Supply Chain Design Decisions Research Articles

Agility and Proximity Considerations in Supply Chain Design

Strategic supply chain design decisions are critical to the long-term success of a business. Traditional facility location models for supply chain design focus on the trade-offs between the costs and benefits of proximity, i.e., the distance between facilities and customers. These strategic-focused models do not consider the supply chain’s agility, i.e., its ability to quickly respond to unexpected fluctuations in customer needs. In this paper, we study the problem of designing a supply chain distribution network under demand uncertainty and analyze how the optimal design characteristics of proximity and agility depend on various input parameters. We are able to draw managerial insights on how agility considerations may invalidate well-established and widely accepted qualitative results derived from traditional models. In particular, we show that it is optimal to increase the density of distribution centers (DCs) when the shortage penalty cost increases, and to decrease the density of DCs when a certain unit transportation cost parameter increases. Through these findings, our work conveys the message that traditional, proximity-based facility location models can be inadequate for designing modern responsive supply chains, and calls for the need to develop a new class of models for the task. This paper was accepted by Yossi Aviv, operations management.

Keep Your Friends Close? Supply Chain Design and Disruption Risk

ABSTRACTIn this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co‐location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co‐location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.

An MILP model for optimization of a small-scale LNG supply chain along a coastline

The world energy demand is continuously increasing and natural gas is one of the strongest candidates to cover the growth. However, natural gas is unavailable in many energy intensive areas and the best way to introduce natural gas to new, scattered, areas is by transporting it as liquefied natural gas (LNG). LNG can be shipped from a large LNG import terminal to consumers through a network of smaller satellite terminals with a combination of sea- and land-based transports. Building up a small-scale supply chain network is expensive and capital intensive. This paper presents a mathematical model to aid in the supply chain design decisions by minimizing the total costs associated with fuel procurement. The use of the model is illustrated by a case study, where the optimal supply chain of LNG for covering certain parts of the energy requirements of a country is designed under different cost structures for LNG and for its land-based transportation.

A supply chain design approach considering environmentally sensitive customers: the case of a German manufacturing SME

Sustainable business development is one of the main topics of research and management in recent years. Since the environmental pillar is a part of the sustainability concept, companies are forced to (re-)design their supply chain according to environmental issues. Both government and other stakeholders, e.g. non-governmental organisations and customers, pay a lot of attention on a company’s environmental performance. Hence there is a risk of losing reputation if a company does not comply with environmental norms. We focus on the impact of customers’ requirements regarding the environmental performance of a product on strategic supply chain design decisions of the manufacturer of the product. Thus, we consider the case of a German manufacturing company and present a mixed-integer linear programming supply chain design model with a demand function that is influenced by sustainability requirements. The company is assumed to be able to improve the environmental performance of the products sold and affect the customer demand positively by designing an environmentally conscious supply chain.

Concurrent Decision-Making of Product Architecture and Supply Chain Configuration

The product architecture at early product design stagte has important effects on product design, and supply chain design decision. Considering the mutual effects between product architecture and supply chian design, this paper proposed an improved current decision-making method for product architecture and supply chain configuration. Taking use of an improved product architecture model expression GBOM, we establish mixed integer programming model to optimize the supply chain configuration, which can concurrently determine the product architecture and supply chain configuration, as well as get minimized the total supply chain cost. The numeric example shows that the proposed method is valid.

Sustainable product and supply chain design decisions under uncertainties

Nowadays enterprises are forced to consider broader aspects while designing and manufacturing products. The scarcity of natural resources as well as stern environmental regulations have obliged enterprises to develop new products that can satisfy profit, people, and planet during its life cycle. In the last decade, many research efforts have incorporated supply chain and sustainability into product design. However, most of the studies provided deterministic based methodologies. Enterprises who intend to improve sustainability under various uncertainties couldn’t find an appropriate method. The method based on fuzzy set theory is proposed in this study to tackle the uncertainty of supply chain and highlight the sustainable design concepts considering product disassemblability, closed loop supply chain performance, and carbon footprint. A scooter company case study is presented to demonstrate the advantage of this methodology. Because the risk of impreciseness of data could be reduced, decision-makers only have to provide suitable regions to express the costs and lead-time instead of exact numbers. This method can support decision-makers to make the most sustainable decisions which contain both product and the closed loop supply chain design based on the concern of lead-time, cost, and carbon footprint. As a result, sustainable product and supply chain design under uncertainties can be addressed at the product design stage.

An optimisation model for the design of global multi-echelon supply chains under lead time constraints

This article presents a mixed integer programming model for the design of global multi-echelon supply chains while considering lead time constraints. Indeed, we impose that the delivery lead time that can be promised by the company must be smaller than the lead time required by the customer. The delivery lead time is calculated based on the lead times of purchasing, manufacturing and transportation that are triggered by the customer order while considering the stock levels of purchased, intermediate and final products that must be kept at the different facilities. Computational studies are conducted in order to analyse the impacts of including lead times on the supply chain design decisions and to prove the solvability of the model.

A hierarchical approach for evaluating energy trade-offs in supply chains

Supply chain design and operational decisions may impact the energy needed to keep the products flowing through to the customers. It is a challenge to determine the energy consumption and even more challenging to understand the impact of design and operational decisions on the energy consumption along the supply chain. This paper presents a hierarchical simulation based approach for estimating the energy consumption to keep the products flowing through a supply chain. System dynamics simulation is used at a high abstraction level to understand the major factors that may affect the energy consumption. Discrete event simulation is then used to delve down in detail for evaluating the critical stages in the supply chain. A case study for a closed loop supply chain of forklift brakes is used as an example of application of the approach.

A novel artificial bee colony algorithm for solving the supply chain network design under disruption scenarios

Today, many supply chain design decisions, such as facility location, are strategic in nature and very expensive to change. However, facilities in real world are always vulnerable to partial or complete disruptions. Owing to this reason, we firstly formulate the supply chain network design problem under disruption scenarios as a mixed-integer nonlinear program which maximises the total profit for the whole system. After that, in order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, we have proposed a novel artificial bee colony ABC algorithm for solving this problem. Unlike other continuous optimisation problems, the supply chain network design problem under disruption scenarios is a classical discrete NP-hard one. Therefore, the proposed ABC algorithm applies discrete operators to generate new neighbouring food sources for the employed bees, onlookers and scouts. Subsequently, the computational simulations reveal very promising results in terms of the quality of solution.

A Strategy Oriented Framework for Food and Beverage E-Supply Chain Management

Several authors have emphasized the importance of analysing the impact of e-business, e-commerce and online-shopping on supply chain and operations management; however, it seems that to date no one has suggested a comprehensive framework that could help identify and support supply chain design decisions for companies about to enter the online-business in the consumer goods retail trade, encompassing the business drivers at a strategic level. This paper aims to bridge the gap between theoretical taxonomies or abstract models and the concrete supply chain design problems encountered by logistics managers who need to take their Food & Beverage retail company into the internet business while also preserving a consistent alignment with their current company strategy. Some insights on this area are presented along with a field study approach and a proposal of a 6-phase framework to jointly manage all the relevant strategic and functional aspects of supply networks.

An investigation on the impact of product modularity level on supply chain performance metrics: an industrial case study

The benefit of integrating product design decisions and supply chain design decisions has been recognized by researchers. Such integration can facilitate better communication between design teams and operations groups. Consequently, potential supply chain risks can be highlighted and addressed before the launch of a new product. Modularization is one of the most critical elements for both product design and supply chain design decisions as it impacts the assembly sequence and hence the selection of component and module suppliers. However, the impact of modularity level on supply chain performance is still unclear, and thus is the focus of this study. The proposed analytical method incorporates both product design and supply chain design functions, and hence, enables simultaneous consideration of these decisions. The supply chain performances of all two-module and three-module design concepts are fully investigated in an effort to explore the impact of modularity level on supply chain performance. Results show that increased modularity is advantageous for the time-based performance of a supply chain network, whereas decreased modularity yields superiority in terms of cost performance.

Agility and Proximity Considerations in Supply Chain Design - An Analytical Approach

Strategic supply chain design decisions are critical to the long-term success of a business. Traditional facility location models for supply chain design focus on the trade-offs between the costs and benefits of proximity, i.e., the distance between facilities and customers. These strategic-focused models do not consider the supply chain's agility, i.e., its ability to quickly respond to unexpected fluctuations in customer needs. In this paper, we study the problem of designing a supply chain distribution network under demand uncertainty and analyze how the optimal design characteristics of proximity and agility depend on various input parameters. We are able to draw managerial insights on how agility considerations may invalidate well-established and widely-accepted qualitative results derived from traditional models. In particular, we show that it is optimal to increase the density of DCs when the shortage penalty cost increases, and to decrease the density of DCs when a certain unit transportation cost parameter increases. Through these findings, our work conveys the message that traditional, proximity-based facility location models can be inadequate for designing modern responsive supply chains, and calls for the need to develop a new class of models for the task.

Design Strategy for the Reverse Supply Chain Based on Time Value of Product

Design strategies for reverse supply chains are relatively unexplored and underdeveloped. The losses due to time delays represent a significant opportunity for asset recovery. Reverse supply chain design decisions should reflect the differences in the marginal value of time among products. The anthors propose that reverse supply chain can be designed for cost efficiency or quick response, and the decision pivots on the product’s time value. The paper analyzes the time value of return product in reverse supply chain, and the products are classified into low and high MVT based time value. In the paper both the efficient, centralized reverse supply chain matching the low MVT product and the responsive, decentralized reverse supply chain matching the high MVT product are designed In addition, the paper presents some key technologies for designing the reverse supply chain.

An Integrative Methodology for Product and Supply Chain Design Decisions at the Product Design Stage

Supplier selection is one of the key decisions in supply chain management. Companies need not only to make the “make” or “buy” decisions but also differentiate across potential suppliers in order to improve operational performance. Product design is an engineering based activity that realizes the customer requirements into functions of a new product. Many studies have pointed out that the integration of product and supply chain is a key factor for profitability and efficiency. However, most studies address supply chain performance after freezing the design of the product; only a few studies discuss when and how to incorporate supply chain decisions during product design. This paper presents a graph theory based optimization methodology to tackle this problem. The supplier selection issue is considered by evaluating its impact on both internal (e.g., ease of assembly) and external (e.g., transportation time) enterprise performances, which are aggregated as supply chain performance at the conceptual design stage. A case study in the bicycle industry demonstrates the advantages of this methodology. The presented mathematical programming formulation enables simultaneous optimization of both product design and supply chain design during the early design stages.

Environmental taxes in Supply Chain Design for recycling waterways sediments

AbstractWaterways sediments is an example of a valuable material that can be recycled. In this study, Design Chain Network of recycling waterways sediments is presented to determine a logistic strategy for real world recycling company in France, and to analyse the influence of integration of environmental taxes on supply chain design decisions. We propose a strategic model for supply chain design with consideration of CO2 emission taxes, multi-modality and the different logistics costs. We show how these features are formulated in a mixed integer programming (MIP) model, thus capturing the role of the environmental taxes and the transportation modes in the strategic design of a supply chain. The models are solved by commercial software Cplex 10.1 and the computational results are compared. The study is followed by the analyses of the results.

Development of a Simultaneous Design for Supply Chain Process for the Optimization of the Product Design and Supply Chain Configuration Problem

:In the complex business environment of today, it is well recognized that paying insufficient attention to product design and the supply chain might result in financial losses due to both demand generation and profitability problems. This article aims to investigate and quantify the potential benefits of a process called Design for Supply Chain, which tackles optimization of product and supply chain design decisions simultaneously via mathematical modeling and optimization methods. The results, based on the optimization with illustrative data, suggest that this concurrent optimization provides better visibility and higher profits by achieving a delicate balance between selecting a better product design and constructing a cost efficient supply chain.

Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty

Models that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.

Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry

This paper analyzed the uniqueness and challenges in designing the logistics system for dedicated biomass-to-bioenergy industry, which differs from the other industries, due to the unique features of dedicated biomass (e.g., switchgrass) including its low bulk density, restrictions on harvesting season and frequency, content variation with time and circumambient conditions, weather effects, scattered distribution over a wide geographical area, and so on. To design it, this paper proposed a mixed integer linear programming model. It covered from planting and harvesting switchgrass to delivering to a biorefinery and included the residue handling, concentrating on integrating strategic decisions on the supply chain design and tactical decisions on the annual operation schedules. The present numerical examples verified the model and demonstrated its use in practice. This paper showed that the operations of the logistics system were significantly different for harvesting and non-harvesting seasons, and that under the well-designed biomass logistics system, the mass production with a steady and sufficient supply of biomass can increase the unit profit of bioenergy. The analytical model and practical methodology proposed in this paper will help realize the commercial production in biomass-to-bioenergy industry.

The Effect of Supply Disruptions on Supply Chain Design Decisions

We study an integrated supply chain design problem that determines the locations of retailers and the assignments of customers to retailers to minimize the expected costs of location, transportation, and inventory. The system is subject to random supply disruptions that may occur at either the supplier or the retailers. Analytical and numerical studies reveal the effects of these disruptions on retailer locations and customer allocations. In addition, we demonstrate numerically that the cost savings from considering supply disruptions at the supply chain design phase (rather than at the tactical or operational phase) are usually significant.

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.