Framework For Supply Chain Design Research Articles

A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea

Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn (Zea mays) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.

Towards Regenerative Supply Networks: A design framework proposal

Anthropocentrism and disciplinary research have not led to effective solutions to sustainability problems. A shift is required towards biocentric, transdisciplinarity-based solutions, to mitigate rebound effects and unintended, negative effects. In the field of Sustainable Supply Network Design, such shift implies on developing resilient solutions that maximize environmental benefits instead of minimizing environmental impacts. Recent researches have showed progress in this direction proposing more integrative approaches, but a framework to design supply networks with the purpose of environmental regeneration is yet to be proposed. This research aims to fill this gap, merging concepts from Regenerative Design, Transdisciplinary Research, Systems Thinking, Social Sciences and Design Sciences. Such framework is regarded as an artefact, and Design Science Research Methodology used for its development: a main problem is identified, objectives for a solution are drawn and the framework is designed. First, the Regenerative Supply Network Design is defined, to guide the elaboration of the Regenerative Supply Chain Design framework, consisting of six steps: (i) description of the network surroundings and identification of a regenerative purpose; (ii) redesign of outputs (iii) network conceptualization, (iv) optimize performance, (v) choose configuration, and (vi) implementation. Among the implications of this research are that (i) practitioners basing their design process in this framework are effectively shifting from anthropocentrism to biocentrism with a clear, defined purpose of environmental restoration and (ii), that the supply networks evolve in the integration with the environment, enhancing eco-systems resilience.

Economic and Environmental Analysis for Advancing Sustainable Management of Livestock Waste: A Wisconsin Case Study.

Livestock waste may cause some air quality degradation from ammonia and methane emissions, soil quality detriment due to in-excess nutrients and acidification, and water pollution issues resulting from nutrient and pathogens runoff to water bodies, which leads to eutrophication, algal blooms, and hypoxia. Despite the significant environmental benefits by performing pollution management of these organic materials, the recovery of value-added products from livestock waste is not a current practice due to the high investment costs required and to the low market values being offered for the products that are recovered. Therefore, we present a supply chain design framework to conduct simultaneous economic and environmental analysis of post-livestock organic material to value-added products. The proposed framework captures techno-economic and logistical issues and can accommodate diverse types of policy incentives obtained at federal and state levels, allowing stakeholders to conduct systematic studies on the effect of incentives on economic and environmental viability of different technologies. We apply the framework to a case study for dairy farms in the State of Wisconsin (U.S.). The framework reveals that, from a purely economic perspective, products recovered from dairy waste are not competitive at current market prices. We also find that incorporating current and potential U.S. government incentives in the form of Renewable Identification Numbers (RINs) and phosphorus credits can achieve economic viability of the recovery of liquefied biomethane and nutrient-rich products. On the other hand, current incentives for electricity generation (Renewable Energy Credits or RECs) would not achieve economic viability. The analysis also reveals that the best strategy to manage waste is to synergize the deployment of technologies that conduct simultaneous recovery of liquefied biomethane and nutrients.

Enhanced models and improved solution for competitive biofuel supply chain design under land use constraints

This paper studies the effect of government regulation on farmland use to balance food and energy production in a competitive biofuel supply chain design framework. We propose a Stackelberg–Nash game model with a direct land-use constraint that captures farmland, food, and fuel market equilibrium. To provide farmers with incentives to comply with the land-use regulation, we implement the land-use constraint through a cap-and-trade mechanism which we show attains equivalent land-use patterns. We further prove the existence of optimal solutions of the two equivalent discretely constrained mathematical program with equilibrium constraints (DC-MPEC) models. Two adaptive “relax-and-tighten” schemes, integrality and Lagrangian duality based relaxation, are proposed to handle the binary variables in a mixed integer formulation of the models. The proposed methodology is tested in a case study for the State of Illinois. The computational results demonstrate the superiority of our customized algorithms to publicly available solvers for solving problems with realistic sizes. Finally, the efficiency of the cap-and-trade mechanism is demonstrated through comparisons of scenarios with and without regulation as well as a sensitivity analysis on the cap-and-trade level. The cap-and-trade policy is found to effectively reduce biomass farmland use and profits of the biofuel industry. It may have different impacts on social welfare depending on problem settings and market parameters. Cap-and-trade levels also affect system behavior and thus should be carefully selected.

Integrated Modeling Framework for Supply Chain Design Considering Multiproduct Production Facilities

Significant benefits can be obtained if the interactions among different decision levels are appropriately addressed and simultaneously solved. In this work, a MILP formulation for the supply chain design is presented which simultaneously takes into account considerations of multiproduct batch production facilities. Usually SC design models used to assume a constant performance and design of the involved plants. Our proposal allows assessment of the trade-offs between decisions of different management levels: from the strategic perspective (nodes selection, supplier selection, material flows among nodes, etc.) until the operative one (production scheduling using campaigns). From several examples, this approach shows that decisions about supply chain and plants are tightly related among them and a general performance cannot be assumed for the production facilities.

A hybrid MCDM model for resilient supplier selection

The aim of this research is to develop a quantitative approach for supplier selection under a disaster environment. Because conflicting criteria co-exist in the industrial scenario it is difficult to satisfy all selection criteria simultaneously when designing supplier selection. The present work incorporates an analytical framework for supply chain design, which may help the decision makers to select a suitable supplier under a disruption scenario. In the four tier process, presented here, the supplier’s weights are initially determined using the TOPSIS and AHP methodology for general selection criteria. A cut-off value for the supplier weight is assigned and the suppliers which are above this cut-off value are selected for the primary selection process. Using AHP-QFD methodology the manufacturer’s critical criteria and resiliency criteria are integrated into the selection process, to determine the subjective factor measures (SFM) for each of the primary selected suppliers. Different cost factors are unified using a normalizing technique to determine the objective factor measure (OFM) for each of the suppliers. Finally, a supplier selection index is calculated in which the decision maker’s attitude plays an important role.

Demonstrating supply chain parameter optimization through beer game simulation

The single player Beer Game supply chain simulation developed using Arena, Visio, Excel, VBA, OptQuest, and Crystal Reports software tools provides a virtual laboratory to analyze various scenarios for optimal design. The model uses animation, directed arrows to show flow of information and flow of material - work in process or finished goods at different stages of transformation, and icons to show an entity such as plant/factory, consumer, warehouse, etc. The model can be scaled up to a larger network of suppliers, OEMs, etc. with multiple products and other features that replicate complexities in an industry supply chain. The detailed analytical presentation of the Beer Game simulation example provides a useful framework for learning about challenges in production logistics systems. The contribution of this paper is to complement and enhance the existing body of knowledge where Beer Game or a similar approach has been utilized in understanding the complexity of managing decision-making in supply chain. In addition, the concept of templates (that is, Excel Workbooks) introduced can be used to build a generic framework for supply chain design of any business operation. The distinguishing feature of this paper is the emphasis given to the integrated system development environment utilizing a simulation software to study, understand, and learn about supply chain management problems. The Bullwhip Effect is an important demand and supply coordination problem that affects numerous organizations (for example, in spite of the efficient Cisco e-hub, still the Bullwhip effect is limiting their performance) which is a major phenomenon incorporated in the Beer Game model.

Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.

Integrated supply chain design using multi-criteria optimization

Supply chain management (SCM) presents significant opportunities for improving profit margins and reducing cost. It encompasses functional and geographical integration as well as integration of tactical and operational decisions. The functional integration aspects involve purchasing, manufacturing, and logistics within the company and across the supply chain. A supply chain design framework has been developed including linear/mixed integer multi-criteria optimization models for determining the identified supply chain design parameters. The selection of suppliers, determination of production quantities, inventory locations and sizes, selection of transportation method and transported quantities in a multi-stage, multi-level supply chain are investigated. Additional criteria including first-time quality (FTQ) and suppliers on-time delivery risk with and without allowing splitting of customer demand between suppliers are considered. An example of typical automotive power train supply chain design and decision issues is used to illustrate the effect of various factors.