The Effectiveness of Inventory Management
A COMPUTER-AIDED INVENTORY MANAGEMENT SYSTEM – PART 2
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A computer-aided inventory management system – part 2: inventory level control
C.Y.D. Liu and Keith Ridgway Reviews inventory policies and lot-sizing techniques in a cutting tool manufacturer
Introduction
In part 1 of this article[1] the design and development of a computer-aided inventory management system (CAIMS) was described. The CAIMS system was developed for a cutting tool manufacturer, PRESTO Tools Ltd, Sheffield, with the objectives of reducing inventory investment and improving productivity, customer-service level and plant efficiency. The CAIMS system consists of four modules incorporating analytical techniques for ABC analysis, forecasting, economic batch quantity calculation and the statistical calculation of the re-order level respectively. In this second part of the article, various inventory policies and lot-sizing techniques are reviewed and the analytical techniques used in the economic batch quantity (EBQ) and re-order level (ROL) modules are described.
review. In this case, the replenishment order quantity is variable and brings the stock to a predetermined level (S ). In the (s,S ) policy is similar to the re-order cycle policy but a variable replenishment order is only placed when the stock falls below a predetermined level (s). In the combined re-order level and re-order cycle policy, replenishment orders are placed periodically and when the stock-on-hand falls below the re-order level. Replenishment orders placed when the re-order level is reached are of fixed size, but those placed at the review are variable. All of these systems are closely related in that a predetermined amount of stock, or period is set to trigger a fixed, or variable replenishment order. The key task is to determine accurately the parameters required to implement a successful inventory policy, i.e. the replenishment order quantity and the re-order level. The calculation of both the re-order level and
11
A computer-aided inventory management system – part 2: inventory level control
C.Y.D. Liu and Keith Ridgway Reviews inventory policies and lot-sizing techniques in a cutting tool manufacturer
Introduction
In part 1 of this article[1] the design and development of a computer-aided inventory management system (CAIMS) was described. The CAIMS system was developed for a cutting tool manufacturer, PRESTO Tools Ltd, Sheffield, with the objectives of reducing inventory investment and improving productivity, customer-service level and plant efficiency. The CAIMS system consists of four modules incorporating analytical techniques for ABC analysis, forecasting, economic batch quantity calculation and the statistical calculation of the re-order level respectively. In this second part of the article, various inventory policies and lot-sizing techniques are reviewed and the analytical techniques used in the economic batch quantity (EBQ) and re-order level (ROL) modules are described.
review. In this case, the replenishment order quantity is variable and brings the stock to a predetermined level (S ). In the (s,S ) policy is similar to the re-order cycle policy but a variable replenishment order is only placed when the stock falls below a predetermined level (s). In the combined re-order level and re-order cycle policy, replenishment orders are placed periodically and when the stock-on-hand falls below the re-order level. Replenishment orders placed when the re-order level is reached are of fixed size, but those placed at the review are variable. All of these systems are closely related in that a predetermined amount of stock, or period is set to trigger a fixed, or variable replenishment order. The key task is to determine accurately the parameters required to implement a successful inventory policy, i.e. the replenishment order quantity and the re-order level. The calculation of both the re-order level and
References: 1. Liu, C.Y.D. and Ridgway, K., “A computer-aided inventory management system – part 1: forecasting”, Integrated Manufacturing Systems, Vol. 6 No. 1, 1995, pp. 12-21. 2. Lewis, C.D., Scientific Inventory Control, Butterworths, London, 1970. 3. Tate, T.B., “In defence of the economic batch quantity”, Operation Research, quarterly, Vol. 15 No. 4, 1964, p. 329. 4. Prichard, J.W., Modern Inventory Management, John Wiley & Sons, New York, NY, 1965. 5. Brown, R.G., Decision Rules for Inventory Management, Holt Rhinehart and Winston, New York, NY, 1967. 6. Ploss, G.W. and Wight, O.W., Production and Inventory Control: Principles and Techniques, Prentice-Hall, Englewood Cliffs, NJ, 1967. 7. Harris, F.W., Operation and Cost, Factory Management Series, A.W. Shaw, Chicago, IL, 1915. 8. Lockyer, K.G., Production and Operation Management, 5th ed., Pitman Publishing, London, 1989. 9. Saunders, G., “How to use a microcomputer simulation to determine order quantity”, Production and Inventory Management, Vol 28 No. 4, New York, NY, 1987, pp. 20-3. 10. Ptak, C.A., “A comparison of inventory models and carrying costs”, Production and Inventory Management, Vol. 4 No. 29, New York, NY, 1988. 11. Lewis, C.D., Scientific Inventory Control, 2nd ed., Butterworths, London, 1980. Conclusion The successful development of the EBQ module has provided the company with a scientific and systematic means of evaluating the most economical batch quantity to manufacture across the entire product range. The sensitivity analysis demonstrates that the EBQ formula and the total cost equation are insensitive to input errors. Adjustments can be made to the EBQ without sacrificing significant savings. This would prove to be useful in companies where costing data are not readily available. The EBQ module provides a stepping stone for more complex C.Y.D. Liu is a Lecturer at the German Singapore Institute, Jurong, Singapore, and Keith Ridgway is the Ibberson Professor of Industrial Change and Regeneration in the Department of Mechanical and Process Engineering, University of Sheffield, Sheffield, UK.