Incorporating Life-Cycle Economic and Environmental Factors in Managerial Decision-Making

Abstract

Recent environmental trends, including (1) an expansion of existing command and control directives, (2) the introduction of market-based policy instruments, and (3) the adoption of extended producer responsibility, have created a need for new tools to help managerial decision-making. To address this need, we develop a nonlinear mathematical programming model from a profit-maximizing firm’s perspective, which can be tailored as a decision-support tool for firms facing environmental goals and constraints. We typify our approach using the specific context of diesel engine manufacturing and remanufacturing. The approach allows the incorporation of traditional operations planning considerations—in particular, capacity, production, and inventory—together with environmental considerations that range from product design through production to product end of life. A current hurdle to implementing such a model is the availability of input data. We therefore highlight the need to involve all departments within businesses and for industrial ecologists and business managers to work together to implement meaningful decision models that are based on accurate and timely data and can have positive economic and environmental impact.

Appendices

Appendix 1: Product Features Assumed in the Base Data

Following is a brief overview of the characteristics of products 1 and 2 assumed in the numerical illustration:

• Product 2 is more “complex” and requires more manufacturing and remanufacturing capacity per unit than product 1.

• Fixed and variable costs of manufacturing and remanufacturing are respectively higher for product 2 than for product 1. Also, holding and backordering costs are higher for product 2.

• Overall, customers of product 2 are less price sensitive and more quality conscious than customers of product 1. Customers of product 1 are less sensitive to the credit offered to induce core returns.

• The market sizes for both new and remanufactured product 1 are respectively larger than those for product 2.

• Emissions attributable to manufacturing and remanufacturing are respectively higher for product 2 than for product 1.

• The performance standard is higher for product 2.

Appendix 2: Illustrative Results

Fig. 14.3

Effect of environmental factors on product mix decision$\left( \begin{aligned} \text{Plotted Values}&=\frac{\sum\nolimits_{t=1}^{10}{{{X}_{1t}}}}{\sum\nolimits_{i=1}^{2}{\sum\nolimits_{t=1}^{10}{({{X}_{it}}+{{Y}_{it}})}}},\,\frac{\sum\nolimits_{t=1}^{10}{{{X}_{2t}}}}{\sum\nolimits_{i=1}^{2}{\sum\nolimits_{t=1}^{10}{({{X}_{it}}+{{Y}_{it}})}}}, \nonumber\\& \frac{\sum\nolimits_{t=1}^{10}{{{Y}_{1t}}}}{\sum\nolimits_{i=1}^{2}{\sum\nolimits_{t=1}^{10}{({{X}_{it}}+{{Y}_{it}})}}},\,\frac{\sum\nolimits_{t=1}^{10}{{{Y}_{2t}}}}{\sum\nolimits_{i=1}^{2}{\sum\nolimits_{t=1}^{10}{({{X}_{it}}+{{Y}_{it}})}}}\end{aligned} \right)$

Fig. 14.4

Effect of environmental factors on design choice of performance $\text{(Plotted Values}={{Q}_{10}},{{Q}_{20}})$

Fig. 14.5

Effect of environmental factors on design choice of remanufacturability $\text{(Plotted Values}={{\Theta }_{10}},{{\Theta }_{20}})$

Fig. 14.6

Effect of environmental factors on core credit decision $\left( \text{Plotted Values}=\frac{1}{10}\sum\nolimits_{t=1}^{10}{{{\Psi }_{1t}}},\,\frac{1}{10}\sum\nolimits_{t=1}^{10}{{{\Psi }_{2t}}} \right)$