Supplier's Cost Research Articles

Incentive Contracts for Capacity Restoration Under Risk of Supply Disruption

We consider a supply chain consisting of a supplier and a buyer. The buyer faces demand as a function of the selling price. There is risk of supply disruption, but the supplier can rebuild his capacity if he has invested for the capacity restoration before a disruption. To motivate the supplier to invest, the buyer can use one of two incentive contracts, namely direct and indirect . With a direct contract, the buyer provides a financial subsidy to share the supplier's capacity restoration cost when disruption occurs. With an indirect contract, the buyer adjusts the wholesale price in the case of disruption to stimulate the capacity restoration. Both contracts allow the buyer to adjust the order quantity in the case of disruption. We analyze decisions of each supply chain member under the two contracts with different commitment strategies (the ex ante commitment (EA) strategy under which the contract is signed before disruption and the ex post commitment (EP) strategy under which the contract is signed after disruption), and study how the supply disruption affects the buyer's selling price to end customers. We show that, from the perspective of either the supplier or the buyer, the incentive contract under the EA strategy is preferred by leading to a greater profit. Further, through numerical experiments, we recognize conditions for the fixed investment cost, probability of disruption, and regular wholesale price, under which each incentive contract encourages the supplier to invest for the capacity restoration, and brings greater profit to each supply chain member.

Supplier Competition and Cost Reduction with Endogenous Information Asymmetry

Problem definition: We consider a buyer sourcing from multiple competing suppliers who exert cost-reduction efforts before procurement contracts are awarded. Academic/practical relevance: The supply chain is subject to the classic hold-up problem—as the lack of a contract commitment hinders suppliers’ incentives to make investment upfront—complicated with supplier competition. Methodology: With deterministic cost-reduction outcomes, suppliers will not exert any effort if this effort is observable, and a pure-strategy equilibrium does not exist if the effort is unobservable. We analyze the mixed-strategy equilibrium with unobservable supplier effort, in which suppliers randomize their efforts and the buyer designs an optimal procurement mechanism. Results: We show that the optimal procurement mechanism can be implemented by a conventional single-price reverse auction with a random reserve price. The mixed strategy of supplier effort generates endogenous information asymmetry on supplier costs that provides suppliers with information rent, which sustains their efforts. The endogenous information asymmetry improves effort efficiency (by inducing positive supplier effort), yet introduces trade inefficiency (by causing the possible failure of trade between the parties). Although increasing supplier competition (measured by the number of suppliers) hurts the effort efficiency, it improves trade efficiency. As a result, the buyer is always better off introducing supplier competition by including more than one supplier in the supply base. However, the desired supply base size (number of suppliers) depends on the product revenue: For high-margin goods, the optimal size is achieved with two suppliers, whereas for low-margin goods, a larger supply base is better for the buyer. We show that the result based on deterministic cost reduction can be established as a limit of the case when uncertainty in cost reduction exists and shrinks to null. Managerial implications: Our study helps to understand the impact of supplier competition when supply-chain parties deliberately make their actions unpredictable to avoid being held up. The findings provide managerial guidance on procurement auction and supply base designs.

ПРИНЦИПИ ЕЛЕКТРОНІЗАЦІЇ ПУБЛІЧНИХ ЗАКУПІВЕЛЬ

Introduction. In the civilized world, the principles of public procurement are based on austerity criteria; fairness and impartiality of contract award decisions; publicity of the procurement process; efficiency of the procurement process; the importance of accountability. The purpose of the research is to analyze the transfer of the procurement system in electronic format as an effective tool for the goods acquisition, works and services in order to financially support the functioning of state structures to serve public needs, which are maintained mainly at the expense of the state and local budgets. Results. The article deals with the concept essence of «electronic procurement» – that is the newest way of procurement, because it is the passing and controlling in the electronic environment of all stages of the procurement cycle, including marketing research, determining the range of economic agents, procedures for purchasing, placing orders, delivering and paying for them, budgeting and planning for purchases by government agencies. An efficient e-procurement system is highlighted, the key to which is the electronicization of the procurement process, which helps to reduce the operating costs of customers and suppliers; speeding up the procurement process and saving time; a wide range of suppliers; simple and convenient management of the procurement process; clear and transparent access to purchasing information. Conclusions. The electronic public procurement system makes it easier for participants to get started with the state, find the right procurement, submit a bid and win bidding, since all the procurement information is available on the procurement page, and in the change case in the tender documentation, all previous revisions of documents remain in the system, which can be downloaded and viewed. Such transparency of the procedure makes it possible to prevent corruption risks and is a way to develop democratic and open processes in society. Keywords: public procurement, participant, customer, Law of Ukraine "On Public Procurement", tender, auction, public procurement, bidding, open bidding.

Making tough choices: A policy capturing approach to evaluating the tradeoffs in sustainable supplier development initiatives

This research explores the tradeoffs that decision makers are willing to make when implementing development initiatives to improve supplier sustainability performance. We employ a policy capturing methodology in which respondents are forced to choose between multiple potential sustainable supplier development initiatives. Data were collected from traditional and executive MBA students. The results suggest that when confronted with tradeoffs, decision makers value improvements in supplier cost savings and injury reduction equally, which was somewhat unexpected. Further, both improvements in supplier cost savings and injury reduction were valued over supplier emissions performance. Because we measure individual tradeoff preferences, multi-level regression analysis was used to better understand the impact of respondent value structure regarding sustainably developing suppliers. Our findings suggest a hierarchy of tradeoff preferences for decision makers as they pertain to sustainable supplier development. As the pressure to ensure supply chain sustainability increases, more firms will engage in sustainable supplier development. The outcomes of the choices they make when choosing between initiatives, and how managers make these choices, will be of increasing interest in both industry and academia. This research answers previous calls for further examination of decision maker tradeoff preferences in sustainable supply chain development.

Supplier portfolio selection based on the monitoring of supply risk indicators

This paper introduces a dynamic optimisation model for a manufacturer's optimal selection of a portfolio of suppliers. The model uses a set of indicators that measure risks imposed by suppliers on the manufacturing supply chain. These indicators measure financial stability, production stability, product quality and cost of suppliers. The model uses a combined simulation-optimisation framework to select suppliers and allocate orders to them based on real-time monitoring of supply risk indicators. This model uses a multi-period order allocation approach based on the viewpoint of a manufacturer in a manufacturing supply chain system. A system dynamics model simulates the interrelations and feedbacks among parties in the supply chain, i.e., suppliers, the manufacturer, and the manufacturing product market. It models the effect of supply risk indicators on a manufacturer's profit over a planning horizon. The result of the simulation is fed to a portfolio optimisation model to determine an optimal supplier order allocation based on the manufacturer's propensity for risk. The model informs the manufacturer to rebalance its supply portfolio in response to early changes in supply risk indicators over a planning horizon. The results show that supplier portfolio selection based on this framework provides higher expected profit and less risks to the manufacturer over the planning horizon. For instance, in our numerical example, the high-level risk averse decision maker made a profit of 5.4% and a risk of 1% less than those of low-level risk averse decision maker at the end of the planning horizon.

Resilient supplier selection in logistics 4.0 with heterogeneous information

Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment. The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

An efficient supplier selection model for pump industry through Best - Worst Method.

Global trend has turned out customers to seek for diverse range of application-based products conferring to their prerequisites. This paper emphasis the supplier selection process in the pump manufacturing industry by adopting zero inventory concept. Major factors such as cost of delivery, capacities, quality, price, stand-alone suppliers, reliability and suppliers profile etc. are the fundamental parameters in the supplier selection process which tends to have a major influence on product delivery and complete satisfaction of the customer. Multi-criteria decision-making (MCDM) technique and best-worst method (BWM) are utilised to avoid ambiguity in supplier selection and improve the on-time receipt (OTR). A novel methodology was developed to analyses supplier selection process and the developed methodology has been validated through a case study.

Bi-objective optimization of the joint replenishment problem in a two-echelon supply chain

In this research, a bi-objective optimisation model for the joint replenishment problem (JRP) of a supply chain comprised of a single supplier and multiple retailers is developed, in which the retailers are assumed to be members of a unique distribution company. The mathematical model minimises the supplier's as well as the retailers' cost subject to some constraints. The constraints are the required storage spaces for any retailer, for any product, and for all the products. The benefit of using the JRP policy is shown based on minimising total cost of supplier and retailers. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimisation algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.

Inventory Control When Selling to Human Newsvendors

We develop inventory control strategies for a supplier who is selling a product to a group of human newsvendors. Recent research in newsvendor experiments using human subjects revealed significant behavioral tendencies in the decision making processes of human newsvendors. If the supplier would incorporate these behavioral tendencies while managing her inventories, her costs would be significantly reduced. Using data from our own experiments, we estimate the possible reductions in supplier costs and determine the factors that significantly impact it. We observe a significant improvement in the supplier’s inventory decision if she estimates the demand distribution with a model that captures the anchoring tendencies instead of assuming aggregate randomness. As the model selection relies much on the data, it is an important task to determine whether the retailers are mean-anchoring, demand-chasing, both, or neither. In addition, we observe that the information about retailers’ behaviors is more beneficial for the supplier when the end-customer demands are more variable. It is certain that more precise information on the data will lead to a better inventory decision for suppliers. The information includes the retailers’ behavioral tendencies, individual-specific order behaviors, and the variability of end-customer demands.

Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus

An operational strategy to optimize building operating energy costs for suppliers and consumers is an important challenge for electrical power utilities. There are various supply-side measures that utilities have to take to ensure continuous energy supply for building heating and air-conditioning. During peak energy demand, utilities are often forced to use more expensive and less efficient generation, thereby increasing the cost of energy. However, some demand-side management practices behind the consumer meter can help in meeting this challenge. One such measure is the use of thermal storage for heating, ventilation, and air-conditioning applications in commercial buildings. There is a gap of adequate knowledge of an optimal control strategy of cold storage operation in buildings adapting to applicable time of day tariffs to minimize annual energy use and annual energy cost of operation. There is also a need to use commercially available tools to avoid the use of complex mathematical models. This study demonstrates strategic controls with six operating modes for using thermal energy storage to shift peak electricity demand, using the time of day tariffs as a decision variable, and reducing operating costs, while also minimizing the size of the system. EnergyPlus was used to model a standard reference large office building for three thermal energy storage system cases: mixed chilled water storage, stratified chilled water storage, and ice storage. An annual average shifting of 25–78% of peak electricity was achieved from the simulation results. The strategy was able to achieve an annual 10–17% cost reduction for consumers using the time of use rates available from a local utility.

Sourcing Contract Under Countervailing Incentives

We study a retailer's sourcing contract when the supplier's reservation profit (offered by his outside options) depends on his cost, which is privately known to only the supplier. An interesting discovery from our analysis is that supply chain coordination may be achieved despite the information asymmetry between the two firms regarding the supplier's type (i.e., his cost). This happens when the marginal contribution of the supplier's cost efficiency to the trade matches with the supplier's marginal reservation profit. In this case, the optimal contract quantities maximize the supply chain profits for the respective supplier types, and no information rent is paid to any supplier type. For the general case, we show that, regardless of his type, the supplier may have an incentive to overstate or understate his cost, depending on whether or not his marginal contribution to the trade exceeds his marginal reservation profit. We demonstrate six possible forms of the optimal contract. Observations from our analysis contrast with those derived from previous studies, complementing the theory of countervailing incentives.

Two-phase selection framework that considers production costs of suppliers and quality requirements of buyers

Buyers are faced with selecting the optimal supplier, while suppliers are left to consider production costs. In this study, we developed a two-phase selection framework that allows buyers to evaluate the performance of suppliers while taking production costs into account for value maximisation. This scheme is a win-win solution capable of promoting long-term relationships between buyers and suppliers. Under the assumption of normality, the first phase involves constructing a new Six Sigma quality capability analysis chart (SSQCAC) which takes production costs into account. The objective is to evaluate all potential suppliers using the 100 × (1–α)% upper confidence limit (UCL) of an integrated Six Sigma quality index (SSQI) QPIh when dealing with products with smaller-the-better (STB), larger-the-better (LTB), or nominal-the-best (NTB) quality characteristics. According to interval estimation theory, this method can have a significant impact on the consumption of resources; i.e. the production costs of the supplier can be decreased by reducing the production quality to below that required by the buyer. The proposed method also filters out unsuitable suppliers in order to simplify the decision problem and reduce computational demands and operational risks/costs without compromising the quality of the final product. In the second phase, a detailed analysis is conducted using Euclidean distance measure to select the optimal supplier from among the remaining candidates. We conducted a real-world case study to evaluate the efficacy of the proposed method. We also conducted comparisons with existing methods to demonstrate the advantages of the proposed method and its managerial implications. Suggestions for future study are also provided.

The Economic Costs of Financial Distress

We estimate the economic costs of financial distress by exploiting cross-supplier variation in real estate assets and leverage, and the timing of real estate shocks. We show that for the same client buying from different suppliers, its purchases from distressed suppliers decline by an additional 10% following a drop in real estate prices. The effect is more pronounced in more competitive industries, manufacturing and durable goods industries, for less-specific goods, and when the costs of switching suppliers are low. Our results suggest that the indirect costs of financial distress are driven mostly by clients reducing their purchases from distressed suppliers.

Route Network Design of Community Shuttle for Metro Stations Through Genetic Algorithm Optimization

This paper investigates an issue for optimizing community shuttle network linked with metro stations. Considering a set of trip demands originating from several bus stops within a large community area, a mixed integer optimization model is formulated to obtain the optimized shuttle route network and the frequency of each route simultaneously, to minimize the total transit system cost, including user and supplier costs. To solve the problem, a solution framework consisting of three primary components is proposed: an initial route network scheme is developed to ensure that any generated network is feasible; a network analysis procedure is performed that assigns the demand of each stop to a set of paths on the generated network according to a certain proportion; a heuristic algorithm is proposed to optimize the service frequency of each route; a genetic algorithm consisting of a set of specifically designed operators is proposed to guide the solution population evolving process. A real-life study is tested successfully using the proposed solution framework. The related sensitivity analysis and impacts of some important variables on the solution are performed.

A Review on Theoretical Development of Vendor-Managed Inventory in Supply Chain

Vendor-managed Inventory (VMI) is a new inventory operation pattern in the supply chain environment, breaking the traditional independent inventory management pattern. It is a kind of cooperative strategies that improving continuously inventory management, which aims at obtaining the lowest cost for both users and suppliers, manages inventory by suppliers under a common agreement, and continuously supervises the implementation of the agreement and amends the content of the agreement. This paper discusses the existing literature of VMI, and reviews the research of VMI from its connotation, theoretical basis and four stages of development, points out the defects and deficiencies, and puts forward suggestions for further research direction.

Willingness Impact to the PAR Optimisation of R-users Community using EMS

The optimisation of residential users’ power usage is measured by Peak-to-Average (PAR) ratio. Higher PAR leads to increase in the cost for both power suppliers and residential users. Suppliers use expensive resources during these peaks, in turn, the residential users will pay more during these times even for the same amount per kW/h. The aim of this study is to investigate the use of an energy management algorithm within an Energy Management System (EMS) to optimise power consumption and to reduce the overall PAR for a community of users. Beyond the group optimisation, the algorithm takes into account the heterogeneous nature of the community by introducing individual household values of willingness to save power and have the energy managed. In conjunction with the concept of community energy management and willingness, the paper also highlights the importance of incentives for power load optimisation to each user. The proposed algorithm is tested on 15 R-users load profiles, selected based on criteria such as house size, temperature level, by measuring the load of each individual user profile during a 24-hour cycle with a 10-minute resolution. The results show that the algorithm provides a 22.66% reduction of the PAR was 22.66% in a multi-user scenario. Other aspects that could influence the PAR reduction are measured, such as the impact of PAR optimisation by individual R-users without considering the community and the impact of different R-users willingness to use the proposed EMS. This indicates that the effective proposed EMS optimisation is significantly accepted at the point of applying it in single and multi-users scenarios in spite of the variance accepted level of these R-users being considered.

Information Asymmetry, Rents, and Cost Risk Allocation: An Investigation of the BMW Supply Chain

This paper studies a dynamic procurement process used by many European automakers and BMW in particular. An automaker (she) must procure components from an upstream supplier (him) to assemble cars in a given production period. Demand for vehicles is stochastic, and retail prices are endogenous. The automaker may or may not be privy to the supplier's raw material product cost, which is also stochastic. In the former case, we say that information is symmetric; in the latter case, information is asymmetric. (Existing work in this area is limited to static models with deterministic demand and symmetric information.) The contracts BMW uses are the material-plus-surcharge (MPS) and the fixed price contract (FP). In that environment, we explain how to choose between the contracts that BMW uses currently, and we identify a new, optimal contract for the asymmetric case that would net the automaker an additional profit boost. We conclude by assessing the empirical support for our predictions using a unique data set of contractual arrangements provided to us by BMW. Our analysis sharpens our understanding of contract choice in dynamic procurement settings and identifies a list of key factors in making such choices.

Regulatory and market disharmony in the Turkish electricity industry

We examine the regulatory and market disharmony in the Turkish electricity industry revealed in the course of introducing competition in retail supply. We specifically show that the design of the support mechanism for renewable energy resources and the level of regulated retail tariffs have left little room for the viability of retail competition in the electricity sector. While the cost of the support mechanism has increased the cost for retail suppliers, the regulated tariffs prevent them from reflecting this cost increase in their prices and from providing commercial offers. This induces customers to switch back to their default regulated tariffs.

Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

Supply chain management and integration play a key factor in contemporary manufacturing concept. Companies seek to integrate itself within a cooperative and mutual benefiting supply chain. Supply chain scheduling, as an important aspect of supply chain management, highly emphasizes on minimizing stock costs and delivery costs. Most previous researches on supply chain scheduling problems assume make-to-order production, which includes delivery cost in lot-size. This practice simplifies the complexity of the problem. Instead, this research discusses make-to-contract production, where the supply chain has a rolling planning horizon that changes according to contracts. Within a planning horizon, two types of interval are defined. The first is frozen interval, in which the manufacturing decision cannot be changed. The second is free interval, where schedules can be adjusted depending on new contracts. This research aims to build a robust rolling supply management schedule to satisfy customers’ needs, by considering supplier, production, and delivery lot-size simultaneously. The objective is to effectively decide a combination of supplier, production, and delivery lot-size that minimizes total cost consisting of supplier cost, finish good stock cost, and delivery cost. Based on the concept, this study designs a problem-solving process that combines the methods of rolling planning horizon and genetic algorithm. Delivery size (DS), finish good stock (FS), and early delivery cost (ED) are the three methods applied; each will provide a guideline to produce a feasible solution. By further considering the fluctuations in practical needs and performing an overall evaluation, a robust and optimal supply chain scheduling plan can be decided, including the optimal lot-sizes of supplier, production, and delivery. In the effectiveness test which considers 3 types of customer demands and 11 types of company cost structures, the simulated data test results suggest that the proposed methods in this study have excellent performance.

Supplier Concentration and Cost Structure

The last three decades witnessed an increasing prevalence of concentrated supplier bases, which lead to higher supply disruption risk. We predict that firms with high supplier-base concentration will choose a more elastic cost structure in response to the increased supply disruption risk. Using a unique dataset of 4,530 firm-year observations hand-collected from supplier information disclosed by Chinese listed firms, we document a positive association between supplier-base concentration and cost elasticity. Furthermore, results from five different cross-sectional tests are consistent with supply disruption risk motivating firms with high supplier-base concentration to increase cost elasticity. Finally, additional tests indicate that firms with high supplier-base concentration use more variable capital assets to achieve higher cost elasticity. Our study contributes to the cost management literature by providing the first large-sample empirical evidence on firms’ cost structure decisions in response to supply-side risk factors.