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This chapter proposes a multiobjective model to design a Closed Loop Supply Chain (CLSC) network. The first objective is to minimize the total cost of the network, while the second objective is to minimize the carbon emission resulting from production, transportation, and disposal processes using carbon cap and carbon tax regularity policies. In the third objective, we maximize the service level of retailers by using maximum covering location as a measure of service level. To model the proposed problem, a physical programming approach is developed. This work contributes to the literature in designing an optimum CLSC network considering the service level objective and product substitution.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.

The aim of this section is to develop a model of the linkage between the macro-dynamics of price and the micro-dynamics of individual buyers and sellers, drawing on classical micro-economic theory (Jehle & Reny, 2001). Jehle and Reny's book is in three parts. The first part discusses economic agents, namely consumers and firms. The second part discusses markets, in other words what happens when the economic agents interact; and welfare, namely the social value of the outcome. The third part is on strategic behaviour, covering game theory, information economics and auctions and mechanism design. Here, the emphasis will be on the core concepts rather than on the mathematical details. In emphasising the core concepts, attention will be drawn to the fact that these core concepts have a much wider range than simply micro-economics.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

This paper focuses on the role of manufacturer brands for resellers within retail channels. This topic is important because of the strategic value of manufacturer brands and the increasing influence of resellers within channels of distribution. Much of the branding research emphasizes a customer-brand knowledge perspective; however, emerging perspectives suggest that brands are also relevant to other stakeholders including resellers. In contrast, channels research recognizes the manufacturer sources of market power, but does not consider the impact of manufacturer “push and pull” strategies within channels. Existing theoretical frameworks, therefore, do not address the reseller perspective of the brand. As a result, the research approach is a multi-method design, consisting of two phases. The first phase involves in-depth interviews, allowing the development of a conceptual framework. In the second phase, a survey of supermarket buyers on brands in several product categories tests this framework. Structural equation modeling analyzes the survey responses and tests the hypotheses. The structural model shows very good fit to the data with good construct validity, reliability, and stability. The findings show that manufacturer support, brand equity, and customer demand reflect the manufacturer brand benefits to resellers. A key contribution of this research is the development of a validated scale on manufacturer brand benefits from the point of view of a reseller. This research shows that the resources that relate to the brand, not just the brand name itself, create value for resellers in channel relationships.