Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

After sales supply chain risk management.

Lean supply chains with cost optimized production and logistics processes in the automotive industry have become a benchmark for other industries. Short delivery times, low inventories and high availability are parameters which assume a robust supply chain. In industrial practice we see, however, that in the After Sales business particularly related to the supply of automotive spare parts, that there are always unforeseen delays in delivery. In order to avoid service level losses on the focal firm level due to missing parts it is necessary to understand the risk structure on the supplier side. For this reason, a risk model for the After Sales inbound SC is developed through this work. Based on an extensive analysis of delivery data a central risk size was derived. Comprehensively researched SC risks are supplemented by After Sales specific risks derived through an empirical supplier survey. A reference network, which is methodologically based on the Bayesian theorem, to control the dynamic relationships was developed. The developed risk model allows for the identification of proactive and reactive measures by top-down and bottom-up analyzes to make lean supply chains for after sales requirements in the best cases robust and resilient. A big advantage of the developed model is not only the ability to quantify the cause and effect of supply chain risks but also to describe the constantly changing risk environment of the supply chain through continuous belief updates within the model. The risk analysis in the developed model potentially reduces the delivery delay of spare parts by 65 percent and diminishes the buffer stock value by 50 percent. To achieve such improvements in the real world organizations must be able to implement measures in explicit SC risk clusters for sustainable supply chain performance and inventory management. Improvements in the internal supplier processes, due to risks like prioritized series supply, or inappropriate after sales supply strategies are necessary. Utilizing the developed After Sales Risk Management Model (ASRIM) organizations will be able to implement proactive risk mitigation strategies, facilitating agile SC performance, while simultaneously reducing buffer stocks

Balancing Demand and Supply in Complex Manufacturing Operations: Tactical-Level Planning Processes

By balancing medium-term demand and supply, tactical planning enables manufacturing firms to realize strategic, long-term business objectives. However, such balancing in engineer-to-order (ETO) and configured-to-order (CTO) operations, due to the constant pressure of substantial complexity (e.g., volatility, uncertainty, and ambiguity), induces frequent swings between over- and undercapacity and thus considerable financial losses. Manufacturers respond to such complexity by using planning processes that address the business’s needs and risks at various medium-term horizons, ranging from 3 months to 3 years. Because the importance of decision-making increases exponentially as the horizon shrinks, understanding the interaction between complexity and demand-supply balancing requires extending findings reported in the literature on operations and supply chain planning and control. Therefore, this thesis addresses complexity’s impact on planning medium-term demand-supply balancing on three horizons: the strategic– tactical interface, the tactical level, and the tactical–operational interface.To explore complexity’s impact on demand–supply balancing in planning processes, the thesis draws on five studies, the first two of which addressed customer order fulfillment in ETO operations. Whereas Study I, an in-depth single-case study, examined relevant tactical-level decisions, planning activities, and their interface with the complexity affecting demand–supply balancing at the strategic–tactical interface, Study II, an in-depth multiple-case study, revealed the cross-functional mechanisms of integration affecting those decisions and activities and their impact on complexity. Next, Study III, also an in-depth multiple-case study, investigated areas of uncertainty, information-processing needs (IPNs), and information-processing mechanisms (IPMs) within sales and operations planning in ETO operations. By contrast, Studies IV and V addressed material delivery schedules (MDSs) in CTO operations; whereas Study IV, another in-depth multiple-case study, identified complexity interactions causing MDS instability at the tactical–operational interface, Study V, a case study, quantitatively explained how several factors affect MDS instability.Compiling six papers based on those five studies, the thesis contributes to theory and practice by extending knowledge about relationships between complexity and demand–supply balancing within a medium-term horizon. Its theoretical contributions, in building upon and supporting the limited knowledge on tactical planning in complex manufacturing operations, consist of a detailed tactical-level planning framework, identifying IPNs generated by uncertainty, pinpointing causal and moderating factors of MDS instability, and balancing complexity-reducing and complexity-absorbing strategies, cross-functional integrative mechanisms, IPMs, and dimensions of planning process quality. Meanwhile, its practical contributions consist of concise yet holistic descriptions of relationships between complexity in context and in demand– supply balancing. Manufacturers can readily capitalize on those descriptions to develop and implement context-appropriate tactical-level planning processes that enable efficient, informed, and effective decision-making

Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment

Electric vehicles (EVs) have received massive consideration in the automotive industries due to their improved performance, efficiency and capability to minimize global warming and carbon emission impacts. The utilization of EVs has several potential benefits, such as increased use of renewable energy, less dependency on fossil-fuel-based power generations and energy-storage capability. Although EVs can significantly mitigate global carbon emissions, it is challenging to maintain power balance during charging on-peak hours. Thus, it mandates a comprehensive impact analysis of high-level electric vehicle penetration in utility grids. This paper investigates the impacts of large-scale EV penetration on low voltage distribution, considering the charging time, charging method and characteristics. Several charging scenarios are considered for EVs’ integration into the utility grid regarding power demand, voltage profile, power quality and system adequacy. A lookup-table-based charging approach for EVs is proposed for impact analysis, while considering a large-scale integration. It is observed that the bus voltage and line current are affected during high-level charging and discharging of the EVs. The residential grid voltage sag increases by about 1.96% to 1.77%, 2.21%, 1.96 to 1.521% and 1.93% in four EV-charging profiles, respectively. The finding of this work can be adopted in designing optimal charging/discharging of EVs to minimize the impacts on bus voltage and line current

Simulation of an automotive supply chain using big data

Supply Chains (SCs) are dynamic and complex networks that are exposed to disruption, which have consequences hard to quantify. Thus, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, the several systems used in SCs generate data with increasingly high volumes and velocities, paving the way for the development of simulation models in Big Data contexts. Hence, contrarily to traditional simulation approaches, which use statistical distributions to model specific SC problems, this paper proposed a Decision-Support System, supported by a Big Data Warehouse (BDW) and a simulation model. The first stores and integrates data from multiple sources and the second reproduces movements of materials and information from such data, while it also allows risk scenarios to be analyzed. The obtained results show the model being used to reproduce the historical data stored in the BDW and to assess the impact of events triggered during runtime to disrupt suppliers in a geographical range. This paper also analyzes the volume of data that was managed, hoping to serve as a milestone for future SC simulation studies in Big Data contexts. Further conclusions and future work are also discussed.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Selecting cost-minimal delivery profiles and assessing the impact on cost and delivery schedule stability in area forwarding inbound logistics networks in the automotive industry

Automobilhersteller in Europa nutzen Gebietsspeditionsnetzwerke, um Frachtkosten im Stückgutverkehr einzusparen. Die Materialflüsse im Logistiknetzwerk werden durch vom Automobilhersteller erzeuge Lieferabrufe gesteuert. Verschiedene Ansätze zur Erzeugung von Lieferabrufen können eingesetzt werden, um die Ziele Kostenreduktion und Lieferabrufstabilität auszubalancieren. Ein vielversprechender Ansatz zur Optimierung der Lieferabrufe, der in der Literatur diskutiert und in der Praxis angewandt wird, sind Anlieferprofile. Geschickt ausgewählte Anlieferprofile können sowohl die Logistikkosten reduzieren als auch die Stabilität der Lieferabrufe erhöhen. In dieser Arbeit wird eine Methode zur Auswahl kostenoptimaler Anlieferprofile zur Steuerung von Gebietsspeditionsnetzwerken in der Automobilindustrie vorgestellt und hinsichtlich des Einflusses auf die Logistikkosten und die Stabilität der Lieferabrufe bei einem Einsatz im rollierenden Planungshorizont im Rahmen einer Fallstudie untersucht. Die wesentlichen Aspekte der Problemstellung werden im Kontext der Planungsprozesse in der Automobilindustrie durchleuchtet, wobei ein besonderer Fokus auf der operativen Bestellmengenplanung liegt. Ein auf einem Dekompositionsverfahren basierender Lösungsalgorithmus für das Planungsproblem wird vorgestellt. Neben einem gemischt-ganzzahligen Modell werden zwei heuristische Lösungsansätze für das Problem vorgestellt. Eine Erweiterung von Modell und Lösungsalgorithmen für den Einsatz in einem zweistufigen stochastischen Verfahren werden präsentiert. Eine Fallstudie mit praxisnahen Daten wird genutzt, um den Einfluss auf die Logistikkosten und die Stabilität der Lieferabrufe darzustellen und einen Vergleich zu neuesten algorithmischen Ansätzen zu ziehen.Automotive manufacturers in Europe use area forwarding based inbound logistic networks to obtain cost advantages in the inbound logistics section. Delivery schedules that are frequently generated by the automotive manufacturers are used to control the material flow in the area forwarding networks. Different delivery schedule generation approaches can be used to balance between the objectives of cost reduction and delivery schedule stability. A promising approach discussed in literature and successfully applied in retailer business are delivery profiles. When chosen wisely, this control rule is said to reduce both logistic cost and schedule instability. In this thesis, a method to select cost-minimal delivery profiles under the consideration of area forwarding networks in the automotive industry is presented and its impact on both cost and delivery schedule stability in a rolling horizon environment is assessed in a case study. To identify the aspects of the problem setting that have to be considered, a description of the planning processes in the automotive industry and the operational order lot sizing in particular is given. An appropriate solution algorithm which uses a decomposition technique to overcome runtime issues is developed. A mixed integer formulation and heuristic algorithms, a sequential algorithm and a genetic algorithm that can be used in the solution algorithm are presented. The model and the solution algorithms are then extended to a two-stage stochastic program in order to consider demand uncertainties in the solution process. A large scale industry case study is then used to assess the impact on both cost and delivery schedules. A comparison with state-of-the-art algorithmic delivery schedule generation approaches is conducted to enlighten the pros and cons of both approaches.Tag der Verteidigung: 13.06.2013Paderborn, Univ., Diss., 201