Consumption-Driven Finite Capacity Inventory Planning and Production Control. A thesis presented to. the faculty of

Transcription

1 Consumption-Driven Finite Capacity Inventory Planning and Production Control A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Gökhan Eğilmez November Gökhan Eğilmez. All Rights Reserved.

2 2 This thesis titled Consumption-Driven Finite Capacity Inventory Planning and Production Control by EĞİLMEZ GÖKHAN has been approved for the Department of Industrial and Systems Engineering and the Russ College of Engineering and Technology by Gursel A. Suer Professor of Industrial and Systems Engineering Dennis Irwin Dean, Russ College of Engineering and Technology

3 3 ABSTRACT EĞİLMEZ, GÖKHAN, M.S., November 2009, Industrial and Systems Engineering Consumption-Driven Finite Capacity Inventory Planning and Production Control (132 pp.) Director of Thesis: Gursel A. Suer Consumption driven finite capacity multi independent item inventory planning problem is studied. Four models are generated to solve the problem to minimize the total cost of inventory carrying, ordering and backordering. The first model reflects the classic (s, Q) policy. Model 2 has a feature of dynamic order quantity which enables system to increase or decrease the amount of items released as production orders. Models 3 and 4 both have the features of dynamic order quantities and dynamic reorder points. Dynamic reorder point is used to allow a production order to be released before reorder point violation occurs with respect to the vulnerability of backlog. In addition system is protected from overproduction and excessive inventory built by limitation parameters for r and Q. As a result, significant amounts of backlogs are prevented and total cost reductions are obtained by model-4 in highly variable demand environments. Approved: Gursel A. Suer Professor of Industrial and Systems Engineering

4 4 ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Gursel A. Suer, my family and my best friend Ali Yasar Yigit for their contributions, support and encouragement on this study.

5 5 TABLE OF CONTENTS Page ABSTRACT... 3 ACKNOWLEDGEMENTS... 4 LIST OF TABLES... 8 LIST OF FIGURES CHAPTER 1: INTRODUCTION Economic Order Quantity Statistical Inventory Control Periodic Review Policy Continuous Review Policy Materials Requirements Planning (MRP) Era Optimized Production Technology (OPT) Just-In-Time Paired Cell Overlapping Loops of Cards with Authorization (POLCA) Demand Management Justification Objective of the Study CHAPTER 2: PROBLEM STATEMENT Definition of the Problem Performance Measures... 44

6 6 CHAPTER 3: LITERATURE REVIEW Some of the Studies without Consideration of Capacity Single Item Multi-Item Some of the Studies with Consideration of Capacity as a Constraint Single Item Multi-item CHAPTER 4: METHODOLOGY Model 1: Multi-item Inventory Planning with (s, Q) Model Introduction Model Framework Conclusion Model-2: Multi-item Inventory Planning with Flexible Order Quantities Introduction Model Framework Conclusion Model 3: Multi-item Inventory Planning with Flexible Order Quantities Subject to Reorder Point- Inventory Status Relation Introduction Model Framework Conclusion... 76

7 Model 4: Multi-item Inventory Planning With Flexible Order Quantities Subject to Reorder Point- Inventory Status Relation with Restricted Ordering Introduction Program Framework Conclusion CHAPTER 5: EXPERIMENTATION & RESULTS Customer Demand Data Generation Determination of Initial Input Experimentation External Parameters of Experimentation Results Comparison of Results Summary of Results Cost Sensitivity Analysis Sensitivity of Backlog Cost Sensitivity of Order Cost Day off Affect CHAPTER 6: CONCLUSION REFERENCES

8 8 LIST OF TABLES Page Table 1 - Characteristics of Different Inventory Control and Production Planning Approaches Table 2 - General Features of Proposed Models Table 3 - Product Inventory Information Table 4 - Reorder Point Violation of Products 2, 5 and Table 5 - Information of Product Table 6 - Products to be produced Table 7 - Modified Order Quantities - Model Table 8 - Numerical example of k Qr Table 9 - Numerical Example for Model 3- Idle Capacity Case Table 10 - Products with Reorder Point Violation Table 11 - Remaining Products and Re-calculated Production Orders Table 12 - Uniform Distributions Used in Demand Generation Table 13 - The variances of demand distributions of products Table 14. Number of observations Table 15 - Number of replications run for models Table 16 - Results of 30 % customer demand load Table 17 - Summary of Results - 30 % Customer Demand Load Table 18 - Results of 40 % customer demand load Table 19 - Summary of Results - 40 % Customer Demand Load... 96

9 9 Table 20 - Results of 50 % customer demand load Table 21 - Summary of Results - 50 % Customer Demand Load Table 22 - Results of 60 % customer demand load Table 23 - Summary of Results 60 % Customer Demand Load Table 24 - Results of 80 % customer demand load Table 25 - Summary of Results - 80 % Customer Demand Load Table 26 - Results of 90 % customer demand load Table 27 - Summary of Results - 90 % Customer Demand Load Table 28 - Summary of Results Table 29 - Cost Parameters of Sensitivity Analysis Table 30 - Cost Sensitivity of Backlog Analysis Results 60 % Load Table 31 - Cost Sensitivity of Backlog Analysis Results 80 % Load Table 32 - Cost Sensitivity of Backlog Analysis Results 90 % Load Table 33 - Cost Sensitivity of Order Analysis Results 30 % Load Table 34 - Cost Sensitivity of Order Analysis Results 40 % Load Table 35 - Cost Sensitivity of Order Analysis Results 50 % Load Table 36 - Cost Sensitivity of Order Analysis Results 60 % Load Table 37 - Cost Sensitivity of Order Analysis Results 80 % Load Table 38 - Cost Sensitivity Analysis Results 80 % Load Table 39 - Cost Sensitivity of Order Analysis Results 90 % Load Table 40 - Day off Analysis Results 30 % Load Table 41 - Day off Analysis Results 40 % Load

10 10 Table 42 - Day off Analysis Results 50 % Load Table 43 - Day off Analysis Results 60 % Load Table 44 - Day off Analysis Results 80 % Load Table 45 - Day off Analysis Results 90 % Load Table 46 - Day off Effect on Total Cost

11 11 LIST OF FIGURES Page Figure 1. Periodic Order-Up to Policy (T, s, R) Figure 2. Periodic Order-Up to Policy (T, R) Figure 3. Continuous Review (s, Q) Policy Figure 4. Continuous Review (s, R) Policy Figure 5. Manufacturing Planning and Control System Figure 6. OPT Framework Figure 7. Process and Transfer Batch Sizes Figure 8: Signal Kanban Figure 9. Sequential Progress of Kanban System Figure 10. Kanban System Framework Figure 11: POLCA System Illustration Figure 12. Idle Capacity Situation Figure 13. Exceeded Capacity Situation Figure 14: Inventory Control Module Figure 15. Model 1- Flowchart Figure 16. Model-2 - Flowchart Figure 17. Model 3 Flowchart Figure 18. Idle Capacity Case - Model Figure 19. Insufficient Capacity Case - Model Figure 20: Model 4 Flowchart... 78

12 12 Figure 21. Idle Capacity Case - Model Figure 22. Insufficient Capacity Case - Model Figure 23. Customer Demand Intervals Figure 24. Variances of Customer Demand Distributions Figure 25. Behavior of Dataset - 30 % Load Figure 26. Behavior of Dataset - 40 % Load Figure 27. Behavior of Dataset - 50 % Load Figure 28. Behavior of Dataset - 60 % Load Figure 29. Behavior of Dataset - 80 % Load Figure 30. Behavior of Dataset - 90 % Load Figure 31. Sensitivity Analysis of Backlog Cost - 60 % Load Figure 32. Sensitivity Analysis of Backlog Cost - 80 % Load Figure 33. Sensitivity Analysis of Backlog Cost - 90 % Load Figure 34. Sensitivity of Order Cost 30 % Load Figure 35. Sensitivity of Order Cost 40 % Load Figure 36. Sensitivity of Order Cost 50 % Load Figure 37. Sensitivity of Order Cost 60 % Load Figure 38. Detailed Sens. Analysis - 80 % Load with Fixed Backlog Cost of $ Figure 39. Sensitivity of Order Cost 80 % Load Figure 40. Sensitivity of Order Cost 90 % Load

13 13 CHAPTER 1: INTRODUCTION Production planning and inventory control has been a vital issue in manufacturing industries for decades. Since inventory control always plays a significant role on meeting customer needs and on the performance of the system, plenty of models and production strategies have been developed to deal with relevant issues. In this section, main production planning and inventory control strategies are briefly explained Economic Order Quantity Economic Order Quantity (EOQ) model is the oldest model which was first published by Ford Harris, in 1915 and reviewed by Wilson in EOQ formula calculates economic lot size considering parameters such as annual demand, inventory carrying cost, ordering cost, and interest rate. Since a known and constant demand rate is assumed and no shortage is allowed, EQO is a robust and insensitive model in terms of cost when input parameters are changed. On the other hand, production systems having varying demand and leadtime need statistical methods to manage their inventory. Over the years, various different EOQ formulations have been developed and published in the literature. EOQ models are mostly single-item planning models. Some of the frequently used performance measures are minimizing shortage and reducing inventory. Various different lot sizing methods such as least-period cost, least-unit cost, part period balancing, etc. have been also developed to deal with known but time-varying demand. These lot sizing methods are also solved for each item individually.

14 Statistical Inventory Control As demand variability increases, EOQ does not provide sufficient inventory management support to manufacturing systems. Since demand rate is not constant in most real life situations, safety stock inventory is needed in reorder point models. Safety stock levels are determined such that shortages are minimized while keeping inventory levels at minimum. Until early 1960s, companies had been using statistical inventory control techniques to manage inventory systems. In the next few paragraphs, inventory policies are classified with respect to review type and ordering rule Periodic Review Policy i. (T, s, R) Periodic Order-Up To Policy with Reorder Point: In (T, s, R) policy, inventory level of corresponding item is checked at equal and predetermined intervals of time (T). This time interval of T is called review period. A predetermined reorder level (s) is checked at each T. If inventory level is less than or equal to the reorder point at checkpoints, an order quantity of (R-I) units is released to the shop floor. Otherwise, no order is given. It is shown in Figure 1.

15 15 Inventory Level (I) 4 4 Leadtime Leadtime : : : Figure 1- Periodic Order-Up to Policy (T, s, R) Time ii. (T, R) Periodic Order-Up To Policy (T, R) policy is similar to (T, s, R) policy. The only difference is that an order is automatically placed at each checking point, since there is no reorder point consideration. Figure 2 shows an example of this policy.

16 16 Inventory Level (I) Leadtime Leadtime Leadtime Leadtime : : Time Figure 2 - Periodic Order-Up to Policy (T, R) Continuous Review Policy In this policy, inventory status is checked continuously. Whenever it drops below or equal to reorder point (s), an order is placed. Depending on the order quantity, there are two main policies, (s, Q) and (s, R). i. (s, Q) Policy In this policy, order quantity is fixed and predetermined by using formulas such as economic order quantity (EOQ). Figure 3 shows an example of this policy.

17 17 Inventory Level (I) Leadtime Leadtime : : Time Figure 3 - Continuous Review (s, Q) Policy ii. (s, R) Policy (s, R) Policy: In this policy, inventory is checked continuously and whenever it drops below or equal to reorder point (s), an order is placed which should be equal to R (Maximum Inventory Level) Inventory. Figure 4 represents an example of this policy. Statistical inventory control techniques were widely used until computer software support became available to handle large amount of data in Materials Requirements Planning (MRP) era.

18 18 Inventory Level (I) 3 Leadtime Leadtime Leadtime : : Time Figure 4 - Continuous Review (s, R) Policy 1.4. Materials Requirements Planning (MRP) Era The problems of manufacturing planning and control are still alive in many aspects and in different shapes since Ford Harris first published the economic order quantity formula in According to Plossl s book, early simple forecasting techniques such as moving average and exponential average were still popular until 1960s. Orlicky, whom is called as the father of modern MRP, first applied Materials Requirements Planning (MRP) in 1961 on J.I. Case Company farm machinery. Before MRP, firms usually forecasted requirements by using simple forecasting techniques such as moving average, exponential smoothing (Robert G. Brown, 1959) for both independent (finished goods) and dependent (parts, subassemblies, raw materials) items. Since there was no

19 19 computer software support to handle tons of data, calculation and forecasting errors were still problems which were not easily surmountable. Orlicky s MRP has made many vital contributions to inventory control and production planning systems. First of all, Bill of Materials (BOMs) shows the components needed for finished products along with their usage. Secondly, MRP made production systems able to provide time-phased requirement records for items to be purchased or manufactured. A general framework of MRP is explained in Figure 5 by Vollman et al. They divided manufacturing planning and control system into three phases as front end, engine and back end. In the front end phase, actual demand and forecasts for independent items (finished products) are considered in building master production schedules of items. Master Production Schedule (MPS) is a build schedule for finished products. In the engine phase, by using bill of materials, routes and MPS, detailed materials planning is done. With routing files, capacity requirements to produce products and parts are calculated. In the back end phase, scheduling of production is planned and executed. Indeed, during execution and scheduling, depending on inventory status and dynamic demand, MPS can be modified to fit into available capacity and meet demand. According to Turbide, MRP has four main steps performed sequentially. The first step is to determine gross requirements of materials by using master production schedules. In the second step, gross requirements are compared with available balance of items on hand and then net requirements are calculated. The third step is to decide order quantities for the items which might have shortages (if no action is taken). One can choose from many lot sizing rules such as lot for lot, fixed quantity, etc. The last step is to decide start time

20 20 of orders by considering leadtimes and backward planning. The most critical factors for a successful MRP system are inventory data, BOMs, leadtime information. The accuracy of forecasting also affects the solution quality. MRP calculates capacity requirements with rough-cut capacity planning techniques such as capacity planning using overall factors (CPOF), capacity bills, and resource profiles while generating MPS. Once MRP records are generated, capacity planning is done by using Capacity Requirements Planning (CRP). However, MRP does not provide enough support to deal with finite capacity loading and leaves this task to the planners. Optimized Production Control is another philosophy to perform inventory planning and production control and is discussed in the next section Optimized Production Technology (OPT) Optimized Production Technology is advancement over MRP system because, it s a sophisticated shop floor control system which combines MRP logic and finite capacity loading for utilization of both bottleneck and non-bottleneck resources. Figure 6 illustrates the framework of OPT. First of all, it combines bill of materials, routing files and master production schedules to build product network which is a kind of tree diagram used to hold records of operational data of each item on network.

21 21 Resource Planning Production Planning Demand Management Rough Cut Capacity Planning Master Production Scheduling Front End BOM Routing File Detailed Material Planning Inventory Status Data Detailed Capacity Planning (CRP) Time Phased Requirement (MRP) Records Engine Material and Capacity Plans Shop Floor Systems Vendor Systems Back End Source: Figure 5 - Manufacturing Planning and Control System

22 22 Data such as required capacities, inventory records, resource capacities, order quantities, minimum batch sizes, alternate machine routings, and labor constraints are going to be used in resource description. Buildnet feature combines product network and resource description. Serve analysis routine identifies the bottleneck and nonbottleneck resources. SERVE routine uses MRP and its infinite capacity assumption and backward scheduling. SPLIT routine separates the network into two networks. The OPT network reschedules the bottleneck resources and the following critical resources by using lot for lot. After SPLIT routine, with the combination of OPT and MRP logics both bottleneck and non-bottleneck resources are identified efficiently and finite scheduling is performed with respect to capacity restrictions. The first main feature of OPT is that, it makes MPS possible to be used directly to schedule shop floor system with its contribution on finite loading through bottleneck resources. The second feature is that, it uses MRP logic for scheduling non-bottleneck resources considering infinite capacity assumption as well. One of the main differences between OPT and MRP is that, OPT enables system to reduce lot sizes until some resources become bottleneck. This allows both inventory and flow time to be reduced. There are two batch size terms used in OPT: process and transfer batch sizes. The process batch size is the main starting batch size. It is broken down into smaller transfer sizes to move peaces faster and among machines and thus WIP and leadtime are reduced.

23 23 Product Network Resource Description BUILDNET Reports OPT/ SERVE Master Engineering Network SPLIT OPT Network Serve Network OPT SERVE Critical Resource Schedule Non-Critical Resource Schedule Reports Reports Source: F. Robert Jacobs, OPT Uncovered: Many Production Planning and Scheduling Concepts Be Applied with or without Software, Industrial Engineering, October, 1984 Figure 6 - OPT Framework

24 24 An example is given in Figure 7 where process batch size is 36 units and transfer batch size is 12 units. Once 12 units are completed at one machine, they all are transferred to the next machine. In the next section, another important approach, called Just-in-Time, will be discussed. Figure 7 - Process and Transfer Batch Sizes 1.6. Just-In-Time In early 80s, Japanese philosophy of production and planning system, Just-in- Time (JIT), had become another strong approach which enables the manufacturing system to be aware of wastes and weak sides of MRP. In 1988, O grady defined the main disadvantages of Orlicky s production planning and inventory control approach (MRP) as higher inventory holding costs, lack of responsiveness and risk of inventory s becoming

25 25 obsolete. He also categorized the problems with MRP as poor inventory level accuracy, inaccurate leadtimes and BOMs, poor MPS, out of date data and poor methodology. JIT has drawn interest among manufacturing and service companies over last two decades. Especially Kanban system at Toyota in Japan has been making an important contribution on naming this new approach as philosophy. From a planning point of view, Kanban system works as a replenishment method that manages and executes production planning and control via control cards (Kanbans) and containers. To apply JIT as a philosophy and Kanban as a replenishment method, manufacturing process should be under control, and quality requirements should be met. However, since MRP, OPT and JIT are discussed from a planning perspective, Kanban will be considered and compared to others in terms of replenishment method. Kanban is a Japanese word which stands for card or signal. Kanban system works based on pull concept whereas MRP supports push method. Whenever there is consumption on a downstream station, the upstream station is alerted by production cards to start manufacturing of parts for the downstream station to use. There are four main types of cards used in Kanban system to keep flow of production under control and execute. 1. Production Kanban: Whenever consumption occurs on a station, production Kanban is released and it triggers manufacturing. 2. Conveyance (Move) Kanban is, a Kanban type, basically used for moving items from the upstream machine or station to the downstream one. 3. Supplier Kanban is similar to conveyance Kanban except that supplier is considered as an upstream station.

26 26 4. Signal Kanban is shown in figure 8. There is a resemblance between signal Kanban and reorder point policy. Signal Kanban includes two components: triangle and rectangular signals. Triangle signal represents reorder point and rectangular one stands for order quantity. It is used between two consecutive stations to trigger production whenever on hand inventory level drops to a minimum level (reorder point). In the example shown in Figure 8, each container has 200 units. Therefore reorder point is 600 units (three containers) and order quantity is 1000 units (5 containers). When two containers are removed, triangular Kanban is exposed and order for 1000 units (five containers) is released. Order Quantity 1000 units Reorder Point 600 units Figure 8 - Signal Kanban Manufacturing in stations and moving buffer stock from previous stations to following ones are basically handled by Kanbans with no need of additional inventory control. Figure 9 shows a brief explanation of Kanban flow in a shop. Since there is no

27 27 need for inventory control, number of Kanbans is the main factor which affects buffer stock sizes. There are several formulas used to calculate of the number of Kanbans. One of the widely used formulas is given in equation 1. % Equation 1 The reorder point is calculated considering leadtime demand and safety stock (SS) as given in equation 2. Equation 2 = Daily Demand * Leadtime in Days + Safety Stock Both the number of Kanbans and reorder point calculations is taking leadtime into consideration. Leadtime is the amount of time between placing an order and receiving the items of the order. Most planning models and systems are suffering from uncertainty of demand during leadtime. Even though, Kanban system works as a reverse logic of MRP, which is shown in Figure 10, leadtime and leadtime demand remain as critical issues in both MRP and Kanban systems. However, Kanban system s consumption-driven production is better and more effective than MRP s combination of forecast and actual order based production. Suri stated that Just-in-Time education, total productive maintenance program, quick changeover program, zero defects, visual workplace etc. are necessary to implement and execute a successful Kanban system. Debates on benefits of pull and push systems still continue. However, Plossl s second

28 28 principle of planning and control of a manufacturing system, there is no single best way to control a manufacturing business, seems still valid. Suri lists disadvantages of JIT. First of all, having lack of ability to perform custom jobs and being designed for repetitive manufacturing systems make JIT hard to implement on such customized manufacturing systems. Secondly, as product variety increases, a classic Kanban system starts having large amount of WIP in spite of its philosophy of maintaining least possible WIP. Thirdly, since Kanban system and cards, container sizes are designated to meet current demand (not forecast), when there is a surging demand in a growing market, this will cause late deliveries, backlogs and shortages. Then he defines an extended method which encompasses both pull and push methodology named POLCA (Paired- cell Overlapping Loops of Cards with Authorization) Paired Cell Overlapping Loops of Cards with Authorization (POLCA) Kanban, a well-known type of pull systems, performs well in repetitive manufacturing environments. In make-to-order or make-to-engineer systems, using Kanban may become a wrong option. In Kanban system, there is a direct relation between cards, containers and product type. However, in make-to-order or engineer-to-order systems, implementing such a direct relation is not handy, since these systems have high product variety that usually creates higher WIP. One of the main contributions of POLCA is that POLCA cards work between two cells and represent jobs instead of products to be performed on corresponding pair of cells. The main context of POLCA is described as a combination of features: (1) a high level material requirements planning

29 29 system (HL/MRP), (2) cellular manufacturing organization, and (3) flat BOMs. Riezebos states that the synchronization of cells in terms of job sequence and routes is the main problem of planning cellular environments and this insufficient synchronization causes waiting times and incomplete product buffers.. According to him, refilling a stock position with respect to product specific control creates inefficiency in especially maketo-order companies. Product specific control is a kind of material control system which replenishes stock position with respect to specific product or component. CONWIP and Generic Kanban are alternative ways of product anonymous control instead of product specific control. The main idea of product anonymous control is that it focuses on releasing order to shop floor regardless of actual product type. POLCA uses route specific control which is a kind of product anonymous control. Suri listed five key features of POLCA. Firstly, releasing authorizations for jobs to cells are created by HL/ MRP. HL/ MRP is a feature which provides tickets to be used in shop floor planning and execution of systems. These tickets provide information about the routes of jobs, sequences of operations, components to be added during corresponding operations, and authorization times depending on lead times.

30 30 Source: Figure 9 - Sequential Progress of Kanban System

31 31 Source: Figure 10 - Kanban System Framework Secondly, POLCA cards are used to control material flow between cells. Kanban system can be used within cells as well. Thirdly, POLCA cards are assigned to jobs rather than products and used between two cells to control synchronization of production and flow by checking jobs over paired cell loops. Fourthly, POLCA card stays with corresponding job during its journey between two consecutive cells. In other words, a job

32 32 cannot be started in a cell until the right POLCA card is released from downstream station and become available for the upstream station for that job and the authorization time (ready time) which is determined by HL / MRP is satisfied. An example is shown in Figure 11 from Suri s book. There are 3 main operations performed. Two cells for operations A and B, one cell for operation C are allocated in system. There are two jobs defined, namely job 1 and job 2. The route for job 1 is A11 - B22 - C31. For job 2, it is B21 - A12 - C31. POLCA cards are designed with respect to shown paired cells. The originating and destination cell and the number of POLCA cards are included on the cards. For job 1, if raw material and A11 /B22 POLCA card are available at the beginning of cell 11, and the authorization time of job 1 is satisfied, then job 1 can be launched to cell 11 for operation A. After the completion of operation A of job 1 on cell 11, A11 / B22 POLCA card gets into the POLCA card queue of cell 22 for operation B with job 1. After job 1 arrives to cell 22, it is required that a free B22 / C31 POLCA card is available for job 1 to be processed on cell 22. When B22 / C31 POLCA card becomes available, job 1 is launched to cell 22 to start operation B. Here, there is a significant difference from Kanban system. Job 1 will carry two POLCA cards on cell 22 for operation B. These are A11 / B22 and B22 / C31. Therefore, both A11/B22 and B22 / C31 POLCA cards stay with job 1, on cell 22 for operation B. Thus, previous cell does not produce for the current cell, while operation B of job 1 is going on. Because of this issue, the term overlapping loop is used in acronym of POLCA. After the completion of operation B of job 1 on cell 22, A11 / B22 POLCA card can be brought back to cell 11 and can be used to launch new jobs.

33 33 For operation C of job 1, B22 / C 31 POLCA card should be available and the authorization time has to be satisfied to start operation C. In addition, if we consider operation C is the last operation for job 1, there is only one POLCA card, B22 / C31, is used for operation C. For job 2, the same procedure is applied as well. POLCA system works well since the POLCA cards make planners and executers sure that a cell only works on jobs for which the following cell also has enough capacity to make it through. POLCA differs from MRP with respect to this point, and offers a better and reasonable shop floor execution. POLCA system ensures that bottleneck resources are considered first during shop floor execution. Suri stated three main advantages of POLCA over MRP and pull systems : 1. Since POLCA cards are used among paired cells and stay with a job during its journey over two cells, they keep cells utilized with jobs which are more eligible to be done in near future by checking both authorization times and availability of corresponding cards before releasing the job to the cells. 2. HL/ MRP feature of POLCA mainly determines authorization times of jobs on their journey in the system with respect to their routes. These times are determined by using leadtimes and MRP logic.

34 34 (Source: Rajan Suri, Quick Response Manufacturing, 1998, Productivity Press, Oregon, Portland) Figure 11 - POLCA System Illustration

35 35 In shop floor execution of POLCA, both POLCA card and job s authorization time should be available to launch the job to the cell. This authorization time keeps system to do necessary jobs. In authorization time determination, POLCA uses leadtime information and BOMs. 3. In standard Kanban system, workstations are placed in close proximity to keep small amount of intermediate inventory and small number of bins and cards. In other words, POLCA cards can go over long loops in terms of physical distance or job route length. Especially in MTO systems, POLCA cards can perform better in terms of shorter throughput time and less WIP of items due to infrequent demand for items. For further information on POLCA, the reader can refer to Suri s Quick Response Manufacturing book and Reizebos s article Demand Management Vollman defines demand management as an activity which manages day-to-day interactions between customers and company. As a response to the question that why is demand management important?, Landvater states that stabilizing production plans and master production schedules through demand management results in vital production improvements, smoother and more effective operations of all planning and execution functions. Demand management in manufacturing planning and control systems is the main issue that is discussed in this study. Demand is the main trigger point in a company regardless of whether it is a service or manufacturing system. If a company does not

36 36 provide sufficient feedback from market place, company strategic plan is directly affected from this situation. For manufacturing companies, demand management includes such activities as forecasting, order entry, order delivery date promising, customer order service, and physical distribution. For planners, there are two main types of demand, namely, actual demand and forecasted demand. Some inventory management and production control systems such as Kanban do not use forecasted demand at all. They solely function based on actual demand. In push systems such as MRP and OPT, demand management includes both forecasted and actual demand. For a company which is seeking successful interactions among demand management, production planning, resource planning and master production scheduling and shop floor execution, as shown in front end part of figure 1 (MRP Framework), the linkage between market place and MPC must be strong. According to Vollman, the connection between demand management and production planning depends on the time interval of production plans, e.g. quarterly, monthly etc. and their statement type such as number of items or money. Three main activities are handled by demand management with respect to the relation with production planning as providing efficient synchronization and communication between market and production plan, and complete demand information. The relationship between demand management and Master Production Schedule varies depending on manufacturing environments. There are three main demand management strategies; Make-to-Stock (MTS), Assemble-to-Order (ATO), and Make-to- Order (MTO).

37 37 i. Make-to-stock: Future sales are forecasted based on historical data. The production orders are released to meet forecasted demand. Finally, customer demand is met from inventory. Therefore MPS works as a build to forecast plan. ii. Assembly-to-Order: In this environment, components and parts are built to the forecasted demand. However, the completion (assembly) of finished product is delayed until an actual order is received. One of the key responsibilities of schedulers is arranging viable customer order promise dates by using available-to-promise information. This approach is useful when different products have common parts and demand is uncertain. iii. Make-to-order: In this environment, the company builds the product only after an order is received. Most of the time, the product is designed to meet the specific requirements of customer. Therefore building to stock would be a risky strategy and is avoided. Some of key principles to have a successful coordination of demand management and production control are: i. All sources of demand should be considered and data should be sufficient in terms of time, quantity, location and source. ii. Available to promise concepts are required to be used during order promising. iii. iv. There should be stabilized and well developed customer service standards MPS should provide coordination for outbound product flows

38 v. MPS and demand management should be coordinated good enough to keep system stable and flexible. 38 In conclusion, demand management is a key requirement to provide stable and efficient coordination between market place (demand) and production planning and control. Demand management should also provide sufficient support to production and planning system in terms of accurate data of orders and forecasts depending on the replenishment type of system in use. Production system type is an important factor on decisions made about demand management strategies Justification In this chapter, seven main inventory planning and production control systems are briefly explained. Changes in time, needs, customer profiles, technology, economy and world have been having vital effects on planning systems. At the beginning of the 20 th century, EOQ was the most appropriate formula to find order quantity and use in inventory control and production planning systems. Its known and constant demand assumption provides robustness and insensitivity against changes made in cost parameters. On the other hand, lack of shortage consideration was a weak side. Lot sizing procedures such as least-unit cost, least-period cost were used to deal with varying demand situation since EOQ was not sufficient. Later on, statistical inventory control techniques (SICT) provided models which are able to deal with stochastic demand environments. However, all these three main methodologies are able to deal with single items. In our proposed approach, we will consider multiple items and stochastic demand

39 39 which are the most complicated situations and reflect the real life the best. On the other hand, MRP, JIT, OPT and POLCA are able to deal with multi-item cases too. MRPs contribution is that dependent and independent items planning became more efficient with the BOMs and MPSs. However, there is still lack of support in terms of finite capacity loading in MRP. JIT does not consider capacity as a constraint, during the execution of orders. It uses the principle of production demand. Production orders are released based on actual consumption. However, when demand and product variety increases and production system is more custom type than repetitive, JIT s advantages may not work. Optimized Production Technology (OPT) includes finite capacity loading by considering bottleneck resources as a decision parameter during the scheduling of production. POLCA s contribution on inventory control and production planning is that high level MRP execution on cellular environment with flat BOMs became possible with Suri s approach. Each approach on inventory control and production planning issues has strong and weak sides as well depending on the production system type, business, strategies, internal and external limits, etc. Some of the characteristics of these approaches are summarized in Table 1. The proposed approach reflects the features of pull system since its production planning is performed based on only consumption and there is no forecasted demand considered. Historical demand data is just used for the estimation of reorder points. From these aspects the proposed approach sits into the JIT and POLCAs category. However, at the same time, capacity utilization and capacity planning is done during production planning and inventory control processes which separates the proposed approach from

40 40 JIT and POLCA. Since the proposed approach works on a daily period and order decisions are made simultaneously with finite capacity allocation, it resembles to OPT. In addition, production system is utilized close to 100 % filling idle capacity with new production orders. Dynamic reorder point consideration makes the proposed approach different from SICT and JIT which are using fixed reorder point and order quantities. In conclusion, the proposed approach attempts to combine some features of pull and push systems, which makes it a combination of consumption-driven and capacitydriven production. Since the proposed approach is a consumption-driven production planning and inventory control system, these features put it into pull category. On the other hand, the proposed approach is a kind of push system, since capacity utilization is considered and system capacity is filled with new orders based on dynamic reorder point consideration when idle capacity is observed based on finite-capacity approach Objective of the Study The overall objective of this study is to propose new procedures for inventory control and production planning in a manufacturing company. These procedures aim to minimize the total cost of inventory carrying, backorder and ordering costs.

41 41 Table 1 - Characteristics of Different Inventory Control and Production Planning Approaches EOQ LOT SIZING PROCEDURES SICT MRP JIT OPT POLCA PROPOSED APPROACH DEMAND DETERMINISTIC CONSTANT x VARYING x STOCHASTIC - x x x x x x NUMBER OF ITEMS SINGLE x x x MULTIPLE x x x x x CAPACITY LOADING Infinite Finite Finite REORDER POINT CONSIDERATION - - Fixed - Fixed - - Dynamic ORDER QUANTITY Fixed Fixed / Varying Fixed / Varying Fixed / Varying Fixed Fixed / Varying Fixed Fixed / Varying SYSTEM TYPE (PULL / PUSH) Push Push Pull Push Pull Push Pull Pull

42 42 CHAPTER 2: PROBLEM STATEMENT In this chapter, the problem to be studied is described in detail along with performance measures considered Definition of the Problem The motivation for this problem comes from a boiler and thermo siphon manufacturing company in Turkey. There are ten products with deterministic processing and setup times. The volumes of products change from 20 liters to 120 liters. The main process of manufacturing is based on manual and machine-based welding. The steps of manufacturing are briefly bending sheet iron, spot welding of vertical body, spot welding of top and bottom lids, finite welding of top, bottom and vertical body and testing with high pressured water. As the volume of product increases, welding time also goes up. Backorder is allowed with a penalty cost. No customer order is considered as lost sale. Backorder cost, ordering cost (setup cost) and inventory carrying cost are included in the total cost function. In this environment, there might be days where no production orders released to the shop and manufacturing system is underutilized. On the contrary, there might be days where production orders are released all at once and capacity may not be sufficient. In this study, an attempt is made to strike a balance among avoiding backorders, maintaining high level of resource utilization, and holding lower inventory. The assumptions made in this study are as follows; Inventory positions checked once a day Equally important products

43 43 A system capacity of eight hours (1440 minutes) per day No overtime allowed Single cell shop floor configuration Partial delivery of customer order allowed Reorder points are estimated based on historical demand data for all products. However, no forecast is used for releasing production orders. Demand is generated on a daily basis and is supplied from on hand inventory, if available, and inventory position is adjusted. If on hand inventory is not sufficient, backorder occurs. As reorder points are violated, production orders are released for replenishment subject to available capacity. There are two main situations that the proposed approach deals with; 1. Idle capacity (underutilization of system) 2. Insufficient capacity (overutilization of system) Figures 12, 13 show illustrations of these two situations as examples. According to Figure 12, the reorder points of products 2, 4 and 10 are violated and production orders are released. The capacity utilization in this case is only 44 %. In this case, the proposed approach deals with the problem of underutilization of the system. An attempt is made to increase the system utilization. On the other hand, in Figure 13, several production orders are released and the capacity utilization is 108 %. Since overtime is not considered, the proposed approach deals with the overutilization of the system. In this situation, alternative methods are considered to lower utilization down to acceptable levels.

44 44 In conclusion, the problem to be studied is defined as the consumption-driven multi-independent item inventory planning and production control problem in a dynamic demand environment with finite capacity planning Performance Measures The tradeoff between the number of orders and lot size is an important factor on the total cost of inventory management. The total cost function consists of three cost elements which are order cost, inventory carrying cost and backorder cost. The total cost function to be used is shown in equation 3. Equation 3 Index t Number of days in the planning horizon Parameters Order cost for item i Inventory holding cost per day per item i Backorder cost per day per item i Variables TC Total cost Number of orders given for item i in day j (0,1) Number of items held per item i in day j

45 45 Number of items backordered per item i in day j The order cost measures the cost of total number of released production orders to shop floor. It is calculated by multiplying the order cost with the number of orders in the entire planning period. Inventory carrying cost is the cost of holding inventory on hand and is computed by multiplying inventory held with the unit carrying cost. Backorder cost is the cost of not meeting the demand of a customer as soon as he orders, in other words, not having enough amounts of items on hand to meet his orders exactly. Thus, the remaining units of order is delayed to the next available time (becomes backorder). The total backorder cost is computed by multiplying the number of backordered units with the unit backorder cost. In addition, it is assumed that no customer and customer order will be lost.

46 Figure 12 - Idle Capacity Situation 46

47 Figure 13 - Exceeded Capacity Situation 47

48 48 CHAPTER 3: LITERATURE REVIEW Many studies have been presented on inventory planning field in this century. Nowadays this field can be called as ocean in terms of many branches and hundreds of studies. Most of the studies related to inventory planning and control of manufacturing area do not include capacity as the part of problem. However, in this study, capacity is considered as constraint. A classification of the reviewed articles is built based on Summers s parameters as Consideration of capacity as a constraint or not Having single item or multi-item 3.1 Some of the Studies without Consideration of Capacity Single Item As the name suggests, these studies focus on a single item. Chikan s Inventory Models book is one of the best sources in terms of classification and explanation of inventory models since first EOQ model was published by Ford Harris in As an earlier study, Koenigsberg modeled single-item multi reorder point inventory policy which enables 1 to k*q orders depending on the amount of violation in reorder point with constant leadtime and deterministic demand. Another continuous review inventory model is applied by Jose, Sicilla and Garcia-Laguna to a single independent item planning problem. It includes parameters such as deterministic demand, shortage and backlogging. Customer impatience is included as a function in this analysis. The cost function consists of setup cost, holding

49 49 cost and shortage cost. The presented approach was used to determine economic lot size, reorder level and total cost.. Most of the companies still use leadtime as a constant variable in their planning system. However, since MRP and related approaches required backward planning from independent forecasted demand to dependent items by considering leadtime, many of firms have still been suffering from shortages or backorders as a result of leadtime errors. In 1958, Wagner and Whitin were the first who studied on dynamic lot sizing problem and found an answer to question of what to do when classic square root formula does not work efficiently due to non-steady state demand rate and varying inventory costs which is difficult to assume as average? on a single item planning problem. Karmarkar criticized the assumption of constant leadtime in planning and examined relationship between lot sizes and leadtime in terms of cost for batch type production oriented shop floors. Kim and Benton issued the same problem which has single independent item with deterministic demand over a finite time horizon based on Karmarkar s study in 1987 about questioning the connection between leadtime and lot size. They defined leadtime as a function of lot size which means that if order quantity is decreased, leadtime is going to be decreased otherwise leadtime is increased as order quantity is increased. Hariga modified Kim and Benton s model which is solving single item (Q, r) problem to find optimal or near optimal lot size as a function of leadtime and he proposed a new procedure which finds optimal or near optimal lot size and reorder point at the same time. As a result, his study produces better results in terms of lot sizes, leadtimes and total cost.

50 50 Gupta and Brennan studied uncertainty of demand and leadtime and their effects on single item with multi-level product structures. A simulation model is run to observe the influence of uncertainty Multi-Item Blyka and Rempa studied a joint replenishment problem which includes multiitem planning and scheduling with the objective of minimizing production and inventory carrying cost on a finite time period.. Downs, Matters and Semple worked on a multi-item planning problem with resource constraints, lags in delivery and lost sales concepts. A linear programming approach for inventory carrying and shortage costs is generated and used with respect to order up to R policy. Zipkin worked on a problem of multi-item planning with stochastic demand and no leadtime consideration. He proposed two alternative approaches; first in first out and longest queue. Both approaches attempt to provide sufficient solutions of when to order and which item to assign.