Ton de Kok

Philips Electronics Synchronizes Its Supply Chain to End the Bullwhip Effect

Demand variability increases as one moves up a supply chain. The demand for finished products is less variable than for subassemblies, which is less variable than for individual components. This phenomenon is known as the bullwhip or Forrester effect. It increases inventory unnecessarily and makes managing the capacity of equipment and personnel difficult. In 1999, Philips Semiconductors confirmed substantial bullwhip effects in some of its supply chains and began developing a collaborative-planning process and tool to reduce them. It sought to reduce inventory and increase customer-service levels by integrating its supply chain planning and control with those of its customers. By applying stochastic multiechelon inventory theory, it developed an advanced planning and scheduling system that supports weekly collaborative planning of operations by Philips Semiconductors and one of its customers, Philips Optical Storage. The project has brought substantial savings. A conservative estimate shows minimum yearly savings of around US

Capacity allocation and outsourcing in a process industry

5 million from

Modeling the planning process in advanced planning systems

300 million yearly turnover. More important, Philips Optical Storage now has a more flexible and reliable supplier that can virtually guarantee quantities and delivery times. Philips Semiconductor is rolling out its new approach to other customers.

Self-imposed time windows in vehicle routing problems

In this paper we consider a production facility producing a uniform product that ships a number of different package sizes to a (number of) stockpoint(s). The packaging capacity owned by the facility is finite and the actually used capacity must be reserved sometime before actual use. Furthermore, the packaging capacity must be allocated among the different package sizes, such that a target fill rate for each package size is achieved. We propose two different capacity reservation strategies, both derived from a periodic review order-up-to-policy. One strategy assumes that excess capacity needs compared with the owned capacity cannot be filled and are postponed to the future. The other strategy assumes that excess capacity needs are outsourced. The objective of the paper is to find cost-optimal policies within each of the two classes of policies and then select the best capacity reservation policy. We present some managerial insights into the impact of various process and cost parameters and the choice of the capacity reservation strategy.

Dynamic shortest path problems: Hybrid routing policies considering network disruptions

Whereas much of the modeling literature in supply chain planning addresses the analysis of decision models and presents solution techniques and much of the empirical literature on planning systems such as ERP and APS software addresses implementation challenges from an organizational perspective, research on the modeling process of capturing the planning process in planning software is very scarce. In this paper, we examine this modeling process. Our approach is based on a normative method for hierarchical planning and presents a case study where this modeling process was used. We analyze the process, relate it to the literature on modeling, and demonstrate the value of the theory in explaining major observations. We conclude that the hierarchical premise on which most planning processes are based is very difficult to capture in an advanced planning system, and find that users and organizations essentially circumvent this problem by creating their own workflows, independent of the systems prescribed one.

Estimating stock levels in periodic review inventory systems

We observe self-imposed time windows (SITW) whenever a logistics service provider quotes a delivery time window to his customer. Once this time window is communicated, the company strives to respect it as well as possible. We incorporate these SITW within the framework of the vehicle routing problem (VRP). Essential to SITW is the fact that the time window is determined by the carrier company and not by the customer. The resulting VRP-SITW is inherently different from the well-studied VRP with time windows (VRPTW) in that in the latter problem the time windows are exogenous constraints imposed by the customers. The second important element of the problem studied in this paper is the uncertainty in the travel times. The basic mechanism of dealing with this uncertainty is the allocation of time buffers throughout the routes, which absorb disruptions. We propose a heuristic solution approach combining an LP model and a local search heuristic. A tabu search heuristic assigns customers to vehicles and establishes the order of visit of the customers per vehicle. Detailed timing decisions are subsequently generated by the LP model, whose output also guides the local search in a feedback loop. We test our algorithm on a number of benchmark instances for the VRP and VRPTW. We highlight the costs involved in integrating SITW with the VRP and we underline the advantages of SITW as compared to VRPTW.

Setting planned leadtimes in customer-order-driven assembly systems

Traffic network disruptions lead to significant increases in transportation costs. We consider networks in which a number of links are vulnerable to these disruptions leading to a significantly higher travel time on these links. For these vulnerable links, we consider known link disruption probabilities and knowledge of transition probabilities for recovering from or getting into a disruption. We develop a framework based on dynamic programming in which we formulate and evaluate different known online and offline routing policies. Next to this, we develop computation-time-efficient hybrid routing policies. To test the efficiency of the different routing policies, we develop a test bed of networks based on a number of characteristics and analyze the results in terms of routes, cost performance and calculation times. Our results show that a significant part of the cost reduction can be obtained by considering only a limited part of the network in detail. The performance of our proposed hybrid policy is only slightly worse than the optimal policy.

A typology and literature review on stochastic multi-echelon inventory models

Calculating the mean physical stock in a simple periodic review inventory system seems to be straightforward. However, the standard approximate expressions appear to yield inaccurate results, especially for low service levels. Low service levels are frequently encountered at intermediate nodes in cost-optimal solutions for multi-echelon systems. In this note, we present an improved approximate method that is both simple and accurate.

Forecasting Techniques in Logistics

We study an assembly system with a number of parallel multistage processes feeding a multistage final assembly process. Each stage has a stochastic throughput time. We assume that the system is controlled by planned leadtimes at each stage. From these planned leadtimes the start and due times of all stages can be derived. If a job finishes at a particular stage and has to wait before the start of the next job(s), a holding cost proportional to the waiting time is incurred. A penalty cost proportional to the lateness is incurred when the last stage of the final assembly process finishes after its due time. The objective is to determine planned leadtimes for each individual stage, such that the expected cost of a customer order is minimized.We derive the recursive equations for the tardiness and earliness at all stages and an exact expression for the expected cost. We discuss the similarity between these expressions and those for serial inventory systems. Based on this observation and a conjecture related to the generalized Newsvendor equations, we develop an iterative heuristic procedure. Comparison with a numerical optimization method confirms the accuracy of the heuristic. Finally, we discuss an application of the model to a real-life case, showing the added value of a system-wide optimization of planned leadtimes compared to current practice.

Buffering against uncertainty in high-tech supply chains

We develop a typology for multi-echelon inventory management. The typology is used to classify and review the extensive research of multi-echelon inventory management under uncertain demand. We identify clusters of model assumptions, research goals and applied methodologies. Based on this review, existing research gaps and avenues for further research are proposed.