Perspectives in supply chain risk management

Multiple-supplier inventory models in supply chain management: A review

The accuracy of intermittent demand estimates

Purpose – The paper seeks to develop a framework for sales and operations planning (S&OP) that is based on previous literature and company interviews. It is designed to help managers understand how effective their S&OP processes are and how to progress to advanced stages.Design/methodology/approach – The S&OP literature, is reviewed and the results of a number of company interviews are presented. These lead to a new framework, with descriptions of each stage, and to implementation insights for managers.Findings – After highlighting key dimensions for establishing a firm's S&OP maturity on a five‐stage framework and, with the use of this framework, exploring in a preliminary way the relationship between firm size or process type (job shop, batch flow, continuous flow, etc.) and its degree of S&OP plan integration, little apparent relationship was found. However, the data suggest that business processes are enablers of S&OP plan integration, but that information technology is not clearly so.Research limitat...

Sales and operations planning: an exploratory study and framework

When designing a sourcing strategy in practice, a key task is to determine the average order rates placed to each source because that affects cost and supplier management. We consider a firm that has access to a responsive nearshore source (e.g., Mexico) and a low-cost offshore source (e.g., China). The firm must determine an inventory sourcing policy to satisfy random demand over time. Unfortunately, the optimal policy is too complex to allow a direct answer to our key question. Therefore, we analyze a tailored base-surge (TBS) sourcing policy that is simple, used in practice, and captures the classic trade-off between cost and responsiveness. The TBS policy combines push and pull controls by replenishing at a constant rate from the offshore source and producing at the nearshore plant only when inventory is below a target. The constant base allocation allows the offshore facility to focus on cost efficiency, whereas the nearshore facility's quick response capability is utilized only dynamically to guarantee high service. The research goals are to (i) determine the allocation of random demand into base and surge capacity, (ii) estimate corresponding working capital requirements, and (iii) identify and value the key drivers of dual sourcing. We present performance bounds on the optimal cost and prove that economic optimization brings the system into heavy traffic. We analyze the sourcing policy that is asymptotically optimal for high-volume systems and present a simple “square-root” formula that is insightful to answer our questions and sufficiently accurate for practice, as is demonstrated with a validation study.

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Global Dual Sourcing: Tailored Base-Surge Allocation to Near-and Offshore Production

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$5 million from $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.

https://pubsonline.informs.org/doi/pdf/10.1287/inte.1040.0116

Philips Electronics Synchronizes Its Supply Chain to End the Bullwhip Effect

A two-supplier inventory model

This dissertation addresses the management of inventory systems. The thesis starts with an exposition on mathematical models that can be used in inventory theory. Then we deal with some information issues related to the demand process. Namely, how to control products that have intermittent demand. Moreover, we investigated the impact of data collection on the customer performance. Next, we investigated to what extend multiple-sourcing can lead to improvements of the inventory system. Finally two demand management strategies are investigated for smoothing demand. The first re-routes large customer orders to alternative stockpoint, whereas the second strategy splits a customer order in a time-phased delivery scheme.

Inventory management systems : Control and information issues

In this paper we compare three methods for the determination of the reorder point s in an (R; s; Q) inventory model subject to a service level constraint. The three methods di er in the modelling assumptions of the demand process which in turn leads to three di erent approximations for the distribution function of the demand during the lead time.The rst model is most common in the literature, and assumes that the time axis is divided in time units (e.g. days).It is assumed that the demands per time unit are independent and identically distributed random variables.The second model monitors the customers individually.In this model it is assumed that the demand process is a compound renewal process, and that the distribution function of the interarrival times as well as that of the demand per customer are approximated by the rst two moments of the associated random variable.The third method directly collects information about the demand during the lead time plus undershoot, avoiding convolutions of stochastic random variables and residual lifetime distributions.Consequently, the three methods require di erent types of information for the calculation of the reorder point in an operational setting.The purpose of this paper is to derive insights into the value of information; therefore it compares the target service level with the actual service level associated with the calculated reorder point.It will be shown that the performance of the rst model (discrete time model) depends on the coe cient ofvariation of the interarrival times. Furthermore, because we use asymptotic relations in the compound renewal model, we derive some bounds for the input parameters within which this model applies. Finally we show that the aggregated information model is superior to the other two models.

The Value of Information in an (R,s,Q) Inventory Model

In this paper we present an approximation method to compute the reorder point s in a (R; s; Q) inventory model with a service level restriction, where demand is modelled as a compound Bernoulli process, that is, with a xed probability there is positive demand during a time unit, otherwise demand is zero.The demand size and replenishment leadtime are stochastic variables.It is shown that this kind of mod- elling is especially suitable for intermittent demand.Furthermore, an approximation for the expected average physical stock is derived.The quality of both the reorder point determination as well as the approximation for the expected average physical stock turn out to be excellent, as is veri ed by discrete event simulation.

F.B.S.L.P. Janssen

Papers

Philips Electronics Synchronizes Its Supply Chain to End the Bullwhip Effect

A two-supplier inventory model

Inventory management systems : Control and information issues

The Value of Information in an (R,s,Q) Inventory Model

On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process