Management science in the era of smart consumer products: challenges and research perspectives

Smart products not only provide novel functionalities, but also may establish new business models, markets, or distribution channels, strengthen relationships with consumers, and/or add smart remote services. While many technical obstacles of such products have already been overcome, the broad market dissemination of smart products still poses some vital managerial challenges for decision makers. In this paper, we outline the technical potential and future trends of smart consumer products, discuss economic challenges in four scopes, namely, preference-based new product development, market analysis, supply chain design, and industry development, and, in particular, we highlight research perspectives for management science in this promising field.     

Pricing policies for complementary products in a dual-channel supply chain

This paper investigates the pricing problem of complementary products in a supply chain with two manufacturers and one retailer, one of the two manufacturers uses dual channels including an online channel and a traditional retail channel to sell its product. We formulate four pricing game models through considering different market power structures of channel members, and derive the corresponding optimal pricing strategies. Based on the theoretical and numerical analysis, we study the effects of consumer channel loyalty, the level of complementarity, and the market power structures on the pricing strategies and the maximal profits of two complementary products. Some interesting and valuable managerial insights are obtained.     

The impact of power structure on the retail service supply chain with an O2O mixed channel

While the Internet has provided a new means for retailers to reach consumers, it has fundamentally changed the dynamic of competition in the retail service supply chain. The mix of offline and online channels adds a new dimension of competition, and one central issue of this competition is the pricing strategy between the two channels. How to set prices for both online and offline channels? What is the impact of the supply chain power structure on pricing decisions and the performance? This research aims to address these questions by focusing on a retail service supply chain with an online-to-offline (O2O) mixed dual-channel. From the Supplier-Stackelberg, Retailer-Stackelberg, and Nash game theoretical perspectives, we obtain the optimal prices and maximum profits for both the retailer and supplier under different power structures. The analysis result provides important managerial implications, which will be beneficial to retailers to develop proper pricing strategies.     

Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand

Several leading manufacturers recently combined the traditional retail channel with a direct online channel to reach a wider range of customers. We examine such a dual-channel supply chain under price and delivery-time dependent stochastic customer demand. We consider five decision variables, the price and order quantity for both the retail and the online channels and the delivery time for the online channel. Uncertainty frequently arises in both retail and online channels and so additional inventory management is required to control shortage or overstock and that has an effect on the optimal order quantity, price, and lead time. We developed mathematical models with the profit maximization motive. We analyze both centralized and decentralized systems for unknown distribution function of the random variables through a distribution-free approach and also for known distribution function. We examine the effect of delivery lead time and customers’ channel preference on the optimal operation. For supply chain coordination a hybrid all-unit quantity discount along a franchise fee contract is used. Moreover, we use the generalized asymmetric Nash bargaining for surplus profit distribution. A numerical example illustrates the findings of the model and the managerial insights are summarized for centralized, decentralized, and coordinated scenarios.     

Optimal inventory policies under decreasing cost functions via geometric programming

In this paper, we establish and analyze two economic order quantity (EOQ) based inventory models under total cost minimization and profit maximization via geometric programming (GP) techniques. Through GP, optimal solutions for both models are found and managerial implications on the optimal policy are determined through bounding and sensitivity analysis. We investigate the effects on the changes in the optimal order quantity and the demand per unit time according to varied parameters by studying optimality conditions. In addition, a comparative analysis between the total cost minimization model and the profit maximization model is conducted. By investigating the error in the optimal order quantity of these two models, several interesting economic implications and managerial insights can be observed.     

The SEXTANT software: A tool for automating the comparative analysis of mental models of dynamic systems

The comparison of mental models of dynamic systems improves our understanding of how people comprehend, interpret, and subsequently influence dynamic management tasks. Approaches to comparing mental models currently used in managerial and organizational cognition research, especially the distance-ratio and the closeness-approaches, have been criticized for not considering essential characteristics of dynamic managerial situations. This paper builds on a recent analysis method developed to compare mental models of dynamics systems, and introduces this mathematical approach to management and organizational researchers by means of the SEXTANT software. It presents the process of mental model elicitation, analysis, comparison, and interpretation. An example with four elicited mental models illustrates the software’s features to analyze and present the results. Then, the software is compared with existing software to map and compare mental models. Our conclusion is that SEXTANT marks a significant step in enabling large-scale studies about mental models of dynamic systems.     

Resource-based strategies and problem structuring: using resource maps to manage resource systems

The resource-based view of strategy seeks to explain why some firms consistently outperform rivals in the same industry by acquiring a unique set of strategic assets (or resources). However, differences between dominant managerial mental models in management teams lead to disagreement at the moment of implementing distinct resource-building strategies. This managerial and cognitive view of strategic decision making and competition lends itself to investigation through problem structuring methods. We suggest that resource maps, as a problem structuring method, can be used to interpret managerial mental models for strategic decision making in terms of resource-building processes. Through resource maps, we represent the system of asset stocks believed to be most important for driving business performance. We illustrate the framework by comparing and contrasting maps of the system of resources (asset stocks) that best characterize two leading firms in the UK commercial radio broadcasting industry.     

Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives

One of the most important steps in the application of modeling using data envelopment analysis (DEA) is the choice of input and output variables. In this paper, we develop a formal procedure for a “stepwise” approach to variable selection that involves sequentially maximizing (or minimizing) the average change in the efficiencies as variables are added or dropped from the analysis. After developing the stepwise procedure, applications from classic DEA studies are presented and the new managerial insights gained from the stepwise procedure are discussed. We discuss how this easy to understand and intuitively sound method yields useful managerial results and assists in identifying DEA models that include variables with the largest impact on the DEA results.     

Inventory management for dual sales channels with inventory-level-dependent demand

This paper studies the inventory management problem of dual channels operated by one vendor. Demands of dual channels are inventory-level-dependent. We propose a multi-period stochastic dynamic programming model which shows that under mild conditions, the myopic inventory policy is optimal for the infinite horizon problem. To investigate the importance of capturing demand dependency on inventory levels, we consider a heuristic where the vendor ignores demand dependency on inventory levels, and compare the optimal inventory levels with those recommended by the heuristic. Through numerical examples, we show that the vendor may order less for dual channels than those recommended by the heuristic, and the difference between the inventory levels in the two cases can be so large that the demand dependency on inventory levels cannot be neglected. In the end, we numerically examine the impact of different ways to treat unmet demand and obtain some managerial insights.     

E-fulfillment and multi-channel distribution – A review

This review addresses supply chain management issues specific to Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and corresponding quantitative models. Our objective is to twofold, namely to enhance the understanding of multi-channel e-fulfillment by documenting the current state of affairs, and to inspire fruitful future research by identifying gaps between relevant managerial issues and available academic literature.     

Dynamic channel control and pricing of a single perishable product on multiple distribution channels

This paper studies dynamic channel control and pricing of a single perishable product distributed through multiple channels with the objective of maximizing the total expected profit over a finite horizon. We consider two types of commissions, namely proportional and fixed commissions, on the third-party channels and utilize stylized linear functions to characterize dependent demand flows from different channels. We show that, the magnitude of the opportunity cost of capacity uniquely determines the optimal channel control, at any given inventory level and periods to go. Consequently, we are able to derive the optimal price offered on each channel as a function of the opportunity cost of capacity in closed form. This significantly reduces the computational complexity of the stochastic dynamic program when parameters are constant with time. When channels are independent, we provide a necessary and sufficient condition for the optimality of a nested channel control policy by commission rates. The same condition is also sufficient for the optimality of the nested channel control policy in a distribution system with two dependent channels. We then characterize the structural properties of the optimal pricing and channel control policies. Finally, we explore the impact of the substitution effect on the channel control through numerical studies and gain managerial insights.     

Decision-network polynomials and the sensitivity of decision-support models

Decision makers benefit from the utilization of decision-support models in several applications. Obtaining managerial insights is essential to better inform the decision-process. This work offers an in-depth investigation into the structural properties of decision-support models. We show that the input–output mapping in influence diagrams, decision trees and decision networks is piecewise multilinear. The conditions under which sensitivity information cannot be extracted through differentiation are examined in detail. By complementing high-order derivatives with finite change sensitivity indices, we obtain a systematic approach that allows analysts to gain a wide range of managerial insights. A well-known case study in the medical sector illustrates the findings.     

Additive super-efficiency in integer-valued data envelopment analysis

Conventional data envelopment analysis (DEA) methods assume that input and output variables are continuous. However, in many real managerial cases, some inputs and/or outputs can only take integer values. Simply rounding the performance targets to the nearest integers can lead to misleading solutions and efficiency evaluation. Addressing this kind of integer-valued data, the current paper proposes models that deal directly with slacks to calculate efficiency and super-efficiency scores when integer values are present. Compared with standard radial models, additive (super-efficiency) models demonstrate higher discrimination power among decision making units, especially for integer-valued data. We use an empirical application in early-stage ventures to illustrate our approach.     

Value-based DEA models: application-driven developments

Data envelopment analysis (DEA) is an approach based on linear programming to assess the relative efficiency of peer decision-making units (DMUs). Typically, each DMU is free to choose the weights of the factors used in its evaluation. However, the evaluator's preferences may not warrant so much freedom. Several approaches have been proposed to allow the incorporation of managerial preferences in DEA, but few address the additive DEA model specifically. This paper presents additive DEA models that use multi-criteria decision analysis concepts to incorporate managerial preferences, and presents the corresponding preference elicitation protocols. The models developed allow the incorporation of preferences at different levels: on valuing performance improvements, on introducing weight restrictions, and on finding adequate targets. These were application-driven developments, resulting from discussing modelling options and preliminary results with the top-level management of a retail chain in the context of an assessment of stores’ performance, also described in this paper.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

企业管理的有效性、管理效率与管理变迁

为了客观公正地评价一个企业的管理有效性,本文深入挖掘了企业管理有效性概念的内涵与外延,并提出根据管理有效性的内涵确定管理效益指标体系,根据它的外延确定管理绩效的测算方法这一基本原则。基于此项原则,建立了管理前沿、管理效率和管理变迁的数理概念和测算方法。利用数据包络分析模型对我国30个省市自治区工业企业2002、2003年的管理效率以及2003年的管理变迁的评测结果表明:同时考察管理效率和管理变迁这两个技术指标,可以更客观、更全面地评价一个企业的管理有效性。     

An economic order quantity model for deteriorating item in a purchasing system with multiple prepayments

In the classic economic order quantity model the purchasing cost of an order should be paid at the time of its receipt. Sometimes, retailers ask purchasers to pay all or a fraction of the purchasing cost in advance and sometimes allow them to divide the prepayment into several equal-sized parts. In this paper, economic order quantity model for a deteriorating product with and without shortage under consecutive prepayments are developed. Numerical examples illustrate the proposed models and solution method and sensitivity analysis provides some managerial insights for managers.     

Optimal inventory policies for an economic order quantity model with decreasing cost functions

In this paper, three total cost minimization EOQ based inventory problems are modeled and analyzed using geometric programming (GP) techniques. Through GP, optimal solutions for these models are found and sensitivity analysis is performed to investigate the effects of percentage changes in the primal objective function coefficients. The effects on the changes in the optimal order quantity and total cost when different parameters of the problems are changed is also investigated. In addition, a comparative analysis between the total cost minimization models and the basic EOQ model is conducted. By investigating the error in the optimal order quantity and total cost of these models, several interesting economic implications and managerial insights can be observed.     

An Organizational Model of Integrated Budgeting for Short-Run Operations and Long-Run Investments

In managerial finance, the short run usually refers to multiperiod models which involve operating plans and cash flow analysis. Long-run models are for capital investment or capital budgeting. Their essential link is the emphasis on cash flow and budgets. This relation is explored with an integrated model and its decompositions by budgets. The decomposition yields subunit models for the short run and long run which are related to earlier models, but yields insight into the organizational use of budgets for coordination and control. To obtain relative efficiency measures on two alternatives organizational budgeting mechanisms, a simulation was performed and analyzed.     

Managerial Decentralization and Decomposition in Mathematical Programming

There are many computational models available now which purport to add to our ability to plan and control organizations in a decentralized fashion. Some of these are custom built for the purpose while others incorporate mathematical programming decomposition procedures. Although comparisons have been made concerning their computational requirements, little analysis seems to have been done to explore the organizational implications of the various methods. A start is made on this task here and some conclusions drawn in terms of the managerial style of-and the motives for-decentralization.