Proactive and Efficient Spare Parts Inventory Management Policies Considering Reliability Issues

Spare parts inventory management plays an important role in many industries. They exist to serve the maintenance planning and a good planning can significantly reduce maintenance cost. This thesis developed a series of non-linear programming models to obtain optimal spare parts replenishment policies for failure-based maintenance in a single period. Both single Part Number case and multiple Part Numbers case with a budget constraint are addressed. Compared with traditional forecasting methods which only consider historical data, our proposed inventory policies take into account reliability issues and predict impending demands based on part failure distributions from two perspectives: failure time and failure numbers. Therefore, optimal order quantity and best order time can be found to realize total cost minimization, as well as a systematic inventory optimization

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions

Maintenance scheduling for modular systems-models and algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 185-188).Maintenance scheduling is an integral part of many complex systems. For instance, without effective maintenance scheduling, the combined effects of preventative and corrective maintenance can have severe impacts on the availability of those systems. Based on current Air Force trends including maintenance manpower, dispersed aircraft basing, and increased complexity, there has been a renewed focus on preventative maintenance. To address these concerns, this thesis develops two models for preventative maintenance scheduling for complex systems, the first of interest in the system concept development and design phase, and the second of interest during operations. Both models are highly complex and intractable to solve in their original forms. For the first model, we develop approximation algorithms that yield high quality and easily implementable solutions. To address the second model, we propose a decomposition strategy that produces submodels that can be solved via existing algorithms or via specialized algorithms we develop. While much of the literature has examined stochastically failing systems, preventative maintenance of usage limited systems has received less attention. Of particular interest is the design of modular systems whose components must be repaired/replaced to prevent a failure. By making cost tradeoffs early in development, program managers, designers, engineers, and test conductors can better balance the up front costs associated with system design and testing with the long term cost of maintenance. To facilitate such a tradeoff, the Modular Maintenance Scheduling Problem provides a framework for design teams to evaluate different design and operations concepts and then evaluate the long term costs. While the general Modular Maintenance Scheduling Problem does not require maintenance schedules with specific structure, operational considerations push us to consider cyclic schedules in which components are maintained at a fixed frequency. In order to efficiently find cyclic schedules, we propose the Cycle Rounding algorithm, which has an approximation guarantee of 2, and a family of Shifted Power-of-Two algorithms, which have an approximation guarantee of 1/ ln(2) ~ 1.4427. Computational results indicate that both algorithms perform much better than their associated performance guarantees providing solutions within 15%-25% of a lower bound. Once a modular system has moved into operations, manpower and transportation scheduling become important considerations when developing maintenance schedules. To address the operations phase, we develop the Modular Maintenance and System Assembly Model to balance the tradeoffs between inventory, maintenance capacity, and transportation resources. This model explicitly captures the risk-pooling effects of a central repair facility while also modeling the interaction between repair actions at such a facility. The full model is intractable for all but the smallest instances. Accordingly, we decompose the problem into two parts, the system assembly portion and module repair portion. Finally, we tie together the Modular Maintenance and System Assembly Model with key concepts from the Modular Maintenance Scheduling Problem to propose an integrated methodology for design and operation.by Eric Jack Zarybnisky.Ph.D

Sustainable supply chain management and decision theory: a qualitative exploration using planetary boundaries and social foundations

The research considers the use of sustainable supply chain management (SSCM) policies as a means to forge a bridge beween the micro scale of individual firm operations and the macro scale of ecological and societal impact(referred to as Kleindorfer's Challenge). Qualitative case study research is undertaken across different economic sectors identified with specific macro-scale challenges that are taken as a more precise and up-to-date definition for sustainability. This research assumes the plaentary boundaries (PB) framework, developed by environmental scientists led by Rockstrom & Steffen et al., and the social foundations (SF) framework, from international development, defined by Raworth & Leach et al. as the basis of the definition used. Eight firms grouped into five case studies are subjected to in-depth investigation into how they relate their own activities to sustainabiity outcomes via their SSCM policy and the barriers they face. To understand the nature of knowledge versus uncertainty within each firm, decision theory is adopted and elaborated in the context of sustainability. In particular, Snowden's Cynefin framework and Keeney's value-focussed decision analysis are adopted as aspects of the dominant logic for each firm. This shapes their decision making abilities when faced with complexities and ambiguities in delivery SSCM in the context of various external pressures (notably from legislative, investor and customer demands). The resulting evidence informs a model of substantive sustainability, whereby firms with substantive impacts are distinguished from those without substantive impacts, in terms of the PB+SF framemworks. This helps firms realise the extent to which they should be concerned about sustainability issues, with some firms having a disconnect between their stated goals and their actual influence, and other firms with substantial impacts receiving insufficient attention from academia and practice

Sustainable supply chain management and decision theory: a qualitative exploration using planetary boundaries and social foundations

The research considers the use of sustainable supply chain management (SSCM) policies as a means to forge a bridge beween the micro scale of individual firm operations and the macro scale of ecological and societal impact(referred to as Kleindorfer's Challenge). Qualitative case study research is undertaken across different economic sectors identified with specific macro-scale challenges that are taken as a more precise and up-to-date definition for sustainability. This research assumes the plaentary boundaries (PB) framework, developed by environmental scientists led by Rockstrom & Steffen et al., and the social foundations (SF) framework, from international development, defined by Raworth & Leach et al. as the basis of the definition used. Eight firms grouped into five case studies are subjected to in-depth investigation into how they relate their own activities to sustainabiity outcomes via their SSCM policy and the barriers they face. To understand the nature of knowledge versus uncertainty within each firm, decision theory is adopted and elaborated in the context of sustainability. In particular, Snowden's Cynefin framework and Keeney's value-focussed decision analysis are adopted as aspects of the dominant logic for each firm. This shapes their decision making abilities when faced with complexities and ambiguities in delivery SSCM in the context of various external pressures (notably from legislative, investor and customer demands). The resulting evidence informs a model of substantive sustainability, whereby firms with substantive impacts are distinguished from those without substantive impacts, in terms of the PB+SF framemworks. This helps firms realise the extent to which they should be concerned about sustainability issues, with some firms having a disconnect between their stated goals and their actual influence, and other firms with substantial impacts receiving insufficient attention from academia and practice