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Freight mode choice and the resulting inventory implications significantly influence a product’s carbon footprint. This paper investigates mode selection under a voluntary carbon emissions constraint. Slower modes such as inland waterways and ocean freight are less expensive and emit less greenhouse gas (GHG), but they require higher inventory levels due to longer lead times. In contrast, faster modes like less-than-truckload (LTL) shipping reduce inventory needs but incur higher transportation costs and emissions. Mode choice thus involves trade-offs between transport cost, inventory holding, lead time uncertainty, and GHG emissions from transportation and warehousing. This paper develops a comprehensive inventory-transportation model under the stochastic demand and lead time to evaluate these trade-offs and guide sustainable freight decisions. The model is a practical toolbox that enables managers to evaluate how freight mode choice and inventory policy affect costs and emissions under different operational scenarios and carbon constraints.

Organizational knowledge is a critical source of competitive advantage for professional service firms. Learning from experience and sustaining past knowledge are critical to the success of such knowledge-driven firms. We use learning curve theory to evaluate learning and depreciation in professional services. Our results, based on seven years of project data collected from an architectural engineering (A/E) firm, show that (a) professional services exhibit learning curves, (b) there is virtually no depreciation of knowledge and, (c) the rate of learning accelerates with experience.

Technology has introduced dramatic new efficiencies to supply chain design, management, and control--but only to those who can open their minds to these new methods and strategies. This book presents innovative articles from eighteen of today's top young Ph.D. scholars, each based on discussions at the 2000 Frank Batten Young Leaders Forum of the College of William and Mary. These \"\"rising stars\"\" from the country's most prestigious operations management programs each take a fresh perspective on current practices and future directions in supply chain management and overall business strategy. Provocative yet valuable questions are asked--and answers provided--on subjects including: * Development of effective performance metrics * Techniques to streamline the order management cycle * Methods to leverage product design and manufacture to reduce supply chain costs * Ways to share knowledge throughout an organization concerning forecasts, manufacturing and sourcing plans, and distribution.

\"Technology has introduced dramatic new efficiencies to supply chain design, management, and control--but only to those who can open their minds to these new methods and strategies. This book presents innovative articles from eighteen of today's top young Ph.D. scholars, each based on discussions at the 2000 Frank Batten Young Leaders Forum of the College of William and Mary.

When analyzing a reorder point, order quantity (r, Q) inventory systems, one important question that often gets very little, if any, attention is: When a stockout occurs, how large is it? This paper is directed at researchers and practicing inventory planners with two objectives. First, we provide several models and algorithms to compute the Expected Shortages When a Stockout Occurs (ESWSO) for a variety of stochastic environments. We show that when ESWSO, is used in conjunction with the traditional fill rate measures it greatly enhances a planners ability to plan for shortages. Second, we develop two cost‐minimizing inventory models—one addressing the backorder and the other the shortage scenario—to show how the ESWSO can be seamlessly integrated into an inventory‐cost framework to specify lot sizes and safety stocks.

There is still considerable doubt and even anxiety among simulation modelers as to what the methodologically correct guidelines or procedures for validating simulation models should be. Epistemically, the approaches one finds in the simulation literature run the gamut from objectivist to relativist with shades in between. At present in the philosophy of science, there appears to be a convergence toward a nonalgorithmic but discursive and nonrelativistic view of the argumentation involved in warranting scientific theorizing. The present paper attempts to give a description of the various philosophical positions as well as to summarize their problems and the kinds of evidentiary arguments they would each allow in arriving at defensible simulation models. From the debate, we attempt to set out a perspective that frees the practioner to pursue a varied set of approaches to validation with a diminished burden of methodological anxiety. Reciprocally this perspective does not let the modeler off of the hook but rather converts the validation problem into an ethical problem in which the practitioner must responsibly and professionally argue for the warrant of the model.

This paper examines the association between the structure of formal intra-firm networks and productivity. We focus on two network structure components-department centralization and centrality, within a four department engineering organization. Centrality indicates the number of connections between one department and others within the organization, while centralization captures how much of those connections are concentrated among the workers within the department. Both of these represent specific managerial decisions in a formal network structure. We use learning curve theory to measure accumulated organizational knowledge, its depreciation and intra-firm transfers. We hypothesize that the departments are more productive, experience less depreciation and realize more knowledge transfer if they have more intra-firm connections among more workers. The findings suggest a significant yet moderate association between the formal network structure and productivity.

This paper investigates the interaction between the economics of production and imperfections in the production process. Specifically, this paper is the first to devise a model in an attempt to provide managers with guidelines to choose the appropriate production run times to buffer against both the production of defective items and stoppages occurring due to machine breakdowns. In addition to providing several structural properties of the model, we show that a manager will always incur a cost penalty when (s)he uses the results of two oft‐cited models‐the EMQ (Economic Order/Manufacturing Quantity) and the NR‐E (No‐Resumption, Exponential machine breakdown)‐to determine production run times.

Namit and Chen (1999) recently developed efficient and accurate algorithms for solving the inventory model with gamma lead-time demand because \"all of the methods for solving the...model...call for tabulated values and perhaps interpolation between them in ever iteration.\" Although they have created an interesting and useful solution to the problem, Tyworth et al (1996) have developed a method that does not require tabulated values to solve the problem. The purpose of this note is to demonstrate the relative simplicity of that approach and comment on the practical use of such models. Unlike the Namit and Chen approach, the current approach easily handles each case with a comparatively simple structure. In addition, this note presents a practical method of estimating the parameters of the gamma distribution and describes a convenient alternative formulation of the current model that assumes a normal approximation of lead-time demand.

Keaton (1995) recently published a paper in which he developed a PASCAL program to calculate stockout probabilities, expected shortages, and reorder points when demand per period has a gamma distribution and lead time is random. Using this program, one can correctly estimate the reorder point (S) that satisfies either a stockout or an order-fill service target for a known reorder quantity (Q) in the standard (S, Q) inventory system. The inventory planner, however, is ultimately interested in determining the levels of S and Q that simultaneously optimize system costs. A nonlinear programming procedure is presented for solving (S, Q) inventory systems problems under gamma demand and random lead-time conditions. These problems include both service and shortage models. It is also demonstrated how these kinds of models can be easily executed in a computer worksheet environment.