The Effectiveness of Inventory Management

Topics: Inventory, Cost, Safety stock Pages: 15 (4768 words) Published: July 11, 2010
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 replenishment order quantity is usually dealt with in isolation. Lewis suggested that for the optimum operation of a re-order level policy, these two quantities should be calculated jointly as one directly influences the other. Lewis recommended a method originally proposed by Tate[3]. Tate’s formula has the advantage of being relatively insensitive to large variations in stockout costs. Lewis illustrated Tate’s formula with an example, and demonstrated that the re-order level evaluated using the joint calculation method was less than that calculated to achieve the same service level using the independent calculation method. The joint calculation method is complicated, and it is difficult to assess whether this additional computational effort is really providing an overall cost saving to the business. Several authors including Prichard[4], Brown[5], and Ploss and Wight[6], have described the use of statistical techniques to calculate the re-order level. This method has been adopted in the ROL module and will be discussed later. The authors wish to thank the staff of PRESTO Tools Ltd, Sheffield for their help during the course of the project.

Inventory policies
Lewis[2] identifies two basic type of inventory policy. The first, in which decisions concerning replenishment are based on the level of inventory held, is known as re-order level policy. The second based on a cycle time, is known as reorder cycle policy. Within these two categories there are several variants including the re-order level policy with periodic reviews, the (s,S) policy and the combined re-order level, and re-order cycle policy. In the re-order level policy, an order for replenishment is placed when the stock-on-hand equals or falls below a fixed value, known as the re-order level. When a replenishment order is placed, the quantity required is fixed. The amount of stock held must be reviewed continuously. In the re-order cycle policy, the stock-on-hand is reviewed periodically and a...

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.
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