976,999 COP 115,827,261 21.07% Conclusions As predicted in the beginning, this is a very good alternative to make production more efficient, if we add a new drying machine and at the same time we make an increase in production as the model suggests. It will be possible to maximize the profits of the Company almost 60% year after year, despite the fact that it has to make a strong investment, its recovery is almost guaranteed in the first three years. One of the cons that may appear in this solution is the economic risk we could face. Remember that coffee is a commodity regulated by an international price, although in recent years to steady state, nothing guarantees us that for the year 2018 and 2019 where it is expected to recover the money invested (Price has to be close to Premium Bag $ 320,000 and Second Bag 80,000). Taking a pessimistic result and implementing solution 3 (Base Model + 1 Dryer + 15% Coffee) we have a safety margin of around 21%, according to Table 7. 63 4.4.2 Base Model + 1 Solar Dryer Another alternative that can be contemplated is the construction of a new solar drying patio or solar dryer, its investment is low and can help, not in the same way as a drying machine that works 24 hours, but may relieve the Bottleneck that is currently being generated in production. For this, if you simulate a drying yard for 600 kg (current size of solar dryer 2). Figure 24 Base Model + 1 Solar Dryer Source Witness Simulation Software Analysing the results of this simulation with the new drying yard we have found very good results, similar to those of the drying machine implementation, although these results must be taken with caution given that this system depends 100% on the Sun, another factor that causes the result is similar is the lower cost of operation of the one against the other. But on the contrary to the previous model, we will not be able to increase the production taking into account that the three solar dyers are working almost to their maximum capacity (saving the time that they remain inoperative either by cleaning or lack of sun). Table 8 Profit Base System + 1 Solar Dryer SunHours Variable Variable Revenue Cost COP273,120,000 COP177,447,000 COP319,120,000 COP190,425,000 Source Author 64 Profit COP95,673,000.00 COP128,695,000.00 Profit variation 0% 35% Implementation and possible financing A solar dryer, is a cement plate covered with a structure of "guadua6" and plastic, which allows the internal circulation of air. The concrete slab is 26 m2 and 5 cm thick (6.5 m long and 4.0 m wide) requires a plastic covering of 7.5 m in length and 6.2 m in width. The upper height of the arch formed by the roof should reach 2.10 meters and the heights of the two lateral ends of the roof about 60 centimeters. Its construction and operation takes about 15 days, and its cost is in the order of 8 million pesos. Table 9 Profit after financial cost Profit Base System Revenue Cost Finance cost Profit Profit variation Base COP273,120,000 COP177,447,000 COP- COP95,673,000 Base + 1 Sun dryer COP319,120,000 COP190,425,000 COP8,000,000 COP120,695,000 0% 26% Source Author The cost of this infrastructure can be assumed by the company without any problem, since its implementation generates a return on investment, covering the total value of the first harvest. Conclusions Although at first the idea of the solar dryer was not taken very seriously, the simulation shows that it is very helpful, with a 10% investment that would be invested in the construction of the solar dryer, it can be done almost the same (of course, without allowing us to expand production). On the other hand, the company would be left without any debt and receiving a high rate of return. One of the biggest cons in this alternative is the dependence of the sun on this model, although the farm is in the tropics and the hours of sun are around 10 hours a day, global warming and constant changes of climate have made that this varied a lot and that in the future we will have rains in times where the sun is expected to shine. This measure we recommend in the short term, its easy construction and low initial value pushes us to move to this system and in the long term investments can be made in more sophisticated infrastructures such as drying machines, which compensate the value for their great capacity and guarantees a 24-hour operation without the weather affect us. 6 Guadua is the most important American bamboo. Due to its quality, the genus has been widely used for house construction along the inter-Andean rivers of Colombia and in coastal Ecuador 65 4.4.3 Base model + Reduction in production This alternative is the easiest one to put into practice, we see that the current bottleneck is costing the company lost in grain quality, which results in a considerable loss of money as analysed in the base model. For the coffee grower it would be profitable to lose a portion of his crop and sell more premium coffee, than to continue the current model, where he is losing about 15% of the profit. In this alternative we will analyse a gradual reduction of the system feed until finding the point where it becomes more profitable to produce. Figure 25 Base model + Reduction in production Source Witness Simulation Software Evidently, according to the thought that a reduction would help to decongest the system and thus have a greater profit, after simulating these 5 scenarios these are the results we have: Table 10 Reductions Scenarios % Decrease Revenue Cost Profit Profit Variation 0% COP273,120,000 COP177,447,000 COP95,673,000.00 0 5% COP277,440,000.00 COP175,393,200.00 COP102,046,800.00 6.66% 10% COP279,120,000.00 COP172,703,600.00 COP106,416,400.00 11.23% 15% COP269,920,000.00 COP164,127,400.00 COP105,792,600.00 10.58% 20% COP256,720,000.00 COP155,685,800.00 COP101,034,200.00 5.60% Source Author 66 The most effective reduction is made when we reduce by 10% the coffee from the harvest, increasing the profits in the order of 12%. Figure 26 Profit vs Production Decrease Profit vs Production Decrease $108,000,000.00 Profit in COP $106,000,000.00 $104,000,000.00 $102,000,000.00 $100,000,000.00 $98,000,000.00 $96,000,000.00 $94,000,000.00 0% 5% 10% 15% 20% % Production Decrease Source Author Implementation This implementation is very simple, the producer has to reduce the number of harvesters, that is, the number of people who are currently harvesting the grain. This has no initial cost, this would lose the harvest of approximately 10% of the plants that are, but their yields are better than if the grain is harvested. Although according to the producer, it is not good for the plants that their harvest is not collected and fall to the ground, attracting pests to the coffee, thus lowering productivity in the long term, something that is currently difficult to estimate by means of being simulation. Conclusions It is an easy way to make 12% more profit, but this has its long-term consequences, the coffee grower does not have to invest any amount of money with respect to the other two proposals, but risks that the next harvests are affected with some plague by the rotting of the grains that are going to stop picking. This further truncates the growth of the company, as this lazy output will affect future harvests. Not recommended in any case. 67 4.4.4 Base Model redistribution As a final test we will modify the current configuration of the dryers, going from a configuration in parallel to a configuration in series, where it has been decided to do two experiments, the first of them is to dry half the time in the drying machine and continue in the process in the solar dryers and the other experiment would otherwise dry half the time in the solar dryers and finish the process in the drying machine. Here we venture out if this measure can be effective or if on the contrary it makes the bottleneck bigger. Everything is aimed at finding solutions where economic investment is not required and have a real impact on production. Figure 27 Alternatives 4 and 5 Source Witness Simulation Software According to the results of the model, this alternative does not good results, since as noted in Table 11the yield in the coffee processing decreased in the two models an approximate 70%. Table 11 Profit alternatives 4 and 5 Scenarios Revenue Cost Profit COP273,120,000 COP177,447,000 COP95,673,000 COP148,960,000 COP116,190,980 COP32,769,020 COP145,280,000 COP117,995,020 COP27,284,980 Base Model 0% 2 Solar Dyer +Dryer Machine Dryer Machine + 2 Solar Dryer Profit variation -65.75% -71.48% Source Author Analysing the reason for this failure, we find that the proposed model accentuates the bottleneck instead of solving it, we no longer have a capacity in the first line of 1600 kg of drying (base 68 model), depending on the model can vary from 1000 kg (2 Solar dryers first) to 600 (drying machine first) and where we detected the problem, as in the previous models the milling machine shows us how the other things are working, since if this is blocked, This will affect the quality of the grain and the money that enters in the company. Figure 28 Milling Machine Statistics Alternative 5 Source Witness Simulation Software According to statistics at the end of the harvest (simulation), this machine will be 75% of its time waiting for a space in the washing tank to deposit the coffee. All this generated by the difference so abysmal between the different machines, because while the mill and the selector takes about an hour to do their work, the dryers spend between 18 and 24 hours. Conclusions Obviously it is a model that the producer will not implement, since it increases the bottleneck for which this study was formulated, this model of drying in series also diminishes the quality of the coffee, because when blocking the system of grinding, the coffee begins to ferment, lowering the quality of the base model from 89% to 70% (ideally 96%), where this increase is affecting the profit. 69 4.5 Results and conclusions In order to take a good decision, statistical weights were given to variables such as Productivity, production growth, external dependencies, quality, cost / benefit and implementation and it was graded depending on the results of the simulation from the lowest result, which is 1 to the highest result that is 10, in order to make a decision that would fit all the needs of the producer. As there are several alternatives within each model, we will work with the highest profits generated. Table 12 Proposals Elections, Author Alternative A.1. Base system + a new drying machine A.2. Base System + 1 Solar Dryer A.3. Base model + Reduction in production A.4/A.5. Base Model redistribution Weight 30% 10% 15% 20% 20% 5% 100% Profit Productivity Grown Possibility External Dependence Quality Cost Imple mentation Total COP152,804,261 9 10 8 8 5 5 7.75 COP120,695,000 8 4 3 8 8 10 6.95 COP106,416,400 8 1 7 8 9 9 7.4 COP32,769,020 1 1 5 3 7 8 3.55 According to the results the best proposal is the investment in a new drying machine, given that it offers a future expansion capacity to the producer and does not depend on any external agent for its operation (besides working 24 hours a day) and the quality of the grain produced is above the standards (96%). Although its cost is high the rate of return is high, paying the entire loan in less than 3 years (only with the money of more raised by the investment made) As mentioned above there are risks that can affect the producer and his finances, although coffee is a commodity with a stable behaviour, there is the possibility of both a high growth and a sharp fall. Noted this risk, we were able to find a possible solution in order to take care of the financial health of the company, in consultations with the National Federation of Coffee Growers of Colombia (Federacion Nacional de cafeteros de Colombia), We found that the price of coffee can be agreed in advance 2 years, i.e. sell a Futures contract, where the two parties would reach a price agreement, thus shielding the coffee grower and encouraging him to make this investment. On the day of publication of this work, the company has initiated the documentary collection to request the money from the bank to make the investment. 70 Conclusion In the study carried out in this work we could find how the tools like BPM and the simulations can be implemented from a big companies like IBM, General Motors, Airbus, to key processes in small production units. In this case we are talking about the companies belonging to the agroindustry sector and the results obtained in this diploma work can help them become more efficient and find its best utility rate. These types of tools are not very common in farms in Colombia, where everything is handcrafted and many decisions are made on the basis of trial and error and making decisions about processes can be expensive. My focus on working with the family business was mainly due to reasons mentioned above, having an additional knowledge given by the Masaryk University, it was intended as a goal that could help not only to my family but also to build better things in my country. Despite the fact I do not change the world by improving the production unit of a small farm, I think I have contributed with my work and effort to make better the place that has been under the domination of war for more than 60 years. The results are obvious, the recommendations given in the work are being made, the decision to invest in infrastructure was waiting for a long time, but with simulate data and with financial planning the coffee producer was encouraged to make the proposed improvements in order of having a better production in key references such as quality and rate of return, at the same time, the producer is aware that it can grow to a certain point (15% of the current production) without re-presenting the same problems of bottlenecks in production. Another thing to be grateful for to the producer is the documentation of all the processes, which had never been done, all the calculations obtained were estimated and handled according to the experience. Although there is much work to be done, a great step has already been taken and an efficient coffee production process has been consolidated, with sustainability and possibilities for orderly growth. 71 72 LIST OF APPENDICES Appendix A Basic Elements on WITNESS Appendix B Production according with the model 73 74 Appendix A Basic Elements on WITNESS Element Description Examples Parts Parts (Entities) are used to represent those discrete items that move around the model. Buffer These are places where parts can be held. Parts can represent:- products (cars, engines, etc.),product batches, calls in a telephone exchange, tiny electronic components or whole computers Buffers can represent: parts awaiting an operation on a factory floor, people in a queue, the space containing aircraft waiting to land. Machine Conveyor Labor Path These are powerful elements which are used to represent anything that takes parts from somewhere, processes them and sends them on to their next destination. These are used to move parts from one fixed point in the model to another over time This element can be used to model both human and physical resources which may be required in the model. A path is an element that parts or labor units can travel along in order to get Machines can represent a machine tool, lathe or a press. A complete shop or a single supermarket checkout. An entire plant or an individual work cell. There are two types of conveyors. •Fixed conveyors maintain a constant distance between parts. If the conveyor stops, the distance between the parts on the conveyor remains the same. •Queuing conveyors allow parts to accumulate. If the conveyor becomes blocked, the parts will slide together until the conveyor is full. For example, tools, people or equipment You can use it to represent the length and the physical route of a real life journey in your model. 75 Designer element Icon Attribute Variables Distribution Function Shift Module Pie chart Timeseries from one element to another element These are characteristics of a specific part or labor unit. Variables are values which can be accessed from anywhere in the model Distributions allow you to build variability into a model by including data which you have collected from the real world. WITNESS provides a large number of built-in functions which you can use to build intelligence into the logic of your model. The shift element is used to simulate a shift pattern which is, in effect, a sequence of working an nonworking periods A module is an element consisting of a collection of other WITNESS elements. A reporting element to display a pie chart. A reporting element to display a time dependent values. For example, the number of cylinders in an engine could be held in an attribute, and you could then use this attribute to determine the amount of time required for tuning and adjustment For example, a variable could be used to record the value of items in an inventory For example, if observations show that the milling operation on type X widgets takes between 5 and 10 minutes but most often takes 8.2 minutes, the information could be introduced into the model using a distribution. For example, you could use a function to detect the number of parts currently on a conveyor. You may also create your own functions. Shift patterns may be applied to labor and other elements in order to simulate shift working. Modules may be used to facilitate "black-box" or hierarchical model building. Pie charts allow you to present simulation results on the screen in the standard pie chart format. Time series allow you to present simulation results on the screen in the form of a graph which plots values taken from the simulation against time. Up to seven values may be plotted with seven different colors 76 Histogram Histograms allow you to present simulation results on the screen in the form of a bar chart. This is useful for determining the range of values observed for some parameter of the simulation. 77 78 Appendix B Production according with the model Base Model Base Model + 1 a new drying machine Base Model + 1 Solar Dryer 79 Base model + Reduction in production Base Model redistribution 80 Works Cited Aalst, W. M. (2004). Business Process Management Don't forget to improve the process. Lecture Notes in Computer Science, 1-65. Aalst, W. M. (2013). Business Process Management: A Comprehensive Survey. International Scholarly Research Notices, 1-37. Aalst, W. M. (2016). Process Mining Data Science in Action. Berlin: Springer-Verlag Berlin Heidelberg. Aguilar-Saven. (2003). 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Hoboken, N.J.: Wiley. 84 Tables TABLE 1 BUSINESS PROCESS MANAGEMENT DEFINITIONS .....................................................................................21 TABLE 2 EXPERIMENTATION AND OPTIMIZATION ....................................................................................................39 TABLE 3 SIMULATION TERMINOLOGY .....................................................................................................................40 SOURCE (LANNER GROUP LTD, 2009) .....................................................................................................................40 TABLE 4 PRODUCTION 2016 VS SIMULATION RESULTS ..........................................................................................55 TABLE 5 EXPANSION BASE MODEL + A NEW DRYING MACHINE ..............................................................................61 TABLE 6 LOAN SIMULATION....................................................................................................................................62 TABLE 7 PROFIT AFTER FINANCIAL COSTS ..............................................................................................................63 TABLE 8 PROFIT BASE SYSTEM + 1 SOLAR DRYER .................................................................................................64 TABLE 9 PROFIT AFTER FINANCIAL COST PROFIT BASE SYSTEM .............................................................................65 TABLE 10 REDUCTIONS SCENARIOS ........................................................................................................................66 TABLE 11 PROFIT ALTERNATIVES 4 AND 5...............................................................................................................68 TABLE 12 PROPOSALS ELECTIONS, AUTHOR ...........................................................................................................70 85 Figures FIGURE 1 BPM HISTORIC VIEW ON INFORMATION SYSTEMS ...................................................................................24 FIGURE 2 BPM LIFECYCLE ......................................................................................................................................28 FIGURE 3 SYSTEMATIC SIMULATION APPROACH. .....................................................................................................33 FIGURE 4 COLLECTING MODEL DATA ......................................................................................................................35 FIGURE 5 WITNESS SIMULATION SOFTWARE ..........................................................................................................42 FIGURE 6 WITNESS SIMULATION SOFTWARE TOOL ..................................................................................................42 FIGURE 7 COFFEE PROCESSING FLOWCHART ..........................................................................................................44 FIGURE 8 COFFEE PLANT. ........................................................................................................................................45 FIGURE 9 COFFEE BEANS .........................................................................................................................................45 FIGURE 10 WASHING TANK .....................................................................................................................................46 FIGURE 11 SOLAR DRYER ........................................................................................................................................47 FIGURE 12 SORTING MACHINE ................................................................................................................................47 FIGURE 13 COFFEE BAGS.........................................................................................................................................48 FIGURE 14 BUSINESS PROCESS OF COFFEE PRODUCTION .........................................................................................52 FIGURE 15 BASE MODEL IN BUILT FACE ...................................................................................................................55 FIGURE 16 CLIMATE BRIGHT SUNSHINE ..................................................................................................................56 FIGURE 17 MODEL BASE RESULTS ...........................................................................................................................57 FIGURE 18 MILLING MACHINE STATISTICS IN THE BASE MODEL. ...........................................................................57 FIGURE 19 MODEL BASE RESULTS ...........................................................................................................................58 FIGURE 20 SELECTOR STATISTICS IN THE BASE MODEL. .........................................................................................58 FIGURE 21 BASE MODEL + A NEW DRYING MACHINE ..............................................................................................60 FIGURE 22 BASE MODEL + A NEW DRYING MACHINE ANALYSIS .............................................................................61 FIGURE 23 PROFIT VS % PRODUCTION INCREASE ...................................................................................................62 FIGURE 24 BASE MODEL + 1 SOLAR DRYER ...........................................................................................................64 FIGURE 25 BASE MODEL + REDUCTION IN PRODUCTION..........................................................................................66 FIGURE 26 PROFIT VS PRODUCTION DECREASE .......................................................................................................67 FIGURE 27 ALTERNATIVES 4 AND 5 .........................................................................................................................68 FIGURE 28 MILLING MACHINE STATISTICS ALTERNATIVE 5 ...................................................................................68 86 Abbreviations 6M Machine, Method, Material, Man, Measurement, Milieu 4P Policies, Procedures, People, Plant/Equipment 7PMG Seven Process Modelling Guidelines ABC Activity-Based Costing APQC American Productivity and Quality Center BAM Business Activity Monitoring Bill-of-Material B2B BPA Business Process Analysis Business Process Model & Notation BPMS Business Process Management System BPR Business Process Reengineering BTO Build-to-Order BVA CEO Business Value-Adding Chief Executive Officer CFO Chief Financial Officer CIO Chief Information Officer CMMI Capability Maturity Model Integrated COO Chief Operations Officer CPO Chief Process Officer CRM Customer Relationship Management CT Cycle Time DBMS Database Management System DCOR Design Chain Operations Reference (product design) DES Discrete-Event Simulation DMS Document Management System EPA Environment Protection Agency EPC Event-driven Process Chain ERP Enterprise Resource Planning FIFO First-In-First-Out HR Human Resources IDF3 Integrated Definition for Process Description Capture Method Internet Service Provider IT Information Technology KM Knowledge Management KPI Key Performance Indicator OMG Object Management Group OS Operating System 87 PD Product Development PDSA Plan-Do-Check-Act PO Purchase Order POS Point-of-Sale PPM Process Performance Measurement ROBAC Role-based Access Control RFI Radio-Frequency Identification RFQ Request for Quote ROI Return-On-Investment TCT Theoretical Cycle Time TOC Theory of Constraints TQM Total Quality Management UIMS User Interface Management System VA Value-Adding VCH Value Creation Hierarchy VCS Value Creation System VRM Value Reference Model WIP Work-In-Progress WfMC Workflow Management Coalition WfMS Workflow Management System 88