Decision Supporting System For Inventory Management
Inherent uncertainties in demands and supply make it difficult for supply chains to achieve optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. An unkempt inventory can take up to one-third of an organization’s annual investment. Therefore, in order to compete 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: how much to order and when to order. Owing to the importance of inventory management this research work presents an Intelligent Decision Support System (DSS) for Inventory Analysis and Control utilizing technology management.
The DSS includes three different types of analyses methods; Price based Analysis, Quantity based Analysis and ABC Analysis. The work extends towards implementation of the suggested DSS to a Pepsi bottling company, inventory. A comparative analysis matrix is formulated for the prior two analyses to isolate the most critical parts in terms of their prices and quantities respectively. The system is developed in PHP. Three main inputs of the system are: inventory items, their prices and quantities consumed over a stipulated time-period. From these inputs, DSS outputs critical items based on price, quantity, annual cost and subsequently critical items based on all three elements together. The implementation was done using Pepsi bottling company as case study. From the results it was deduced that almost 20% of the items consume a major amount of a budget being expended on inventory maintenance, ranging from 60% to 80%. This calls attention towards these 20% critical items segregated by the software and demands detailed analysis and procurement procedures to be carried out for them in order to minimize inventory costs.
1.1 Background of the Study
Over the last several decades there has been much speculation about the role of computers in management. Predictions that computers would take over many management functions encouraged counter claims that computers could have only minimal impact since most management functions cannot be automated. The experience to date has fallen between the two extremes. Although very few management functions have been automated, advances in information retrieval, processing, and display technologies have certainly led to significant computer applications that help people perform management functions. Ever since, Management Information System replaced Electronic data process system as the popular term denoting computer applications in business, computer aided decision making in organizations has been the object of high hopes. Although the computer industry has enjoyed remarkable success in transforming the way business transactions and data are processed. MIS and management science professionals have been disappointed by the relatively limited use of these systems for managerial decision making. In these circumstances decision support systems emerged as new, practical approach for applying computers and information to the decision problems faced by management.
Decision support systems (DSS) represent a point of view on the role of the computers in the management decision making process. Decision support implies the use of computers to [Alter, Steven, 2000] Assist managers in their decision processes in semi-structured and unstructured tasks, Support, rather than replace, managerial judgment, Improve the effectiveness of decision-making rather than its efficiency. The second term in phrase is support. A DSS supports and does not replace the manager. This emphasis on enhancement of decision making exploits those aspects of computers and analytical techniques that are appropriate for the problem and leaves the remainder to the manager. Many problems have components that can be structured and others that require subjective assessments. In pricing some consumer products, for example, management intuition alone is inadequate, a computer model alone is also inadequate, but the two together may be most effective.
Compared with effectiveness, efficiency implies a narrowing of focus in order to get a specific job done. Typically, it takes the form of minimizing time, cost, or effort to complete a given activity. Effectiveness, on the other hand implies a broadening of focus in order to find out what set of activities should be considered. It requires defining and searching a decision space to become more confident that the goal itself is relevant and appropriate,
In the aggregate, inventories affect the economy through business cycles. Individually they provide the means by which we can effectively organize operations such as purchasing, manufacturing and distribution so that ultimately the end user receives any desired level of service. At the level of the firm, inventory is among the largest investment made and therefore logically deserves to be treated as a major policy variable highly responsive to the plans and style of top management. In general the larger the inventory the easier it is to plan operations and work force levels, the easier it is to reduce costs of purchasing, manufacturing and shipping, the easier it is to provide prompt customer service (Aggarwal, S., 2011). At the same time, a larger inventory also requires a larger investment of money and has associated with higher costs such as storage, handling, risk of obsolescence and data processing. The management tries to balance these latter costs against the advantages achieved from stocking larger amounts in inventory As such solving an inventory model is a structured problem. But some features like policy analysis, analyzing alternate models, coordinating multiple items etc,, make the inventory system design semi- structured. So the analytical power and computational ability of the computer can be rightly combined with the intervention of the management to formulate a decision support system.
1.2 Statement of the Problem
Developments in information technology (IT) over the past two decades have enabled many organizations to establish computer-based information management system (MIS) to improve inventory management. Most of these systems are mainly used to record transactions, produce management reports, and monitor inventory status. They lack the ability to help inventory managers to choose the correct model by analyzing the data kept by the system to identify the inventory environment. To maintain optimal inventory operations in a company, the management will have to continuously review and update the accumulated data, the selection of suitable models and the computation of new optimum values of ordering decisions. This is needed because the conditions in relation to the demand, the cost elements, the supply, the lead time, etc., are likely to change with time. The complexity of such a problem will greatly increase as a result of a large number of stock items in the inventory.
Pepsi bottling company for instance stock thousands of items which results in a very large number of records being generated. The analysis of such huge amounts of information is beyond manual human capacity or traditional computational methods. For a company to be more competitive, it has to face the challenge of reducing the processing costs. This issue is closely related to the effectiveness of inventory management which can help to reduce both the storage and the labour operating expenses. A small percentage reduction in inventory can be transformed into significant operating profit.
1.3 Objective of the Study
The main objective of the decision support system for inventory management is to provide advice to inventory managers to achieve an efficient and effective inventory management practice. To achieve the stated objective, the following specific objectives were laid out:
- Provide a system which provides a graphical presentation of sales and stock over a period of time to aid in decision making.
- To reveal the pattern of individual stock purchase and sale and identify the vital or critical information regarding the stock
- Develop a system which helps the management in planning, monitoring, and optimizing resources to ascertain their financial position at any time.
- The system should be able to forecast based on some present information the model to use in stocking the system.
- To provide higher level authentication mechanism to prevent unauthorized access.
1.4 Scope of the Study
The research focuses on deriving a smart solution system, also referred to as decision support system, to help lower high inventory costs due to lack of forecasting, analysis and control in Pepsi bottling company. The proposed system employs three different methods which take in to account the major issues of price and quantity of items being replenished and maintained. The system works by using real data from already-established computerized inventory database. The proposed system is designed to extract data from inventory database of the co-operating companies, analyze the data to specify the demand patterns and lead time distributions. It uses this information to select an appropriate inventory model for that particular environment, calculate the optimal order quantity, update the inventory status of the item, and present output to the user in both numerical and graphical format.
1.5 Significance of the Study
This thesis will be of utmost importance to mangers that use inventory support system as it helps to lower high inventory costs due to lack of forecasting, analysis and control in regular inventory management system. The proposed system employs three different methods which take in to account the major issues of price and quantity of items being replenished and maintained. Therefore, the developed system significantly reduces inventory costs and help maintaining an optimal level of merchandizing inventory.
Also the result of this study will be of importance to scholars researching the field of automatic health management as it will serve as a reference to them. As a reference material, it could generate other researchers’ interests in the unfinished part of this research, especially in the full implementation and deployment of the system.
1.6 Limitation of the Study
Most constraint experienced during the course of writing this project is during the actual software development. PHP algorithm needed to provide accurate decision pattern for manages to follow were difficult to obtain. This made me go for the available but less preferred codes for the implementation. Although the project intended objectives were met, better codes would give a strong and faster system.
Also detailed information about the major operations of my case study was difficult to obtain, the personnel manager was a little diplomatic in answering my questions in order to reveal information that may indent the company’s image, though that did not stop me from writing and researching for detailed information.
1.7 Project Organisation
This study is developed under five chapters.
The first chapter introduces the research topic, stating the background of the intended project, statement of the problems, project objectives, its significance to the society and overall scope.
The second chapter reviews related literature on decision support system and their usage in inventory management. It analyses previous research works, their limitations and need for the development of better decision system for experts.
The third chapter discusses the methodology used for the project development, the limitations of the currently used detection system and reasons the intended system should be chosen over the current system. It also showcases the design processes of the new system.
Chapter four showcases the actual running of the developed system. Here proper tests are done to check the strength of the developed system. The developed system is analyzed to determine its conformation with the stated objectives.
Chapter five gives the summary of the project, gives the conclusion and recommends approaches for better system.
1.8 Definition of Terms
To store information in a computer system or process it by computer.
Decision Support System:
An aspect of management information system that is focused on enabling its users make timely decisions with well programmed software systems.
A record of a business’s current assets, including property owned, merchandise on hand, and the value of work in progress and work completed but not sold.
A combination of related parts organized into a complex whole
Summary, Conclusion and Recommendation
The need for a Decision Support System for Inventory Analysis and Control has become inevitably. In order to refrain from having an inventory go dead it is of utmost importance to stay abreast with the number and condition of items in that particular inventory. In this regard both periodic and continuous techniques can be used for appraising the stats of the stocks. Once the figures are accurately determined it is yet again very important to be able to further determine the level at which a particular item’s stock needs to be maintained. For which calculations and analysis are mandatory. The research presents three methods to go by the analysis of any inventory for further placing purchasing orders a) Quantity Based Analysis b) Price Base Analysis and ABC Analysis. All three bring items of critical importance in lime light before making decisions.
The application of these methods on the available data showed 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 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, it was incurred 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.
Based on the critical items from the aforementioned analyses the DSS finally outputs those 20% of the items that consume up major amounts of budget being expended on an inventory, ranging from 60% to 80%. This calls attention towards these 20% critical items and demands detailed analysis and procurement procedures for them in order to minimize inventory costs.
The results and findings of the project work can be applied to various inventory stocks apart Pepsi Bottling Company. The DSS can also be incorporated with EOQ analysis for more precision in calculation and determination of the critical items.
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