Business Sustainability III 1 Bahadir Akyuz, Adriana Araújo, Leonilde Varela, António Monteiro University of Minho School of Engineering 4800-058 Guimarães Portugal bhakyuz@gmail.com, dricafaraujo@gmail.com leonilde@dps.uminho.pt, cmonteiro@dem.uminho.pt Management Tool based on MRP and Lot-sizing A case study for comparing different cases Abstract MRP enables orders planning to provide an efficient and effective way to meet demands on a company. Although it is not a modern planning tool it still keeps its importance within ERP systems. Improved forms of MRP are used commonly by many companies. By using such systems Companies plan their material flows according to their daily problems. Moreover, one of the complicated problems arising in the context of MRP is lot-sizing, which has a large number of rules to effectively plan orders. In this study a management tool for supporting MRP and lot-sizing is presented, by using three different cases, to determine effectiveness of lot-sizing rules. The proposed tool is based on Arena simulation software, and is able to determine total cost and total order number of L4L, EOQ, PBB and LUC for orders planning. Keywords Material Requirement Planning, MRP, Lot-Sizing, L4L, EOQ, LUC, PBB 1. Introduction Material Requirements Planning (or MRP for short) is a production control and inventory control system which uses detailed material lists, inventory situation and master production planning (Browne, Harhen & Shivnan, 1988). Generally it is based on computers (software) but for some relatively easy situations it is possible to build this system by hand. This paper aims at putting forward a tool for supporting MRP and Lot sizing decision making and is structured as follows: section 2 presents a short literature review about MRP systems. Section 3 refers to the main structure of MRP systems description. Section 4 summarizes the Lot sizing rules used and section 5 presents the results obtained for the proposed case studies. Finally, section 6 resumes some of the main conclusions. 2. Literature review It is largely known that it is still important in much kind of companies to carry on performing production planning functions before and along the production process. Over the years some improvements have taken place. Production organizations have used various systems to record and handle their material situation to achieve in their production. In professional sense, Material Requirements Planning (MRP) was used in the Second World War manually, then originated in 1960s, computer based tools, in USA. After USA, it spread to Europe countries. Joseph Orlicky, one of the developers of MRP, has affected this development period of MRP with his studies and first detailed textbooks on this area. When MRP was founded in 1960s, it was quite simple. At that time period also some new features were installed into MRP. Today, a MRP system includes MPS (Master Production System), PAC (Production Activity Control), RCCP (Rough Cut Capacity Planning), CRP (Capacity Requirements Planning) and DRP (Distribution Resources Planning) (Browne, Harhen & Shivnan, 1988).
Business Sustainability III 2 Today, while MRP systems and its advanced forms are widely used, APICS (American Production and Inventory Control Society) has an important place for this situation. They provide a common area for all companies which use MRP, bringing some standards for MRP systems (Mabert, 2007). Additionally MRP is the main pillar of ERP (Enterprise Resources Planning) which is indispensable for many modern companies (Snapp, 2012). 3. Structure of MRP Management Requirement Planning Systems need some information to run effectively. This information basically includes production quantity, production method and inventory records that an organization has currently available. Due to this reason, MRP requires Master Production Schedule (MPS), Bill of Materials (BOM) and an Inventory Record File (IRF). Master Production Schedule (MPS) shows which product, in which quantity and in which time is needed to be delivered to the customer or to be ready. To gain this information, MPS benefits from the exact orders which customer identify, through all the needed details and forecasts, which is related to the number of orders expectation. The MRP system should be able to determine a feasible plan to implement requirements of MPS. Bills of Material (BOM) specifies how to make or produce a product. Each final product consists of intermediate products which get together in certain way and variants led to a BOM that keeps their list in hierarchy. Generally it looks easy, but for example to produce a main board of a computer it is needed to gather hundreds of small pieces. BOM holds the list of rules and quantities of ingredients, and components to create the final product. It can be called as product tree (Chase, R.B., Jacobs, F.B., Aquilano, N.J., 2004, p. 591). To assemble a LCD TV, basic requirements are: panel (screen), front cover, back cover, main board, and cables. For an effective MRP system, BOM is extremely important because a possible mistake in BOM increasingly reflects on the production process, while it includes multiplications (Stevenson, W.J., 2002, p. 646). When companies update their production or assembly process about a final product, they should also update BOM file of that product. Figure 1 shows a tree for product P structure. Inventory Records File (IRF) is the third one of primary inputs of MRP, which stores information about current status of products the company/ organization. This information includes not only product amount on hand but also product supplier, safety stock amount, and production lead time, among other information (Stevenson, W.J., 2002, p. 646). MRP system accesses status of product according to time buckets of Inventory Record File, which represents the specific time intervals. Figure 1. Product Tree of P. If a Company has enough number of A and B, it takes time (lead time of P ) to get/produce P or when a company orders part G it takes time to arrive to the company localization. In Table 1, all lead times can be seen. These times will be used for the MRP calculations. Table 1. Lead times of parts In the other hand company should know how many products are demanded. This information is provided by the Master Production Schedule (MPS). In this case according to the MPS, the company should meet 20 units of product P demand, for the example to be considered. Third input of MRP was in the Inventory Records File (IRF). The Company should consider revising their inventory before ordering or producing new products. According to the IRF file, the company has stock of the products and components as shown in Table 2.
Business Sustainability III 3 Table 2. Stock quantities 4. Lot-sizing Until now, can be solved the problem of knowing what quantity of products the company needs for each time planned. After that, the management system has another decision point, which has to decide how much quantity of products the company should order (or produce) to satisfy the demand needs, and this is called as Lot-Sizing (Chase, R.B., Jacobs, F.B., Aquilano, N.J., 2004, p. 604). Lot sizes aim to meet company s needs and they can meet these part requirements in once for one or more time horizons planned. Therefore, Lot-Sizing is a problem which makes MRP more complicated. In this paper it is targeted to minimize total costs, which include setup cost and holding cost, based on various lot-sizing methods (Stevenson, W.J., 2002, p. 651). Setup cost is a term used for a cost that the company has to pay for each lot and it is generally fixed. Other elements of the total cost include a holding cost, which is about companies stocks. To make stock of each product has a cost. If a company stocks large number of products, it has to pay more, as because of that the total holding cost increases. All companies have to decide quantities of orders (lot sizes) to minimize their cost. In order to decide, there are many different lot-sizing methods that can be used. According to Ho, most commonly ones are, Part Period Balancing (PPB), Silver-Meal (SM), Lot for Lot (L4L), Economic Order Quantity (EOQ), Least Unit Cost (LUC) and Least Total Cost (LTC) methods, which are used to determine lot sizes (2008, p. 5098). Some of these methods require complicated calculations, while others require basic ones in order to determine the lots size. Each method has some advantages and it is not possible to say that one of these methods is always better than others (Ho and Ho, 1999, p. 153). For each specific situation different lot sizing rules may be more or less advantageous. In this study details of 4 most commonly used lot sizing rules are provided and a comparison of these methods is presented for different situation. These 4 techniques are L4F, PPB, LUC and EOQ, which are going to be explained next. 4.1. Lot for Lot (L4L) In this dynamic lot sizing rule, orders are set to exactly match the net requirements. There is no stock, no safety stock, and no anticipation of next orders with this technique and it is efficient when a company has frequent orders and setup cost is not very expensive, while holding cost is expensive. 4.2. Part Period Balancing (PPB) This is one of the dynamic techniques which try to balance setup and holding cost (DeMatteis, 1968). This method firstly creates an economic part period (EPP), which is a ratio of the setup cost to holding cost. Then the system calculates the holding cost of all probable order quantities, namely for a first period, for first and second period, for first, second and third period, and so on. The order quantity, which has closer value to economic part period is chosen by the use of this technique. 4.3. Least Unit Cost (LUC) The least unit cost method is another dynamic method which has some similarities with part period balancing method. It uses the same determining method with PPB that calculates ordering and inventory cost for each possible lot size and divides them by number of total unit, then chooses lot size with minimum unit cost. After determining first lot size, it implements same process next orders and determine the next lot. 4.4. Economic Order Quantity (EOQ) This technique varies from the first three rules because of being static and this method is recommended for cases where relatively independent demand exist, or non dependent demand (Heizer, Render, 2001, p. 589). This model, which companies commonly applies, uses an estimate of total annual demand, setup and holding cost (Mabert, 2007, p. 346). According to this estimate value, it provides order quantities. This model is not designed for discrete time periods. Estimate of EOQ is obtained by:
Business Sustainability III 4 EOQ = (2*D*S / H) where D is annual demand, S is the setup cost per order and H is the holding cost per product. 4.5. Comparison of lot-sizing rules In this section 3 different cases and their lot sizing cost for L4L, EOQ, PPB, LUC will be presented, for varying values of setup and holding cost. In order to calculate the total cost for different cost values and methods, The Arena Simulation software was used in this study. Calculations are based on 8 time periods. Before determining the costs, it is useful to mention that the EOQ method generally gives large cost values because the organization has usually some inventories at the end of this 8 period-based horizon, so it would be useful each time the EOQ approach is used next another method should be applied to finish all the inventory at the end of the 8 periods. There will be considered 3 different cases and assumptions made about stable, wavy and seasonal production situation, which will be detailed for each case. Their comparison will be determined according to the total cost versus rate of setup cost and holding cost. These ratios are not definite indicators for every situation because the total cost varies according not only to the setup and holding costs but also regarding quantities of orders. For a new situation it is better to implement the simulation again and analyze new results. For the different ratios, holding cost is kept as 0.5 per product and setup cost is taken values between 1 and 800. Therefore, ratios vary between 2 and 1600. For the evaluation performed for each one of the cases cost graphs show these ratios versus the total cost. 5. Case Study 5.1. Case-1 In this case it is aimed to determine costs according to a stable schedule. As it can be seen in Figure 2, demand quantities are almost the same for each component. (Order quantities are respectively 94, 91, 107, 113, 116, 110, 97 and 116.) With this production schedule it is assumed that quantities of demands of production or assembly are almost exact through an annual period and it can be easily forecasted according to past information and data. In order to compare costs of each lot-sizing for case-1, Figure 2 should be considered. Figure 2. Order quantities of case-1. When the setup cost is relatively smaller in the beginning, firstly L4L then LUC seem likely to be the most advantageous approach, as the ratio of setup cost and holding cost is relatively smaller, so the system prefers to avoid to stock, which is the aim of L4L. LUC gives almost the same cost values for the beginning, while it meets demands with less order number. After that, the more setup cost is implemented for order, the less advantageous L4L shows and the most dynamic and complicated methods, PPB and LUC provide less costs. But, there is no regular relation between ratio and their cost. While LUC is more advantageous between ratio value of 600 and 1200, PBB takes advantage between 1200 and 1800. It is not possible to find direct relation after one point to state that one of the methods is more advantageous. In general, EOQ almost seems that it provides the biggest cost value, but the point which was mention at the beginning of this section is important in this sense. For very big number of setup cost it seems to be a more profitable tool to determine order quantity than L4L, although it makes some stocks for the next orders. For very small numbers of holding cost, L4L method does not seem to be a useful method.
Business Sustainability III 5 5.2. Case-2 In the second case, it is assumed that the company has a wavy demand schedule. This means company s production plan depends on customer demand. Demand quantity can even be almost zero, after a period which has a very large number. Figure 3 shows the demands for case 2 (respectively 94, 4, 33, 146, 7, 185, 27 and 10). For these situations, it is hard to forecast the next period demand or total demand. In order to compare costs of each lot-sizing for case-2, Figure 3 should be considered: Figure 3. Order quantities of case-2. In the first period, the most profitable method seems a lot similar to case-1, as the same reason lies on this reality. From beginning for ratio value of 80, the two most advantageous methods are L4L and PBB. (Second method was LUC after L4L in case 1). Similarly PBB provides less cost with less number of orders. It will be advantageous to use PBB while the company has some problematic with its suppliers like absence of communication or being very far away from a supplier. After beginning part of the chart, L4L loses its advantage, LUC and PBB seeming likely to be most advantageous methods. But like in the first case, it is not possible to say that PBB or LUC is more profitable than another one for any given internal, as it depends on ratio value, so on setup and holding costs. On the other hand EOQ seems to be the least advantageous approach while it starts to give more benefit than L4L in an earlier point than case-1. 5.3. Case-3 Finally, in the third case, it is urged that to handle an approximation of seasonal production with two periods representing a season. The Company has the information that demand of one season level will be relatively more, after a less-demanded season. As it is seen in Figure 4, orders are almost in the same level for the seasons two periods. (Exact numbers are 31, 36, 178, 184, 33, 39, 197 and 174). Figure 4 shows the cost analysis for case-3: Figure 4. Order quantities of case-3. As usual, L4L is one of the most efficient methods in the beginning. Now other two methods, PBB and LUC accompany it, not just one of them and differently L4L loses its less cost performance, very earlier than the other cases. PBB and LUC try to give best result in an irregular way while ratio of setup cost and holding cost increases. It is hard to estimate that one of these methods is advantageous. PBB gives less cost for some intervals while LUC has less value for other intervals. Here it is easily seen that EOQ takes advantage of
Business Sustainability III 6 providing less cost than L4L, earliest in the whole cases. If the effect of disadvantage of making stock of EOQ is considered, it can be predicted that EOQ is more advantageous. 6. Concluding remarks While it was purposed to develop time schedules for production in 1960s, MRP has grown on its importance by using advanced and various tools which run together in an effective and efficient way to assist to all major functions of companies. In modern days, it can be simply said that dynamism is getting more important for all organizations. In terms of this moving world, Information dynamism of MRP keeps a major role. In order to get more profitable and desirable results Companies still try to reach it through MRP tools and, therefore, they have to be aware of the need for updating their information from day by day to an hourly basis, or even minutely. In MRP, lot-sizing problem take one of most important roles. Like its main system, dynamism and information updating is really inevitable for lot-sizing to be valuable. In this study there were analyzed 3 different cases and it was shown that in different numbers of demand orders, even very small changes affect profitability of an organization. In this point, accurate values in Master Production Schedule (MPS) provide more desirable results. Likely, if all information is kept updated in BOM and IRF, a company will be able to more easily reach its goals. For lot-sizing problems, it is advisable for organizations to develop a dynamic way to determine orders quantity because the associated cost can vary significantly for different amounts of demand and/ or different values of setup and holding costs. Therefore, they should develop and design their plans in a highest possible frequency. Acknowledgments The authors wish to acknowledge the support of the Fundação para a Ciência e Tecnologia (FCT), Portugal, through the grant Projeto Estratégico UI 252 2011 2012 reference PEst-OE/EME/UI0252/2011. References Browne J., Harhen J., Shivnan J. Production Management Systems: A Cim Perspective, Addison-Wesley Publishing Company, 1988. Chase, R.B., Jacobs, F.B., Aquilano, N.J., Operations Management for Competitive Advantage. International Edition, New York: McGraw-Hill Companies, Inc., 2004. DeMatteis, J.J., An Economic Lot-Sizing Technique: The Part-Period Algorithms, IBM Systems Journal (30:38), 1968. Heizer. j., Render, B., Operations Management. Sixth Edition, New Jersey: Prentice-Hall, Inc., 2001. Ho, C., Ho, S.K., Evaluating the effectiveness of using lot sizing rules to cope with MRP system nervousness, Production Planning and Control (10:2), 1999. Ho, C., Exploring the compatibility of dampening procedures and lot-sizing rules in MRP systems under uncertain operating environments, International Journal of Production Research (46:18), 2008. Ho, C., Ireland, T.C., Mitigating forecast errors by lot-sizing rules in ERP-controlled manufacturing systems, 50(11), 2012, 3080-3094 Mabert, V.A. The early road to material requeriments planning, Journal of Operations Management 25 (2007), 346 356. Moon, Y.B. and Phatak, D., 2005. Enhancing ERP system s functionality with discrete event simulation. Ind. Manage. + Data Sys., 105 (9), 1206 1224. Plenert, G., Focusing material requirements planning (MRP) towards performance, European Journal of Operational Research (119), 1999. Snapp, S., The History of MRP and DRP, 2012, retrieved at 16th June, 2013 from http://www.scmfocus.com/ scmhistory/2012/08/the-history-of-mrp-and-drp/ Stevenson, W.J., Operations Management. Seventh Edition, New York: McGraw-Hill Companies, Inc., 2002.