Perspectives in supply chain risk management

We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a suppliers percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse.

Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firms costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.

https://pubsonline.informs.org/doi/suppl/10.1287/mnsc.1060.0515/suppl_file/mnsc.1060.0515-sm-tomlin_companion_5_06.pdf

On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks

Publisher Summary This chapter discusses challenges for developers while using groupware applications. To understand the problems encountered by groupware applications, it is essential to realize that most interest in groupware development is found among the developers and users of commercial off-the-shelf products who previously focused exclusively on single-user applications. In addition to technical challenges, groupware poses a fundamental problem for product developers: Because individuals interact with a groupware application, it has all the interface design challenges of single-user applications, supplemented by a host of new challenges arising from its direct involvement in group processes. A groupware application never provides precisely the same benefit to every group member. Costs and benefits depend on preferences, prior experience, roles, and assignments. Although a groupware application is expected to provide a collective benefit, some people must adjust more than others. Ideally, each individual benefits, even if they do not benefit equally. Most groupware requires some people to do additional work to enter or process information required or produced by the application.

https://pi4.informatik.uni-mannheim.de/pi4.data/content/courses/2005-ss/cscw/material/grudin_EightChallengesForDevelopers.pdf

Groupware and social dynamics: eight challenges for developers

Recent trends in modeling of deteriorating inventory

Large increases in productivity are typically realized as organizations gain experience in production. These "learning curves" have been found in many organizations. Organizations vary considerably in the rates at which they learn. Some organizations show remarkable productivity gains, whereas others show little or no learning. Reasons for the variation observed in organizational learning curves include organizational "forgetting," employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.

https://science.sciencemag.org/content/sci/247/4945/920.full.pdf

Learning curves in manufacturing.

Partial table of contents: PRELIMINARIES Approaches to Scheduling Book Summary ONE-MACHINE PROBLEMS Single Machine: Foundations Mathematical Solution Methods: New Directions Single Machine: Timing MULTI-MACHINE PROBLEMS Embedded Problems Parallel Machines/Batch Machines Shop Routing FLOW SHOPS AND JOB SHOPS Scheduling Flow Shops: Makespan Scheduling Job Shops: Basic Methods PROJECT SCHEDULING AND MANAGEMENT Network Project Scheduling Resource-Constrained Project Scheduling OTHER ISSUES Model Extensions Planning, Scheduling, and Control Appendices Index

Heuristic scheduling systems : with applications to production systems and project management

Beam search is a technique for searching decision trees, particularly where the solution space is vast. The technique involves systematically developing a small number of solutions in parallel so as to attempt to maximize the probability of finding a good solution with minimal search effort. In this paper, we systematically study the performance behaviour of beam search with other heuristic methods for scheduling, and the effects of using different evaluation functions to guide the search. We also develop a new variation of beam search, called filtered beam search which is computationally simple yet produces high quality solutions.

Filtered beam search in scheduling

Increasing product complexity, manufacturing environment complexity and an increased emphasis on product quality are all factors leading to uncertainties in production processes. These uncertaintie...

A periodic review, production planning model with uncertain capacity and uncertain demand—optimality of extended myopic policies

We examine the problem of scheduling a given set of jobs on a single machine to minimize total early and tardy costs. Two dispatch priority rules are proposed and tested for this NP-complete problem. These were found to perform far better than known heuristics that ignored early costs. For situations where the potential cost savings are sufficiently high to justify more sophisticated techniques, we propose a variation of the Beam Search method developed by researchers in artificial intelligence. This variant, called Filtered Beam Search, is able to use priority functions to search a number of solution paths in parallel. A computational study showed that this search method was not only efficient but also consistent in providing near-optimal solutions with a relatively small search tree. The study also includes an investigation of the impacts of Beam Search parameters on three variations of Beam Search for this problem.

http://akturk.bilkent.edu.tr/ie573/e_t.pdf

The single machine early/tardy problem

Forward algorithms that solve successively longer finite horizon problems and that possess good stopping rules such as a planning horizon seem better suited to the needs of a manager facing a partial information environment than the more common procedure of selecting a horizon [0, T] in advance. In this light, the Wagner and Whitin forward algorithm with a planning horizon procedure for the dynamic lot size model goes far beyond computational savings. Building on additional results due to Zabel, we develop new planning horizon procedures and near planning horizon procedures. A brief sketch of how to develop similar results for the production smoothing problem suggests that the basic methodology developed possesses some generality. We present an extensive empirical study that reports that Wagner-Whitin planning horizons were found for a reasonably small subset of problems within 500 periods, while planning horizons, or at least near planning horizons, were found universally by the modified procedure. The Zabel procedure was intermediate in power. The number of periods until a "near" horizon seems to be given empirically by \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $t = 5\sqrt{2K/hD}$ \end{document} for the linear stationary case, where K is the setup cost, h the holding cost, and D the average demand rate. Extensions useful for a broad subset of the general concave cost case are also given.

Thomas E. Morton

Papers

Heuristic scheduling systems : with applications to production systems and project management

Filtered beam search in scheduling

A periodic review, production planning model with uncertain capacity and uncertain demand—optimality of extended myopic policies

The single machine early/tardy problem

Planning Horizons for the Dynamic Lot Size Model: Zabel vs. Protective Procedures and Computational Results