Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, 2006: 339-345 Sakarya University, Department of Industrial Engineering
Using Artificial Intelligence in Material Requirement Planning (MRP) Özgür Şaştım Kırıkkale University, Engineering Faculty, Department of Industrial Engineering
Serkan Köroğlu Kırıkkale University, Engineering Faculty, Department of Industrial Engineering Yahşihan - Kırıkkale/Turkey Phone: 0318 357 35 71 Fax: 0318 357 24 59 e-mail:
[email protected] Mustafa Yüzükırmızı Kırıkkale University, Engineering Faculty, Department of Industrial Engineering Yahşihan - Kırıkkale/Turkey Phone: 0318 357 35 71 Fax: 0318 357 24 59 e-mail:
[email protected] Süleyman Ersöz Kırıkkale University, Engineering Faculty, Department of Industrial Engineering Yahşihan - Kırıkkale/Turkey Phone: 0318 357 35 71 Fax: 0318 357 24 59 e-mail:
[email protected] Abstract For managing the Small and Medium-sized Enterprises (SME’s) more efficient and productive, Material Requirement Planning (MRP) software should be used. But the complexity of the MRP software and the lack of qualified personnel make the MRP usage less effective. In this study, our aim is to supply the MRP software with Artificial Intelligence for using the software easier and making more useful. Keywords: MRP, Artificial Intelligence, Defectives of MRP
1. Introduction The enterprises have to use the new techniques for competing in the competitive environment of the changing World’s conjuncture. Otherwise it becomes difficult to supply the dynamism of the market. In the production sector, which mostly consist of SME’s, there is no possibility to reach the quality and production agility with traditional methods. For SME’s making more competitive expansions are related with their productivity. Hence it is unavoidable for enterprises to use the MRP software. However the complexity of MRP software and lack of qualified personnel makes this usage less. When planning the material needs, the computer algorithms can not provide the necessary flexibility. The MRP software is deficient in different sectors, different products, with changes in the supply times, capacity, demand of customer, amount of products, amount of the inventories and deciding the appropriate location of the inventory and causes uncertainty conditions. In this study; to make the MRP software easier to use and applicable for all alternative processes, they should be empowered with Artificial Intelligence (AI) techniques and this will effect the performance of the enterprises greatly. The MRP software which targets the inventory level to a minimum and arranges the materials in needed place on needed time can make important contributions to the enterprise’s production processes and customer satisfaction. MRP is a production control program that improves the production efficiency and service supplied to the customer. MRP arranges the material needs for the related terms with the production planning made for product inventory information and Bill of Material (BOM) information. On the other hand MRP can not provide the intended flexibility because of the structure of classical programming. In existing structures, the AI techniques must be used to solve problems and to improve the performance of the software, so with the new structure combined with AI can adapt the real life conditions.
2. Material Requirement Planning (MRP) MRP is a scheduling and control technique that is used to make the inventory investments in the minimum levels, to increase the production, efficiency and improve the service delivered to the customer. MRP is a computer - aided system that arranges the production planning and control activities. MRP provides an efficient master plan, control of the materials and re-arranging the plans according to the possible changes. MRP makes the inventory level minimum and arranges the materials in needed place on needed time. MRP software compares the availability of the inventories and orders with the main production plan, arranges the factory capacity and lead time by the goals planned. MRP makes the main schedule for the product, for the raw materials that form the finished product, for the half-finished product and for the assembly parts. While preparing the detailed schedule of materials, other required components can be determined. To make the main schedule of the product; the delivery time and order time must be determined. Main schedule arranges the detailed product plan for the products according to periodic delivery. BOM arranges the amount of components that consist of the products. The inventory data gives information about all the components for today and tomorrow. MRP software processes these data and demands of the products, finally calculates the time and amount of order successfully. MRP is a system that controls the information and material flow; starting from production to sales location to the customer and makes programming and planning. The unwanted scraps; machine stops, delivery problems, change in the customer order plan and other lames can be re-organized. On the other hand; planning for the future capacity requirement planning, capital planning, re-arranging labor force can be also done in the Computer Integrated Manufacturing (CIM) with MRP. A MRP system can produce some
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outputs and these outputs can be inputs for other systems; facility planning systems, marketing systems, Job Shop Control Systems and Capacity Requirement Systems.
3. Defective Sides of MRP MRP system needs the Main Production Schedule (MPS), inventory information and BOM data input to work. These data exist as modules in the MRP systems. MRP system collects these inputs, processes and gives order reports, priority order reports, capacity planning reports and performance control reports as outputs. And also as an output of MRP system related reports of the modules are given. MRP systems collects the information of products from MPS, uses the BOM and inventory level information to calculate the exact demands of the inventory, planned and given order quantity. It determines the amount of which components on what time and when to order these components. At this point with the purchase orders and components of the product, MRP prepares the Production Program. After these processes some outputs are collected. The outputs of the MRP system provide the needed material program for every period demanded from MPS. The mainly outputs of MRP are; giving order, the maintaining of planned order in the future time period, re-programming and delay reports. With these reports, performance, inventory forecasting, obligation of buying orders and demand resource reports can also be produced. However, MRP system which intends to determine the gross and exact needs of the enterprises in the inventory units seems to be efficient but has some defective sides. These defective sides are; determining the best application to obtain the MPS, determining the lot sizes, determining the customer demands, capacity requirement planning, inventory levels and locations. These uncertainties cause the enterprises to be away from the appropriate conditions. (Ersöz, 1998) From at least one perspective, MRP has been a stunning success. The number of MRP systems in use by American industry grew from a handful in the early 1960’s to 150 in 1971. The American Production and Inventory Control society (APICS) launched its MRP Crusade to publicize and promote MRP in 1972. By 1981, claims were made that the number of MRP systems in the United States had risen as high as 8.000. In 1984 alone, 16 companies sold $ 400 million in MRP software. In 1989, $ 1.2 billion worth of MRP software was sold to American industry, constituting just under one-third of the entire American market for computer services. At the micro level, various surveys of MRP users have not painted a rosy picture either. (Hopp and Spearman, 1996) Booz, Allen, and Hamilton, from a 1980 survey of over 1.100 firms, reported that much less than 10 percent of U.S. and European companies are able to recoup their investment in an MRP system within two years. In a 1982 APICS-funded survey of 679 APICS members, only 9.5 percent regarded their firms as being Class C or Class D users. To appreciate the significant of these responses, it must be noted that the respondents in this survey were both APICS members and material managers-people with strong incentive to see the MRP system looking as good as possible! In this light, their pessimism appears most revealing. A smaller survey of 33 MRP users in South Carolina arrived at similar numbers concerning system effectiveness; it is also reported that the eventual total average investment in hardware, software, personnel, and training for an MRP system was $795.000, with a standard deviation of $1.191.000. Such discouraging statistics and mounting anecdotal evidence of problems have led many critics of MRP to make strong disparaging statements, such as; MRP is a “$100 billion mistake”, “90 percent of MRP users are unhappy”, and “MRP perpetuates such plant inefficiencies as high inventories”.
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This barrage of criticism has prompted the proponents of MRP to defend it. While not denying that it has been far less successful than they had hoped when the MRP crusade was launched, they do not attribute this lack of success to the system itself. The APICS literature (e.g. Orlicky as quoted by Latham 1981), cited the following four problems as the cause of most MRP system failures: 1. Lack of top management commitment. 2. Lack of education of those who use the system. 3. An unrealistic MPS. 4. Inaccurate data, including BOM and inventory records. (Hopp and Spearman, 1996) i) Defectives of MPS MPS is the mechanism that executes the MRP process. This plan is a detailed list that consists when to produce which product is obtained from demand forecast and sale orders. MPS is a schedule consists of arranging customer orders, demand forecasts, labor force, machine and material needs in an appropriate order. The output of the MRP system is mainly interested with this schedule therefore the decrease in orders, increase in orders, and cancellation of orders must be included. The inputs of MPS are demand and orders of finished product so the outputs must be in the type of finished product. The time period of MPS is the whole of planning term. Planning term must be longer than the supply of whole products. MPS determines the relationship between marketing and production functions by defining the customer orders and demand forecast with the production values in periods. MPS is a production plan with object of minimizing costs according to the capacity constraints. For a long term the amount of resources may not be constant. Then the MPS can be thought as the tool of planning the necessary changes in the amount of sources. Improving the main plan is an experiment process. The usefulness of the main plan is related with both describing realistic production and marketing entities. If the production is different than it is planned because of changing conditions then some preventions must be done, i.e. working over-time, including extra labor force… with these preventions, production lot must be approximately equal to planned. If preventions don’t work then MPS must be controlled and reviewed. When obtaining the MPS, MRP leaves the decisions to users to decide the appropriate method. With the characteristics of the problem and properties of the method it would be difficult to obtain the method that gives the more efficient solutions. When preparing the detailed schedule; the appointments of different alternatives can not be decided by the system. In the capacity planning when a discrepancy occurs with the given input data, furthermore if the material plan is over the capacity limits, warning takes place. Hence the solution of this problem is not explained and scarce resources can not be appointed. Warnings take place as review of the MPS and increase in capacity. The capacity is not related with the lead time parameters and queues are not included. In this process capacity planning is done after MPS and MRP. The constraints; except material constraints; are not included in the process. ii) The Defectives of Inventory Information Module Inventory records consist of every entity condition in the inventory. All the inventory records must be decided separately. These records must be updated with all deliveries, adding new entities or removing entities. Therefore the completeness should be provided.
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Inventory records should include; the time for backorders, order amounts and other property information. Some materials are stored in the warehouses and some come to warehouse due the scheduling process. For the materials that come to warehouse; entrance of material, order, obtaining time, obtaining place and order quantities information must be recorded. These records must be in an order. There are MRP system process and some factors affecting this process. The factors that effect the calculation of needs can be listed as following: a) Structure of the product: Production process that consist of material, part and subassembly part with some production level. b) Lot size: The order of the planning orders according to their economical amount. (In this condition order size can be higher than the exact demand size.) c) The different lead time of parts that is the component of finished product. d) Multi needs of any inventory entity that is used in finished product’s some process. e) The multi needs of any inventory entity that is used in one or more parts. f) During the planning term scheduling of finished product’s needs. MRP system determines the gross component needs from MPS and product structure records and deducted parts from the existing amounts according to inventory records. In the beginning of the scheduling process the inventory amount is taken as usable amount in production as “open orders” consisting of open buy orders and work orders that is hoped to be delivered. MRP both includes the ready amount and the on-hand orders. There are some methods for determining lot sizes. In the existing MRP softwares only one method is used. The existence of several lot-size determination methods proves that all of them are weak. To make a choice between these methods, there is a performance criterion to realize the effects of lot sizes on planned orders. With the existing methods it is obvious to provide the optimum solution. In the MRP system customer orders, material demand times and lead times are recorded as if they are known. The prevent this problem emergency inventory is hold for both entrance and exit. Except these inventories there is no need to hold inventory. However, if Just In Time (JIT) production system is not in use, the work-in-process (WIP) inventory better solution than raw materials or finished products inventory. There are some methods to determine the amount and location of WIP. In MRP these methods do not take place. iii) The Defectives of Bill of Materials (BOM) To produce the finished product; usage of all parts, components are needed. In the structure of BOM, the amount information of all parts; parts name and part number information are collected through and part number information are collected through the below of the finished product. Also BOM provides lead time of the components and stage codes information. BOM information is used in the enterprise greatly. In this information; product definition, the needed parts to build the product, the control of the engineering changes in the production, which materials are necessary for service parts and finished product, and to provide the MPS which parts to produce and which parts to buy; exist. Every time planned lead times are used as the same value in the MRP system. Lead times can change according to components of product, real capacity and the load of job shop.
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4. Artificial Intelligence and MRP Artificial Intelligence is formed from the processes; recognizing information researching the cause-reason relationship, recognizing and development of some comprehension techniques, with a large number of experiments by using computer. (Chorafas, 1992) AI uses Computer and Intelligence to help to solution of problems and provides the operations to be more productive and work at optimum. AI applications produce new generation and alternative information. AI techniques consists of four parts; Genetic Algorithms, Fuzzy Logic, Neural Network and Expert Systems. Genetic Algorithm (GA) is used in the problems whose mathematical model can not be produced and solution area is wide. It takes the evaluation process of the metabolisms in the nature. The basic of GA is being randomize and producing successful solutions. Fuzzy Logic (FL) is used to model the uncertainty of life problems mathematically. It is related with the degrees of occurring of the events. The most important property of fuzzy is the membership function. Neural Networks (NN) is used in speaking, capturing, signing, optimization problems, and robotic control applications and consists of doing work like human activity, in computer. Expert Systems (ES) is a computer software which is used for planning, clarifying, translation, consulting services, like human expert activities, uses automatically opinion techniques. In the conditions of being late or finishing early of lead-times in MRP, AI techniques can be applied. And the most appropriate planning for the production in the system can be done. In the JIT production system, for only one type of product, using linear programming and FL, this problem has been solved with an algorithm developed by Wang (1998). Determining the lot sizes can also be done by using AI techniques. Alternatives for the amount of lot sizes can be produced by this way for the inventory model selection. An Expert system was developed which calculates the lot sizes for the chosen inventory method in (Anagun, 1997) MRP is a planning process hence it makes planning only with given information concept. During this process, it doesn’t determine required process. AI techniques can be used to determine the process how to be made. While giving decision for MRP, AI techniques can make big additions. To support the MRP system with Decision - Support systems; ES, NN, simplex and Cased-Based Reasoning (CBR) techniques were used and alternative solutions were provided. (Warley, 2002) In the MRP process customer demand and sales forecasts are accepted as certain according to the forecasts, and are used as if exact information. Hence the production can not be realistic, and causes to make over inventory, not to supply the demand and not to use the resources at optimum level. Therefore the production costs increase and efficient production can not be achieved. To balance the customer demand and production, FL and linear programming were used to provide an efficient solution. The production amount for the different customers were tried to be optimum. (Nguyen, 2005) An Information System can be included to MRP which produces solution techniques with AI. If a MRP system works with this technique, to support the system AI solutions can be used. A software produces solution with NN and CBR were integrated to ERP systems and run. (Huin, 2003) In the inventory problems for the demand there is an uncertainty for demand caused from being randomize and fuzzy. When determining demand; FL and optimization can be used to find a solution. For the inventory problems FL and optimization were used to develop a model (Li, 2001). For the inventory control a model was developed to adjust the inventory level with dynamic production system. (Samantha, 2000)
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The uncertainties in MRP can be prevented by working MRP integrated with AI techniques. The uncertainties that can not be modeled mathematically can be modeled by AI techniques can make alternative solutions and suggestions.
5. Conclusion The SME’s must use the MRP to manage the production process more efficient. The enterprises which realize the production process more efficient and productive with MRP can decrease the costs. But the existing MRP softwares have problems about this issue and causes being unsuccessful. The defective sides of MRP systems must be detected and some improvement must be made, so MRP can be more useful and can decrease the costs. The uncertainties of existing MRP softwares can be avoided by using AI techniques. When obtaining MPS; which method gives the most efficient solution, determining lot sizes, customer demands, material obtaining and demand time, capacity planning, detecting the amount and the location of the inventory, some uncertainties occur. With eliminating these uncertainties by AI techniques, enterprises can work more efficient, productive and realistic with the existing conditions. As a conclusion, it is time to abandon MRP fixes and move toward a new generation of production control methods that exploit both the simplicity and robustness of the recorder point/AI ideas and the sophistication and power offered by modern computer technology.
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