Network DEA model for supply chain performance evaluation

This paper constructs an alternative network DEA model that embodies the internal structure for supply chain performance evaluation. We take the perspective of organization mechanism to deal with the complex interactions in supply chain. Three different network DEA models are introduced under the concept of centralized, decentralized and mixed organization mechanisms, respectively. Efficiency analysis including the relationship between supply chain and divisions, and the relationship among the three different organization mechanisms are discussed. As a further extension, we investigate internal resource waste in supply chain.     

二阶段加法DEA模型的生产可能集与生产前沿面

针对二阶段加法DEA模型的中间要素的特殊性,构造生产可能集及其公理体系,由此定义生产前沿面,并建立DEA有效和生产前沿面之间的等价关系.通过构造一个多目标规划模型,建立该问题的Pareto有效解与DEA有效之间的等价关系.     

DEA models for supply chain efficiency evaluation

An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Two hurdles are present in measuring the performance of a supply chain and its members. One is the existence of multiple measures that characterize the performance of chain members, and for which data must be acquired; the other is the existence of conflicts between the members of the chain with respect to specific measures. Conventional data envelopment analysis (DEA) cannot be employed directly to measure the performance of supply chain and its members, because of the existence of the intermediate measures connecting the supply chain members. In this paper it is shown that a supply chain can be deemed as efficient while its members may be inefficient in DEA-terms. The current study develops several DEA-based approaches for characterizing and measuring supply chain efficiency when intermediate measures are incorporated into the performance evaluation. The models are illustrated in a seller-buyer supply chain context, when the relationship between the seller and buyer is treated first as one of leader-follower, and second as one that is cooperative. In the leader-follower structure, the leader is first evaluated, and then the follower is evaluated using information related to the leader's efficiency. In the cooperative structure, the joint efficiency which is modelled as the average of the seller's and buyer's efficiency scores is maximized, and both supply chain members are evaluated simultaneously. Non-linear programming problems are developed to solve these new supply chain efficiency models. It is shown that these DEA-based non-linear programs can be treated as parametric linear programming problems, and best solutions can be obtained via a heuristic technique. The approaches are demonstrated with a numerical example.     

A geometrical approach for generalizing the production possibility set in DEA

Consider a Data Envelopment Analysis (DEA) study with n Decision Making Units (DMUs) and a model with m inputs plus outputs. The data for this study are a point set, {a¹,…,a ⁿ }, in Open image in new window . A DMU is efficient if its data point is located on the efficient frontier portion of the boundary of an empirical production possibility set, a polyhedral envelopment hull described by the data. From this perspective, DEA efficiency is a purely geometric concept that can be applied to general point sets to identify records with extreme properties. The generalized approach permits new applications for nonparametric frontiers. Examples of such applications are fraud detection, auditing, security, and appraisals. We extend the concept of DEA efficiency to frontier outliers in general envelopment hulls.     

A DEA game model approach to supply chain efficiency

Data envelopment analysis (DEA) is a useful method to evaluate the relative efficiency of peer decision making units (DMUs). Based upon the definitions of supply chain efficiency, we investigate the efficiency game between two supply chain members. It is shown that there exist numerous Nash equilibriums efficiency plans for the supplier and the manufacturer with respect to their efficiency functions. A bargaining model is then proposed to analyze the supplier and manufacturer's decision process and to determine the best efficiency plan strategy. DEA efficiency for supply chain operations is studied for the central control and the decentralized control cases. The current study is illustrated with a numerical example.     

Performance evaluation of purchasing and supply management using value chain DEA approach

Purchasing and Supply Management (PSM) today is increasingly becoming more important to senior management due to its potential to strategically influence both operational performance as well as financial performance outcomes. However the cross-functional nature of many PSM activities has led to inadequate data collection and performance measurement resulting in weak performance evaluation methodologies and mixed results. We address this gap in the current study, firstly by using an external assessment survey methodology that complements the internal perceptional measures of PSM performance, to collect data for a sample of over 120 firms across the globe with more than 3 billion US dollar turnover, representing seven industry sectors. Next, we develop a comprehensive performance measurement framework using the classical and two-stage Value Chain Data Envelopment Analysis models, which make use of multiple PSM measures at various stages and provide a single efficiency measure that estimates the all-round performance of a PSM function and its contribution to the long term corporate performance in each of these seven industry sectors. The relevance of this measurement methodology is demonstrated through an in-depth analysis of the distribution of efficiencies within and across industry sectors and through the estimation of target PSM performance levels.     

An economic evaluation model of supply chain flexibility

With the advent of the supply chain management concepts, business communities have been realizing that being competitive as a single company is no longer adequate; instead, competitiveness requires consideration of all channels in the supply chain. Despite its importance, the availability of the literature addressing supply chain flexibility is still limited to date. Although relationships between various types of flexibilities have been established, the degree to which one type of flexibility affects the other types and the system performance remains to be investigated. In particular, there is a lack of rigorous analytical models elucidating the relationships between the degree of flexibility in a system and the system level of performance. In this paper, a supply chain flexibility model is developed comprising labor flexibility, machine flexibility, routing flexibility, and information technology, with total system flexibility measured by an economic index. Outputs from the model can assist in making suitable production decisions to produce multiple products under an uncertain environment. Example solutions are given. This paper can help economic evaluation when supply chain flexibility and the factors affecting flexibility are to be improved. It can also assist in making supply chain flexibility-promotion decisions.     

Decomposing the efficient frontier of the DEA production possibility set into a smallest number of convex polyhedrons by mixed integer programming

This paper extends the works by Olesen and Petersen (2003), Russell and Schworm (2006) and Cooper et al. (2007) about describing the efficient frontier of a production possibility set by the intersection of a finite number of closed halfspaces, in several ways. First, we decompose the efficient frontier into a smallest number of convex polyhedrons, or equivalently into a new class of efficient faces, called maximal efficient faces (MEFs). Second, we show how to identify all MEFs even if full dimensional efficient faces do not exist. Third, by applying the MEF decomposition to various real-world data sets, we demonstrate the validity of the MEF decomposition and how it can contribute to the DEA literature. Finally, we illustrate how to use the identified MEFs in practice.     

Supply chain performance assessment and supplier and component importance identification in a general competitive multitiered supply chain network model

In this paper, we develop a multitiered competitive supply chain network game theory model, which includes the supplier tier. The firms are differentiated by brands and can produce their own components, as reflected by their capacities, and/or obtain components from one or more suppliers, who also are capacitated. The firms compete in a Cournot–Nash fashion, whereas the suppliers compete a la Bertrand since firms are sensitive to prices. All decision-makers seek to maximize their profits with consumers reflecting their preferences through the demand price functions associated with the demand markets for the firms’ products. We construct supply chain network performance measures for the full supply chain and the individual firm levels that assess the efficiency of the supply chain or firm, respectively, and also allow for the identification and ranking of the importance of suppliers as well as the components of suppliers with respect to the full supply chain or individual firm. The framework is illustrated through a series of numerical supply chain network examples.     

Evaluating returns to scale and congestion by production possibility set in intersection form

This research proposes a new method to estimate returns to scale(RTS) of decision making units(DM Us) with multiple inputs and outputs.The state of return to scale includes increasing RTS,constant RTS,decreasing RTS and evidence of congestion.The method is based on the production possibility set in the intersection form given by a set of linear inequalities.We propose and prove the necessary and sufficient conditions for the RTS estimation.With the new procedure,to estimate the RTS of a DM U is simply to ch...     

An integrated production and inventory model to dampen upstream demand variability in the supply chain

We consider a two-echelon supply chain: a single retailer holds a finished goods inventory to meet an i.i.d. customer demand, and a single manufacturer produces the retailer’s replenishment orders on a make-to-order basis. In this setting the retailer’s order decision has a direct impact on the manufacturer’s production. It is a well known phenomenon that inventory control policies at the retailer level often propagate customer demand variability towards the manufacturer, sometimes even in an amplified form (known as the bullwhip effect). The manufacturer, however, prefers to smooth production, and thus he prefers a smooth order pattern from the retailer. At first sight a decrease in order variability comes at the cost of an increased variance of the retailer’s inventory levels, inflating the retailer’s safety stock requirements. However, integrating the impact of the retailer’s order decision on the manufacturer’s production leads to new insights. A smooth order pattern generates shorter and less variable (production/replenishment) lead times, introducing a compensating effect on the retailer’s safety stock. We show that by including the impact of the order decision on lead times, the order pattern can be smoothed to a considerable extent without increasing stock levels. This leads to a situation where both parties are better off.     

Generating strong defining hyperplanes of the production possibility set in data envelopment analysis

In data envelopment analysis (DEA), identification of the strong defining hyperplanes of the empirical production possibility set (PPS) is important, because they can be used for determining rates of change of outputs with change in inputs. Also, efficient hyperplanes determine the nature of returns to scale. The present work proposes a method for generating all linearly independent strong defining hyperplanes (LISDHs) of the PPS passing through a specific decision making unit (DMU). To this end, corresponding to each efficient unit, a perturbed inefficient unit will be defined and, using at most m+s linear programs, all LISDHs passing through the DMU will be determined, where m and s are the numbers of inputs and outputs, respectively.     

Impact of information errors on supply chain performance

This study investigates how information errors affect supply chain performance when a retailer and a supplier share the end-customer demand information. An Enterprise Resource Planning (ERP) system, often used to share information in a supply chain, is not perfect and often contains erroneous information. Companies contemplating the use of ERP systems must evaluate the benefit of using them despite the existence of errors in the system. Our study quantifies the impact of information errors by comparing the supplier costs with and without errors. Our analytical and computational results indicate that the detrimental impact of errors outweighs the beneficial impact of information sharing when the variance of information errors exceeds the variance of end-customer demands. Therefore, when that happens, it is best to operate as if the information is not available. Finally, if it is possible to expend effort and reduce information errors, we present an analytical model for determining the optimal level of investment.     

Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management

The current paper presents a comprehensive methodology for supplier selection. In the first stage, the linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the criteria. The Axiomatic Fuzzy Set clustering (AFS) method, which handles ambiguity and fuzziness in the supplier selection problem effectively, is applied to cluster the suppliers and evaluate each potential supplier that aims at obtaining initial supplier ranking. In the second stage, the Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to determine the weight of various quantitative and qualitative criteria. To address multiple decision criteria in supplier ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to select the final suppliers. A numerical example composed of 30 suppliers and 6 criteria is studied, and the experimental results show that the proposed evaluation framework is suitable for supplier selection decisions even with the dependent criteria/attributes.     

Towards a multi-objective performance assessment and optimization model of a two-echelon supply chain using SCOR metrics

This paper aims at multi-objective performance assessment and optimization of a multi-period two-echelon supply chain consisting of a supplier and a manufacturer. On the basis of the assessment system of the supply-chain operations reference model, the supply chain’s performance is investigated with respect to costs, assets, agility, reliability and responsiveness. First, methods to quantify these five performance attributes are put forward. Then a multi-objective mathematical programming model is developed for production decision making of components and products so that the supply chain’s performance frontier formed with Pareto efficient performance values can be achieved. Thereafter a simple augmented \(\epsilon \) -constraint method is proposed for searching for all Pareto efficient solutions of the multi-objective mathematical programming problem. Finally, efficiency of the method is demonstrated with a numerical example and a sensitivity analysis is implemented to reveal effects of capacity expansion on supply chains’ performance.     

Price fluctuations,information sharing,and supply chain performance

We consider a supply chain consisting of one supplier with finite production capacity and a retailer facing independent and identically distributed demands from end-customers. Existing research advocates that, in a decentralized setting, the retailer and the supplier using stationary order up to policies is efficient. We show that in the presence of information sharing, the supply chain performance can be improved by the supplier offering fluctuating prices. We study two specific settings: (1) the supplier only knows the parameters of the retailer’s inventory policy; and (2) the supplier knows the day-to-day inventory levels at the retailer as well. After establishing structure of optimal policies and developing efficient solution procedures, we perform an extensive computational study to determine the extent of the improvements realizable in the supply chain. We observed that for setting 1, an improvement was realized only when the end-customer demands were highly variable. Even then, the improvement in supply chain performance was less than 1%. Whereas, for setting 2, the improvement in supply chain performance averaged around 5.0% with a maximum of 16.3%.     

A supply chain model with direct and retail channels

We study a dual channel supply chain in which a manufacturer sells to a retailer as well as to consumers directly. Consumers choose the purchase channel based on price and service qualities. The manufacturer decides the price of the direct channel and the retailer decides both price and order quantity. We develop conditions under which the manufacturer and the retailer share the market in equilibrium. We show that the difference in marginal costs of the two channels plays an important role in determining the existence of dual channels in equilibrium. We also show that demand variability has a major influence on the equilibrium prices and on the manufacturer’s motivation for opening a direct channel. In the case that the manufacturer and the retailer coordinate and follow a centralized decision maker, we show that adding a direct channel will increase the overall profit. Our numerical results show that an increase in retailer’s service quality may increase the manufacturer’s profit in dual channel and a larger range of consumer service sensitivity may benefit both parties in the dual channel. Our results suggest that the manufacturer is likely to be better off in the dual channel than in the single channel when the retailer’s marginal cost is high and the wholesale price, consumer valuation and the demand variability are low.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.