Strategic Supply Chain Design Research Articles

Bi-criteria allocation of customers to warehouses using a particle swarm optimisation

Success of a firm largely depends on its supply chain decisions. Minimising cost and maximising service level are the contending objectives in strategic supply chain design. Researchers have been attempting to evolve an effective and efficient solution procedure for multi-objective supply chain models. In this paper, a multi-objective particle swarm optimisation (MOPSO) algorithm is proposed for bi-criteria allocation of customers to multiple warehouses. The proposed algorithm is developed in Matlab platform and tested. The results show that the proposed approach is capable of producing high-quality Pareto-optimal solutions.

Incorporating uncertainty into a supplier selection problem

Abstract Supplier selection is an important strategic supply chain design decision. Incorporating uncertainty of demand and supplier capacity into the optimization model results in a robust selection of suppliers. A two-stage stochastic programming (SP) model and a chance-constrained programming (CCP) model are developed to determine a minimal set of suppliers and optimal order quantities with consideration of business volume discounts. Both models include several objectives and strive to balance a small number of suppliers with the risk of not being able to meet demand. The SP model is scenario-based and uses penalty coefficients whereas the CCP model assumes a probability distribution and constrains the probability of not meeting demand. Both formulations improve on a deterministic mixed integer linear program and give the decision maker a more complete picture of tradeoffs between cost, system reliability and other factors. We present Pareto-optimal solutions for a sample problem to demonstrate the benefits of the SP and CCP models. In order to describe the tradeoffs between costs and risks in an analytical form, we use multi-parametric programming techniques to more completely analyze the alternative Pareto-optimal supplier selection solutions in the CCP model. This analysis gives insights into the robustness of the solutions with respect to number of suppliers, costs and probability of not meeting demand.

Successful partnerships for the forest biorefinery

Industrial BiotechnologyVol. 4, No. 4 FEATURE COMMENTARYSuccessful partnerships for the forest biorefineryMatty Janssen, Virginie Chambost, and Paul R. StuartMatty JanssenSearch for more papers by this author, Virginie ChambostSearch for more papers by this author, and Paul R. StuartSearch for more papers by this authorPublished Online:22 Dec 2008https://doi.org/10.1089/ind.2008.4.352AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetailsCited byThe biorefinery transition in the European pulp and paper industry – A three-phase Delphi study including a SWOT-AHP analysisForest Policy and Economics, Vol. 110Cross-sector collaboration in the forest products industry: a review of the literatureCanadian Journal of Forest Research, Vol. 48, No. 11Systematic assessment of triticale-based biorefinery strategies: investment decisions for sustainable biorefinery business models31 August 2018 | Biofuels, Bioproducts and Biorefining, Vol. 12Continuous feeding of spent ammonium sulphite liquor improves the production and saccharification performance of cellulase by Penicillium oxalicumBioresource Technology, Vol. 245Biorefinery strategies: exploring approaches to developing forest-based biorefinery activities in British Columbia and Ontario, Canada12 August 2016 | Technology Analysis & Strategic Management, Vol. 29, No. 5Characteristic properties of organosolv lignin/polylactide copolymers10 February 2017 | Journal of Wood Chemistry and Technology, Vol. 37, No. 3Technological innovation systems for biorefineries: a review of the literature15 March 2017 | Biofuels, Bioproducts and Biorefining, Vol. 11, No. 3Implementing the Bioeconomy on the Ground: An International Overview14 April 2017Forest bioeconomy in Ontario – A policy discussionThe Forestry Chronicle, Vol. 93, No. 01Life cycle assessment of bio-based sodium polyacrylate production from pulp mill side streams: case study of thermo-mechanical and sulfite pulp millsJournal of Cleaner Production, Vol. 131Reasons to apply operability analysis in the design of integrated biorefineries24 December 2014 | Biofuels, Bioproducts and Biorefining, Vol. 9, No. 2Methodology for biorefinery portfolio assessment using supply-chain fundamentals of bioproducts23 April 2014 | Biofuels, Bioproducts and Biorefining, Vol. 8, No. 5Scenario-based strategic supply chain design and analysis for the forest biorefinery using an operational supply chain modelInternational Journal of Production Economics, Vol. 144, No. 2Biorefinery Opportunities in the Pulp and Paper IndustryForest biorefineries – A business opportunity for the Finnish forest clusterJournal of Cleaner Production, Vol. 19, No. 16 Volume 4Issue 4Dec 2008 InformationCopyright 2008, Mary Ann Liebert, Inc.To cite this article:Matty Janssen, Virginie Chambost, and Paul R. Stuart.Successful partnerships for the forest biorefinery.Industrial Biotechnology.Dec 2008.352-362.http://doi.org/10.1089/ind.2008.4.352Published in Volume: 4 Issue 4: December 22, 2008PDF download

The product-relationship-matrix as framework for strategic supply chain design based on operations theory

The strategic design of supply chains can be analysed building on recent theory development in operations management. Schmenner and Swink [1998. On theory in operations management. Journal of Operations Management 17 (1), 97–113] have identified the theory of performance frontiers and the theory of swift, even flow, which integrate various concepts from operations strategy and management. Transferring these theories to the supply chain level allows five strategic decision fields in supply chain management to be outlined: Products and Services, Partners and Partnerships, Plants and Stocks, Processes and Planning and Control. These decision fields form the backbone of the product-relationship-matrix, a conceptual framework for supply chain management. This is built by combining a life-cycle dimension with a partnership dimension. The framework conceptualises both the content and process of such decisions. This is illustrated by five case studies from different industrial sectors.

Integrated design of supply chain networks with three echelons, multiple commodities and technology selection

We consider a strategic supply chain design problem with three echelons, multiple commodities and technology selection. We model the problem as a tri-echelon, capacitated facility location problem that decides on the location of plants and warehouses, their capacity and technology planning, the assignment of commodities to plants and the flow of commodities to warehouses and customer zones. We use a mixed-integer programming formulation strengthened by valid but redundant constraints and apply Lagrangean relaxation to decompose the problem by echelon. Lagrangean relaxation provides a lower bound that is calculated using an interior-point cutting plane method. Feasible solutions are generated using a primal heuristic that uses the solution of the subproblems. Unlike common practice in the literature, the decomposition does not aim at getting easy subproblems, but rather at getting subproblems that preserve most of the characteristics of the original problem. Not only does this provide a sharp lower bound but also leads to a simple and efficient primal heuristic. We can afford to have relatively difficult subproblems because the interior-point cutting plane method used to solve the Lagrangean dual makes clever and selective choices of the Lagrangean multipliers leading to fewer calls to the subproblems. Computational results indicate the efficiency of the approach in providing a sharp bound and in generating feasible solutions that are of high quality.

A continuous DC programming approach to the strategic supply chain design problem from qualified partner set

We present a new continuous approach based on the DC (difference of convex functions) programming and DC algorithms (DCA) to the problem of supply chain design at the strategic level when production of a new market opportunity has to be launched among a set of qualified partners. A well known formulation of this problem is the mixed integer linear program. In this paper, we reformulate this problem as a DC program by using an exact penalty technique. The proposed algorithm is a combination of DCA and Branch and Bound scheme. It works in a continuous domain but provides mixed integer solutions. Numerical simulations on many empirical data sets show the efficiency of our approach with respect to the standard Branch and Bound algorithm.

CLOCKSPEED‐BASED STRATEGIES FOR SUPPLY CHAIN DESIGN

This paper discusses a framework for strategic supply chain design that rests on an assortment of conceptual approaches. These approaches include benchmarking fast‐evolving industries to posit principles of supply chain dynamics and integrating supply chain design into the concurrent processes of product and manufacturing system design. These approaches yield insights into sourcing strategy as well as implementation of concurrent engineering.