Modelling of ill-known requirements and integration in production planning

Making decisions on the base 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 base 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 by period without loss of information; it is illustrated on an example in the last section

Management of the risk of backorders in a MTO-ATO /MTS context under imperfect requirements

International audienceProduction systems are often classified according to the way production is released, e.g. make-to-stock (MTS), make-to-order (MTO), assembly-to-order (ATO) or engineer-to-order (ETO). The choice of a type of production depends on the decoupling point between customer and supplier. In some supply chains, like in the aeronautical sector, a customer may work according to a MTO process (since his product is highly specific) while his supplier works with a MTS process (since he delivers variants of standards components). This situation sets specific problems that are seldom considered in the literature, especially when collaboration between actors is required for an efficient management of the supply chain, which is the case when uncertainties are present. In this paper, we propose a method based on fuzzy modelling allowing a customer to choose a plan taking into account the uncertainty on his requirements when he works in MTO-ATO while his supplier is in MTS

Fuzzy uncertainty modelling for project planning; application to helicopter maintenance

Maintenance is an activity of growing interest specially for critical systems. Particularly, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance center is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans at short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multiresource, multi-project planning (respectively Rough Cut Capacity Planning and Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on tactical level considering uncertainties in macro-tasks work contents, and modelling the continuous workload on operational level considering uncertainties in tasks durations. We model uncertainties by a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy RoughCut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP).We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster which aims at building a center for civil helicopter maintenance

Robust Production Plan with Periodic Order Quantity under Uncertain Cumulative Demands

International audienceIn 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

Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

"This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). 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Integration of the supplier capacity for choosing the less risked schedule within an uncertain environment

This communication addresses the integration of the supplier capacity in the procurement planning process of a customer within a supply chain. Since this supply chain evolves within an uncertain environment, uncertainties may be due to an ill-known demand (prevision) and to the customer production system (scraps, imprecise processing time...). Thus, we propose a collaborative process where the customer aims at taking the less risked decision. The integration of the supplier capacity in the gross requirement enables to assess the risk of back order so that the feasibility of the requirement plan. It then enables the customer to make the decisions which limit this risk

Operations research models and methods for safety stock determination: A review

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project no. 39479, Funding reference: POCI-01-0247-FEDER-39479]

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications