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Supply chains have expanded rapidly over the decades, with the aim to increase productivity, lower costs and fulfil demands in emerging markets. The increasing complexity in a supply chain hinders visibility and consequently reduces one’s control over the process. Cases of disruption such as the ones faced by Ericsson and Enron, have shown that a risk event occurring at one point of the supply chain can greatly affect other members, when the disruption is not properly controlled. Supply chain management thus faces a pressing need to maintain the expected yields of the system in risk situations. To achieve that, we need to both identify potential risks and evaluate their impacts, and at the same time design risk mitigation policies to locate and relocate resources to deal with risk events. This dissertation aims to analyse how supply chain risks could be effectively managed. This is done firstly by positioning the research agenda in supply chain risk management (SCRM). Then, methods for effective management of supply chain risk are identified and analysed. In order to find these, we develop a research framework in which the supply chain system is divided into subsystems based on the operations of make, source and deliver; as well as on material, financial and information flows. Furthermore, research questions are raised in order to understand the impact of risks on supply chains, to identify the performance measures for monitoring supply chains, and to determine risk mitigation strategies for improving system performances. This dissertation includes a bibliometric analysis of relevant literature of SCRM published in recent years. Based on the co-citation analysis, we identify the changing interest in SCRM, from performance-focused individual issues in the early years to integrated system issues with management perspective in recent years. We also identify the growing importance of information issues in SCRM. However, there is a relative lack of research into risk mitigation focusing on information flows in the literature. This dissertation also develops a conceptual model for analysing supply chain risk. The adoption of tools from the established field of reliability engineering provides a systematic yet robust process for risk analysis in supply chains. We have found that the potential use of a stand-alone tool of Failure Modes and Effect Analysis (FMEA) or a hybrid application of Fault Tree Analysis (FTA) and Analytical Hierarchy Process (AHP), will be most appropriate in SCRM. Apart from above mentioned studies, this dissertation then includes three manuscripts respectively investigating the risk mitigation policies in SCRM. First, we suggest a dynamic pricing policy when facing supply yield risk, such as price postponement, where price is determined only after receiving the delivery information. This postponed pricing, can improve the balance between supply and demand, especially when the delivery quantity is small, demand has a low uncertainty and there is a wide range when demand is sensible to price change. In another paper, a system dynamics model is developed to investigate the dispersion of disruption on the supply chain operation as well as along the network. Based on this simulation model, policies are tested to observe their influence to the performance of the supply chain. The study results support the benefit of a dual-sourcing strategy. Furthermore, information sharing, appropriate order splitting and time to react would further improve the supply chain performance when disruption strikes. In the last paper, we study how capacity should be expanded when a new product is introduced into the market. The major risk here is due to a quick capacity expansion with large investments which could be difficult to recover. Using the Bass diffusion model to describe demand development, we study how capacity expansion, together with sales plan could affect the economics of the system. Using sales information for the forecast, delaying the sales and adding initial inventories, should create a better scheme of cash flows. This dissertation contributes in several ways to the research field of SCRM. It plots research advancements which provide further directions of research in SCRM. In conjunction with the conceptual model, simulations and mathematical modelling, we have also provided suggestions for how a better and more robust supply chain could be designed and managed. The diversified modelling approaches and risk issues should also enrich the literature and stimulate future study in SCRM.

This thesis addresses industrial energy issues from a production economic perspective. During the past decade, the energy issue has become more important, partly due to rising energy prices in general, but also from a political pressure on environmental awareness concerning the problems with climate change. As a large user of energy the industry sector is most likely responsible for a lot of these problems. Things need to change and are most likely to do so considering current and assumed future governmental regulations. Thus, the energy intensive process industries studied and focused on in this thesis exemplify the importance of introducing a strategic perspective on energy, an appropriate approach for planning, as well as the possibilities of including energy issues in a production and supply chain planning model. The thesis aims to provide models, methods and decision support tools for energy related production and supply chain planning issues of relevance for process industries as well as for other energy intensive industries. The overall objectives are to analyze the strategic importance of energy management, production and supply chain planning, and the opportunities provided when energy is included in a production and supply chain planning model. Three different studies are carried out, analyzed, and presented as in this thesis. The first study is a case study at a specialty chemicals company and resulted in the first paper. Since the energy issue is not only a cost issue driven by supply and demand, but also a political issue due to its environmental aspects, it is likely to believe that political influence and especially continuity will have escalating effect on the energy intensive process industry sector. Thus, the strategic dimension of energy is highly relevant in this thesis. The importance of organizational integration, having a main responsible person, locating core business, and political continuity are addressed as prerequisites for including energy into the corporate strategy. Regarding long term profitability, the importance of correctly utilizing the energy system by appropriate energy planning and with respect to energy efficiency and effectiveness in both flexibility and investment issues are addressed. Further on, the quest of finding alternative revenue while striving for a proper exergy usage is addressed. The second study is a multiple case study with four different case companies involved; pulp, specialty chemicals, specialty oils, as well as a pulp and paper company. The need for improved production and supply chain planning is also addressed where for instance the lack of planning support for process industries is still an area of improvement. The production and supply chain planning in process industries is found to be rather poor compared to regular manufacturing companies. The planning methods found are often tailor made and adapted to the individual characteristics that are typical for many process industries. It has further on been difficult to distinguish similarities and differences among process industries regarding these planning issues and thus hard to generalize. The third study focuses on mathematical modelling and programming developing a combined supply chain and energy optimization model for a pulp company. Taking the first papers together there are reasons to believe that a planning and optimization model that take energy aspects in consideration, as a previously missing link, will contribute to improve the operations in process industries. A clear impact of involving energy issues into the supply chain planning is shown. The results show that a different production schedule is optimal when the energy issues are applied, and depend on, for instance, variations in energy prices such as the one for electricity. This is shown by using a model for a supply chain where the energy flow, and especially the utilization of by-products, also is involved.

The dynamic lotsizing problem concerns the determination of optimally produced/delivered batch quantities, when demand, which is to be satisfied, is distributed over time in different amounts at different times. The standard formulation assumes that these batches are provided instantaneously, i.e. that the production rate is infinite. Using a cumulative geometrical representation for demand and production, it has previously been demonstrated that the inner-corner condition for an optimal production plan reduces the number of possible optimal replenishment times to a finite set of given points, at which replenishments can be made. The problem is thereby turned into choosing from a set of zero/one decisions, whether or not to replenish each time there is a demand. If n is the number of demand events, this provides 2n−1 alternatives, of which at least one solution must be optimal. This condition applies, whether an Average Cost approach or the Net Present Value principle is applied, and the condition is valid in continuous time, and therefore in discrete time. In the current paper, the assumption of an infinite production rate is relaxed, and consequences for the inner-corner condition are investigated. It is then shown that the inner-corner condition needs to be modified to a tangency condition between cumulative requirements and cumulative production. Also, we have confirmed the additional restriction for feasibility in the finite production case (provided by Hill, 1997), namely the production rate restriction. Furthermore, in the NPV case, one further necessary condition for optimality, the distance restriction concerning the proximity between adjacent production intervals, has been derived. In an example this condition has shown to reduce the number of candidate solutions for optimality still further. An algorithm leading to the optimal solution is presented.

To make a difference among the energy intensive process industries, this dissertation addresses production planning and supply chain planning problems related to industrial energy management issues. The energy issue is turning more and more important from different angles, involving price as well as environmental problems due to climate change leading to political pressure on all energy users. The process industry sector is one of the largest users of energy, and thus important to analyse. Process industries are also capital intensive and operate on large and expensive process equipment, making it imperative to plan their production well in order to reach preferable capacity utilisation. Therefore this dissertation strives to locate the most important energy management issues for the long term profitability of process industries, and investigates the symbiotic effects of including energy issues in production and supply chain planning. Three different studies at three case companies are carried out, analysed, and presented in five papers. The cases represent the process industry sectors: chemicals, pulp, and steel. Both qualitative case study methodologies as well as quantitative mathematical modelling and optimisation approaches have been practiced. The research questions are analysed from both an energy system and from a production process point of view, separately as well as combined. Energy is somewhat considered to be the main workforce for process industries and this dissertation exemplifies some of its most important dimensions in this context. Several prerequisites for putting energy management on the strategic agenda are located in a specialty chemical industry where the importance of introducing a strategic perspective on energy, the way energy is used, and the possibilities of increasing alternative revenue from utilising by- and/or co-products differently are pinpointed. Approaches for including energy issues in planning processes are also suggested in terms of a MILP model for the entire supply chain of a pulp company, including decisions on purchase and transportation of raw maerials, production allocation, energy mix, and distribution. Another example is presented based on the perspectives of economics of scale and lot sizing through economic order quantity principles in a steel company. By using real company data, energy smart approaches in planning and scheduling are developed with respect to the most important intersections between the production processes and their supporting energy system. The accumulated resource intensity and embedded energy could, and probably should, hence be more fairly reflected in the product price. The research finally shows some possible impact with including energy issues in a production and supply chain planning model. By planning differently, production prioritisations can be done, and it is not only possible without any large investments, but also prosperous with savings on both energy and money within reach. To conclude, planning of production and supply chain has either a direct or an indirect impact on the energy cost-effectiveness of a company. This dissertation argues that such impact also exists in its mutual form, and is very important when the energy issues are large enough, as they often are in the energy intensive process industry sector. Decision makers should thus beware of the short end of the stick that might be devastating in the long run, but also aware of all the possibilities that can bring success and prosperity when the future begins.

The dynamic lotsizing problem concerns the determination of optimal batch quantities, when given required amounts appear at discrete points in time. The standard formulation assumes that no shortages are allowed and that replenishments are made instantaneously. For the case when no shortage is allowed, previously it has been demonstrated that the inner-corner condition for an optimal production plan in continuous time reduces the number of possible replenishment times to a finite set of given points at which either a replenishment is made, or not. The problem is thus turned into choosing from a set of zero/one decisions with 2 n −1 alternatives, of which at least one solution must be optimal, where n is the number of requirement events. Recently, the instantaneous replenishment assumption has been replaced by allowing for a finite production rate, which turned the inner-corner condition into a condition of tangency between the cumulative demand staircase and cumulative production. In this paper we investigate relationships between optimal cumulative production and cumulative demand, when backlogging is permitted. The production rate is assumed constant and cumulative production will then be a set of consecutive ramps. Cumulative demand is a given staircase function. The net present value (NPV) principle is applied, assuming a fixed setup cost for each ramp, a unit production cost for each item produced and a unit revenue for each item sold at the time it is delivered. Among other results, it is shown that optimal cumulative production necessarily intersects the demand staircase. Instead of having 2 n −1 production staircases as candidates for optimality, there are 2 n −1 production structures as candidates. These are made up of sequences of batches, of which the set of batches may be optimised individually. Also is shown that the NPV of each batch has a unique timing maximum and behaves initially in a concave way and ends as convex. Results for the average cost approach are obtained from a zeroth/first order approximation of the objective function (NPV).

MRP theory provides a theoretical background for multi-level, multi-stage production-inventory systems (material requirements planning in a general sense) together with their economic evaluation, in particular applying the net present value principle. The theory combines the use of input-output analysis and Laplace transforms, the former for capturing product structures, and the latter for incorporating timing, including time lags, lead times, and output delays. In this paper, we consider any production policy, when given any external demand as a vector-valued function of time. It is shown that in order for available inventory to be kept at finite levels at any time, the lot-for-lot (L4L) solution must be valid for the time averages of production and deliveries, irrespective of the policy followed. This analysis is carried out using properties the Laurent expansions of the transforms involved.

The purpose of this paper is to investigate the research development in supply chain risk management (SCRM), which has shown an increasing global attention in recent years. Literature survey and citation/co-citation analysis are used to fulfil the research task. Literature survey has undertaken a thorough search of articles on selected journals relevant to supply chain operations management. Meanwhile, citation/co-citation analysis uses Web of Sciences database to disclose SCRM development between 1995 and 2009. Both the approaches show similar trends of rising publications over the past 15 years. This review has piloted us to identify and classify the potential risk associated with different flows, namely material, cash and information flows. Consequently, we identify some research gaps. Even though there is a pressing need and awareness of SCRM from industrial aspect, quantitative models in the field are relatively lacking and information flow risk has received less attention. It is also interesting to observe the evolutions and advancements of SCRM discipline. One finding is that the intellectual structure of the field made statistically significant increase during 2000-2005 and evolved from passively reacting to vague general issues of disruptions towards more proactively managing supply chain risk from system perspectives.

The integrated planning of transportation of raw material, production and distribution of products of the supply chain for a large pulp company is studied. The planning period is one year and a number of time periods are considered. The tactical decisions included in the model regard transportation of raw materials from harvest areas to pulp mills, production mix and contents at pulp mills, distribution of pulp products from mills to customers either via terminals or directly, and the selection of potential orders and their levels at customers. A mixed integer linear programming model for the supply chain problem is developed. The main solution method in this paper is a Lagrangian heuristic method based on Lagrangian decomposition. The heuristic divides the problem into two subproblems representing different physical stages in the supply chain, each including several time periods. The advantage of the proposed solution approach is that it generates feasible solutions of high quality in short time. A number of cases based on real data is analysed. Original Publication:Helene Lidestam and Mikael Ronnqvist, Use of Lagrangian decomposition in supply chain planning, 2011, Mathematical and computer modelling, (54), 9-10, 2428-2442.http://dx.doi.org/10.1016/j.mcm.2011.05.054Copyright: Elsevierhttp://www.elsevier.com/