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ABSTRACTThis article investigates the effectiveness of a tactical demand‐capacity management policy to guide operational decisions in order‐driven production systems. The policy is implemented via a heuristic that attempts to maximize revenue by selectively accepting or rejecting customer orders for multiple product classes when demand exceeds capacity constantly over the short term. The performance of the heuristic is evaluated in terms of its ability to generate a higher profit compared to a first‐come‐first‐served (FCFS) policy. The policies are compared over a wide range of conditions characterized by variations in both internal (firm) and external (market) factors. The heuristic, when used with a Whole Lot order‐processing approach, produces higher profit compared to FCFS when profit margins of products are substantially different from each other and demand exceeds capacity by a large amount. In other cases it is better to use the heuristic in conjunction with the Split Lot order‐processing approach.

ABSTRACTCurrent research and practice in the field of production and inventory planning is dominated by hierarchical, material requirements planning‐based methods. The objective of this research is to determine the merits of a major redirection for the design of manufacturing planning systems—a direction that more directly exploits the capabilities of modern computer technology. We test the relative performance of two fundamentally different classes of manufacturing planning systems: (1) those that make no use of sequencing and scheduling information in the planning of material deliveries (for example, traditional MRP‐based systems), and (2) those that use sequencing and scheduling information to plan delivery of materials. We first derive an analytical expression for the expected benefit (in terms of flow time reduction) of using scheduling information for a single machine system. We then describe and test a simple heuristic for predicting the improvement that could be realized in more complicated multimachine systems, under certain conditions. Results from controlled simulation experiments, over a wide range of operating environments, suggest that although the value of scheduling information is influenced by the operating environment, it could be substantial.

Schedule instability is a major problem in companies using material
requirements planning (MRP) systems. The effectivenesss of using buffer
stock to combat nervousness in the master production schedule (MPS) of
an MRP system is investigated. An example scenario illustrates the need
for caution in using buffer stock for reducing schedule instability.
Detailed simulation results are presented which suggest the need for
further research to understand the role of buffer stock in achieving
stable master production schedules.

ABSTRACTManaging the trade‐off between achieving a stable master production schedule (MPS) and being responsive to changes in customer requirements is a difficult problem in many firms where providing a high level of customer service is viewed as an important competitive factor. One alternative for managing this trade‐off is to freeze an agreed portion of the MPS. This paper investigates the impact of adjustments in the design parameters of MPS freezing methods on two performance measures (MPS lot‐sizing cost and stability) under stochastic demand conditions in a rolling planning horizon environment given a service level target. Simulation experiments are reported which indicate that many of the conclusions regarding the design of MPS freezing methods obtained under deterministic demand conditions hold under stochastic demand.

PurposeThe purpose of this paper is to determine if internal integration, information sharing, and training constitute direct antecedents to organizations' warning and recovery capabilities. Assuming that organizations periodically face various supply chain risks, the authors intend to show that managers can develop these antecedent competencies in ways that bolster their supply chain risk management (SCRM) capabilities.Design/methodology/approachTo understand the relationships between the antecedents and SCRM capabilities, the authors used Q-sorts and confirmatory factor analysis to develop new warning and recovery measures. The authors then collected survey data from 231 hospital supply managers and analyzed these records using structural equation modeling.FindingsThe results indicate that internal integration and training positively affect organizations' warning and recovery capabilities, in both a direct and indirect manner. The authors also illustrate how managers can leverage their SCRM capabilities to affect operational performance.Research limitations/implicationsThese results suggest that by developing antecedent competencies like internal integration and training, firms may bolster their warning and recovery capabilities, and ultimately operational performance of the organization.Originality/valueThe findings provide hospital supply organizations and other inventory management teams with a novel approach to managing an evolving array of supply chain risks. Rather than investing in costly risk management techniques, like inventory stocks, organizations can use internal integration and training to improve their SCRM capabilities.

PurposeThe purpose of this paper is to test a method for improving decision‐making at a tactical level – i.e. at the shop floor level. This is done by testing the use of the binary sorting algorithm (BSA) to improve decisions concerning quality improvement efforts among machine operators in a plastics manufacturer.Design/methodology/approachThe method used was the "action‐research" approach since the researchers were actively involved in the training, implementation, and use of the BSA.FindingsIt was found that using the BSA to prioritize quality improvement efforts by machine operators reduced the "scrap‐from‐line" rate in a plastics manufacturer.Research limitations/implicationsThe study should be replicated in different companies and industries using multiple methods because action‐research is limited in its generalizability. Also, since the researchers are actively involved in the process, our observations could not be considered objective.Originality/valueTo the authors' knowledge, this is the first application of the BSA to improve decision‐making at a tactical shop‐floor level in a manufacturing company.

Population growth, urbanization and economic development drive the use of resources. Securing access to essential services such as energy, water, and food, while achieving sustainable development, require that policy and planning processes follow an integrated approach. The 'Climate-, Land-, Energy- and Water-systems' (CLEWs) framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. The approach was first introduced by the International Atomic Energy Agency to conduct an integrated systems analysis of a biofuel chain. The framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. Its multi-institutional application to the case of Mauritius in 2012 initiated the deployment of the framework. A vast number of completed and ongoing applications of CLEWs span different spatial and temporal scales, discussing two or more resource interactions under different political contexts. Also, the studies vary in purpose. This shapes the methods that support CLEWs-type analyses. In this paper, we detail the main steps of the CLEWs framework in perspective to its application over the years. We summarise and compare key applications, both published in the scientific literature, as working papers and reports by international organizations. We discuss differences in terms of geographic scope, purpose, interactions represented, analytical approach and stakeholder involvement. In addition, we review other assessments, which contributed to the advancement of the CLEWs framework. The paper delivers recommendations for the future development of the framework, as well as keys to success in this type of evaluations.

Population growth, urbanization and economic development drive the use of resources. Securing access to essential services such as energy, water, and food, while achieving sustainable development, require that policy and planning processes follow an integrated approach. The 'Climate-, Land-, Energy- and Water-systems' (CLEWs) framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. The approach was first introduced by the International Atomic Energy Agency to conduct an integrated systems analysis of a biofuel chain. The framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. Its multi-institutional application to the case of Mauritius in 2012 initiated the deployment of the framework. A vast number of completed and ongoing applications of CLEWs span different spatial and temporal scales, discussing two or more resource interactions under different political contexts. Also, the studies vary in purpose. This shapes the methods that support CLEWs-type analyses. In this paper, we detail the main steps of the CLEWs framework in perspective to its application over the years. We summarise and compare key applications, both published in the scientific literature, as working papers and reports by international organizations. We discuss differences in terms of geographic scope, purpose, interactions represented, analytical approach and stakeholder involvement. In addition, we review other assessments, which contributed to the advancement of the CLEWs framework. The paper delivers recommendations for the future development of the framework, as well as keys to success in this type of evaluations.