Bergische Universiẗat, Bachelorarbeit--Wuppertal, 2006.

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The Planning of Supply Chain Management System for Industrial Materials Intermediary

Implementing Build to Order (BTO) strategies instead of usual Build to Stock have the potential of sorting out some individual problems of the automotive industry (e.g. high stock of finished goods). BTO reconnects the customer to the Supply Chain by focusing on him/her and making him/her to the starting point of the production process. Consequently some objectives like stock reduction as well as an increase of market shares and profits are linked with BTO. However, some requirements and preconditions have to be fulfilled to realize BTO strategies. First of all flexibility across processes, products and volume have to be assured. Therefore actions and strategies like Direct Order Booking, integration of suppliers and 3rd party logistics, platforms and modular design could be adopted. Furthermore, it's necessary to take measures to reduce the Order to Delivery (OTD) time generally and the time of information flow, which currently accounts for more then 85% of the delay in the production and planning process, specially. Promoting IT along the whole Supply Chain, might be the key to success in this case. Finally it should be noted that BTO could not be the overall solution for the whole automotive industry. It will only be applicable if some circumstances are satisfied. Therefore the premium segment (customers going for individuality and prestige) as well as the European market (customers with relatively high willingness to wait) are the possible fields of implementing BTO in the near future. (author's abstract) / Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Management

This thesis proposes a novel synergy of the classical scenario and robust approaches used for strategic supply chain optimization under uncertainty. Two novel formulations, namely the naïve robust scenario formulation and the affinely adjustable robust scenario formulation, are developed, which can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded by the infinity-norm. The two formulations are applied to a classical farm planning problem and an energy and bioproduct supply chain problem. The case study results demonstrate that, compared to the scenario formulation, the proposed formulations can achieve the optimal expected economic performance with smaller number of scenarios, and they can correctly indicate the feasibility of a problem. The results also show that the affinely adjustable robust scenario formulation can better address uncertainties than the naïve robust scenario formulation. Next, a strategic optimization problem for an industrial chemical supply chain from DuPont was studied. The supply chain involves one materials warehouse, five manufacturing plants, five regional product warehouses and five market locations. Each manufacturing plant produces up to 23 grades of final products from 55 grades of primary raw materials. The goal of the strategic optimization is to determine the capacities of the five plants to maximize the total profits of the supply chain system while satisfying uncertain customer demands at the different market locations. A mathematical model is developed to relate the material and product flows in the supply chain, based on which the classical scenario approach and the affinely adjustable robust scenario formulation were developed to address the uncertainty in the demands. The case study results show the advantages of the affinely robust scenario formulation over the scenario formulation. Using the affinely adjustable robust scenario formulation often results in problems with very large sizes, which cannot be solved by regular optimization solvers efficiently. In order to exploit the decomposable structure of the formulation, Dantzig-Wolfe decomposition is studied in the thesis. Two approaches to implement Dantzig-Wolfe decomposition are developed, and both approaches involve the solution of a sequence of linear programming (LP) and mixed-integer linear programming (MILP) subproblems. The computational study of the industrial chemical supply chain shows that a combination of the two Dantzig-Wolfe approaches can achieve an optimal or a near-optimal solution much more quickly than a state-of-the-art commercial LP/MILP solver, and the computational advantage increases with the increase of number of scenarios involved in the problem. / Thesis (Master, Chemical Engineering) -- Queen's University, 2014-03-24 20:39:42.761

Den växande e-livsmedelshandelns Supply Chains har studerats genom hållbarhetsglasögon där syftet var att ”[…] ge en fördjupad förståelse över hur svenska elivsmedelsaktörerarbetar med frågor rörande Sustainable Supply Chain Management”. Syftet fullgjordes genom en kvalitativ studie av tre organisationer, i vilka totalt 9 beslutsfattare intervjuades enligt ett semi-strukturerat intervjuförfarande. De data som samlades rörande e-livsmedelsaktörernas arbete med SSCM har i sin tur analyserats med hänvisning till en i förväg fastställd teoretisk referensram. Vår studie avslöjar att två av de svenska e-livsmedelsaktörerna är exemplariska och framstående med att införliva hållbarhet i sin Supply Chain. De engagerar sig aktivt i de initiativ för Sustainable Supply Chain Management som i litteraturen identifierades som nödvändiga för en verkligt hållbar Supply Chain. De har en allomfattande och övergripande strategi för att minimera den sociala risken, reducera resursutnyttjande och förorenande biprodukter i hela Supply Chain samt för att förbättra organisatoriskt inbäddade ohållbara vanor. Det tredje företaget saknar emellertid kapacitet och marknadsinflytande att engagera sig i SSCM. Studien åskådliggör även att en klimatneutral e-livsmedelsdistribution osannolikt förverkligas inom en snar framtid, såvida inte marknaden interveneras och företagen tvingas internalisera kostnaderna för utsläpp.

This thesis presents an optimization model which helps retailers to reduce product costs by taking advantage of parts commonality in manufacturing and production areas, when selling similar units with uncertainty in demands. The concept of component commonality can be often found in the assemble-to-order system, which is the foremost concept used by prominent manufacturing companies in the global market. The method developed uses genetic algorithm (GA) to solve real world optimization problems that contain integer values for parts and finished items, and uncertain information. Numerical examples are solved using generated stochastic scenarios to show the impact of uncertainty on solutions. This impact is verified using two important criteria, Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS). The obtained solutions present significant monetary benefits for the manufacturer illustrating the importance of the model presented here for retailers.

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Zugl.: Lüneburg, Univ., Diss., 2007

Zugl.: Köln, Universiẗat, Diss., 2008.