Strategies for dynamic appointment making by container terminals

We consider a container terminal that has to make appointments with barges dynamically, in real-time, and partly automatic. The challenge for the terminal is to make appointments with only limited knowledge about future arriving barges, and in the view of uncertainty and disturbances, such as uncertain arrival and handling times, as well as cancellations and no-shows. We illustrate this problem using an innovative implementation project which is currently running in the Port of Rotterdam. This project aims to align barge rotations and terminal quay schedules by means of a multi-agent system. In this\ud paper, we take the perspective of a single terminal that will participate in this planning system, and focus on the decision making capabilities of its intelligent agent. We focus on the question how the terminal operator can optimize, on an operational level, the utilization of its quay resources, while making reliable appointments with barges, i.e., with a guaranteed departure time. We explore two approaches: (i) an analytical approach based on the value of having certain intervals within the schedule and (ii) an approach based on sources of exibility that are naturally available to the terminal. We use simulation to get insight in the benefits of these approaches. We conclude that a major increase in utilization degree could be achieved only by deploying the sources of exibility, without harming the waiting time of barges too much

Optimal ordering and pricing in a quick response system

Cataloged from PDF version of article.Quick response systems enable retailers to estimate customer demand more accurately, and improve stocking decisions for perishable products with uncertain demand. Retailers place separate orders for a product at two different times before the selling season. Following the initial order, additional market information is obtained, and the second-order amount is decided based on an improved demand forecast. In some cases, purchase cost associated with the second order is uncertain, and demand for the product during the season depends on the selling price. We present a solution procedure for finding the optimal order quantity and selling price in this setting. We also study the case where any desired portion of the initial order can be cancelled after updating the demand forecast. In the numerical study. the optimal price is observed to be relatively insensitive to changes in demand variability. (C) 2009 Elsevier B.V. All rights reserved

FORWARD SHIPPING OPTIONS FOR GRAIN BY RAIL: A STRATEGIC RISK ANALYSIS

Grain hauling railroads began offering shipping alternatives in the late 1980s that have made transportation decisions more strategic. Shippers now confront alternatives ranging from nearby and unguaranteed ordering to various durations of forward and guaranteed shipment. A dynamic stochastic simulation model was developed to analyze grain shipping and merchandising strategies that integrate these alternatives.railroads, grain merchandising, logistics, simulation, risk analysis, Crop Production/Industries, Marketing,

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

Inventory and pricing decisions in a single-period problem involving risky supply

Cataloged from PDF version of article.We explore an extension of the single-period (newsboy) inventory problem when supply is uncertain. We look into the negotiations between a newsvendor (retailer) and a manufacturer when there is competition from a second supplier. There is a chance that the second supplier will not be able to deliver the product. The retailer can maximize his expected profit by optimally allocating his order between the two suppliers. The retailer’s ordering problem is analyzed in conjunction with the manufacturer’s related pricing problem. The effects of demand and supply uncertainties on the optimal decisions of the parties are explored using numerical examples. We also explore extension of the retailer’s problem to the cases of order cancellation, price-dependent demand, and demand-dependent supply availability. & 2008 Elsevier B.V. All rights reserve

Optimal inventory decisions when offering layaway

This paper presents an inventory management policy for a retailer offering a layaway programme. Layaway is a service provided by retailers that allows budget constrained consumers who have sufficiently high valuations to pay for a product in several instalments rather than at once and obtain the product that has been reserved for them at the end of the payment period. If a consumer defaults on payments, then the reserved item is released back into store inventory. In this paper, we first determine the retailer's optimal order decisions when layaway is offered. We find that the order quantity under a layaway programme decreases with the likelihood of consumers not finishing their layaway plans and that it is not always profitable for a retailer to offer a layaway programme. We then identify the market conditions under which the retailer would benefit from a layaway programme. Lastly, we consider an extension to capture the influence of the timing of consumer defaults

Developing an Agent Based Heuristic Optimisation System for Complex Flow Shops with Customer-Imposed Production Disruptions

The study of complex manufacturing flow-shops has seen a number of approaches and frameworks proposed to tackle various production-associated problems. However, unpredictable disruptions, such as change in sequence of order, order cancellation and change in production delivery due time, imposed by customers on flow-shops that impact production processes and inventory control call for a more adaptive approach capable of responding to these changes. In this research work, a new adaptive framework and agent-based heuristic optimization system was developed to investigate the disruption consequences and recovery strategy. A case study using an Original Equipment Manufacturer (OEM) production process of automotive parts and components was adopted to justify the proposed system. The results of the experiment revealed significant improvement in terms of total number of late orders, order delivery time, number of setups and resources utilization, which provide useful information for manufacturer’s decision-making policies.