DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR INVENTORY ANALYSIS AND CONTROL

Abstract

Uncertainties inherent in customer demands and market supply make it difficult for supply chains to achieve optimum inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. An unkempt inventory can take up to one-third of the annual investment of an organization. Therefore, in order to compete with the contemporary business world in an efficient manner with invariably erratic demands, it is not only challenging to develop an intelligent system to maintain and control an optimum level of inventory but has also become mandatory. The two most important issues to address in inventory management are a) how much to order? and b) when to order? i.e; lot sizing and lot timing. Inventory optimization involving both ‘analysis and control’ attempts to find the stocking of inventory that best meets specified cost and availability goals. Owing to the importance of inventory management this research work presents an intelligent decision support system (DSS) for Inventory Analysis and Control. This DSS includes three different types of analyses methods; Price based Analysis, Quantity based Analysis and ABC Analysis, to take appropriate control measures for optimization of an inventory. The work extends towards implementation in the form of case study of an inventory plant of the eighth largest oil and gas producing company of Pakistan i.e. Magyar Olaj és Gázipari Részvénytársaság (MOL), Hungarian Oil and Gas Company. Data from the company was evaluated on quantity, price and annual cost basis for the aforementioned methods. A comparative analysis matrix was formulated for the prior two analyses to isolate most critical parts in terms of their prices and quantities. The results and findings conform to the theory and successfully manifest the idea of categorization of inventory items in term of not only quantity and price but also the annual costs expended by the company on each of them. The system is developed in MATLAB software. Three main inputs of the system are; Inventory Items, Price of Each Item (USD) and Quantity Consumed of Each Item. From these inputs Decision Support System outputs; Critical Items based on Price, Quantity, Annual Cost and subsequently Critical Items based on all three elements i.e. price, quantity and annual cost. The DSS further calculates percentage contribution of all categories in Price Based Analysis, Quantity based Analysis and ABC Analysis including %age contribution of low price items to the entire inventory cost, %age contribution of medium price items to the entire inventory cost, v %age contribution of high price items to the entire inventory cost, %age contribution of small quantity items to the entire inventory cost, %age contribution of medium quantity items to the entire inventory cost, %age contribution of large quantity items to the entire inventory cost, %age contribution of category A items, %age contribution of category B items, %age contribution of category C items. The application of these methods on the available data tells that according to Price Based Analysis out of the total revenue, 85.7% is spent on only 1.25% of the total items. 10.1% on 8.45 % items and 4.2 on 90.3 % of the total items. Whereas the Quantity Based Analysis depicts that out of a total of 100%, 0.33% of revenue is spent on 33.01 % quantity of items, 0.76% on 33.33 % of items and the rest of 98.89% on only 33.66% quantity of the total items. From the ABC analysis results show that only 23% of total items constitute to 60% of annual cost. These are the items of utmost importance and shall be given maximum time and consideration while putting their orders. Whereas, 36% of items constitute 22% of annual costs and 41% of items make up the rest of the 18% contribution to annual costs. Simulation results highlight that almost 20% of the items consumed up to major amount of a budget being expended on inventory maintenance, ranging from 60% to 80% of Oil and Gas exploration company’s inventory. This calls attention towards these 20% critical items and demands detailed analysis and procurement procedures to be carried out for them in order to minimize inventory costs.