When a major disruption occurs, an organization’s performance is usually negatively affected. The great recession of 2008 – 2009 was such a disruption which had global implications that had not been seen since the great depression that started in the 1930s. This thesis is intended to contribute to the understanding of how leanness and slack resources affect firm performance in the presence of disruptions that test supply chain resilience, or the ability to restore the firm’s performance to its original condition after encountering stress or a large disturbance. These disruptions may not only affect the firm’s financial performance during the disruption but also well after the disruption has occurred. Two industries with differing supply chains, food and beverage, and electronics and computer, were investigated. The study is based on archival data (N=10,020 and 668 firms) with observations from just before and just after the great recession, a disruption that affected the entire global economy. Our results suggest (1) the effect of inventory leanness and slack resources on firm performance is industry specific; and (2) variation in firm performance is less in the post-disruptive period than in the pre-disrupted period. Overall, our findings call for a contingency perspective to specify the level of inventory leanness and slack resources when determining their impact on firm performance to support supply chain resilience.

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This thesis intended to increase the understanding of bullwhip effect in electrical appliance industry in the Chinese market. In the supply chain management, the bullwhip effect is a phenomenon can never be ignored. The bullwhip effect has being defined as information distortion when orders move form downstream enterprises to the supplier (Lee et al 1997b). The distortion information was amplified step by step, and finally propagates to the enterprise marketing, logistics, manufacture and other fields. The existence of the bullwhip effect weakens the ability to add value and competitiveness of the supply chain. Hence, enterprises must collaborate and jointly mitigate the bullwhip effect to reach groups coexist. This work focus on the electrical appliance industry in China, and based on the successful experience of the case company, Haier Group, to formulate recommendations. Firstly, this research analysis the four causes of the bullwhip effect: demand forecast, price fluctuations, order quantity and short game (Lee et al 1997a). After analyze these causes, this study begins to identify the impacts which the bullwhip effect bring to the members of supply chains. The most obvious impacts can be defined as inaccurate forecasting, inadequate customer service and high inventory cost. Next, the paper evaluate the measures of Haier implement to dampen the bullwhip effect. Information sharing, the key point to solve the problem has been use in Haier. In addition to this, the Just in Time (JIT) strategy which include JIT purchase, JIT delivery and JIT distribution is another important measure for Haier to achieve the goal of mitigate the bullwhip effect.  Overall Every Control and Clear (OEC) management was created by Haier Group and aims to improve the supply chain management, is another countermeasure to deal with the bullwhip effect. Besides, the inventory management also have an important role in gaining the control of bullwhip effect. In summary, the successful experience on dampening the bullwhip effect of Haier can shine a light for electrical appliance industry in China on solving the similar problem. Information sharing is always the key point to mitigate the bullwhip effect, and related instructions should build to remove the barriers of sharing information.

The work presents a framework for analyzing complex decision making in policy from the perspective of planning power supply mix for Ontario. Concepts of sustainability are introduced and analyzed followed by an in-depth view of two case studies. The first analyzes the power supply mix for Ontario and the second analyzes policy impacts in Germany and Denmark. A linear programming model, including energy storage is then developed that would yield an optimized sustainability based development policy for electricity production in Ontario. Future work is recommended to calibrate and run the model. The analysis discusses the new model in relation to the first case study and provides a mechanism to evaluate tradeoffs traditionally unquantifiable, to yield a strategic plan for electricity development in Ontario.

The work presents a framework for analyzing complex decision making in policy from the perspective of planning power supply mix for Ontario. Concepts of sustainability are introduced and analyzed followed by an in-depth view of two case studies. The first analyzes the power supply mix for Ontario and the second analyzes policy impacts in Germany and Denmark. A linear programming model, including energy storage is then developed that would yield an optimized sustainability based development policy for electricity production in Ontario. Future work is recommended to calibrate and run the model. The analysis discusses the new model in relation to the first case study and provides a mechanism to evaluate tradeoffs traditionally unquantifiable, to yield a strategic plan for electricity development in Ontario.

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Physical distribution networks are integral parts of modern supply chains. When faced with a question of which node in a network is more important, cost immediately jumps to mind. However, in a world of uncertainty, there are other significant factors which should be considered when trying to answer such a question. The integrity of a network, as well as its robustness are factors that we consider, in making a judgement of importance. We develop algorithms to measure several properties of a class of networks. To accelerate the optimization of multiple related linear programs, we develop a modification of the revised simplex method, which exploits several key aspects to gain efficiency. We combine these algorithms and methods, to give rankings of the relative importance of nodes in networks. In order to better understand the usefulness of our method, we analyse the effect parameter changes have on the relative importance of nodes. We present a large, realistic network, whose nodes we rank in importance. We then vary the network's parameters and observe the impact of each change.