Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length

The most common solution strategy for stochastic supply-chain man-agement problems that are analytically intractable is simulation. But, how can we be sure that the optimal solution obtained by simulation is in fact the true optimal solution? In this paper we try to shed light on this question. We report the results of an extensive simulation study of a base-stock controlled production-inventory system. We tried different values of base-stock levels (R) to determine, via simulation, which was the value that minimized the total inventory holding and backordering costs per period. For 25 different cases (and 100 replications each), we compared the optimal solution obtained from simulation (Rs*) with the true optimal base-stock level (Ra*) obtained from an analytical result, with the goal of obtaining a lowerbound of 95% matches. Results show that when the traffic in-tensity increases, the run length necessary to achieve a minimum of 95% matches increases too, and when the backorder cost increases, the number of matches de-creases for each specific run length. In most of the cases simulated, 100,000 de-mands were enough to achieve reasonably reliable results.Postprint (published version

On Model-Based Systems Engineering for Design, Management, and Governance of Protective Systems

PresentationProtective systems failure can be catastrophic, and originates in management failure. These systems rely on a document-based approach, which involves handling disjointed artifacts that are expensive to maintain and may become inconsistent and obsolete. We propose a framework for managing process safety that pioneers the modeling of protective systems according to the tenors of model-based systems engineering (MBSE). The framework embeds management and governance, and harmonizes regulations and inconsistent industry guidelines. Potential users include enterprises and regulators in the chemical process safety industry and the energy sector. The framework starts the development of more sophisticated standards to prevent catastrophic protective systems failures

Designing Supply Chains for New Product Development

Research and development (R&D) supply chains are often designed without the process discipline and rigor that typically characterize the development of products emerging from R&D programs. This book should help everyday supply chain practitioners involved in research and new product development, who are migrating their products to full commercialization. The book should also aid decision makers looking to improve the overall effectiveness and efficiency of their supply chain. When new products are developed, a significant divide typically emerges in trying to commercialize the product while attempting to meet project demands for cost, schedule, and quality. Simply put, in many cases the supply chains developed to accomplish R&D functions are usually woefully inadequate to meet the demands of large-scale commercial applications. This book recounts the real-world work efforts, rigor, and discipline used to transition from a supply chain supporting R&D functions to a world-class supply chain capable of supporting a multibillion-dollar hydrocarbon recovery project

Design of stockless production systems

In make-to-stock production systems finished goods are produced in anticipation of demand. By contrast, in stockless production systems finished goods are not produced until demand is observed. In this study we investigate the problem of designing a multi-item manufacturing system, where there is both demand- and production-related uncertainty, so that stockless operation will be optimal for all items. For the problem of interest, we focus on gaining an understanding of the effect of two design variables: (i) manufacturing speed—measured by the average manufacturing rate or, equivalently, the average unit manufacturing time, and (ii) manufacturing consistency—measured by the variation in unit manufacturing times. We establish conditions on these two variables that decision makers can use to design stockless production systems. Managerial implications of the conditions are also discussed.13 page(s

Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length

The most common solution strategy for stochastic supply-chain man-agement problems that are analytically intractable is simulation. But, how can we be sure that the optimal solution obtained by simulation is in fact the true optimal solution? In this paper we try to shed light on this question. We report the results of an extensive simulation study of a base-stock controlled production-inventory system. We tried different values of base-stock levels (R) to determine, via simulation, which was the value that minimized the total inventory holding and backordering costs per period. For 25 different cases (and 100 replications each), we compared the optimal solution obtained from simulation (Rs*) with the true optimal base-stock level (Ra*) obtained from an analytical result, with the goal of obtaining a lowerbound of 95% matches. Results show that when the traffic in-tensity increases, the run length necessary to achieve a minimum of 95% matches increases too, and when the backorder cost increases, the number of matches de-creases for each specific run length. In most of the cases simulated, 100,000 de-mands were enough to achieve reasonably reliable results

Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis

We present a heuristic optimization method for stochastic production-inventory systems that defy analytical modelling and optimization. The proposed heuristic takes advantage of simulation while at the same time minimizes the impact of the dimensionality curse by using regression analysis. The heuristic was developed and tested for an oil and gas company, which decided to adopt the heuristic as the optimization method for a supply-chain design project. To explore the performance of the heuristic in general settings, we conducted a simulation experiment on 900 test problems. We found that the average cost error of using the proposed heuristic was reasonably low for practical applications.Production Inventory Simulation Regression analysis Supply-chain management