Strategic Design Decisions Research Articles

A Generalized Benders Decomposition based algorithm for an inventory location problem with stochastic inventory capacity constraints

This paper deals with an inventory location problem with order quantity and stochastic inventory capacity constraints, which aims to address strategic supply chain network design problems and is of a nonlinear, nonconvex mixed integer programming nature. The problem integrates strategic supply chain networks design decisions (i.e., warehouse location and customer assignment) with tactical inventory control decisions for each warehouse (i.e., order size and reorder point). A novel decomposition approach that deals with the nonconvex nature of the problem formulation is proposed and implemented, based on the Generalized Benders Decomposition. The proposed decomposition yields a Master Problem that addresses warehouses location and customer assignment decisions, and a set of underlying Subproblems (SPs) that deal with warehouse inventory control decisions. Based on this decomposition, nonlinearity of the original problem is captured by the SPs that are solved at optimality, while the Master Problem is a mixed integer linear programming problem. The master is solved using a commercial solver, the SPs are solved analytically by inspection, and cuts to be added into the Master Problem are obtained based on Lagrangian dual information. Optimal solutions were found for 160 instances in competitive times.

Process design decisions in community-based collaboration: implications for implementation and collateral social benefits

Collaborative decision-making processes are conducted widely in public affairs at various scales, from community to metropolitan, regional and national. They vary from highly formalized and prescribed processes, such as “regulatory negotiations”, to collaborative planning processes directed by urban planners or other public agency staff, to rather informal processes within community-based organizations. While substantial effort has been invested in identifying the benefits of collaboration, we ask; do the benefits materialize in each case? We researched one ad hoc community collaborative in Oregon, USA, widely considered a success in the aftermath of agreement. We were interested in the implementation of agreement provisions, as well as in any social benefits from the process. Relying on published documents, surveys and interviews one year after the process ended, and additional interviews and on-site observations five years later, we found that despite the initial enthusiasm and confidence in actions proposed in the final report and social relationships strengthened during the process, evidence of long-term success on these two dimensions was mixed. This case provides a cautionary note to process facilitators, urban planners, public administrators and stakeholder and citizen participants. Participants face many strategic and process design decisions. Among these are choices about institutional linkages, stakeholders, the decision rule, and clarity about the purpose and goal of the process. While the degree of “success” of any collaborative may not be our call as observers, we contend that implementation and social impacts are critical to assessing the value of these processes. Consequently, we propose that participants and facilitators alike should understand and consider appropriately the micro-decisions that can and do add to critical implications on these two important dimensions.

Cost and environmental trade-offs in supply chain network design and planning: the merit of a simulation-based approach

We develop a simulation-based optimization methodology for the joint design and planning of globalized supply chains (SCs) under minimization objectives for cost and CO2 emissions. The assumptions required for the analytical optimization of such SCs include deterministic lead times, no-backorder occurrences at central distribution facilities, and powers-of-two replenishments of inventories at the SC nodes. The paper aims to investigate whether and how will the decisions that stem from the analytical optimization process change when these assumptions are relaxed. The proposed methodology is employed in a realistic SC structure. The results reveal that (i) the optimal order cycles of the nodes of the SC are not necessarily powers of two, (ii) the central distribution facilities have the option to operate at lower service levels, (iii) the network’s cost and CO2 emissions under optimality are reduced by approximately 0.9%, and (iv) the strategic network design decisions align with those of the analytical solution.

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.

Developing Tacit Knowledge of Complex Systems: The Value of Early Empirical Inquiry in Healthcare Design

Every year, more than 200,000 patients in Canada acquire an infection while receiving healthcare and more than 8,000 die as a result (Public Health Agency of Canada, 2013). Infections can arise from the entry and multiplication of a microorganism in a person’s tissue (Public Health Ontario, 2012a, 2012b). As healthcare workers perform tasks, they circulate among patients, objects, and surfaces, thereby creating opportunities for transmitting microorganisms and possibly contributing to the spread of hospital-acquired infections. Newborns hospitalized in neonatal intensive care units are particularly vulnerable because of inherent risk factors such as low birth weight, underlying illness, undeveloped immune systems, and greater skin permeability. Other risk factors include poor staff-topatient ratios, crowded environments, and exposure to invasive devices (Public Health Ontario, 2012a, 2012b). Infection prevention and control has been the subject of much study in medical and epidemiological research and a variety of best practice guidelines have been developed to support healthcare workers and related stakeholders. Yet, despite the availability of information, managing healthcare-associated infections remains a challenge because the relevant explicit knowledge is not being adequately developed and mobilized as tacit knowledge for use the front lines. Some researchers have called for a human factors perspective to help address challenges in designing for infection prevention and control, but relatively few studies have been conducted to date. Researchers also suggest that empirical inquiry is needed to better inform the design process, and particularly the design of complex systems where attention to detailed processes and interactions can support the success of an intervention. A human factors approach can help designers develop a deeper understanding of work processes, technology considerations, as well as physiological, psychological, cultural, and organizational factors. The need is particularly pressing in low-resource healthcare environments where funds, time, and human resources may be scarce and strategic design decisions based on evidence are needed to support meaningful and effective changes. With this in mind, a human factors study was conducted in an existing neonatal intensive care unit to identify the influence of product and environment design on infection prevention and control and to inform recommendations for improvement. In this case study, we illustrate how the application of an empirical, methodical approach can help design professionals and stakeholders develop tacit knowledge of complex systems – knowledge that can be used to better inform design priorities, the design process, decision making, and the allocation of resources to help maximize improvements. “ ” To know that you do not know is the best. To think you know when you do not is a disease. Recognizing this disease as a disease is to be free of it.

Benders decomposition for a strategic network design problem under NAFTA local content requirements

Global production and sourcing strategies of multinational corporations are strongly influenced by the increasing number of Free Trade Agreements (FTAs). Based on the special local content requirements of the North American Free Trade Agreement (NAFTA) for automotive goods, the impact on strategic network design decisions is investigated. The presented model explicitly integrates the different NAFTA legal options to calculate the local content for automotive goods. Furthermore, it considers the possibility to underachieve the local content requirement and to pay penalty duties for NAFTA cross-border deliveries instead. Plant fixed costs have a significant impact on the local content fulfillment and have to be allocated in accordance with the actual plant utilization and the different local content calculation options. Due to the resulting non-linearity of the mixed-integer program, a solution algorithm based on Benders decomposition is presented. In addition, we introduce multiple Benders cuts to improve the efficiency and applicability to real-world planning problems. Compared to piecewise linearization approaches, the run-time can be improved significantly. In a numerical study, the impact of local content requirements on the strategic network design is shown and the different NAFTA options to calculate the local content for automotive goods are compared with each other. Furthermore, computational experiments are performed to evaluate the applicability and efficiency of Benders decomposition.

A guiding framework to maximise the power of the arts in medical education: a systematic review and metasynthesis.

A rich literature describes many innovative uses of the arts in professional education. However, arts-based teaching tends to be idiosyncratic, depending on the interests and enthusiasm of individual teachers, rather than on strategic design decisions. An overarching framework is needed to guide implementation of arts-based teaching in medical education. The objective of this study was to review and synthesise the literature on arts-based education and provide a conceptual model to guide design, evaluation and research of the use of the arts in medical education. A systematic literature review using the PubMed and ERIC databases. Search terms included humanism, art, music, literature, teaching, education, learning processes, pedagogy and curriculum. We selected empirical studies and conceptual articles about the use of creative arts, imagery and symbolism in the context of professional education. Data synthesis involved a qualitative content analysis of 49 included articles, identifying themes related to educational characteristics, processes and outcomes in arts-based education. Four common themes were identified describing (i) unique qualities of the arts that promote learning, (ii) particular ways learners engage with art, (iii) documented short- and long-term learning outcomes arising from arts-based teaching and (iv) specific pedagogical considerations for using the arts to teach in professional education contexts. The arts have unique qualities that can help create novel ways to engage learners. These novel ways of engagement can foster learners' ability to discover and create new meanings about a variety of topics, which in turn can lead to better medical practice. At each of these steps, specific actions by the teacher can enhance the potential for learners to move to the next step. The process can be enhanced when learners participate in the context of a group, and the group itself can undergo transformative change. Future work should focus on using this model to guide process design and outcome measurement in arts-based education.

Balancing design freedom and brand recognition in the evolution of automotive brand styling

Designers faced with the task of developing a new product model of a brand must balance several considerations. The design must be novel and express attributes important to the customers, while also recognizable as a representative of the brand. This balancing is left to the intuition of the designers, who must anticipate how customers will perceive the new design. Oftentimes, the design freedom used to meet a product attribute can compromise the recognition of the product as a member of the brand. In this paper, an experiment is conducted for measuring changes in ten styling attributes common to both design freedom and brand recognition for automotive designs from four brands, Audi, BMW, Cadillac, and Lexus, using customer responses to two- and three-dimensional vehicle designs created and presented interactively through a crowdsourced web application. Results show that while brand recognition is highly dependent on the manufacturer, two brands have strong negative relationship between design freedom and brand recognition, suggesting that these two manufacturers face a significant challenge when evolving their respective brand styling. This study is a first effort toward quantifying and predicting tradeoffs between design freedom and brand recognition, contributing to existing efforts that augment human intuition during strategic design decisions.

A Process to Analyze Strategic Design and Management Decisions Under Uncertainty in Complex Entrepreneurial Systems

This paper proposes a process to support strategic design and management of complex entrepreneurial systems under uncertainty. The process is designed to help designers and decisions-makers readily identify valuable opportunities for flexibility as a way to deal pro-actively with and improve strategically the lifecycle performance of the system as it is designed, deployed, and managed in operations. The process relies on the novel concept of a layered design structure matrix DSM, which provides gradually a systems-level representation of the enterprise system starting at an early stage, and gaining more details as more information becomes available about the system and its environment. The resulting layered DSM is used to identify performance-enhancing flexibility strategies in the design and management of the system. The flexibility, providing the right, but not the obligation, to change the system in the face of uncertainty provides contingencies to minimize exposure to downside risks, and to enable the system to capture upside opportunities-thereby improving expected lifecycle performance. The process is applied as a demonstration to the analysis of a startup system in the sector of mobility on-demand transportation. Strategic-level opportunities for flexibility are identified systematically, and real options analysis is used to demonstrate quantitatively lifecycle performance improvement as compared to baseline design concepts.

Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming

Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study.

Defining user experience goals to guide the design of industrial systems

The key prerequisite for experience-driven design is to define what experience to design for. User experience (UX) goals concretise the intended experience. Based on our own case studies from industrial environments and a literature study, we propose five different approaches to acquiring insight and inspiration for UX goal setting: Brand, Theory, Empathy, Technology, and Vision. Each approach brings in a different viewpoint, thus supporting the multidisciplinary character of UX. The Brand approach ensures that the UX goals are in line with the company's brand promise. The Theory approach utilises the available scientific knowledge of human behaviour. The Empathy approach focuses on knowing the actual users and stepping into their shoes. The Technology approach considers the new technologies that are being introduced and their positive or negative influence on UX. Finally, the Vision approach focuses on renewal, introducing new kinds of UXs. In the design of industrial systems, several stakeholders are involved and they should share common design goals. Using the different UX goal-setting approaches together brings in the viewpoints of different stakeholders, thus committing them to UX goal setting and emphasising UX as a strategic design decision.

Global Supply Chain Network Design Incorporating Disruption Risk

Supply chain networks have expanded globally in today's business environment due to cost efficiencies, advanced technology, and market growth. This expansion makes the supply chains more vulnerable to disruption risks in different countries. A disruption in one country can cause serious global impacts. In this paper, the authors formulate a multi-criteria optimization model for supporting strategic supply chain network design decisions. The model considers disruption risk of supply chain components (i.e., facilities and transportation links) as well as profit and customer responsiveness as conflicting criteria. This consideration is important since disruption at any supply chain component may lead to the disruption of the entire supply chain network. They apply goal programming (GP) techniques to handle multiple and conflicting network design objectives. The authors present a numerical example to illustrate how to incorporate disruption risk when making strategic supply chain decisions. The results demonstrate how supply chain network designs that over emphasize profit may include inexpensive supply chain components with high disruption risk. Therefore, more attention must be paid to managing potential disruptions and designing supply chain networks that balance profit and risk. They discuss tradeoffs among multiple design solutions and identify opportunities for future research.

A simulation-based decision support system for industrial field service network planning

Technical field services for industrial machinery and equipment have become increasingly important for original equipment manufacturers. To deliver services to their customers, companies have to build up new core competencies and infrastructure, a challenge due to the high complexity and dynamics of this business. To assist companies in the strategic design of their network and the planning of resources for delivering industrial field services, we present a model-driven decision support system that uses discrete event simulation to support decision makers in various aspects of strategic design and tactical planning. The benefits of the decision support system include the creation of a generic framework that makes it possible to create simulation models of different field service networks for multiple purposes. Specifically, the system can be used to support various tactical planning and strategic design decisions while keeping investments low in terms of time consumption and money spending. In addition, the paper closes an identified gap involving a lack of decision support for the management of field service networks. An application of the decision support system in an exemplary case is used to illustrate potential applications and benefits.

A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study

This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently.

A Bayesian Method for Predicting Future Customer Need Distributions

This article presents a Bayesian method to predict future customer need distributions based on forecasts of external factors and forecast accuracies. It is critically important to accurately predict future customer needs when strategically deciding what product to develop. However, prediction is challenging because customer preference changes as external factors that influence customer preference change over time. Consequently, customer needs at the time of product design decisions and at the time of new product market introductions may be significantly different. While external factors can be forecasted, forecasts are not necessarily accurate. This paper outlines a Bayesian approach to incorporate accuracies of forecasts in predicting future customer need distributions. An illustrative example demonstrates impacts of forecast accuracies on customer need distributions and on a strategic design decision. In this example, the customer need is ‘fuel-efficient vehicle’, the external factor is the gasoline price index (defined as gasoline price divided by consumer price index), and the strategic design decision is the selection of the next vehicle from gasoline-powered, hybrid, and electric. Sensitivity analysis is performed in order to study how a strategic design decision changes with forecasts and forecast accuracies.

The Legacy of Office Landscaping:

In this text I relate the Rolex Learning Centre (RLC) at EPFL in Lausanne by Japanese architects SANAA to the historic example of the Bürolandschaft [office landscape] invented by German management consultants Eberhard and Wolfgang Schnelle in the late 1950s. In doing so I posit the RLC as the most advanced and most elaborate contemporary example of a workplace architecture that mirrors the post-Fordist knowledge economy. The text introduces the world’s first Bürolandschaft Buch und Ton for the Bertelsmann corporation: the design’s political aspirations and the economic context in Post-War Germany, its cybernetic design method, as well as strategic design decisions. I then discuss the interior economy of EPFL’s new architectural icon and how its interior economy differs from the political and organisational aspiration of its post-war Europe inventors.

Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach

Market players, such as competing manufacturing firms and retail channels, can significantly influence the demand and profit of a new product. Existing methods in design for market systems use game theoretic models that can maximize a firm’s profit with respect to the product design and price variables given the Nash equilibrium of the market system. However, in the design for uncertain market systems, there is seldom equilibrium with players having fixed strategies in a given time period. In this paper, we propose an agent based approach for design for market systems that accounts for learning behaviors of the market players under uncertainty. By learning behaviors we mean that market players gradually, over time, learn to play with better strategies based on action–reaction behaviors of other players. We model a market system with agents representing competing manufacturers and retailers who possess learning capabilities and based on some prespecified rules are able to react and make decisions on the product design and pricing. The proposed agent based approach provides strategic design and pricing decisions for a manufacturing firm in response to possible reactions from market players in the short and long term horizons. Our example results show that the proposed approach can produce competitive strategies for the firm by simulating market players’ learning behaviors when they react only by setting prices, as compared to a game theoretic approach. Furthermore, it can yield profitable product design decisions and competitive strategies when competing firms react by changing design variables in the short term—case for which no previous method in design for market systems has been reported.

A possibilistic programming approach for closed-loop supply chain network design under uncertainty

The design of closed-loop supply chain networks has attracted more attention in recent years according to business and environmental factors. The significance of accounting for uncertainty and risk in such networks spurs an interest to develop appropriate decision making tools to cope with uncertain and imprecise parameters in closed-loop supply chain network design problems. This paper proposes a bi-objective possibilistic mixed integer programming model to deal with such issues. The proposed model integrates the network design decisions in both forward and reverse supply chain networks, and also incorporates the strategic network design decisions along with tactical material flow ones to avoid the sub-optimalities led from separated design in both parts. To solve the proposed possibilistic optimization model, an interactive fuzzy solution approach is developed by combining a number of efficient solution approaches from the recent literature. Numerical experiments are conducted to demonstrate the significance and applicability of the developed possibilistic model as well as the usefulness of the proposed solution approach.

A Methodology for Supply Chain Design An Application to Auto-part Industry

A supply chain is an alliance of independent business players, such as supplier, manufacturing, and distribution processes that perform the critical functions in order reach a business objective delivering value to customer and companies. Strategic design decisions as number of supply chain levels, numbers of nodes at each level, distance between nodes and definition of operations for each node contribute for better performance of the production and delivery of a variety of products to the customers. This paper presents an auto-part automotive supply chain redesign case study that has as objective to delivery modules to the assemble plant at lowest cost, high quality, and short lead time. It starts with a review of the legal procedures as the fees and taxes of the concerned States and after an analysis of logistics costs, values of labor, agility of the entire system and competitiveness, essential for the automobile company in the current scenario, are considered. At the end indexes of performance of the actual and proposed supply chain are compared to show the advantages of a new design.

GIS-BASED RESTORATION SYSTEM FOR HISTORIC TIMBER BUILDINGS USING RFID TECHNOLOGY/GIS PAREMTA ISTORINIŲ RĄSTINIŲ PASTATŲ RESTAURAVIMO SISTEMA TAIKANT RFID TECHNOLOGIJĄ

Using geographic information system (GIS) and other established technologies, this paper develops an information management system that integrates graphic and non‐graphic data to enhance the effectiveness of historic building restoration work in Taiwan. Radio frequency identification (RFID) is used to permit real‐time tracking of assembly components and thus enhance restoration management efficiency. Restoration data stored in the memory of RFID tags can be changed and updated according to the needs of a specific restoration phase. The use of RFID handheld readers both improves data collection efficiency and system mobility. In the developed system, timber structure restoration data and drawings in the GIS can be accessed online immediately for use in the restoration process and to help advise strategic design, construction, and management decisions. Data of each timber element, including restoration sequence, restoration contractors, supervisors, evaluated strength capacity etc can be retrieved remotely through a handheld PDA reader‐writer used onsite. Data of component construction sequence during disassembly or re‐assembly, critical to project success, can be transmitted in a synchronized manner to the developed host system to achieve a level of accuracy and reliability significantly greater than that typically achieved using traditional reconstruction management approaches. Santrauka Taikant geografinę informacinę sistemą (GIS) ir kitas technologijas, straipsnyje plėtojama informacinė valdymo sistema, integruojanti grafinius ir negrafinius duomenis, didinanti istorinių pastatų restauravimo efektyvumą Taivane. Sudedamiesiems komponentams stebėti realiajame laike taikomas radijo dažnių identifikavimas (RFID), taip didinant restauravimo valdymo efektyvumą. Saugomi RFID ženklių atmintyje restauravimo duomenys gali būti keičiami ir atnaujinami pagal specifinės restauravimo fazės reikmes. Taikant RFID delninius skaitytuvus gerėja tiek duomenų kaupimo efektyvumas, tiek sistemos mobilumas. Sukurtoje sistemoje rąstų statinio restauravimo duomenys ir piešiniai (brėžiniai) geografinėje informacinėje sistemoje gali būti iš karto pasiekiami kompiuteriniu tinklu ir taikomi restauravimo procese bei padėti strategiškai projektuojant, konstruojant, taip pat ir valdymo sprendimams priimti. Kiekvieno rąsto elemento duomenys, restauravimo seka, restauravimo vykdytojai, prižiūrėtojai, stiprybių galimybės ir kita gali būti pasiekiami nuotoliniu būdu su delniniu PDA skaitytuvu-rašytuvu darbo vietoje. Komponentų konstravimo eilės tvarkos išmontuojant ar sumontuojant duomenys gali būti sinchroniškai perduodami sukurtai valdančiai sistemai siekiant daug didesnio tikslumo ir patikimumo lygio, nei įprastai pasiekiama taikant tradicinius restauravimo valdymo taikymus.