How Inventory Cost Influences Introduction Timing of Product Line Extensions

In the industry with radical technology push or rapidly changing customer preference, it is firms' common wisdom to introduce high‐end product first, and follow by low‐end product‐line extensions. A key decision in this “down‐market stretch” strategy is the introduction time. High inventory cost is pervasive in such industries, but its impact has long been ignored during the presale planning stage. This study takes a first step toward filling this gap. We propose an integrated inventory (supply) and diffusion (demand) framework and analyze how inventory cost influences the introduction timing of product‐line extensions, considering substitution effect among successive generations. We show that under low inventory cost or frequent replenishment ordering policy, the optimal introduction time indeed follows the well‐known “now or never” rule. However, sequential introduction becomes optimal as the inventory holding gets more substantial or the product life cycle gets shorter. The optimal introduction timing can increase or decrease with the inventory cost depending on the marketplace setting, requiring a careful analysis.     

An integrated vendor-buyer cooperative inventory model for items with imperfect quality

This article develops a model to determine an optimal integrated vendor-buyer inventory policy for flawed items in a just-in-time (JIT) manufacturing environment. The aim is to minimize the total joint annual costs incurred by the vendor and the buyer. The proposed model extends the integrated vendor-buyer inventory model by accounting for imperfect quality items. The expected annual integrated total cost function is derived and an analytic solution procedure is proposed to determine the optimal policy. Finally, numerical examples are also given to illustrate the solution procedure presented in this article.     

An inventory model with random discount offerings

We consider an inventory model in which a supplier makes deal offers with random discount prices at random points in time. Assuming that discount offerings follow a Poisson process and discount price is a discrete random variable with a known distribution, we propose a continuous-review control policy for the model and derive optimality conditions for the policy parameters. The model is then extended to the case of multiple suppliers that offer discount deals with supplier-specific Poisson processes and discount prices. Numerical examples are presented to demonstrate cost savings due to discount offers.     

The effects of component commonality in an infinite horizon inventory model

This paper studies the effects of component commonality in the context of an infinite horizon inventory model. Three models are proposed that are characterized by different degrees of component commonality. Assuming the three models all follow the same inventory policy, exact service level measures are derived and incorporated into cost optimization problems. With the infinite horizon assumption, potential setup cost reductions can be evaluated due to the inclusion of common components. The results indicate that, as expected, commonality incurs significant cost savings; what is new and unique is that setup cost may increase or decrease when commonality is present. In addition, when the behaviour of the optimal solutions is examined, it is found that some of the well-known properties suggested by the existing one-period models do not hold for this infinite horizon model.     

Inventory Models with Continuous and Poisson Demands and Discounted and Average Costs

We develop a new, unified approach to treating continuous‐time stochastic inventory problems with both the average and discounted cost criteria. The approach involves the development of an adjusted discounted cycle cost formula, which has an appealing intuitive interpretation. We show for the first time that an (s, S) policy is optimal in the case of demand having a compound Poisson component as well as a constant rate component. Our demand structure simultaneously generalizes the classical EOQ model and the inventory models with Poisson demand, and we indicate the reasons why this task has been a difficult one. We do not require the surplus cost function to be convex or quasi‐convex as has been assumed in the literature. Finally, we show that the optimal s is unique, but we do not know if optimal S is unique.     

有限时域与采购价格上升情形下的最优订货策略

研究了有限时域下采购商面对价格上升时的订货策略问题.在分析问题的基础上提出一种新的最优采购策略,并分析了价格上升幅度对订货量的影响,以经典EOQ模型的总成本为基准,比较了本文提出的策略与文献已有策略在成本节约上的差异.本文对库存总成本的计算方法更加精确;分析表明在有限时域背景下采购商的临时订货量决定于价格上涨的幅度、在库库存以及时段长度.     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

A multicriteria model for supporting setup reduction investment decisions

One critical manufacturing challenge of the 1990s is for firms to effectively apply new operations management techniques while embracing wider philosophies such as total quality management (TQM) and computer integrated manufacturing (CIM), etc. Setup cost and/or time reduction is one such technique capable of producing many benefits for manufacturing firms, including reduced inventory, better equipment utilization, and improved quality. It is thereby viewed as an important component of just-in-time (JIT) manufacturing practice. Existing problems with the setup reduction decision include the many factors that must be considered, as well of an absence of validated and usable models for estimating potential benefits from setup reduction investment made in different contexts. This paper discusses the attainment of gains from setup reduction mainly by improving existing equipment and work practices rather than purchasing new equipment or technology. The model proposed in this paper is based on the application of the analytic hierarchical process (AHP) on seven weighted factors to obtain a preliminary indication as to whether investment in setup reduction is desirable in a given manufacturing context, and the expected benefits of such investment. A flexible scaling system, thus obtained, allows the model to handle a wide range of managerial predispositions to setup reduction.     

Adoption of just-in-time based quality assurance and purchasing practices

Feasibility of transforming purchasing and quality control operations from conventional to just-in-time (JIT) practices is discussed. The study is confined to the dock-to-stores area of an engine manufacturing plant. Components used in the study are selected from a representative sample of inventory classes, commodity groups and agreement types with vendors. A time/cost analysis is carried out for the components selected so as to evaluate the cost effectiveness of the conventional purchasing and quality control. Findings on savings in terms of inventory-related costs and inventory turnover through the implementation of JIT practices are reported. A transformation framework is then developed to determine the suitability of individual components for JIT based practices. Cases are presented to demonstrate the functioning of the framework.     

An optimal run-mode scheduling for oil refinery production–a case study on Taiwan CPC Corporation

The refinery industries have been going through very difficult time from fierce competition and high price of crude oil. To increase compatibility, it is essential to increase efficiency in the inventory, production and transportation. Refineries start generating revenues while their products are sold in the market. This article examines the possible costs of additional inventories and recurrent times of refineries derived from early or late shipment in a continuous production system. Then, based on the developed mathematical programming model, an optimal production structure with the minimum total cost of production, inventory and start-up is proposed. Tolerance analysis is conducted to cope with uncertain environment. A case study on applying the proposed model to perform scenario analysis for CPC Corporation, Taiwan is demonstrated. The results show that the developed model is able to provide useful information towards developing cost-effective oil refinery strategies.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Trade-off between quoted lead time and price

In a make-to-order environment, lead time and price can play a crucial role in determining the financial success of a firm. Their importance increases when demand is sensitive to the quoted lead time and price. A model is presented which uses the quoted lead time and price as a mechanism to determine the optimal demand level. The relationships between the model parameters and their impacts on the firm's profit is also analysed. In addition, the effect of the number of job requests, and the mean processing time are examined. Based on the results presented in this paper, there is clear indication that the firm's profit is sensitive to the inventory holding rate, and that the inventory holding cost component is redundant in the presence of a tardiness cost component.     

Economic efficiency when prices are not fixed: disentangling quantity and price efficiency

This paper proposes an approach to compute cost efficiency in contexts where units can adjust input quantities and to some degree prices so that through their joint determination they can minimise the aggregate cost of the outputs they secure. The model developed is based on the data envelopment analysis (DEA) framework and can accommodate situations where the degree of influence over prices ranges from minimal to considerable. When units cannot influence prices at all the model proposed reduces to the standard cost efficiency DEA model for the case where prices are taken as exogenous. In addition to the cost efficiency model, we introduce an additive decomposition of potential cost savings into a quantity and a price component, based on Bennet indicators.     

大宗物资集中采购的调运计划研究

大宗物资调运问题是一个受多因素影响的复杂问题,一般来讲,其调运数量大、地域跨度大、时间紧。如何协调供需以及运输能力,保证物资采购部门的经济利益是本文所要研究的问题。本文首先给出了大宗物资集中采购的调运模式,然后针对采购部门在大宗物资调运过程中遇到的困难,为采购部门提出了以获取数量折扣、准时采购、降低库存成本和协调运输能力为准则的调运计划模型。数值计算表明应用本文所建模型编制调运计划能够达到降低采购费用、协调运输能力的目的,为大宗物资调运计划的编制提供了一种较为有效的方法。     

A Multi‐Supplier Sourcing Problem with a Preference Ordering of Suppliers

We study a sourcing problem faced by a firm that seeks to procure a product or a component from a pool of alternative suppliers. The firm has a preference ordering of the suppliers based on factors such as their past performance, quality, service, geographical location, and financial strength, which are commonly included in a supplier scorecard system. Thus, the firm first uses available inventory from supplier 1, if any, then supplier 2, if any, and so on. The suppliers differ in costs and prices. The buyer firm seeks to determine which suppliers to purchase from and in what quantities to maximize its total expected profit subject to the preference ordering constraint. We present the optimal solution to this problem, and show that it has a portfolio structure. It consists of a sub‐set of suppliers that are ordered by their underage and overage costs. This portfolio achieves a substantial profit gain compared to sourcing from a unique supplier. We present an efficient algorithm to compute the optimal solution. Our model applies to component sourcing problems in manufacturing, merchandizing problems in retailing, and capacity reservation problems in services.     

A review of RFID technology and its managerial applications in different industries

Radio frequency identification (RFID) is an emerging technology that is increasingly being used in supply chain management. RFID technology plays an important role in supporting logistics and supply chain processes because of their ability to identify, trace and track information throughout the supply chain. The technology can provide suppliers, manufacturers, distributors and retailers precise real time information about the products. This accurate knowledge of the inventory would result in lower labor cost, simplified business processes and improved supply chain efficiency. If properly used, it has the potential to cut ordering lead time and cost on inventory control, increase the accuracy of inventory information, help avoid stockouts and boost the number of inventory turns. In this paper, we provide an overview of the current state of RFID applications in different industries and its impact on business operations. We provide extensive literature survey and develop a framework for future research areas in this field.     

Optimizing lot size and setup time in a JIT environment: a multiple objective application

The just-in-time (JIT) system has been studied extensively and implemented by a number of US firms as an ell'ective production system. The core of JIT involves determination of lot size and setup time reduction so as to increase manufacturing flexibility while minimizing the inventory level. This decision problem usually involves multiple conflicting objectives and mixed-model production. In this paper, goal programming (GP) is applied to a real-world JIT problem involving fabrication of different automotive and industrial rubber composite belts. The model results provide new insights concerning the conflicting nature of several goals, especially between meeting demand and reducing setup or idle time. Also, the GP solution is superior to the current JIT practice of the company.     

Product Launches and Buying Frenzies: A Dynamic Perspective

Buying frenzies caused by a firm's intentional undersupplying of a new product are frequently evident in several industries including electronics (cell phones, video games), luxury automobiles, and fashion goods. We develop a dynamic model of buying frenzies that incorporates the firm's manufacturing and sale of a product over time and characterizes the conditions under which inducing such frenzies is an optimal strategy. We find that buying frenzies occur when customers are sufficiently uncertain about their valuations of the product and when they discount the future sufficiently but not excessively. We propose measures of “customer desperation” and of the extent of scarcity to measure the depth and breadth of buying frenzies, respectively. We also demonstrate that such frenzies can have a significantly positive effect on firm profits and partially recover the loss due to non‐commitment to future prices. This study provides managerial insights on how firms can influence market response to a new product through production, pricing, and inventory decisions to induce profitable frenzies.     

Inventory control with seasonality of lead times

The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions.     

Integrated procurement- production system in a just-in-time environment-modelling and analysis

Classically, economic lot size models have been used separately for procurement and production subsystems. However, when the raw materials are used in production, the procurement policies are dependent on the schedule and the batch size for the product. Hence, it is necessary to unify the procurement and production policies. The just-in-time JIT environment provides an ideal setting for such a coordination between the procurement and production policies. The model proposed here is a traditional inventory model recast into a model for a JIT system for a single product, multistage batch environment aiming at the minimization of total variable cost and thereby determining the batch sizes for the product and raw material order sizes. A JIT system aims at minimizing setup time and this feature is captured in the proposed model. The computational experience reported in this paper is based on a number of simulated problem sets. The possible domain of application is also highlighted.