Strategic Analysis of Dual Sourcing and Dual Channel with an Unreliable Alternative Supplier

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148383/1/poms12938_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148383/2/poms12938.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148383/3/poms12938-sup-0001-AppendixS1.pd

Sustainable sourcing of strategic raw materials by integrating recycled materials

In this paper we investigate a manufacturer's sustainable sourcing strategy that includes recycled materials. To produce a short life-cycle electronic good, strategic raw materials can be bought from virgin material suppliers in advance of the season and via emergency shipments, as well as from a recycler. Hence, we take into account virgin and recycled materials from different sources simultaneously. Recycling makes it possible to integrate raw materials out of steadily increasing waste streams back into production processes. Considering stochastic prices for recycled materials, stochastic supply quantities from the recycler and stochastic demand as well as their potential dependencies, we develop a single-period inventory model to derive the order quantities for virgin and recycled raw materials to determine the related costs and to evaluate the effectiveness of the sourcing strategy. We provide managerial insights into the benefits of such a green sourcing approach with recycling and compare this strategy to standard sourcing without recycling. We conduct a full factorial design and a detailed numerical sensitivity analysis on the key input parameters to evaluate the cost savings potential. Furthermore, we consider the effects of correlations between the stochastic parameters. Green sourcing is especially beneficial in terms of cost savings for high demand variability, high prices of virgin raw material and low expected recycling prices as well as for increasing standard deviation of the recycling price. Besides these advantages it also contributes to environmental sustainability as, compared to sourcing without recycling, it reduces the total quantity ordered and, hence, emissions are reduced

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies

Planning of outsourced operations in pharmaceutical supply chains

In this dissertation, we focus on the planning and control of supply chains where part of the supply chain is outsourced to a contract manufacturer(s). Supply Chain Management deals with the integration of business processes from end-customers through original suppliers that provide products, services and information that add value for customers (Cooper et al., 1997). In a narrow sense, a supply chain can be ‘owned’ by one large company with several sites, often located in different countries. Planning and coordinating the materials and information flows within such a worldwide operating company can be a challenging task. However, the decision making is easier than in case more companies are involved in a supply chain, since the sites are part of one organization with one board and it is likely that the decision makers have full access to information needed for the supply chain planning. Outsourcing is an ‘act of moving some of a firm’s internal activities and decision responsibilities to outside providers’ (Chase et al., 2004) and it has been studied extensively in the literature.Outsourcing is developing in many industries, but in this dissertation, we focus on outsourcing in the pharmaceutical industry, where outsourced supply chain structures are rapidly developing. Recent studies show that the global pharmaceutical outsourcing market has doubled from 2001 to 2007 and it is expected to further increase in the upcoming years. In the pharmaceutical industry, the outsourcing relationship is typically long-term and customers often require high service levels. Due to high setup costs, production is conducted in fixed large batch sizes and campaign sizes. The cumulative lead time within the supply chain is more than one year, whereas the customer lead time is about two months. In this industry, production activities are outsourced for three main reasons. First, intellectual property legislation requires outsourcing the production activities to a contract manufacturer that owns the patent for specific technologies that are needed to perform the production activities. Second, expensive technologies or tight (internal) capacity restrictions also result in outsourcing. Third, to limit the supply uncertainty, companies outsource to have an external source producing the same product next to an internal source. This dissertation deals with the planning and control of outsourced supply chains, which are supply chains where part of the supply chain is outsourced to a contract manufacturer. Most supply chain operations planning models from the literature assume that the supply chain is planned at some level of aggregation and that further coordination is conducted at a more detailed level by lower planning levels. These concepts implicitly assume that the lower planning level and the operations are conducted within the same company with full information availability and full control over the operations, which is not case when part of the supply chain is outsourced. Hence, the objective of this dissertation is to obtain insights into the implications of outsourcing on the supply chain planning models. First, we review the literature on outsourcing research and we find that little is known on the operational planning decisions in an outsourced supply chain and on the implications of outsourcing on the operations planning. The literature on outsourcing at the operational level uses outsourcing purely as a secondary source to control performances such as the delivery reliability. Consequently, we discuss two case studies that we conducted into outsourced supply chains to understand the implications of outsourcing on the supply chain operations planning function, where the contract manufacturer is the only source of supply. The main implications of the planning and control of outsourced supply chains can be summarized in three categories: limited information transparency, limited control over the detailed planning and priorities at the contract manufacturer, and contractual obligations. Below, we discuss these in more detail. In order to decide on the release of materials and resources in a supply chain, it is required that the decision maker is able to frequently monitor the status of the supply chain. In an outsourced supply chain, the outsourcer does not have access to all relevant information of the entire supply chain, especially not to the available capacity in each period, also because the contract manufacturer serves a number of different (and sometimes even competing) outsourcers on the same production line. Moreover, the contract manufacturer plans and controls its part of the supply chain based on rules and priorities that are unknown to the outsourcer. This results in facing an uncertain capacity allocation by the outsourcer. Another implication is that the contract manufacturer requires by contract to reserve capacity slots prior to ordering. These reservations are subject to an acceptation decision, which means that part of the reservation quantity can be rejected. The accepted reservation quantity bounds the order quantity that follows later on. Therefore, another main insight from the case study is that in an outsourcing relationship, the order process consists of different (hierarchically connected) decisions in time. In the ordering process, the uncertain capacity allocation of the contract manufacturer should be incorporated. Hence, the order release mechanism requires a richer and more developed communication and ordering pattern than commonly assumed in practice. In a subsequent study, we build on this insight and we design three different order release mechanisms to investigate to what extent a more complicated order release function improves (or deteriorates) the performance of the supply chain operations planning models. The order release mechanisms differ in the number of decision levels and they incorporate the probabilistic behaviour of the contract manufacturer. Based on a simulation study, we show that a more advanced order release strategy that captures the characteristics of outsourcing performs significantly better than a simple order release strategy that is commonly used in practice. We also discuss the conditions for a successful implementation of the more advanced order release strategy. In another study, we study the case where the contract manufacturer is a second source next to an internal manufacturing source for the same product and where the outsourcer faces inaccurate demand forecasts. The two sources are constraining the supply quantities in different ways. Its own manufacturing source is more rigid, cheaper and tightly capacitated, whereas the contract manufacturer is more flexible but more expensive. In that study, we compare the performance of two different allocation strategies by a simulation study in which we solve the model in a rolling horizon setting. The results show that the rigid allocation strategy (the cheaper source supplies each period a constant quantity) performs substantially better than the dynamic allocation strategy (each period the allocation quantities are dynamic) if the parameters are chosen properly. In another study, we study the outsourcer’s problem of deciding on the optimal reservation quantity under capacity uncertainty, i.e., without knowing what part of the reservation will be accepted. In that study, we develop a stochastic dynamic programming model for the problem and we characterize the optimal reservation and order policies. We conduct a numerical study where we also consider the case where the capacity allocation is dependent on the demand distribution. For that case, we show the structure of the optimal policies based on the numerical study. Further, the numerical results reveal several interesting managerial insights, such as that the optimal reservation policy is little sensitive to the uncertainty of the capacity allocation from the contract manufacturer. In that case, the optimal reservation quantities hardly increase, but the optimal policy suggests increasing the utilization of the allocated capacity. We also study the outsourced supply chain from the contract manufacturer’s perspective. In that study, we consider the case where the contract manufacturer serves a number of outsourcers with different levels of uncertainty. The contract manufacturer faces the question of how to allocate the contractual capacity flexibility in an optimal way. More precisely, we focus on the contract manufacturer’s decision to make the acceptation decision under uncertainty. The more the contract manufacturer accepts from an outsourcer, the more risk is taken by the contract manufacturer, as the outsourcer might not fully utilize the accepted reservation quantity. However, we assume that the outsourcer is willing to pay an additional amount to compensate the contract manufacturer for that risk. We develop a mixed-integer programming model, which optimizes the allocation of capacity flexibility by maximizing the expected profit. Offering more flexibility to the more risky outsourcer generates higher revenue, but also increases the penalty costs. The allocated capacity flexibilities are input (parameters) to the lower decision level, where the operational planning decisions are made and demands are observed. The simulation results reveal interesting managerial insights, such that the more uncertain outsourcer gets at least the same capacity flexibility allocated as the less uncertain outsourcer. Moreover, we have seen that when the acceptation decision is made, priority is given to the less uncertain outsourcer, because that information is the most valuable. However, we see the opposite effect when orders are placed, namely that priority is given to the more uncertain outsourcer, i.e., the most paying outsourcer, as no uncertainty is involved anymore. These insights are helpful for managers of contract manufacturers when having contract negotiations with the outsourcers. We believe that the results and insights that we obtained in the various research studies of this dissertation can contribute to solving the broader real-life problems related to the planning and control of outsourced supply chains. We also discuss potential managerial implications of our findings explicitly addressing the management decisions that may be affected by using the insights from our studies. Considering the operational implications of outsourcing when taking the strategic outsourcing decision will lead to a different and a better estimate of the transaction costs and probably to a different strategic outsourcing decision. Based on our research, we think that the transaction cost estimate will be higher if the outsourcer and the contract manufacturer do not agree on operational issues, such as the multi-level order release mechanism. From a tactical point of view, the outsourcer may include the options of postponement and cancellation in the contract, even if the contract manufacturer would charge little extra for these options. The results show that the benefits of including these options are substantial. Moreover, we showed that controlling a contract manufacturer operationally in the same way as an internal manufacturing source leads to a nervous ordering behaviour with a lot of changes and a lot of panicky communication between the outsourcer and the contract manufacturer. Combining the insights from different studies, one can also conclude that including little reservation cost is beneficial to both parties; it leads to a win-win situation. The outsourcer with a high level of demand uncertainty secures sufficient capacity allocation from the contract manufacturer and avoids more expensive penalty costs. For the outsourcer with less demand uncertainty, it is wise to set the contract such that the reservation costs are subtracted from the total paid amount. Moreover, this outsourcer may gain competitive advantage if his competitors operate in the same market by securing sufficient capacity allocation (by paying little reservation costs). For the contract manufacturer, including reservation cost is also beneficial, as it leads to a better match between the outsourcer’s reservation and ordering behaviour

Design and control of carbon aware supply chains

In this dissertation the impact of carbon emissions on the design and control of supply chains is studied. Increasing awareness for global warming and the role of greenhouse gasses in this has made companies more aware of carbon dioxide emissions caused by supply chains. As a result of this awareness, carbon emission regulations have been developed enforcing companies to incorporate a carbon cost (for certain activities in certain regions). Moreover, companies are voluntarily restricting their carbon emissions by specifying emission reduction targets, as a response to pressure from customers and stakeholders. In this dissertation we develop models with emission regulation and also with voluntary emission targets. We study well-known trade-offs in the field of operations management, such as between inventory and transport costs, by incorporating a carbon emission component, historically often neglected, and investigate the impact of the emissions on decisions. It is important for companies to take carbon emissions explicitly into account in decision making as carbon related costs are expected to increase in the future. Carbon emissions can be reduced to a certain extent by taking efficiency measures that both reduce emissions and costs. As companies can also invest in these measures from a pure cost perspective, we do not consider them in this dissertation. Moreover, it is likely that these measures yield insufficient emission reductions to achieve global emission targets. Hence, to achieve substantial emission reductions, measures that require investments, or increase operational costs, might be necessary. We explore several strategies for companies to reduce carbon emissions and investigate when a certain strategy is cost-effective. Examples of emission reduction strategies are to switch transportation to a mode with lower emissions, or to invest in production technology or off-shore production capacity. The focus of the research is on production companies and their carbon emissions generated during production and transportation activities, either to facilities of the same company or from suppliers or to customers. When considering emissions from transportation, we assume that transport is executed by a third party logistics service provider, as is often seen in practice. As a result, the control of the production company over the transport (emissions) is limited. The optimization of the load of the vehicle, and the traveled route is outside the control of the production company. However, the production company can decide which transport mode, or combination of modes be used, which determines the emissions to a large extent. In Chapters 2, 3, and 4, this emission reduction opportunity is studied in settings with one or multiple products and imposing the use of one or two modes. Then, in Chapter 5, the focus is extended to include emissions from production. We consider a company facing emission regulation for production and consider the possibility to invest in cleaner technology or to offshore production to a location without emission regulation. We next present a summary of the models and results presented in Chapters 2 through 5. First, in Chapter 2, we study the transport mode selection decision for a single product subject to emission regulation. We investigate the impact of different types of emission regulations and investigate under what circumstances a transport mode switch may occur. A transport switch implies that the selected transport mode in a setting with emission regulation differs from the selected mode in absence of emission regulation. The tradeoff under consideration is that a fast mode results in low inventory costs but in high transportation costs and emissions (costs), and vice versa. In a setting with stochastic demand we consider an order-up-to inventory policy including an emission cost. To accurately estimate the carbon emissions from transportation, we use a carbon emission measurement methodology based on real-life data and incorporate it into an inventory model. We observe that not the emission cost but the product characteristics, such as weight, density, and value, mainly determine which transport mode is selected. Consequently, a switch to a less polluting transport mode only results for a very high emission cost or if a product has a low weight or density or a high value. We find that even though large emission reductions can be obtained by switching to a different mode, the actual decision depends on the regulation and non-monetary considerations, such as lead time variability. Then, in Chapter 3, we consider a multi-item setting in which a self-imposed emission reduction target is set for a group of items. One item represents a combination of a particular product and a particular customer for which regular shipments occur, which determines the demand, product characteristics and the distance to be traveled. As the choice of transport mode (and corresponding transport costs) is up to the production company, the quoted price to the customer is also a decision variable. Since a single emission constraint is set for a group of items, the model is a constrained multi-item deterministic problem which can be solved using Lagrangian relaxation. Setting an emission target for a group of items allows for taking advantage of the portfolio effect: reducing emissions first where it is overall less costly. For a fixed emission target the transport mode that minimizes the total logistics cost is selected. If a range of emission targets are considered and we compare the cost-minimizing solutions, then it appears that two opportunities exist for the producer to reduce emissions: first of all, to select a mode that results in lower emissions per product shipped, and secondly to select a slightly higher sales price which results in lower demand and hence lower emissions. In a case study, we apply our model (with fixed sales price) to a business unit of Cargill and observe that emissions can be reduced by 10% at virtually no cost increase. Emissions can be reduced by at most 27% which results in a 30% cost increase. In an extension in which the sales price can be set, we observe that the portfolio effect results in at most 20% profit savings, a value which is relatively robust to price-sensitivity of demand. As in this case study road transport is the most polluting mode, larger emission reductions can be expected when air transport is used for shipments. Next, in Chapter 4 we examine the possibility to use two supply modes for a given product simultaneously, which is referred to as dual sourcing in inventory literature, in a multi-item emission-constrained setting with stochastic demand. By using two supply modes, a fast and a slow, one can combine the low transport costs and emissions (the slow mode) with being highly responsive (the fast mode) when required, i.e. in case of a stock out situation. As has been investigated in the literature using dual sourcing may result in lower expected period costs than using only a single mode. From an emission perspective using dual sourcing is beneficial compared to single sourcing since emission reductions can be achieved on a continuous scale. In some situations switching all shipments to a less polluting mode is too costly. Dual sourcing may then provide a large part of the emission reduction at a lower cost than using only the slow mode. We assume that a so-called single-index policy is used, which specifies two order-up-to levels: one for each mode. As a result of this policy, the fast mode is used when the demand in a certain period exceeds a certain value. Making use of a special case with exponentially distributed demand, we provide structural insights for a single product model. Then we extend these results to a model with two products and an aggregate emission constraint which provides insight into the more general situation with n products. In a numerical study we observe that if dual sourcing results in a cost decrease, then emissions can be reduced to a large extent without increasing the costs compared to using only a single mode. For a two-product setting we study if setting an emission constraint for a group of items is more or less beneficial if the products are more similar with respect to the value for one variable. We observe that the demand variability, and not so much for product weight and the penalty cost factor, has a large impact on how beneficial dual sourcing is, i.e. less similar products benefit less from dual sourcing. Lastly, we study the investments of a production company in production technology and capacity under asymmetric and uncertain emission regulation in Chapter 5. Asymmetric emission regulation refers to the fact that in different regions of the world different, or no, emission regulations exist and as result the emission price differs from region to region. We consider a producer of an energy-intensive good which incurs an emission cost for emissions generated during production. The company is deciding how much to invest in production technology in the regulated market, and how much capacity to build in a location with no emission regulation, the unregulated market. As emission regulation may result in off-shoring production and an increase in total emissions, regulators can implement measures to combat this undesirable effect. We refer to these measures as anti-leakage policies and study for each policy how it affects the company’s investment decisions and ultimately global emissions. We consider three different anti-leakage policies: Border Tax, which imposes a cost for products imported into the regulated region, Output-based allocation, which reimburses a certain emission cost per product produced in the regulated market, and Grandfathering, which reimburses a lump sum of emission cost, provided actual emissions exceed the amount. We consider four scenarios, one without an anti-leakage policy (baseline scenario) and three for the anti-leakage policies just described, and determine the optimal investment strategy and also the production strategy, which specifies how much to produce in each location given an emission cost realization, and the global emissions. We have observed that four possible strategies exist, two of which are to invest and produce only in one market (either the regulated or the unregulated) and two involve investment in both markets. When an anti-leakage policy is implemented and we compare the investments to the baseline scenario two effects may occur. First of all, less capacity may be built in the unregulated market, while not changing the production strategy. Secondly, it may result in the selection of a strategy with more production in the regulated market. We have applied our model to a data set based on a European-based cement producer and conducted a full factorial study for several important parameters. Overall we have observed that the grandfathering policy is preferred from both the company’s and regulator’s perspective. It is however, important to set the reimbursement not too low or too high. Finally, in Chapter 6 we present the conclusions of the research presented in this dissertation and provide directions for future research

Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Structuring postponement strategies in the supply chain by analytical modeling

fi=vertaisarvioitu|en=peerReviewed

Demand Modeling And Capacity Planning For Innovative Short Life-Cycle Products

This dissertation focuses on demand modeling and capacity planning for innovative short life-cycle products. We first developed a new model in the class of stochastic Bass formulations that addresses the shortcomings of models from the extant literature. The proposed model considers the common fact that the market potential of a product is not fixed and might change during a life-cycle due to exogenous (e.g., economic- or competitors-related) or endogenous (e.g., quality-related) factors. Allowing this parameter (market potential in the Bass model) to follow a geometric random walk, we have showed that the future demand of a product in each period follows a lognormal distribution with specific mean and variance. We also developed a novel stochastic capacity expansion model that can be used by a make-to-order manufacturer, who faces stochastic stationary/non-stationary demand, in order to optimally determine policies for specifying the sizes of capacity procurement. In addition to the cost of expansion decisions, the proposed risk-neutral expansion model considers procurement lead-times, irreversibility of investments, and the costs associated with lost sales and unutilized capacity. We provide necessary and sufficient conditions for the derived optimal policy. We then present an exact solution method, which is more efficient than classical recursive methods. Additionally, three extensions of the proposed expansion model that can address more complicated settings are presented. The first extension increases the capability of the model in order to tackle capacity planning for a multi-sourcing scenario. Multi-sourcing is a case in which the manufacturer can procure capacity from two supply modes whose marginal expansion costs and lead-times are complementary. The second extension addresses a scenario in which an installed capacity can be used for producing future generations of a product. The last extension accounts for salvage value of the installed capacity in the model and provides the necessary and sufficient conditions for the optimal policy. Finally, using the proposed stochastic Bass model, we present the results and managerial insights gathered from numerical experiments that have been conducted for the stochastic capacity expansion models