LIMITATIONS AND PERFORMANCE OF MRPII/ERP SYSTEMS SIGNIFICANT CONTRIBUTION OF AI TECHNIQUES

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1 19 th International Conference on Production Research LIMITATIONS AND PERFORMANCE OF MRPII/ERP SYSTEMS SIGNIFICANT CONTRIBUTION OF AI TECHNIQUES J. Oleskow, P. Pawlewski, M. Fertsch Institute of Management Engineering, Poznan University of Technology, Strzelecka 11 Str., Poznañ Abstract An important characteristic of any manufacturing system is its ability to maintain an immutable performance under changing operations conditions, the disturbances and unpredictable events that can influence the performance of system s objectives. The determination of how much to produce of each component in each time period, in a synchronized way, arises in MRPII/ERP systems. The aim of this research is to contribute to the development of more realistic MRPII/ERP systems. Powerful tools for solving wide spectrum of non clear defined problems in the field of production optimization and scheduling are artificial intelligence techniques like genetic algorithms, simulated annealing, tabu search. In the following paper authors present a review of AI techniques that might be successfully implemented for all sorts of the MRP problems with special impact to lot sizing problems. The potential benefits and limitations of certain techniques are being described as well as our research domain. Keywords: MRPII/ERP systems, genetic algorithms, simulated annealing, tabu search 1 INTRODUCTION In today's dynamic and turbulent business environment, there is a strong need for the organizations to become globally competitive. This means that, in order to produce goods tailored to customer requirements and provide faster deliveries, the enterprise must be closely linked to both suppliers and customers. Otherwise it becomes difficult to supply the dynamism of the market. In the production sector, there is no possibility to reach the quality and production agility with traditional methods. Many companies now realize the need for the development of world class systems and methodologies, as well as acquiring the productivity tool that will let them be in a commercial position to offer competitive manufacturing resource planning that assures customers of quality goods and services and compliance with international quality requirements on different industry fields. Material requirements planning (MRP) is regarded as one of the most widely used systems for production planning and control in industry. It has gained significant popularity in the 1970s when Orlicky (1975) and Wright (1984) rigorously advocated the potentials and benefits of MRP. Orlicky s book, MRP The New Way of Life on Production and Inventory Management, formalized the fundamental concepts and principles of MRP in MRP is a production control program that improves the production efficiency and service supplied to the customer. The issues related to material requirement planning logic (MRP) in large business application has remained relatively consistent even as systems become more and more complex and evaluated to currently available enterprise resource planning systems (ERP). The key characteristics include the coordination of assembly and purchased component requirements through time-phased order releases and the reduction of setups through the aggregation of common part requirements. Despite the sophistication of commercial software using MRP logic it had been criticized for leading to high work-in -progress inventory and long lead times [1]. There is a wide range of conflicting perspectives regarding the usefulness of MRPII/ERP systems, and popular literature provides many negative accounts describing the limitations and inaccuracies of existing MRPII/ERP systems. Although implementing MRPII/ERP systems can be very expensive, and require large project teams, they have become widely used; initially in large companies, and more recently also in medium-sized companies. Furthermore, developments in Internet technology have enabled MRPII/ERP systems to he used for information sharing with suppliers and customers, and new ERP-based applications are emerging: for instance, salesforce automation, customer relationship management, data mining and supply-chain management systems [2]. An important characteristic of any manufacturing system is its ability to maintain an immutable performance under changing operations conditions, the disturbances and unpredictable events that can influence the system s ability to achieve its performance objectives [3]. In existing structures within MRPII/ERP systems, the AI techniques are recommended to 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 THE PURPOSE OF MRP SYSTEMS MRP computer systems or MRP logic comprised by evolving MRPII/ERP systems serve the organization by providing the functions below: In terms of Inventory [4]: Determine the number of parts, components, and materials needed to produce each end item. Determine the right part, right quantity, & right time to order parts. Provide time schedules for ordering materials & parts.

2 Maintain a bill of materials sequencing the assembly parts of the final product ("schematic, product structure tree"). Priorities: Order for the right due date, keep the due date valid. Capacity: Plan to optimize the use of plant & equipment capacity, Plan an accurate Objectives: MRP has the same objectives as any inventory management system 1. To improve customer service 2. Minimize inventory investment 3. Maximize production operating efficiency According to fundamental philosophy of Material Requirements Planning: the materials should be expedited (hurried) when their lack would delay the overall production schedule and de-expedited (delayed) when the schedule falls behind and postpones their need [6]. When MRP systems are implemented properly they allow firms to realize the following benefits: ability to price more competitively, reduce sales price, reduce inventory, better customer service, better response to market demands, ability to change the master schedule, reduce setup and tear-down costs, reduced idle time. In addition to these benefits, MRP systems also: Gives advance notice so managers can see the planned schedule before actual release orders. Tell when to de-expedite as well as expedite. Delays or cancels orders. Changes order quantities. Advances or delays order due dates. Aids capacity planning.[4] Despite of beforehand mentioned advantages we cannot forget about various shortages which arise from real world MRPII/ERP applications. We will define them precisely in the next section. 3 LIMITATIONS OF MRP ASSUMPTIONS Traditionally, MRP system implementation has been based on the assumption of a deterministic environment. Thus, demand and lead times have been assumed to be deterministic. In a typical manufacturing environment, nevertheless, this assumption is invariably violated. This conflict between the assumption and reality in the implementation of MRP is often advanced as the reason for the failure of MRP to fulfill its promise. 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 (Master Production Schedule), 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 [5]. The APICS literature, 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 [6]. The inaccuracy of the bill of materials and inventory database is a common problem with MRP systems. Inaccurate bills of materials mean inaccurate material and capacity plans. Providing a management system that will facilitate data accuracy will likely require major adjustments in strategic management approaches Being able to cope with the uncertainty of the manufacturing environment is of course not a new concern, even if it is more and more relevant with regards to the present industrial context. Many uncertainties in Material Requirements Planning (MRP) systems are treated as controllable elements, with a variety of buffering, dampening and other approaches being used to cope with them [7]. However, such approaches are often found wanting, forcing enterprises into emergency measures to ensure delivery performance. MRPII/ERP systems use fixed lead-time to plan for material purchase and product manufacture. This ignores real life uncertainties of supply unavailability and variability of queue, set-up and run times on the shopfloor. Net requirement patterns generated do not consider the availability of resource simultaneously, but identify resources required as a separate, subsequent activity. MRPII/ERP systems may be loaded with a predetermined scrap rate. Any increase in this rate will automatically render due date uncertain unless corrective measures are taken. Such measures may well impact upon other products in the system, an effect that is not normally monitored [8]. Existing traditional methods have also other weakness which considerable limit their practical applications. For example they search for optimum considering all possible cases, therefore when planning horizon is becoming longer the number of alternative schedules increase dramatically. Moreover these methods establish lot sizes for individual items only on one level in the bill of material structure (BOM) hence have got false assumption of the demand for the item that is constant. Summing up our dissertations the most commonly shortages of MRP systems, from our point of view, are following: 1. Limitations on the length of the MRP planning horizon over which optimal order schedules can be found. Usefulness in practical situations is questionable since large numbers of alternative schedules would need to be considered and, in addition, optimal short-term schedules would not necessarily result in optimization of inventory over the long term. 2. Limited use in manufacturing industry owing to the complexity of the procedures required to generate optimal or near-optimal schedules. These have often been found to be difficult for operating personnel within manufacturing organizations to understand. 3. Existing methods treat the lot sizing problem as a single-stage process, but MRP is a multistage process and, hence, any lot sizing techniques must consider all items whose demand is related, both horizontally and vertically, throughout the bill of material (BOM) structures [9]. There are conducted various researches and in particular the following issues are considered as analysis domains: a. the impact of forecast processing frequency, especially when combined with a variable frozen period in production planning ; b. the impact of the MRP procedure running frequency; c. the impact of lead time uncertainty. [10]

3 19 th International Conference on Production Research Regardless of the types of MRP/ERP systems used within the MRP-planned manufacture, uncertainty that could occur during the manufacturing process is indifferent. For instance, scrap could be caused by the poor quality of raw material, machine variation or labour mistakes. Therefore we must enable a complete consideration of all combinations of uncertainties under an MRP-planned manufacture for preventing discrete examination on uncertainty An uncertainty categorization structure can be developed using the systems theory to categorize uncertainty into input and process. This structure has also addressed the uncertainty that occurs in the supply and demand chain of the manufacturing process. We categorize uncertainty in following mode table 1 and table 2. Table 1 Uncertainty connected with input INPUT : Late Supply Forecast errors Customer order changes Table 2 Uncertainty connected with process PROCESS: Interoperation move time Queue waiting time Process lead-time Variability in set-up and run time Tooling unavailability Material unavailability Operator absence Machine breakdown Late supply Variability in resource supply Capacity loading Order released prematurely Engineering changes Lot-sizing and planning horizon System uncertainty WIP lost Safety stock changes Record errors Unplanned transactions Process yield loss Ouality variation Scrap Allocation not issued in expected quantity Order released in unplanned quanlily The most important of which are: 1. There is no evidence for the existence of a detailed structure of types of uncertainty. This absence results in past research not identifying the relative significance of uncertainties and only the effects of uncertainty being examined. 2. Most of the previous research has studied uncertainty discretely and only some research has considered uncertainty combinations. Even within the combinations, only specific combinations of uncertainty were examined. 3. Input uncertainty has received more research attention, especially at external demand compared with other uncertainties. This finding shows a suboptimal approach in coping with uncertainty because evaluation is not made on the relative significance of uncertainty by considering all possible types of uncertainty. 4. Little research has been identified in examining interactions between uncertainties. After review of various studies we see that so far many approaches have been proposed to deal with uncertainties in an MRP environment. Some specific solutions have been proposed which are applicable under a given set of circumstances, but there are no general solution methodologies that would aid the practicing manager in this environment. For example, as buffering is performed in response to uncertainty, reducing uncertainty would lead to a reduction in buffer size and possibly reduction in the number of locations. However, reduction in uncertainty can be obtained by either fixing the MPS or by means of frequent rescheduling. Uncertainty may exist at all levels in a product structure, demand uncertainty at the end-item level, and yield, capacity, lead time as well as supply and demand uncertainty at the component and sub-assembly level. [7,11,12,13,14] An important shortcoming in most of the previous work in this area has been the limited amount of realism in the models and approaches. None of the works reviewed benchmarks the parameters used in the studies with any industrial data. Given the widespread use of MRPII/ERP systems, such data to ground models could and should be used. Because the applicability and usefulness of the approaches in MRPII/ERP systems would seem to be the degree of realism built into the approach, it would only be logical to focus on the models that get closer to the real environment. 4 AI TECHNIQUES EMBEDDED IN MRPII/ERP SYSTEMS Powerful tools for solving wide spectrum of non clear defined problems are artificial intelligence technique like fuzzy logic, genetic algorithm, neural networks, simulated annealing, tabu search etc. hence they can be also applied in the field of production optimization and scheduling. 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 [15]. 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. The most commonly applied AI techniques comprise 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

4 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.[16] Our example of AI application in the field of MPR systems is lot sizing problems domain. It is one of the most important problems arising in the field of operation planning. One variable in MRPII/ERP system design is the selection of method to determine how much to order (the lot-sizing rule), once the MRPII/ERP system has determined it is now time to order. The determination of when to order involves the perpetual-inventory logic behind the MRPII/ERP system, the result of which is timephasing order requirements. How much to order, within the context of MRPII/ERP systems, is a current topic of interest to both production/material planners and academics. The crucial objective of lot sizing problem is in MRPII/ERP systems is to reduce the total manufacturing cost including setup cost, inventory holding cost, overtiming cost etc., while trying to satisfy the customer s requirements with the limited capacity [17]. The sizes of lots have to be carefully chosen in order to avoid schedule with a large number of lots of very small size that need large setup time on machines Lot sizing problem greatly affect the performance of manufacturers but are often an afterthought in most factories. MRPII/ERP systems ask users to provide lot sizes as an input for their calculations, but most people have difficulties to point the optimal lot-size regarding following issues: how much of each product should be run at one time, should lots be split, when the production of items should start. The theoretical framework for effective solution of lotsizing problems in MRPII/ERP systems should fulfill the following requirements: encompasses the entire product structure multilevel dimension include the ability to deal with multi-item systems regard capacity constrains be able to deal with rolling time horizon deal with varying cost and demand reduce system nervousness Based on defined by authors requirements for lot-sizing technique within framework of MRPII/ ERP system application the simulated annealing, tabu search and genetic algorithms seems to be appropriate. More detailed research we provide with respect to Genetic Algorithms. Table 3 Comparison of selected AI techniques in the field of lot sizing problems Method/approach Area of application Benefits Limitations Simulated Annealing -Multi-item unconstrained lot sizing problems - single- and multistage capacity constrained lotsizing problems with set up times and set up cost Genetic algorithms -multi-item lot sizing problems - single- and multistage capacity constrained lotsizing problems with set-up times and set up cost Tabu search Single and multilevel lot sizing problems with and without constraints - might be used for problem where a desired global optimum is hidden among many, poor, local optima - flexibility, able to manage with e.g. varying cost, varying demand - able to deal with complex problems - objective function can consider criteria other than costs, such as delivery times, quality levels - the same procedure can be used whatever the demand pattern, order or holding costs, order cycles, lengths of planning horizon and demand distributions, - not restricted in the number of planning periods taken into consideration, - flexibility, - ability to deal with a large number of parameters. - reduce the computational complexity - might be used for complex and large optimization problems - quick response time and provide good optimal and sub-optimal solutions - the annealing cooling procedure is not transparent to the user and might lead to the lost of insight of lot sizing problem - the simulated method is rather slow - neighborhood must be defined (representation method is required) -heavy data structure of the encoding -indirect encoding - ability to handle unfeasible solutions (necessity to adopt a punishment rule) - structure and memory length, stopping rule and aspiration criterion must be defined in a suitable manner - technique is complicated - handle with unfeasible solutions is required - neighborhood must be defined (representation method is required) The industrial lot-sizing problem determines the best replenishment strategy that size of replenishment and

5 19 th International Conference on Production Research timing of the production quantities. Finding an optimal solution for dynamic multistage production systems under some special assumptions still suffers from computation complexity. Several heuristic approaches have been proposed in various aspects of the lot-sizing problem but they can only guarantee the local optimal solution. The genetic algorithm approach, simulated annealing and tabu search may be appropriate tools to overcome this draw back. AI techniques can be used to determine the process how to be made. While giving decision for MRP, AI techniques can make big additions. 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. In the inventory problems for the demand there is an uncertainty for demand caused from being randomize and fuzzy. When determining demand; fuzzy logic and optimization can be used to find a solution. Our current research domain is optimization of lot sizes in company with changeable range of goods requiring complex and specialist machines, tools and special materials. All these factors have a tremendous impact on the total costs level. During production process planning we face up to arising conflict: on the one hand efficient loading machines and tools is required, on the other hand we must keep material consumptions at the lowest level. Lot sizing planning must be carried out quickly and efficiently using all available data, striving to eliminate or reduce production standstills. In given company lot sizing optimization becomes really challenging problem. Actually we develop solely genetic algorithm-based method for more effective and less time-consuming problem solution. A natural extension of this research study (using GAs) will be to optimize lot-sizes through various AI techniques as well as to optimize lot-sizes, safety stocks and safety lead times simultaneously. This should give us an even lower total cost when using safety lead times and safety stocks at the same time. 5 SUMMARY In practice, resources are finite and if optimum benefits cannot be obtained from the application of traditional approaches, no significant enterprise improvement will result by leaving the significant uncertainty within the system. Hence, the role of artificial techniques is crucial for more optimum application MRPII/ERP systems. Enterprises must use the MRP to manage the production process more efficient. If they realize the production process more efficient and productive with MRP can decrease the costs. But the existing MRPII/ERP 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 AI ideas and the sophistication and power offered by modern computer technology. REFERENCES [1]Enns S.T. (2001): MRP performance effects due to lot size and planned lead time settings. International Journal of Production Research, vol. 39, No.3, pp [2]Caldwell, B, and Stein, T, (1998) 'New IT agenda', 30 November: 30-8, [3] Patty W. Cheng, Effective Use of MRP-Type Computer Systems to Support Manufacturing, Master of science in Industrial and Systems Engineering, March 1997, Blacksburg, Virginia [4] Chase, R. B., and Aquilano, N. J. Production and Operations Management. R.D. Irwin, Inc [5] Ersöz S., An Expert System For Production & Marketing Integration, Doktora Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, [6] Joseph A. Orlicky 1975 Material Requirements Planning: The New Way of Life in Production and Inventory Management New York: McGraw-Hill. [7] D.R. Guide, R.Srivastawa, A review of techniques for buffering against uncertainty with MRP systems, Production Planning & Control, 2000, vol. 11, no. 3, 223 ± 233 [8]Buzacott J. A., Shanthikumar J.G., Safety stocks versus safety lead time in MRP controlled production systems. Management Science, 40, 1994, [9] Stockton D.J., Quinn L., Khalil R.A.: Use of genetic algorithms in operations management Part 1: applications, Proc. Instn.Mech.Engrs, vol.218, part B:J. Engineering Manufacture, 2004, [10] Caridi M., Cigolini R., Managing safety and strategic stocks to improve materials requirements planning performance, Proc Instn Mech Engrs Vol 216 Part B: J Engineering Manufacture, 2002, [11] BLACKBURN. J. D.. KROPP. D. H. and MILLEN. R. A A comparison of strategies to dampen nervousness in MRP systems. OMEGA Internalionat Journal of Management Science [12 BYRNI;, M. D. and MAPFAIRA, H An investigation of the performance of MRP planning in an uncertain manufacturing environment. Proceedings of the I4th National Conference on Manufacturing Research. Derby. UK. pp I6. software has problems about this issue and causes being unsuccessful. The defective sides of MRPII/ERP 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 MRPII/ERP software can be avoided by using AI techniques. When obtaining MPS, 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

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