Inventory Management - Research Paper Example

Inventory Management Abstract This project intends to develop an efficient inventory management system. Its main role is to create a bridge on the gap that exists between the intense theory of inventory management and its practice in the real world to assist inventory managers to achieve a successful and effective inventory management system. The system that this project proposes intends to achieve a unique identification and model selection facilities. The inventory system incorporates a collection of techniques that identifies demand and lead-time patterns and a rule base for the selection of good inventory model. All this takes into consideration the aspects of a practical situation. Introduction The literature review of this project will only review the relevant literature on inventory management. The review will cover the elements of inventory management then go through four main sections of intelligent inventory management. The first three parts will cover the principal domains of inventory management: inventory modeling, expert systems, and the decision support systems. The fourth part is the intelligent decision support system discussion, which advances the two previous parts. Elements of Inventory Management Inventory is the stock of goods kept on hand by an entity for future and current use of meeting the customers demand. The inventory is of importance to an entity in both the financial and operational perspective. First, inventory contributes a main investment for any firm. Inventory constitutes approximately 30 to 55 per cent of current assets in manufacturing companies while constituting approximately 70 to 85 per cent of current assets to wholesaler and retailer companies. On the other perspective, from the operational point of view, inventories put operating flexibility to company (Andreasen et al., 2013). Keeping of adequate inventory by manufacturing processes will facilitate smooth production process. Holding of inventory by wholesaler and retailers facilitates good customer service which in return gives the companies good public image. The main aim of inventory management is to balance between having low inventory and the high return on investments. Functions of Inventory Inventory has a variety of functions, which should be summarized to facilitate a good inventory management. First, the main aim of holding inventory is to meet customers demand for the product. This is because it is impossible to have delivery of products or production of goods exactly the same time when the consumers need them. It is therefore wise to keep a reasonable level of inventory to meet this expected or anticipated consumer demand. Secondly, it is wise to keep addition inventory inform of buffer or safety stocks since the demand is usually not known with certainty. The buffer is kept due to the variations of the unexpected demand. Inventory is also kept to meet the demand that has a cyclical or seasonal nature. It is quite common for firms to produce goods during periods when the demand is low. This action is to meet the high cyclical demand during which their production capacity may be low. On the hand, retailers might decide to have a large inventory to facilitate the peak cyclical demand or for the display intentions of attracting consumers (Black, 2012). Inventory costs and decisions Despite the many functions and benefits of inventory, a proper decision has to make concerning inventories to enhance its effectiveness. The management should study the demand of their products to make appropriate decisions concerning the means of monitoring inventory, the amount of inventory to be ordered and the point when orders are to be placed. This has to be done while trying as much as possible to minimize inventory costs. The inventory costs are divided into four distinct categories (Brand, 2006). The first cost to consider is the item cost. This is the cost of purchasing or producing a specific item. The consideration of this cost is highly important since the management should be keen to take advantage of the discounts offered at certain duration of time. The second cost is referred to as the ordering cost. This is the cost incurred because of purchasing orders with orders with suppliers or the organization of production within a production plant. This cost includes computational costs, collection of required data costs, transportation costs, telephone costs, inspection, and receiving costs. The next cost is the holding costs. This is the cost associated with keeping of inventory for a given duration of time. The holding costs usually consists expenses incurred by the inventory in relation to heat and electricity, spoilage and obsolescence, insurance and tax, the cost of capital and other expenses of running the warehouse such as security. The last cost is the stock out costs that shows the economic consequences of running short of stock (Burns, Forbes & Forbes, 2013). Inventory Modeling History of Inventory Modeling The Economic order quantity was the first mathematical inventory model to be developed. Early scholars in the fields of economics and business made many attempts in a bid to give explanations how the model could be put in practice. Later, a discovery was found that the EOQ model appears a little insensitive to the errors made in specification of some appropriate cost parameters and in the demand estimation. The significance of the EOQ model does not only rely on its historical point of view but on the fact that many other models that came after it had there designs based on the model (Byrski, 2012). This early mathematical model technique on inventory management had little application at the period. This was because the new concepts needed a little more time for maturation to occur. During that stage, improvement of the details and the original claims on increase of performance and productivity could be proven by a test of duration. After the invention of the EOQ model, many other inventory management models were designed during the time. Some of these include the newspaper carrier problem or the Christmas tree problem, which was developed during the Second World War. It was the first probabilistic model that dealt with stochastic inventory models (Drury, 2012). The Dynamic Programming technique was another model that was developed during the time. It was the first model of dynamic programming formulation to involve non-stationary demand. With the technological advancements in the 1970s, there was greater diffusion of computers that facilitated the development of many more mathematical models that represent the inventory systems. Modeling Classifications and discussions of the Survey Papers There have been many research papers and publications on inventory modeling for over half a century now. This intense proliferation of the inventory models has facilitated further research and publication of status surveys of these models and the inventory theory in general. The early authors did a mathematical description of the status of inventory modeling reviewed some of the structures of optimal solutions. They did this by making some assumptions such as the one concerning cost function with uncertain and certain demands. The discussions of different solutions, which are dependent upon whether the cost function is concave, convex, or proportional, are made. Much attention was focused on the multi-echelon and multi-item problems since they dominated research on inventory at the time. These surveys listed some of the quantifiable variables that affected the inventory formulas. These variables were grouped into six categories: models that determine optimum inventory policies; lot-size optimization; optimization different specific management objectives; optimizing specialized inventory situations models; advance mathematical theories applications; and the bridging the gap between inventory practice and theory models. Under these six categories, there was a presentation of approximately 70 models that cover simple models to more complex multi-item and multi-echelon models. There was a discussion of two simulation methods in the sixth category with an intention of bridging the gap between inventory theory and practice. One of the models finds the parameter of exponential which smoothers the demand forecast while the other model computes control limits by practicing simulation of the inventory system. In another publication of a survey paper on operations research in inventory management, the objectives and limitations that encounter decision makers in inventory management. Due to this, there was an introduction of a new class of inventory cost known as system control cost. Despite the aim of this paper not being the presentation of theoretical developments of various inventory models, it had to do a brief review of standard problems that were outlined and a number of the problems encountered in research. These problems included intermittent and erratic demand service parts, and uncertainty related to future demand. The practical solutions addressed to these problems would facilitate the practice of good inventory management. This survey made an essential contribution to facilitate the bridging of the gap between inventory management theory and practice. The survey outlined a number of suggestions that would facilitate this process. It suggested that more attention was to be directed towards the formulation of an accurate model and obtaining of a good solution instead of the usual getting of optimal solutions to mathematical interesting problems. The improvement of the current conditions by an understandable decision rule is better than the optimal solutions that are neither accepted nor understandable by management. The publication also suggested the need of easily understandable procedures on organizations, especially the smaller ones. The use of simple and clear implementations aids such as tables and graphs would be of great use in this aim. The survey advocates for the taking of a more explicit account of control costs that would assist in the decision of which control system to be used. It also expressed the need to put more emphasis on convincing the decision makers not to replace the decision system since it of great use to them and it essential for them to co-operate. A recent survey paper on independent demand inventory modeling discussed the classical EOQ model in detail. The survey did the introduction of the key costs of inventory and the main assumptions of the EOQ models. It did a description of the development of several generations of the EOQ model by advancing its basic assumptions. These models help in determining inventory problems and can greatly handle real management situations such as EOQ with discounts, EOQ with finite supply or production rate, integer quantity EOQ, shortage allowed EOQ, variable holding EOQ, and constraints EOQ. The main contribution of this publication to inventory management theory was the systematic classification of the models into six groups: Dynamic demand models, Stochastic models, Perishables models, Capital or volume constraints, Joint-ordering systems and Inventory devaluation and control. The publication does not put much focus on the contents of each class of models but discusses the order used in arranging the classification of these models. This makes the publication to be of assistance to both the operational researchers who seek to do further research on the topic and to academics who want to acquaint themselves with this broad field. From the discussions conducted so far, it is evident that the earlier publications focused on the description and development of mathematical models on inventory management while the recent publications puts much effort in revealing the problems in inventory modeling and seek methods to promote and develop the existing models. The authors of these publications reviewed these models from different viewpoints while considering some of the difficulties in the models. These reviews have greatly helped researchers on this field to have a good overview of inventory modeling. Recently, researchers have gone an extra mile by classifying the inventory systems to promote the application of the inventory models. This assists managers to pin point an appropriate model from the large inventory literature that exists (Girlich & Chikán, 2012). A recent survey is similar to the previous one on classification of inventory management models. The only difference is that this publication classifies inventory systems based on four aspects. These aspects are; environmental parameters, inventory-related costs, structure of the system and operating policies. The system structure usually refers to the number of suppliers, number of items, and the number of echelons. The environmental parameters include the lead-time distribution, nature of demand variation, size of backlogging permitted and shelf size. The inventory policies that are put in consideration are the review period, order size, order level and reorder point. The inventory- related costs are four in number: shortage, carrying, procurement and replenishment costs. The main advantage of this approach is that aids in the classification of any inventory systems while incorporating all the relevant information (Pradhan & Buchroithner, 2012). Computerized Inventory Systems The surveys and the publications of inventory models and management provide vital information that can be of use to the decision makers who use the models. The main challenge is that the application of these models would involve large number of numerical calculations in making inventory decisions. It is extremely hard to perform these calculations manually and because of this, there has been the development of computerized inventory over the past five decades to assist in solving these problems. The IBM Company introduced an inventory package in the early 1960s known as Inventory Management Programme and Control Techniques (IMPACT). This package is useful for the retail and whole trade. After this first development in inventory technology, other computer-manufacturing firms such as ICL and Siemens followed suit in developing similar systems since they were in great demand at the time (Pigeon, 2014). During the 1980s, there was development of many inventory decision support systems. There was the development of a microcomputer based decision support system known as the Integrated Decision System for Inventory Management. The computer-based system is very useful in making decisions on inventory control parameters such as safety stocks, order quantities, and order points. It does this at both the aggregate levels and the individual item. The system has a main program that which interacts with several other subroutine functions that are transparent to its users. The latest version of the IDSIM is written in an interactive mode to facilitate its implementation in the decision making process. The system allows data and constraints alerting by the user in a bid to perform a sensitivity analysis at different points during the entire execution program. There was another development on the inventory control system on the microcomputer. This system applies a decision support system best for single-item inventory problems with stochastic and deterministic demand distributions. The deterministic situations include systems with time-varying demand and linear demand. Order point review and order-up-to-level review systems are expressed in the stochastic case. This program runs on IBM computers and other compatible microcomputers. In addition to this, it has a friendly user interface and table and graphical aids (Wright & Slaybaugh, 2013). Decision Support Systems The DSS are structured with the intention to bring change and evolution. Therefore, the DSS field forms part of an evolutionary process in operational research and development of information technology to support the management in decision-making. The technological advances in the early 1970s facilitated the decentralization of computing resources and allowed the application of computing technology to managerial tasks in a bid to enhance the use of computers in making better decision. The synthesis of the interactive computing technology and organizational decision-making assisted the focus on practical decision support. The first crucial synthesis of these two significant contributions was the Management Decision Systems that explained an experimental project investigating the application of the new technology in supporting decision making by management. This brought a behavioral perspective into computer science and as a result led to the introduction of technical innovations of interactive inventory computing into organizational structures. Conceptual foundations of decision support were carried out during the early 1970s and because of this, academics built some experimental systems with the assistance from innovative managers. Various names were used on the first computer-based systems to be developed but the term DSS came up from the paper that which did a combination of grouping managerial activities within organizations. This creates a framework for the application of computer-based information in organizations. The DSS research gained momentum around 1975 and reached a critical mass point by 1979 (Jaber, 2013). A scrutiny through the literature on inventory management and DSS, gives a wide range of explanations of what a DSS should do, what it should consist of and how it should attempt to do it. The synthesis of definitions of DSS provided by scholars describe DSS as computer-based system that: utilizes data and models, supports decision makers instead of replacing them, Puts focus on the effectiveness of the decision processes rather than their efficiency, and comes up with solutions to problems of varying degrees. The main components of DSS according to an influential framework are the Data base, Model base and the Dialogue system. The database is comprises of the internal data, the external data and the overall data base management system. The model base comprises of the model base management system and the decision models. The dialogue system only comprises of the interface. The current advanced informational technology is being included into DSS instead of the transformation of operational research into DSS. This is because the DSS faces threats of becoming obsolete in the near future (Muller, 2013). Experts Systems An expert system is commonly referred to as the embodiment within a computer knowledge based component from a highly skilled source in such a way that the system can offer an intelligent decision or advice about a processing function. The most desirable feature of the system is that it can justify its own line of reasoning on demand. It does this in a way directly intelligible to the inquire. This program gives high-level performance on solving problems that are difficult for the human expertise to give their solutions. Expert system thus solves complex problems in a well-defined area of knowledge by showing the logical steps leading to the solution (Horngren, 2012). The structure of expert systems is designed in a manner that makes them have three main components: a user interface, an inference engine, and a knowledge base. The knowledge base is referred as the heart of an expert system because the expert system power is mainly derived from the wealth of knowledge of a domain that is efficiently encoded and not from the strengths of is reasoning methods. Thus, expert systems must have a rich knowledge base even if there methods might be poor. Due to this reason, the expert systems and similar programs are known as knowledge-based systems (KBS). Knowledge representation is a crucial factor in the designing of a KBS. It refers to the encoding of the knowledge into the system. Some of the techniques used in encoding the knowledge are the semantic networks, production rules, and frames (Jagels, Coltman & Coltman, 2012). The semantic network is a unique representation technique that is widely used for prepositional information. The structure of the semantic network is well represented in a graphical mode and has arcs and nodes connecting them. The nodes are circles while the arcs are the lines connecting these nodes. The nodes are objects in the organizations while the arcs are links, which show the relationships between the objects to provide a structure for organizing knowledge. A frame is a structure that contains information regarding a single entity. It mainly comprises of a set fillers and slots. A slot is a frame component that refers to certain attribute of the frame entity. The fillers are range values or simply values of slots. A frame system is an efficient device of describing a mechanical device such as an automobile but due since the discussion is on inventory management, it further explanations will not be made. A frame is very useful in recording high-level languages such as LISP and Pascal. The slot fillers and slots correspond to the fields and range of values of a record (Neuner & Deakin, 2013). The production rules are largely characterized by two clauses: the antecedent clause and the consequent clause. The production rule is a very flexible mechanism that is essential in the representation of different types of knowledge such as sufficiency, action, and conclusion. In many of the effective expert systems, the knowledge is mainly in the productions rules form due to their outstanding advantages. The first advantage is modularity. Each rule in the system defines some knowledge in a manner that is significantly different from the other rules. The other advantage is flexibility. There is room for the addition of new rules to the knowledge base without affecting the existing rules. The last advantage is in connection with modifiability. The system is quite easy to change some rules with little influence on the unchanged ones (Hansen & Mowen, 2013). The knowledge base comprises of factual knowledge in addition to the rules. This knowledge is mainly in the form facts through which representation of information is generated with the user or similar assertions of the nature through a dialogue. The inference engine comprises of a control strategy that draws a sequence of inferences from a knowledge base. The common control strategies are the forward chaining and the backward chaining. The backward chaining is goal driven and it is important if the determination of its value is done. The inference engine therefore searches for the knowledge-based rules that will value the goal. This will serve as the new goals and search will continue. It is common to find backward chaining in many expert systems because it generates more questions sequentially. Forward chaining starts its operations from a point where they have the values of the expressions and therefore their function is to infer these values until a goal is reached. Forward chaining is popular in expert systems that get information directly from measuring components or users (Gazely & Lambert, 2013). Expert systems applications The expert systems came up because of the intelligent research in inventory management. Despite the enthusiasm of theoretical research declining, the practical application of these models on inventory management is on the rise. MYCIN, PROSPECTOR, and XCON are three important expert systems that have a significant impact on the field. MYCIN is the most popular expert systems and has many applications apart from inventory management such as the diagnosing of microbial diseases in blood. It uses a knowledge base of rules and is forms part of the backward chaining systems. There is introduction of confidence measurements to aid in the handling of imprecision involved in diagnosis. PROSPECTOR is a geological exploration expert system. The system plays a vital role in making contributions towards the use of inference networks. The inference network is the tree in which have nodes that correspond with the assertions. The PROSPECTOR nodes calculate the probability values from their various inputs in three ways; OR nodes, AND nodes and weighted combination nodes. The calculation of the OR and AND is done through a fuzzy logic approach by substituting the maximum and minimum respectively. The weighted combination nodes comprise of complex computations that are in accordance with the Bayesian probability theory. XCON was developed because of the commercial expert systems. The program performs repetitive recognition functions using a set of values (Epstein & Lee, 2014). Intelligent Inventory Management Systems Inventory management has become a complex problem in the recent times due to the technological advancements and real life situations. Successful and efficient inventory management requires sophisticated methods that can cope with the ever-changing environment. The applications of the expert systems have promoted researchers to come up with intelligent inventory management systems. Conclusions The practitioners in operational research in general have used the classical mathematical approaches for decision and modeling. These approaches are responsible for the foundation of theory in lead to optimal solutions. Despite their effort, it is unfortunate that the real world problems are structured in a way that makes them too complex for the ordinary mathematical modeling. The complex nature of these problems experienced in the business and industrial sectors require efficient and innovative ways to solve them. The recent advances in operational research and Expert systems techniques provide tools that address the need for solving these problems. Consequently, the advance in the application of knowledge based systems to aid management in the decision-making in various areas of business and industry has contributed towards intelligent inventory management. Recommendations The research comes up with some recommendations that will facilitate an intelligent inventory management. Organizations should come up with an inventory knowledge based system. The system should be comprised with a pattern identifier, model base, data manager, rule base, on-help system, and a user interface. This system will facilitate the unique feature of being able to recognize the demand pattern and because of that choose an appropriate inventory model. This system differs from the knowledge based system discussed in detailed in the in the publications. The development of this system should be entirely based on practical situations. It should co-operate with the service and manufacturing companies. To ease functioning of the pattern identifier, it is necessary to develop a data manager to aid in the processing of the history of demand data and other basic inventory information. Further Work The work in this literature review has not exhausted the field of inventory management; therefore, further studies should be conducted in a bid to fill the gap. There has been examination and experimentation of the inventory knowledge based systems but no installation of the system in the real world is done. A practical installation would be of great assistance in improving, consolidating and validating the system to ensure effectiveness, performance and functioning of the system. It is wrong to assume that a software developer will think in the way an actual user of the system will do. Therefore, further studies on the real life application of the system should be used. References Andreasen, K., Melzer, T., George, L., Lüthi, L., Rijn, I., & Stokhof, M. (2013). Inventory. 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