Per Joakim Agrell

Risk, information and incentives in telecom supply chains

Supply chain management involves the selection, coordination and motivation of independently operated suppliers. The central planners perspective in operations management translates poorly to vertically separated chains, where suppliers recurrently seem to object to benevolent information sharing and centralized decision rights. Seen from the suppliers perspective, such resistance may very well be rational. A downstream assembly line disclosing reliable information on actual and forecasted sales puts itself at a disadvantage when bargaining on share of chain profits. In this paper, we use a minimal agency model to contrast known optimal mechanisms with the actual practice in the telecommunications industry. A three-stage supply chain under stochastic demand and varying coordination and information asymmetry is modeled. A two-period investment-production game addresses the information sharing and specific investment problem in the telecom industry. The observed price-quantity contracts under limited commitment are shown to be inadequate under realistic asymmetric information assumptions. More a result of gradually evolving changes in bargaining power than coordination efforts, the upstream urge to coordinate may further deteriorate performance in terms of our model

Incentive plans for productive efficiency, innovation and learning

In many industries where production or sales is delegated to a number of subunits, the central management faces the classical problem how to induce continuous efficiency improvements, organizational learning and transfer of knowledge with a minimum of control exercised. This paper draws on recent results regarding regulatory frameworks to construct simple, yet powerful incentive schemes for decentralized production under asymmetric information. The theoretical foundation is based on principal–agent theory (cf. Laffont and Tirole, Econometrica 56 (1986) 614–641) and extensions to production theory by Bogetoft (Management Science 40 (1994) 959–968). The proposed incentive system is operational and makes use of available information to provide positive incentives for participation in the dynamic development of the entire organization.

A Dual Approach to Nonconvex Frontier Models

This paper extends the links between the non-parametric data envelopment analysis (DEA) models for efficiency analysis, duality theory and multi-criteria decision making models for the linear and non-linear case. By drawing on the properties of a partial Lagrangean relaxation, a correspondence is shown between the CCR, BCC and free disposable hull (FDH) models in DEA and the MCDM model. One of the implications is a characterization that verifies the sufficiency of the weighted scalarizing function, even for the non-convex case FDH. A linearization of FDH is presented along with dual interpretations. Thus, an input/output-oriented model is shown to be equivalent to a maximization of the weighted input/output, subject to production space feasibility. The discussion extends to the recent developments: the free replicability hull (FRH), the new elementary replicability hull (ERH) and the non-convex models by Petersen (1990). FRH is shown to be a true mixed integer program, whereas the latter can be characterized as the CCR and BCC models.

Interactive multiobjective agro-ecological land use planning: The Bungoma region in Kenya

The development of a third world country requires a conscious balance between different planning and policy issues, such as population growth rate, gross national income, self-reliance and long-term sustainable ecological development. This paper reports on a cross-disciplinary project to design a decision support system (DSS) that aims to assist government policy makers in planning the regional agricultural development of the Bungoma region in Kenya. The DSS is based on the agro-ecological zones (AEZ) model, a previously developed non-interactive optimization model that provides an agro-ecological and economic assessment of various types of land uses, including cash-crops, food production, grazing, forestation and farming. This work extends the decision analytic scope of the AEZ model to explicitly incorporate a multicriteria optimization formulation that facilitates a direct trade-off analysis between the various decision criteria within a user-interactive decision support modeling framework. The DSS uses in-depth information about the Bungoma region, extracted from a large-scale FAO database on Kenya that includes information on various climatic and soil characteristics (e.g., thermal and moisture regime, soil type, slope class) and socio-economic data (e.g., projected growth rate and product demand patterns) for 90000 agro-ecological cells. At each stage of the analysis, our system offers the decision maker several alternative planning strategies with different suggested land uses for over 100 different types of crops, fuel wood and livestock land utilization types for evaluation, allowing the decision maker to take into account trade-offs between a number of planning and policy criteria, including food output, net revenue, gross value of output, self-sufficiency, production costs, arable land use and degree of erosion. Furthermore, the DSS facilitates a direct assessment of the stability of each solution with respect to the food output and erosion in any particular cell and for any given climatic scenario. Through a Geographic Information System (GIS) interface, the AEZ system can depict proposed solutions graphically within a PC environment. A PC spreadsheet implementation of our multicriteria DSS is illustrated with real data from the Bungoma district and an expert decision maker.

Allocating fixed resources and setting targets using a common-weights DEA approach

Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally, DEA is used in ex post evaluation of actual performance, estimating an empirical best-practice frontier using minimal assumptions about the shape of the production space. However, DEA may also be used prospectively or normatively to allocate resources, costs and revenues in a given organization. Such approaches have theoretical foundations in economic theory and provide a consistent integration of the endowment-evaluation-incentive cycle in organizational management. The normative use, e.g. allocation of resources or target setting, in DEA can be based on different principles, ranging from maximization of the joint profit (score), combinations of individual scores or game-theoretical settings. In this paper, we propose an allocation mechanism that is based on a common dual weights approach. Compared to alternative approaches, our model can be interpreted as providing equal endogenous valuations of the inputs and outputs in the reference set. Given that a normative use implicitly assumes that there exists a centralized decision-maker in the organization evaluated, we claim that this approach assures a consistent and equitable internal allocation. Two numerical examples are presented to illustrate the applicability of the proposed method and to contrast it with earlier work.

An interactive multicriteria decision model for multipurpose reservoir management: the Shellmouth Reservoir

Reservoir management is inherently multicriterial, since any release decision involves implicit trade-offs between various conflicting objectives. The release decision reflects concerns such as flood protection, generation of hydroelectric power, dilution of wastewater and heated effluent, supply of municipal, agricultural and industrial water, maintenance of environmental standards and satisfaction of recreational needs. This paper presents a framework for analysing trade-offs between several decision criteria and includes the dilution of heated effluents from downstream thermoelectric power generation in an optimization model for reservoir management. The model is formulated and analysed in an interactive multicriteria decision-making (MCDM) modelling framework. Rather than providing specific target levels or ad hoc constants in a goal-programming framework as proposed elsewhere, this multicriteria framework suggests a systematic way of evaluating trade-offs by progressive preference assessment. The MCDM model, based on a Chebyshev metric and a contracted cone approach, is learning-oriented and permits a natural exploration of the decision space while maintaining non-dominated decisions. A detailed case study of the Shellmouth Reservoir in Manitoba, Canada serves as an illustration of the model.

A multicriteria framework for inventory control

An interactive multicriteria framework for an inventory control decision support system is presented. Previous formulations of the simultaneous order quantity, safety stock and service-level problem have assumed explicit preference articulation, although comparisons of total annual cost and e.g. service level are complex without the knowledge of local trade-off ratios and the nondominated set. The procedure is constructive in the sense that the preference structure of the decision maker is assessed progressively under the exploration of the solution space. The framework is intended for inclusion in a decision support system for production and operations management or to be used as a separate module for strategic inventory control. Implementations as FORTRAN modules and spreadsheet macros are available.

Frontier-based performance analysis models for supply chain management: State of the art and research directions

Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources. Acknowledging the lack of systems view, the need to identify system-wide and individual effects as well as incorporating a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in network DEA modeling, in particular two-stage models, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating current work in this range using the unified notations and comparison of the properties of the presented models, the paper is closed with recommendations for future research in terms of both theory and application.

Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Several researchers have proposed various fuzzy methods for dealing with the ambiguous and random data in DEA. In this paper, we propose three fuzzy DEA models with respect to probability-possibility, probability-necessity and probability-credibility constraints. In addition to addressing the possibility, necessity and credibility constraints in the DEA model we also consider the probability constraints. A case study for the base realignment and closure (BRAC) decision process at the U.S. Department of Defense (DoD) is presented to illustrate the features and the applicability of the proposed models.

A Caveat on the Measurement of Productive Efficiency

Abstract The correspondence between used performance measures and enterprise objectives, such as profit maximization and cost minimization, is fundamental for manufacturing companies. This paper identifies, and critically examines, a minimal set of relevant properties that a productivity index needs to satisfy to rightly assess performance development of a decision-making unit. Commonly applied and suggested productivity measurement techniques, such as partial efficiencies, total factor productivity (TFP), index number approaches, integrated partial efficiencies and operational competitiveness ratings, are analyzed in order to assess the alignment with superior objectives. There is a considerable spread in the results of this class of models and the interpretation may prove difficult or misleading. As these apparently less complicated productivity measures increasingly are employed as a component in evaluation of manufacturing efficiency, the question is of high managerial relevance. In particular, the paper points out inconsistencies with properties related to commensurability, monotonicity, and implications of maximizing behavior. Based on this viewpoint, issues such as the consistency with profit maximization will be shown extra interest. The paper also provides a critique of previous work in non-parametric efficiency analysis, where properties have been postulated or based on other arguments. The findings suggest that there is no globally superior measurement technique to be found in this class and that care should be taken when evaluating managerial performance not to penalize rational behavior.