Caroline Thierry

A review of Vendor Managed Inventory (VMI): from concept to processes

In the modern supplier–customer relationship, Vendor Managed Inventory (VMI) is used to monitor the customers inventory replenishment. Despite the large amount of literature on the subject, it is difficult to clearly define VMI and the main associated processes. Beyond the short-term pull system inventory replenishment often studied in academic works, partners have to share their vision of the demand, their requirements and their constraints in order to fix shared objectives for the medium/long-term. In other words, the integration of VMI implies consequences for the collaborative process that links each partners different planning processes. In this article we propose a literature review of VMI. Based on the conceptual elements extracted from this analysis, we suggest a VMI macro-process that summarises both operational and collaborative elements of VMI.

Cooperative subcontracting relationship within a project supply chain: A simulation approach

Abstract The management of supply chains is of the highest interest from both the industrial and the academic point of view since several years. The emerging notion of “Project supply chain” is less studied but also very appealing. In this paper, we study the interaction of different agents within this kind of supply chain. The major issue of such an interaction is the degree of cooperation between the agent and the capacity of each of them to forecast the impact of their behaviour. A simulation tool which studies the relationship between a principal contractor (in charge of project management) and a subcontractor (dealing with the management of a scarce resource) is proposed here, that aim at enabling both actors to understand the impact of their behaviour on risks and delivery dates.

Design of cooperative processes in a customer-supplier relationship: An approach based on simulation and decision theory

Performance improvement in supply chains, taking into account a customer demand in the tactical planning process is essential. It is more and more difficult for customers to ensure a certain level of demand over a medium term horizon as their own customers ask them for personalisation and fast adaptation. It is thus necessary to develop methods and decision support systems to reconcile the order and book processes. In this context, this paper intends firstly to relate decision under uncertainty and the industrial point of view based on the notion of risk management. This serves as a basis for the definition of an approach based on simulation and decision theory that is dedicated to the design of cooperative processes in a customer-supplier relationship. This approach includes the evaluation, in terms of risk, of different cooperative processes using a simulation-dedicated tool. The evaluation process is based on an exploitation of decision theory concepts and methods. The implementation of the approach is illustrated on an academic example typical of the aeronautics supply chain.

Modelling of ill-known requirements and integration in production planning

Making decisions on the basis of uncertain forecasts is one of the key challenges for efficient supply chain management. This article suggests the use of the theory of possibility for building a procurement plan on the basis of ill-known requirements. These requirements, expressed in quantities by date, may come from various sources: forecasts or orders for instance. The possible types of imperfection pervading requirement are analysed, and a unified representation model is suggested. A method is then described for calculating a plausible demand per period without loss of information; it is illustrated with an example in the last section.

Cooperation to Reduce Risk in a Telecom Supply Chain

The telecom market is highly unpredictable and evolves at a very fast rate, making it extremely difficult to forecast demand accurately. These characteristics of the telecom supply chain lead to a high level of risk. One of the possible solutions for better decision making and improvement of local and global performance is the establishment of cooperative relationships within the chain. Our article presents a system and implementation methodology that aims to evaluate the risks of the actors′ behaviors (resource planning strategies, production and supply control strategies, and information sharing strategies) on the performance of the individual supply chain actors and of the supply chain as a whole. This risk is evaluated according to the level of the risk attraction of the decision maker and a risk evaluation diagram is provided to the decision maker.

MRP with imprecise demand and uncertain lead time

Taking into account the uncertainty of real data in the planning process is a real challenge for nowadays companies. It is suggested in this communication to take into account the demand uncertainty, but also the uncertainty on the lead times, for deciding which quantities of components should be released, and when. In that purpose, the various situations that may happen are summarized in a graph which design is detailed here.

Robust material requirement planning with cumulative demand under uncertainty

Abstract In this paper, we deal with the problem of tactical capacitated production planning with the demand under uncertainty modelled by closed intervals. We propose a single-item with backordering model under small uncertainty in the cumulative demand for the Master Production Scheduling (MPS) problem with different rules, namely the Lot For Lot rule and the Periodic Order Quantity rule. Then we study a general multilevel, multi-item, multi-resource model with backordering and the external demand on components for the Material Requirement Planning (MRP) problem under uncertainty in the cumulative demand. In order to choose robust production plans for the above problems that hedge against uncertainty, we adopt the well-known minmax criterion. We propose polynomial methods for evaluating the impact of uncertainty on a given production plan in terms of its cost and for computing optimal robust production plans for both problems (MPS/MRP) under the assumed interval uncertainty representation. We show in this way that the robust problems (MPS/MRP) under this uncertainty representation are not much computationally harder than their deterministic counterparts.

Deciding under Ignorance: In Search of Meaningful Extensions of the Hurwicz Criterion to Decision Trees

The major paradigm for sequential decision under uncertainty is expected utility. This approach has many good features that qualify it for posing and solving decision problems, especially dynamic consistency and computational efficiency via dynamic programming. However, when uncertainty is due to sheer lack of information, and expected utility is no longer a realistic criterion, the approach collapses because dynamic consistency becomes counterintuitive and the global non-expected utility criteria are no longer amenable to dynamic programming. In this paper we argue against Resolute Choice strategies, following the path opened by Jaffray, and suggest that the dynamic programming methodology may lead to more intuitive solutions respecting the Consequentialism axiom, while a global evaluation of strategies relying on lottery reduction is questionable.

Scheduling and Restoring the Consistency of a Project in a Multi-Project Context

Abstract Project organisation often characterises small batch production planning. In this context each project has its own objectives. Nevertheless the multi-project context (resources shared with other projects, availability of components, due dates) has to be considered. In this framework, a decision support system is proposed to project managers. This system adopts a decision-makers point of view while taking into account the constraints of the multi-project context. This system either proposes a solution to the project scheduling problem -whenever such a solution exists- or displays explanations on the problem inconsistency and the possible actions likely to restore the consistency.

Robust production plan with periodic order quantity under uncertain cumulative demands

In this paper, we are interested in a production planning process in collaborative supply chains. More precisely, we consider supply chains, where actors use Manufacturing Resource Planning process (MRPII). Moreover, these actors collaborate by sharing procurement plans.We focus on a supplier, who applies the Periodic Order Quantity (POQ) rule to plan a production integrating the uncertain procurement plan sent by her/his customer. The uncertainty of the procurement plan is expressed by closed intervals on the cumulative demands. In order to choose a robust production plan, under the interval uncertainty representation, the min-max criterion is applied. We propose algorithms for determining the set of possible costs of a given production plan - due to the uncertainty on the cumulative demands.We then construct algorithms for computing a robust production plan with respect to the min-max criterion: the algorithm based on iterative adding constraints and the polynomial algorithms under certain realistic assumptions.