INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD)

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1 INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD) International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue 1, January- June (2012), pp IAEME: Journal Impact Factor (2011): (Calculated by GISI) IJIERD I A E M E OPTIMIZING THE UNEVENNESS IN PRODUCTION SCHEDULING THROUGH MATHEMATICAL APPROACH: A CASE STUDY S. K. Gupta 1, Dr. V. K. Mahna 2, Dr. R. V. Singh 3, Rajender Kumar 4 1 Research Scholar & Associate Professor, Department of Mechanical Engineering, FET, MRIU, Faridabad, India Executive Director & Dean Academics, FET, MRIU, Faridabad, India Professor & Head, Department of Mechanical Engineering, FET, MRIU, Faridabad, India Asst. Prof., Department of Mechanical Engineering, FET, MRIU, Faridabad, India gupta.sarojkumar@gmail.com, rajender629@yahoo.com ABSTRACT World over manufacturing has received a great deal of attention in the recent few years for efficient utilization of resources, because of the market becoming customer oriented. There is still no consensus on how best to measure and manage production within manufacturing industries as there are numerous variables which change from industry to industry. In real world, the global competition is forcing entrepreneur s to cut corners on customer deliveries and input production cost. Under the circumstances production scheduling is an important matter especially in the organizations wherein the product consists of no. of models of the same product. This case reveals that the production is either in the form of overproduction or underproduction as and when compared with planned production. The situation of underproduction isn t that serious as is the case of overproduction since overproduction is generation of unnecessary inventories either in the form of WIP or finished goods. Therefore, this is nothing except generating a form of waste which is fundamentally against the principles of Lean Manufacturing. The situation of unnecessary and undesirable inventories results in lockup of funds, which could be otherwise utilized effectively. Mathematical modeling may be an answer to remove such waviness and unbalances through application of averages, variance and deviation. The study was conducted and data collected for the purpose of optimization in XYZ Company manufacturing Shock Absorbers with 203 different varieties catering to need of OE suppliers and replacement market. The most advantageous manufacturing priorities for a given competitive situation are also discussed in this paper. Keywords: Quality, Overproduction, Underproduction, Lean Manufacturing, Inventory. 74

2 1.0 INTRODUCTION Over the last two decades there has been enormous increase not only in demand but also in production supply of variety of products. In most of the manufacturing industries, the main issue of productivity is production planning. The materials play an important role so far as its contribution towards the product and also the cost are concerned. Therefore, a systematic control is essential so far as materials are concerned to avoid obsolescence, unnecessary storage and material handling including the delayed deliveries. Figure 1 shows the various inputs to production to make the system efficient and effective. Figure 1 Resources that must be managed effectively Twenty keys are discussed for workplace improvement in manufacturing industry as shown in Figure 2 [8]. These keys must be considered in order to achieve continuous improvement. These 20 keys are arranged in a circle that shows the relations between the keys and their influence on the three main factors: quality, cost, and lead time. The arrangement in the circle is not categorical, and some keys offer benefits in more than one factor. There are four keys outside the circle. Three of them (keys 1, 2, and 3) must be implemented before the rest, and key 20 is the result of implementing the other 19 keys. 75

3 Figure 2 Twenty keys for workplace improvement The lack of planning generates imbalance in the planned targets and subsequently deliveries. As a result of this, the customer demand gets nullified resulting into loss of revenues. The uncertain production plan and scheduling also leads to very heavy inventory of raw materials, WIP and undesired movements of materials. This leads to unnecessary breakup of lots considered for manufacturing. The result is loss of man-hours, machine-hours, material input and also increase in the input cost. All these are a type of waste which does not add any value. "A more liberal and extensive reciprocity in the production and sale of commodities is necessary, so that the overproduction of the organizations can be satisfactorily disposed off to the market. The problem of unevenness in planning and scheduling can be addressed through process of categorization of the demand factors pertaining to various models. This categorization shall provide a whip to the planning and scheduling department to keep a close watch and amend the actions where-so-ever required. Once, the objective of smoothening the production is achieved 80-85% then it becomes easy to meet the market requirements. This paper addressed the data bifurcation in which the products are refers to three main categories such as Production at par, overproduction, underproduction. This paper presents mathematical optimization with application of methodology for finding Co-efficient of variation [5]. 76

4 2.0 LITERATURE REVIEW Data filtering has become a key technology for modern production control systems. The goal of data bifurcation or filtration is a route to the right decision of controlling and optimizing the production. In order to deal with high variety of products, a very small variety of products requires the attention for getting the maximum efficiency. The VED technique is the best adopted as a routine practice for optimal use of resources and elimination of out of stock situations in the production house. The VED analysis will facilitate planning department to engage entire energy to limited area only leading to reducing overproduction to bare minimum including best utilization of funds [2,11]. Optimization is about utilizing the resources in organized manner to achieve higher efficiency. Optimization is thus, about achieving high utilization, high efficiency and high effectiveness at bare minimum cost [4]. Mathematical Optimization refers to the selection of a best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain, including a variety of different types of objective functions and different types of domains. 3.0 CASE STUDY A study was undertaken in a company manufacturing shock absorbers and having a wide variety of the product numbering 203. The period of this study was based on a data spread over the period of last six month as per Annexure 1. Table 1 shows the performance of actual production as compared with planned production targets. This variation has been graphically represented through Bar Chart as given in Figure 3. Table 1 Gross average monthly production Months Planned Production Actual Production Jan Feb' March' April' May' June' Average

5 Variation in Actual & Planned Production Production Quantity Series1 Series2 Series3 Series4 0 Jan 12 Feb'12 March'12 April'12 May'12 June'12 Months Detail 3.1 Inferences Figure 3 Gross average monthly productions On analysis of data given in Annexure 1, it was found that on an average 13 % production was inline with the plans ac in Annexure 2, 20% of the production on an average was overproduced i.e. over and above the planned production as in Annexure 3, and in balance cases it was observed that 67% of the production was in the category of underproduction as compared to palmed production as in Annexure 4. The process of straightening the plans is a quite heavy task therefore, the principles of VED analysis was followed for purpose of categorization so that effective controls could be introduced. Table 2 shows the VED analysis of over production, under production and production at par. The categorization establishes that production at par is the lowest followed by overproduction and further followed by under production. Table 2 Actual Performance Production Data Actual Model Contribution Production at Par 13% Over Production 20% Under Production 67% Total 100% 78

6 Model Contribution Production at Par, 13% Under Production, 67% Over Production, 20% Production at Par Over Production Under Production Figure 4 Pie chart for category bifurcation The production data reveals over planning as compared to manufacturing capacity. This variation is almost to the tune of 28% as given in the Figure 3. This over planning will result in unnecessary pressure on material planning, unnecessary occupancy of space, lockup of funds, obsolescence in spoilage of materials and frequent movement of the stocks. Table 3 provides comparative analysis of underproduction; overproduction and production at par in terms of no. of product produced in six months and the contribution of each category so-far-as volumes manufactured are concerned. Table 3 Comparative Analysis on Production Data Production Detail Production Quantity Percentage Contribution Production at Par % Over Production % Under Production % Total % It is concluded from Table 3 above that the Production Planning and Control Department of said company is working with no exposure to the technological advancements. It is evident that the 79

7 production data doesn t match with the planned production. In most of the categories, it is underproduction followed with overproduction and further very small percentage matching with the planned data. The data was further analyzed and it was observed that there is a huge variation from the standard deviation. This was further normalized with the help of mathematical modeling and the details are as per enclosed table. This normalized solution can remove the waviness to a great extent by introducing controls on categorized items only. Table 4 gives details regarding category-wise breakup of VED analysis. This table establishes that a mere control of 35 items out of 203 items (17%) will help in eliminating the imbalances in the production and improving the overall performance. Table 4 Results Summary Sheet Sr. No. Description Total No. of Items Category Bifurcation Items for Control V E D Total 1 Under Production (17%) Over Production (30%) Balanced Total Table 5 is based on optimized data including the percentage smoothness of both categories i.e. over production and under production. The category of overproduction being serious in nature has been smoothened to an extant of 92% with control on 12 items (0.6% of total varieties). Table 5 Optimization Results Production Total Produced Quantity Optimized Quantity Percentage Detail No. of Quantity No. of Quantity Contribution Products Products Over % Production Under % Production Total % 4.0 CONCLUSION In this competitive globalization, almost all the manufacturing organizations realize the value of on time delivery of their products to market or stockholders. The amount of product varieties was found under production to be tune of 67%, over production 20% and remaining 13% production just matching with the actual plans giving rise to huge build up of inventories all around. This also results in non compliance of deliveries and thereby an unsatisfied customer. Under production is the confirmation of living in dreams while it is quiet evident that the planned data is much beyond capacities and therefore away from realm. This statement is further confirmed 80

8 by the fact that the data depicts 62% of quantities produced are underproduction as compared with planned production. The situation is not so serious except that there is extra amount of burden for procurement to cater the needs of the planned production. Similarly, the fate of over production is also bad as it results in build up of unnecessary inventories either in the form of WIP or finished goods. This is contrary to fundamental principles of Lean Manufacturing. The amount of over production is to the tune of 36% of the item produced. The co-efficient of variation when applied to this situation optimizes the given data in a fashion that mere strict control of 9 % of the items produced can provide smoothness up to 38% and further control of 8% of items can remove waviness up to 35%. Therefore, the overall results can be improved up to 73% by controlling 35 items as given in the table above. REFERENCES 1. Dilworth, J. B., (1993). Production and Operations Management: Manufacturing and Services. Mc-Graw Hill Publication, 4th Edition. 2. Grewal C.S. and Sareen K.K. (2006). Development of model for lean improvement: A case study of automobile industry. Industrial Engineering Journal, Vol. 35(5), pp Hines, P., Rich, N. & Esain, A. (1997). "Creating a Lean Supplier Network: A Distribution Industry Case". Proceedings of the Logistics Research Network Conference, Huddersfield, U.K.: University of Huddersfield, pp Khare, S. (2006). Optimizing the Organization: Unleashing the potential of practices, processes and people. Tata Mc-Graw Hill Publishing Company Limited, New Delhi, pp Kobayashi, I. (1998). 20 Keys to Workplace Improvement. Productivity Press, Cambridge, revised edition, MA. 6. Ohno, T. (1988). "Toyota Production system: Beyond Large Scale Production". Productivity Press, Cambridge, MA. 7. Pavnaskar, S.J., Gershenso, J.K and Jambekar, A.B. (2003). "Classification scheme for lean manufacturing tools, International Journal of Production Research, Vol. 41, pp Sardana, S., Kumar, R., Bajwa, M. and Gulati, P. (2011). Application of SPC tool for finding variation in the Process Output (A Case Study). International Journal of Industrial Engineering Research and Development, ISSN: , Vol. 2, Iss. 1, May-Oct., 2011, pp Schroeder, R. G., (1993). Operations Management: Decision making in the Operations Function, Mc-Graw Hill Publication, 4th Edition. 10. Stevenson, W.J. (1996). Production and Operations Management, Irwin McGraw-Hill, 5th ed., New York. 11. Womack, J.P., and D.T. Jones, From Lean production to the Lean Enterprise, Harvard Business Review, pp (1994). 81

9 Annexure 1: Planned Production V/S Actual Performance from time period of January to June, 2012 Sr. No. Part No. January February March April May June Plan Actual Plan Actual Plan Actual Plan Actual Plan Actual Plan Actual

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11

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13 Total Annexure 2 Planned V/s Actual Production: Production at Par Optimization Sr. No. Product Code Plan Production Actual Production

14 Total Annexure 3: Analysis of Planned V/s Actual Production: Underproduction Optimization Sr. No. Code Total Deviation Average Plan Actual Prod Deviation in days %age Contribution Cumulative %age

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17 Total Sr. No. Annexure 4: Analysis of Planned V/s Actual Production: Overproduction Optimization Code Total Deviation Average Prod Deviation in days %age Contributio n Cumulative %age Sr. No

18 Total