ON LEAD TIME MANAGEMENT IN INVENTORY MODELS

Most of the literature dealing with inventory problems assume lead time as prescribed whether deterministic or probabilistic. In certain cases lead time can be reduced but at an added cost. By reducing lead time, customer service and responsiveness to production schedule changes can be improved and reduction in safety stocks can be achieved. In this paper we present two models which can be used to determine the optimal length of lead time and order quantity that minimizes the total inventory expected cost. The first model is an extension of a model proposed in the literature. In the second model, a functional form relating lead time cost to lead time duration which is more flexible from implementation point of view is proposed. Numerical examples are presented to illustrate the procedures developed

ON LEAD TIME MANAGEMENT IN INVENTORY MODELS

Most of the literature dealing with inventory problems assume lead time as prescribed whether deterministic or probabilistic. In certain cases lead time can be reduced but at an added cost. By reducing lead time, customer service and responsiveness to production schedule changes can be improved and reduction in safety stocks can be achieved. In this paper we present two models which can be used to determine the optimal length of lead time and order quantity that minimizes the total inventory expected cost. The first model is an extension of a model proposed in the literature. In the second model, a functional form relating lead time cost to lead time duration which is more flexible from implementation point of view is proposed. Numerical examples are presented to illustrate the procedures developed

Inventory management systems:Control and information issues

Abstract: This dissertation addresses the management of inventory systems. The thesis starts with an exposition on mathematical models that can be used in inventory theory. Then we deal with some information issues related to the demand process. Namely, how to control products that have intermittent demand. Moreover, we investigated the impact of data collection on the customer performance. Next, we investigated to what extend multiple-sourcing can lead to improvements of the inventory system. Finally two demand management strategies are investigated for smoothing demand. The first re-routes large customer orders to alternative stockpoint, whereas the second strategy splits a customer order in a time-phased delivery scheme.

Analysis of an inventory management model in a manufacturing process based on material requirements planning (MRP)

Optimal inventory management is crucial for the efficient operation of businesses, as it accounts for over 50% of the total invested capital. Inadequate inventory management can lead to high costs and large profits. Modern inventory management models focus on quantity and time, rather than costs, aiming for smaller and more frequent purchases within the economical quantity of purchase and national transport. The material requirement planning (MRP) model is one such model, focusing on quantity and time rather than costs. This approach is essential for businesses to make informed decisions regarding inventory decisions and maintain a competitive edge in the market. Keywords – Inventory Management, Operation, Time, Costs, Material Requirement Planning (MRP) Mode

Analysis of an inventory management model in a manufacturing process based on material requirements planning (MRP)

Optimal inventory management is crucial for the efficient operation of businesses, as it accounts for over 50% of the total invested capital. Inadequate inventory management can lead to high costs and large profits. Modern inventory management models focus on quantity and time, rather than costs, aiming for smaller and more frequent purchases within the economical quantity of purchase and national transport. The material requirement planning (MRP) model is one such model, focusing on quantity and time rather than costs. This approach is essential for businesses to make informed decisions regarding inventory decisions and maintain a competitive edge in the market. Keywords – Inventory Management, Operation, Time, Costs, Material Requirement Planning (MRP) Mode

The Effect of Inventory on Purchase Incidence: Empirical Analysis of Opposing Forces of Storage and Consumption

Behavioral studies and recent empirical research suggest higher levels of inventory on hand can lead consumers to increase consumption. Inventory on hand is therefore posited to exert two countervailing forces on the probability of purchase incidence. First, higher levels of inventory reduce the likelihood of purchase as the consumer feels less pressure to buy. At the same time however, theory suggests higher levels of inventory may drive up the rate of consumption, thereby increasing the probability of incidence. We develop an empirical model that explicitly captures these two effects. The elasticity of purchase incidence with respect to inventory derived from the model is shown to capture these opposing forces in a simple and intuitive way. The analytical expression allows calculation of a threshold below (above) which the net effect is positive (negative). The model is estimated on ten product categories from the Stanford Market Basket database and is shown to fit better than both the standard nested logit approach and an alternative formulation developed by Ailawadi and Neslin (1998). The threshold values have plausible magnitudes and are intuitive across categories: butter, margarine and crackers have relatively low thresholds implying that inventory build up does not drive consumption; ice cream and soft drinks have relatively large thresholds (below which the inventory pressure to consume more outweighs the effect to delay purchase). Implications for retail management are discussed. --Choice Models,Consumption,Inventory,Purchase Incidence

Distribution Network Design and Customer Service

In distribution network design, it is implicit that transportation costs, travel distances, and transit times are tightly correlated. Therefore, one can argue that models directing at minimizing travel distances not only minimizes transportation costs, but also minimizes transit times. The center of gravity, and its various extensions, is an example of such a model. Quantitative analyses such as mathematical programming and stochastic models, the transportation costs are often the only factors of interest. A universal metric for customer service is the customer’s lead time – the time it takes to get the right quantity of the right product to the right place. If the right quantity of the right product is available, then the leadtime is the time it takes to take the goods to the right place. For example, when inventory is available, the time to get the product from the warehouse to the customer consists of the time to process the order plus the time it takes to transport it to the customer. These times do not vary much. Moreover, customers generally are aware of and accustomed to them. If the required quantity of a product is not available, the lead-time is based on two components - inventory availability and product acquisition time. Product acquisition time is the time to get the product back in stock. This is the time to process and ship the product from some other location such as another warehouse, a manufacturing plant or a supplier. In this paper, we examine the impact of distribution network design on customer’s lead time. We conclude that the number of shipping locations may have some effect on customer’s lead time. However, the effect of outbound transportation on lead-time can be small relative to product acquisition time. Acquisition time is the time to get the product back in stock. Production inventory management determines this component of the lead-time, not distribution managemen

Inventory policy planning for spare parts and its application in the heavy-duty truck and bus industry

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Engineering. Johannesburg, November 1994.Inventories are produced, used (e.g. for raw materials, supplies, spare parts, and So forth) or distributed by every organisation. Moreover, inventories represent a major investment from the perspectives of both individual firms and entire national economies. In addition, enormous costs are incurred in the planning, scheduling, control and actual carrying out of replenlshment-Iprocuretnentl related activities. Interest in the subject of inventory management is constantly increasing, yet Silver and Petarsonlll (P(eface) found that "although invi ,~ory management ha.l been studied in considerable depth from a theoretical perspective, yet, those of us who, throuah consulting work, come into. clos>!)contact with mananerlal decision procedures in this arer are repeatedly surprised to find how limited, and ad hoc, many of the existing decision systems actually are. The rate at which theory has been developed has far outstripped the rate at which decision practices of firms have been successft,Jlly upgraded. A major g~o has existed between the theoretical solutions, on the one hand, and the real world problems, on the other". Inventory control is the science-based art of ensuring that lust enough inventory (or stockl is held by an organisation to meet both its internal and external demand commitments economically. There can be disadvantages in holding either too much 01 too little inventory. Therefore, inventory control is primarily concerned with obtaining the correct inventory with compromise between these two extremes. The control and maintenance of inventories is a problem common to all enterprises in any sector of a given economy. The primary aim of this study is to identify What the inventory policy of a company shoull;I be to Secure a reduction in inventory-related costs while maintaining a high level of customer service. Lewis(2) defines two bMlt:~ tvpes of inventory policy. Those in which decisions concerning replenishment are based on the lellel of inventory held, are known as "fixed-order quantity models" or "re-order level policies" and those in which such declslons arc made on a time basis are known as "fixed-time period models" or "re-order cycle policies". According to Nadder(3) (7I 11) the basic distinction between fixed-order quantity models and fixed-tlme period models is that the former are "event-triggered" while the latter are "time-triggered". That is, a fixed-order quantity model initiates an order when the "event" of reaching a specified re-order level occurs. This event may take place at any time, depending on the demand for the items considered. In contrast, the fixed-time period model is limited to placing orders at the end of a predetermined time period; hence, the passage of time alone "triggers" the model. In this thesis, we shall discuss both classical inventory models and heuristic models. We shall also conduct an investigation into the factors affecting high levels of inventory ~ mainly lead times (supplier and internal lea' times) in relation to spare 9arts in the heavv-dutv truck and bus industry. The thesis also suggests guidelines for controlling stock or these types of commodities in a practical environment. This will be done by either researching the existing inventory models or developing new inventory models or a combination of both, the intention being not to look for absolute optimisation, but rather to achieve significant improvements over current operations.GR 201

Performance Evaluation of Stochastic Multi-Echelon Inventory Systems: A Survey

Globalization, product proliferation, and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty and their widefspread applications to industry. These developments are based on three generic methods: the queueing-inventory method, the lead-time demand method and the flow-unit method. In this paper, we compare and contrast these methods by discussing their strengths and weaknesses, their differences and connections, and showing how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes. Our objective is to forge links among research strands on different methods and various network topologies so as to develop unified methodologies.Masdar Institute of Science and TechnologyNational Science Foundation (U.S.) (NSF Contract CMMI-0758069)National Science Foundation (U.S.) (Career Award CMMI-0747779)Bayer Business ServicesSAP A