Full fixed cost recovery lot sizing with quantity discounts

Purchasers must often make lot sizing decisions when facing price schedules of price-quantity discounts. It is important to determine the supplier's pricing philosophy when establishing a solution procedure.One approach is to evaluate total costs at all of the appropriate break points. This offers limited information: a lot size and a set of total costs. This is especially true in the case of full fixed cost recovery pricing. In actual practice price schedules can be extremely lengthy: indeed, it may be in the supplier's best interests to offer comprehensive discount schedules. This situation complicates the purchaser's decision making process.An efficient alternative, which solves the price-quantity discount problem when the supplier insists on a full fixed cost recovery schedule, is presented. Computations are reduced to a few simple steps; the result is a least total cost lot size for a simple linear package price model given parameters obtained by an appropriate analysis of the supplier's price-quantity discount schedule. A starting point is determined and the choice of the lot size is made using a simple criterion. Rapid convergence is assured, given a reasonably well-behaved schedule.     

Time-sensitive supply chain disruption recovery and resource sharing incentive strategy

Market demand is becoming increasingly time-sensitive in competitive environments. Hence, supply disruptions will have a more serious impact on the profits of supply chains. This study applies a Stackelberg competition between a single supplier and a single manufacturer in a time-sensitive supply chain in a cloud manufacturing environment. We aim to address the supplier’s production capacity recovery issues and the manufacturer’s incentive decision issues after supply disruption. We find that the supplier is in a weak position when the information is symmetrical. The manufacturer can encourage the supplier to shorten the recovery time by raising the unit wholesale price. When the supplier’s unit production cost remains unchanged but the unit wholesale price increases, the profit of the supplier first increases and then decreases. In addition, under the centralized decision-making setting, the optimal recovery time of the supplier is shorter and the optimal unit market price of the product is lower than that under decentralized decision-making. We further find that resource sharing can shorten the optimal recovery time, but it does not necessarily play an incentivizing role.     

Supply chain decisions and coordination in the presence of an imperfect spot market

This study considers a supply chain consisting of a commodity supplier and a final product manufacturer with uncertain demand. In addition to purchasing from the supplier through a forward contract, the manufacturer can adjust their inventory by trading the commodity in an online spot market after observing the actual demand. However, the spot market is imperfect in that transactions cannot be certainly realized and come with additional transaction costs. Furthermore, the spot price is volatile such that overly relying on the spot market is unwise. To investigate how the spot market affects the decisions and coordination in a supply chain, we develop a game-theoretical model incorporating spot trading. We derive the optimal ordering decision in a centralized supply chain, as well as the supplier's and manufacturer's equilibrium pricing and ordering decisions in a decentralized supply chain. The impact of the imperfect spot market on the optimal decisions and profits is analyzed. This study also demonstrates how the supply chain can be coordinated in the presence of an imperfect spot market. Finally, a numerical analysis is performed to examine the analytical results. Our results indicate that the spot market can generally improve the performance of the centralized supply chain and benefit the manufacturer in the decentralized one. However, it can be detrimental to the supplier. The supply chain can be coordinated by a revenue-sharing contract, and both parties' profits can be improved. Our findings suggest that the manufacturer could take advantage of the spot market, and the supplier should attempt to integrate or coordinate the supply chain to share the benefits of spot trading.     

The effects of tariff increases on supply base complexity: A conceptual framework

Recent protectionist trends around the world have raised interest in newly enforced or increased tariffs and their effects on global supply chains. For firms sourcing globally, tariff introductions or increases significantly affect importing costs, which ultimately affect product costs. Such tariff changes may incentivize firms to adjust their supply base to mitigate these cost increases, thus altering the structure and complexity of firms' supply bases. In this paper, we first characterize the U.S. tariff landscape from 1997 to 2017. We then develop a conceptual model to explain how the severity and timing uncertainty of expected tariff increases influence a firm's speed of adapting to the changing tariff environment. Specifically, we explore firms' propensity to form or delete ties to suppliers, which influences supply base complexity. Moreover, we consider factors that moderate the relationship between severity, timing uncertainty, and supply base complexity, including a tariff's geographical scope, a firm's relative purchase spend, and supply risk. Our conceptual model offers both research and managerial implications.     

Optimality and consistency in a differential game with non-renewable resources

Imperfect competition amongst buyers for a non-renewable resource is modeled as a Stackelberg differential game. The buyers may act as a cartel or behave non-cooperatively in setting tariffs, which the sellers take as given. The buyer's optimal policy is not consistent when extraction costs depend on the stock level. Both optimal and consistent, unit and ad valorem tariff's are analyzed. When a buyers' cartel uses a consistent tariff, the sellers may prefer to behave as perfect competitors or a monopolist, depending on the functional form of demand and cost. Non-cooperative buyers who are constrained to act consistently are unable to extract any rent from competitive sellers.     

Strategic inventory in capacitated supply chain procurement

We study the strategic role of inventory in a sequential two‐period procurement setting, where the supplier's capacity in the first period is limited and the retailer has the option to hold inventory. We compare the equilibrium under a dynamic contract, where the decisions are made at the beginning of each period, and a commitment contract, where the decisions for both periods are made at the beginning of the first period. We show that there is a critical capacity level below which the outcomes under both types of contracts are identical. When the first period capacity is above the critical level, the retailer holds inventory in equilibrium and the inventory is carried due to purely strategic reasons; as capacity increases, so does the strategic role of inventory. The supplier always prefers lower capacity than the retailer, and the difference between supplier‐optimal and supply‐chain optimal capacities, and the corresponding profits, can be significant. Finally, we find that the retailer's flexibility to hold inventory is not always good for the participants or for the channel. Copyright © 2008 John Wiley & Sons, Ltd.     

Supply chain vulnerability assessment: A network based visualization and clustering analysis approach

Supply chains are large, complex, and often unpredictable. Purchasing and supply managers and supply chain risk managers need methods and tools to enable them to quickly understand how unexpected disruptions in the supply chain start and grow and to what extent will they negatively impact the flow of goods and services. This paper introduces a methodological approach that can be used by both researchers and managers to quickly visualize a supply chain, map out the propagation path of disruptive events from the supply side to the end customer and understand potential weaknesses in the supply chain design; taking into account the structure, connectivity, and dependence within the supply chain. The approach incorporates a Petri net and Triangularization Clustering Algorithm to offer insights into a supply chain network's vulnerabilities and can be used to efficiently assess supply chain disruption mitigation strategies, especially in complex and difficulty to analyze supply chain systems.     

A model of the firm in time and space

We consider a multi-plant monopoly that sells to markets which are geographically separated and which stores product over time via an inventory capability. It is assumed that plant average production cost is U-shaped and that, if the output of a plant's production run were sold to a single market at only one point in time, the plant would operate on the falling portion of its average cost curve. Hence, it is in the interest of the firm to aggregate markets, both spatially and temporally, to lower average production cost. We develop the optimal joint interplant spacing-inventory policy. We also consider the effects changes in freight costs, storage costs, and interest charges have on the firm's optimal policy.     

Supply chain finance and impacts of consumers’ sustainability awareness

Several new methods have been proposed for supply chain finance (SCF) with bank credits, but none of them mentions how to solve the borrowers’ moral hazard problems in SCF. This paper examines the moral hazard problem in supply chain financing with procurement contract (or purchase order). We show that since supply chain is an up-down directed structure, when financing with the procurement contract, the supplier’s effort monitoring task can be rendered to the procurement contract, which can secure the supplier’s optimal effort and capital choices in production. Hence, compared to separate lending, the supplier’s credit rationing problem can be mitigated, and most importantly, banks’ under-estimation on the supplier’s default risk and the over-estimation on the retailer’s default risk will both decrease. We further show that the retailer’s corporate social responsibility expenditure can increase consumers’ brand recognition, thus when facing demand shocks arising from consumer’s unexpected concerns, the retailer can better stabilize the firm value.     

Optimal structure of joint inventory-pricing management with dual suppliers and different lead times

We consider a joint inventory-pricing control problem in a single-product, periodic-review, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide properties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.     

Data center supply chain configuration design: A two-stage decision approach

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.     

供应链理念下我国企业存货管理方法研究

从我国企业存货管理的现状入手,提出了存在的问题,分析了其产生的原因,在此基础上结合我国实际,着重对供应链理念下考虑货币时间价值的存货计价模型进行了介绍并结合案例进行了分析,指出了今后企业在存货管理决策方面应构建起以货币时间价值为理念的存货控制管理方法。     

The role of supply management innovativeness and supplier orientation in firms’ sustainability performance

Questions about the influencing factors and measurement of firms' sustainability performance have attracted growing research interest, as the requirements for sustainability have steadily increased. This study examines whether supply management innovativeness and supplier orientation make positive impacts on firms' overall sustainability performance. An empirical study based on a survey targeting large- and medium-sized manufacturing companies in Finland shows that innovativeness in supply management considerably influences a firm's overall sustainability performance and that supplier orientation positively relates to sustainability performance. The benefits of innovativeness in supply management and strategic supplier orientation are directly realised in sustainability performance. Therefore, supply management is not only a gatekeeper against sustainability risks arising from the supply base but is also a function by which new ideas aiming to influence supply markets and firms' sustainability are presented.     

A cost minimization production planning model for continuous process chemical plant operations

The management of large continuous process chemical plants oftentimes must make operating decisions within a rapidly changing economic environment. This paper describes a decision making aid in the form of a production planning model which is immediately responsive to such changes. The model provides cost minimization solutions to “what if” questions of management through the use of linear programming. Implementation upon an interactive computer system provides a user oriented model. A specific type of chemical plant operation involving 13 decision variables illustrates the approach. Five example real-world situations demonstrate the approach's capability.     

需求确定型多级库存优化模型研究

多级库存管理对于企业来说具有重要的意义,在需求确定的情形下对库存模型进行了分析,建立了基于确定需求的多级库存数学模型,包括一个单目标模型和一个多目标模型,使供应链的效率得到了有效提高。     

Why locate manufacturing in a high-cost country? A case study of 35 production location decisions

In this paper, we examine in detail 35 final assembly location decisions to gain understanding of the manufacturing location decision from strategy and economic policy perspectives. We are particularly interested in the decision to locate final assembly specifically in a high-cost (high GDP per capita) environment. In contrast with the earlier literature, we focus not just on manufacturing activities themselves, but also the key linkages between production, market, supply chain, and product development. These linkages are examined using three key concepts from theories of organization design: formalization, specificity, and coupling. Using these concepts, an analysis of the micro-structure of each case reveals important commonalities that inform our understanding of location decisions. We conclude by discussing the policy implications of our findings.     

Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.     

A behavioral study of supply manager decision-making: Factors influencing make versus buy evaluation

This paper investigates behavioral factors influencing a supply manager's decision to insource or outsource the manufacture of a product component. To do so we posit a theoretical framework that integrates the heretofore distinct operational make–buy literature and the behavioral decision-making literature. Within the framework three factors influencing the make–buy decision are brought into account: the decision-maker's perception of supply risk or “strategic vulnerability”, the degree of core competency represented by the product component under consideration and the formality of the information about supply alternatives. The results of a controlled experimental survey show that: strategic vulnerability and core competency do influence the make–buy decision, strategic vulnerability has greater influence than core competency and information formality moderates the make–buy decision when the strategic vulnerability and core competency conditions are mixed. The practical implications of these results include the notion that management can ensure a more rational make–buy decision if they understand the biases that influence the decision and point these biases out to the decision maker.     

A fuzzy multi-objective programming approach for determination of resilient supply portfolio under supply failure risks

The main contribution of this paper is to develop a new decision tool that interprets strategies for determination of resilient supply portfolio under supply failure risks. The strategic decisions include the allocation of emergency capacities to be pre-positioned at backup suppliers, the output of which can be increased in the event of mitigating a shortage caused by another supplier's failure. The model contains three objective functions – minimising the total cost, minimising the net rejected items and minimising the net late deliveries – while satisfying capacity and minimum order quantity requirement constraints. A weighted additive fuzzy multi-objective model is proposed to simultaneously consider the imprecision of information and the relative importance of objectives for determining the allocation of order quantity and emergency capacity to each supplier. The application of the proposed model is illustrated using an example case of global supply chains with different supplier characteristics.