Supply Chain Uncertainty Research Articles

Logistics Capability, Supply Chain Uncertainty and Risk, and Logistics Performance: An Empirical Analysis of Australian Courier Industry

The purpose of this paper is to examine the applicability of logistics capability for mitigating supply chain uncertainty and risk and improving logistics performance in the Australian courier industry. Based on the resource-based view, a quantitative research study is designed to investigate the relationships among three latent variables: logistics capability, supply chain uncertainty and risk, and logistics performance. Structural equation modelling is used to analyse the data. The results indicate that there are significant relationships between logistics capability, supply chain uncertainty and risk, and logistics performance. The results provide insights into supply chain uncertainty and risk management. This paper provides empirical support for the resource-based view as it applies to mitigating supply chain uncertainty and risk management in the Australian courier industry. These findings suggest that managers might develop and deploy logistics capability to support and enable supply chain risk management strategies.

Knowledge management system for risk mitigation in supply chain uncertainty: case from automotive battery supply chain

Automotive battery supply chain include battery manufacturer, sulphuric acid suppliers, polypropylene suppliers, lead suppliers, transportation service providers, warehouses, retailers and even customers. Due to the increasingly dynamic condition of the environment, supply chain actors were required to improve their ability to overcome various uncertainty issues in the environment. This paper aims to describe the process of designing a knowledge management system for risk mitigation in supply chain uncertainty. The design methodology began with the identification of the knowledge needed to solve the problems associated with uncertainty and analysis of system requirements. The design of the knowledge management system was described in the form of a data flow diagram. The results of the study indicated that key knowledge area that needs to be managed were the knowledge to maintain the stability of process in sulphuric acid process and knowledge to overcome the wastes in battery manufacturing process. The system was expected to be a media acquisition, dissemination and storage of knowledge associated with the uncertainty in the battery supply chain and increase the supply chain performance.

What Determines the Range of Supply Chain Integration? Comparison of Korean and Japanese Steel Firms

Supply chain strategies depend on competitive environments as well as the nature of the particular business. Consequently, supply chain integration (SCI) strategies should also be evaluated under the conditions a firm is facing. This paper examines the determinants of range of supply chain integration. By comparing the SCI strategies of Korean and Japanese steel firms, the paper shows that SCI should be evaluated in light of supply chain flexibility and uncertainty. Additionally, it is argued that although SCI with customers helps diminish demand uncertainty, it can also bring inflexibility, which is a customer 'lock-in' phenomenon.

Simulation based multiple disturbances evaluation in the precast supply chain for improved disturbance prevention

Disturbances are inevitable and ubiquitous in the precast supply chain. Systematic evaluations of the impact of individual disturbances to prioritize the prevention of various disturbances are critical for realizing the full potential of logistical profits. This is especially true when the disturbance prevention budget is insufficient. Whereas, existing studies focused primarily on single disturbance analyses or precautions evaluation. This study proposes an overall disturbance evaluation model to assess the uncertainty of precast supply chain. The model is developed using ARENA-based discrete event simulation methods, and aims to address the lack of systematic multiple disturbances evaluation before the implementation of precaution measures. The model constructs include a real-world precast supply chain environment which considers four categories of disturbances derived from suppliers, manufacturers, logistics, and customers. In the model, the importance of disturbance is evaluated from both operational and economic perspectives, and the upper bound for the implementation cost of each precaution measure is provided. Finally, eleven scenarios with different levels of exposure to disturbances are investigated to test the validity of the proposed approach. Numerical results indicate that system failure is the most critical disturbance in this precast supply chain, followed by PC quality problem, PC reproduction owing to repair failure, and the material quality problem. The findings could facilitate the multiple disturbance management in the precast supply chain.

An Empirical Examination of Supply Chain Sustainability in Turkish Automotive Sector: Using the PLS-SEM Approach

In today’s dynamic environment, it has become a necessity for firms to better control their supply chain operations against supply chain vulnerabilities. Little consideration has been paid to the relationship between business environment, supply chain risks, supply chain vulnerability, supply chain performance and sustainability. Hence, we aim with this study to unveil the influence of supply chain variables on supply chain performance and sustainability. The proposed model consists of 10 hypotheses to disclose the relationship between 6 main constructs; Supply Chain Uncertainty, Supply Chain Risks, Supply Chain Performance, Collaborative Planning Systems, Vulnerability, and Supply Chain Sustainability. The hypotheses are validated by empirical study with 213 domestic and foreign automotive companies operating in Turkey. The results of this study indicate that supply chain sustainability is primarily affected by supply chain performance and collaborative planning systems. The findings of this study could provide the necessary point of view for the managers working in supply chain management area to comprehend the dynamics behind sustainable supply chains.

Analyzing Supply Chain Uncertainty to Deliver Sustainable Operational Performance: Symmetrical and Asymmetrical Modeling Approaches

The purpose of this study is to analyze different types of supply chain uncertainties and suggest strategies to deal with unexpected contingencies to deliver superior operational performance (OP) using symmetrical and asymmetrical modeling approaches. The data were collected through a survey given to 146 supply chain managers within the fast moving consumer goods industry in Thailand. Symmetrical modeling is applied via partial least squares structural equation modeling (PLS-SEM) in order to assess the theoretical relationships among the latent variables, while asymmetrical modeling is applied via fuzzy set qualitative comparative analysis (fsQCA) to emphasize their combinatory causal relation. The empirical results support the theory by highlighting the mediating effect of supply chain strategy (SCS) in the relation between supply chain uncertainty (SCU) and firms’ OP and, hence, deliver business sustainability for the firms, demonstrating that the choice of SCS should not be an “either-or” decision. This research contributes by providing an illustration of a PLS-SEM and fsQCA based estimation for the rapidly emerging field of sustainable supply chain management. This study provides empirical support for resource dependence theory (RDT) in explaining the relation between SCU and SCS, which leads to sustainable OP. From a methodological standpoint, this study also illustrates predictive validation testing of models using holdout samples and testing for causal asymmetry.

Integrating operational planning decisions throughout the forest products industry supply chain under supply and demand uncertainty

ABSTRACT In the face of both supply and demand uncertainty, the forest products industry needs advanced supply chain management models that can significantly improve their competitiveness in global markets. This paper aims to provide a decision support tool for integrating operational planning decisions with inventory management of all agents in a multi-product forest industry supply chain under supply and demand uncertainty. A pulp mill is considered as the nodal agent and an integrated simulation-based optimization model is developed, which minimizes the cost of the entire supply chain for different customer satisfaction levels, while material and information flow both upstream and downstream of the pulp mill. The incorporation of a merchandizing yard helps in managing risks associated with supply and demand uncertainty in the forest products industry supply chain. There is a net annual cost saving of $17.4 million by including a merchandizing yard in the supply chain. However, there is an increase in handling, holding and transportation costs. Comparing the shortage cost to the handling, holding and transportation costs, it is observed that as long as the shortage cost is above $6.80 per m3, it is viable to keep the merchandizing yard. The merchandizing yard not only absorbs supply shocks for the pulp mill, but also reduces the safety stocks on the downstream side. This integrated supply chain model can be used for operational planning decisions that minimize overall cost for any agent in the supply chain.

Analysis of challenges inhibiting the reduction of waste in food supply chain

The main objective of this paper is to identify and analyse the effectiveness of challenges inhibiting the reduction of waste in Indian agri-food supply chain (AFSC). The reduction of food waste impacts positively all three dimensions of sustainability (economic, social, and environmental). Thirty-three challenges inhibiting reduction of waste in AFSC are identified by a review of the literature and a consultation with experts in the Indian food industry and academia. These challenges are grouped using Exploratory Factor Analysis into a super-set of nine challenges. The inter-relationship and respective dominance among these nine challenges is then determined using Interpretive Structural Modelling and MICMAC analysis. The group of independent challenges (food characteristics, supply chain uncertainty, market infrastructure, and food policy and regulation) have higher driving power and low dependency, and require maximum attention. These four challenges constrain decisions at the three-decision-making echelon (strategic, tactical, and operational) and each of the supply-chain echelons. The group of dependent challenges (supply chain partnerships, operational capability, and supply chain networks) have high dependence and low driving power and are resultant effects. The challenge, information technology, with high driving and dependence power, is a linkage variable. It acts as an enabler of dependent variables, and it mitigates the complexities due to food characteristics and uncertainty. The challenge, consumer behaviour, with low driving and dependence power, is an autonomous variable. It has little influence on waste reduction in Indian AFSC. This study highlights the importance of conducting region-specific study of supply chains and promotes sustainable practice.

Reprint "Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach"

Product lifecycle uncertainties in Closed-Loop Supply Chains (CLSCs) are costly and frequently unavoidable. So the aim of this paper is to develop efficient flexible long-term capacity planning policy for CLSCs that considers social responsibility or a supply chain with Reverse Logistics Social Responsibility (RLSR). This aim is to answer an important research question on how to tackle the lifecycle with its inherited uncertainty to achieve optimal sustainability dimensions performance. Here, a single-product System Dynamics (SD) model of the supply chain with RLSR is used. This SD model considers interrelated sustainability dimensions and adopts the product lifecycle with its inherited uncertainties, such as the length of the product lifecycle, pattern of the product lifecycle, and residence index. Finally, a mathematical model of the developed policy is constructed and a simplified non-linear multi-objective algorithm is proposed to solve this mathematical model. In addition, Taguchi Design is used to minimize the number of simulations needed in the numerical experiment. The findings of this study show that the developed policy could be used to tackle the lifecycle with its inherited uncertainty to optimize the sustainability dimensions performance. These findings have some limitations, however. The findings underscore this paper's contribution to the relatively limited but important academic knowledge on capacity planning development for research on social responsibility issues in CLSCs. In practice, the results will give managers a better understanding of how to tackle product lifecycle uncertainties in RLSR and will therefore lead to better capacity planning to achieve optimal sustainability dimensions performance.

Supply chain uncertainties linked to information systems: a case study approach

PurposeThe purpose of this paper is to study the following research question: how can information systems (ISs) be the real sources of uncertainty for supply chain management (SCM), despite their positive roles in the effectiveness of modern supply chain (SC) organization?Design/methodology/approachA typology of the uncertainties linked to ISs and their usage is developed. Then, five explanatory case studies conducted in diverse industries allow to characterize this typology.FindingsThe case studies allow to identify the most frequently recurring uncertainties, namely, “limited IS capacity” and “data unreliability.” In addition, “company size” does not seem to be a relevant dimension for assessing the vulnerability of a SC to uncertainties linked to ISs. On the other hand, the “business sector,” with its implied power influences, was found to be a relevant dimension.Research limitations/implicationsThis research does not allow to rank uncertainties according to their level of criticality.Practical implicationsThis paper enables SC and information systems managers, as well as ISs/SC project managers to be aware of the need to consider ISs not only as the vectors for performance, but also as the factors for uncertainty in SCs.Originality/valueISs are generally considered to be a vector of performance. Nevertheless, their use remains problematic in many companies. This paper falls within both the ISs and SCM research areas. In the latter field, typologies of the uncertainties are common, but often ignore the uncertainties linked to ISs and their usage. The authors suggest envisaging ISs as the potential factors for uncertainty in the context of SCM.

A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization

Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.

Suppliers’/customers’ production efficiency uncertainty and firm credit risk

This study explores the effects of business counterparties’ (i.e. suppliers’/customers’) production efficiency uncertainty (PEU) on corporate credit risk by employing American bond observations of manufacturing firms. Empirical results of this study show that customers’ PEU is positively related to corporate bond yield spreads whereas suppliers’ has an opposite effect. The former result shows the importance of demand uncertainty while the latter one suggests that the benefits of supply chain integration or information sharing exceed the costs of supply chain uncertainty. We also find that the effects of suppliers’/customers’ PEUs on bond yield spreads are significantly affected by the information flow risk within the supply chain. In addition, the customer-side effect becomes weaker during the financial crisis period, whereas the supplier-side one is insignificantly affected. These empirical results are robust when controlling for potential endogeneity problems and employing an alternative sample which consists of the bond observations with both supplier and customer identification information. Finally, it has to be noticed that our conclusions are only applicable to manufacturing industries.

Implementing fuzzy rank function model for a new supply chain risk management

Intensive business competition has encouraged enterprises to focus more on their core activities to outsource the management of supply chain. Issues such as political crises, demand fluctuations, strategy variations, financial instability and natural disasters are the factors that can contribute to emergence of uncertainty and risk in the supply chain. Effective management of such risks is essential for reducing the vulnerability of the supply chain. Supply risk management is the most basic concept of the supply chain risk management; therefore, this study examines this subject by developing a mathematical model consisting of a set of equations to quantify typical risk such as delayed delivery, substandard quality, natural disasters and financial risk of the supplier. This model is designed to select and allocate orders to suppliers to minimize the supply risk imposed by the mentioned risk factors. A fuzzy-order function is implemented to turn this crisp model into a fuzzy supply chain risk assessment model. The defuzzified solutions of the fuzzy model show significant improvements over the result of crisp model. Uncertainty in supply chain and intense competitiveness between organizations and managers create different challenges. To effectively manage these challenges, new management approaches are used to strengthen and improve the effectiveness of organizations. The results show that the fuzzy model yields better results compared to the deterministic model. Moreover, it is demonstrated that the ability of the model for activities risk control is improved. The work presented addresses the same challenging problems that others have tried to address, but the methodology presented in this paper is more efficient in terms of execution performance and accuracy.

The role of uncertainty in supply chains under dynamic modeling

Article history: Received April 4 2016 Received in Revised Format June 16 2016 Accepted June 27 2016 Available online June 27 2016 The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client’s order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach. © 2017 Growing Science Ltd. All rights reserved

A grey-based approach for managing uncertainties and performance in automotive supply chains

Automotive supply chains are among the most complicated chains to manage because of the involvement of enormous number of suppliers and the associated uncertainties. Any disruption in the chain has a great impact on the performance of the entire chain. Uncertainties in supply chains interfere with their performance and managing uncertainties and risks to reduce this interference is a costly affair. The supply chain managers, therefore, need to prioritise uncertainties in terms of their negative impact on the performance. This will help them identify and adopt an appropriate mitigation strategy to reduce the effect of any kind of uncertainty on a given parameter of performance. Since the occurrence of any kind of uncertainty and its effect on a given performance parameter cannot be ascertained for a selected time horizon, a grey-based modelling technique has been developed to model the relationship between uncertainty and performance.

Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach

Product lifecycle uncertainties in Closed-Loop Supply Chains (CLSCs) are costly and frequently unavoidable. So the aim of this paper is to develop efficient flexible long-term capacity planning policy for CLSCs that considers social responsibility or a supply chain with Reverse Logistics Social Responsibility (RLSR). This aim is to answer an important research question on how to tackle the lifecycle with its inherited uncertainty to achieve optimal sustainability dimensions performance. Here, a single-product System Dynamics (SD) model of the supply chain with RLSR is used. This SD model considers interrelated sustainability dimensions and adopts the product lifecycle with its inherited uncertainties, such as the length of the product lifecycle, pattern of the product lifecycle, and residence index. Finally, a mathematical model of the developed policy is constructed and a simplified non-linear multi-objective algorithm is proposed to solve this mathematical model. In addition, Taguchi Design is used to minimize the number of simulations needed in the numerical experiment. The findings of this study show that the developed policy could be used to tackle the lifecycle with its inherited uncertainty to optimize the sustainability dimensions performance. These findings have some limitations, however. The findings underscore this paper's contribution to the relatively limited but important academic knowledge on capacity planning development for research on social responsibility issues in CLSCs. In practice, the results will give managers a better understanding of how to tackle product lifecycle uncertainties in RLSR and will therefore lead to better capacity planning to achieve optimal sustainability dimensions performance.

Supply chain uncertainty and environmental management

This manuscript examines the impact of supply chain uncertainty on environmental management spending in manufacturing plants. Building on the attention-based view of the firm (ABV), the basic premise is that with increased uncertainty in the supply chain, managers’ attention to environmental management lessens which in turn leads to (i) fewer resources devoted to green issues within the plant and (ii) a bias to use resources toward less disruptive pollution control approaches rather than pollution prevention approaches. Data from a survey of 251 Canadian manufacturing plants was used to test the link between the level of uncertainty in the supply chain and environmental management decisions. The results indicate that supply chain uncertainty does not have a substantial impact on the level of environmental spending in a plant but has a substantial and significant impact on the allocation of the spending between pollution prevention and pollution control. More particularly, as supply chain uncertainty increases, organizations shift their resources away from pollution prevention to favor pollution control approaches.

Managing Information Processing Needs in Global Supply Chains: A Prerequisite to Sustainable Supply Chain Management

Sustainability is an important topic in supply chain management research and practice. For buying firms, one of the most pressing challenges associated with sustainable supply chain management is that they frequently do not possess sufficient information on what is occurring in their complex supply chains, as demonstrated by numerous incidents lacking sustainability. Using eight in‐depth case studies across four industries and elaborating on information processing theory, we identify three forms of sustainability‐related uncertainty that each firm is facing in its supply chain. We refer to them as task uncertainty, source uncertainty, and supply chain uncertainty. The study shows that the extent to which these uncertainties translate into information processing needs depends on a newly identified boundary condition labeled uncertainty intolerance. With respect to the management of such information processing needs, prior research has pointed primarily at matching information processing needs with fitting information processing capacity and secondly at mitigating information processing needs with corrective measures. This study illuminates how some innovative firms occasionally employ a more radical sustainability‐driven supply chain modification mechanism. In doing so, this research exemplifies how sustainable supply chain management may eventually turn from an amendment to a firm's daily business to a decisive factor for shaping future supply chains. In addition, the study constitutes a nascent step to elevate information processing theory to the supply chain level.

Sourcing truckload capacity in the transportation spot market: A framework for third party providers

Due to uncertainty in supply chains and carrier networks stretched thin by demand, it is sometimes necessary for shippers to procure capacity for shipments on the transportation spot market. The transportation spot market is a mechanism by which unfulfilled and urgent demand is satisfied. Shipments are tendered one at a time on a load-by-load basis. Because of the often severe shortage of spot market capacity and its relatively high and volatile prices, shippers must actively as opposed to passively seek carrier capacity. Often, they turn to third parties. This research considers third party brokers (3PL, or non-asset carriers) operating in the spot market. On behalf of shippers, these brokers take responsibility for shipments and secure capacity for them. The paper proposes a behaviourally-based conceptual framework that uses 3PL broker data to improve the search for capacity on the spot market. It seeks to improve the effectiveness of the search process by combining (1) broker data on the availability of carrier capacity, (2) a priori information on a particular carrier’s likely response to various prices to service a load (the reservation price), and (3) the ability to source multiple loads simultaneously (bundling). These components are incorporated into a framework that enables brokers to better prioritize the carriers they contact, thereby improving outcomes in terms of lower carrier costs and reduced search effort.

Application of Dynamic Value Stream Mapping in Warehousing Context

Uncertainty within supply chains increases the risk of not meeting objectives. Warehouses can absorb some of these uncertainties, by accumulating inventory. This accumulation has led many to consider warehouses as a source of waste in supply chains. Hence, there is limited research that seeks improving intrinsic warehouse efficiency; particularly in the context of Lean concepts and Value Stream Mapping (VSM). Since, warehouses seek to absorb uncertainty in supply chain by holding inventory; this uncertainty absorption may introduce variability to warehousing function itself. Therefore a methodology is required, which can capture the embodied dynamic within warehousing function. This paper reflects Lean concepts and, in particular, VSM to warehousing context and introduces some methods and guidelines to assure the proper application of VSM in what is an uncertain and dynamic system. In this paper, warehousing function is formulated based on some abstract processes which vary on their output status. This formulation facilitates identifying value-adding activities as one of the most substantial steps, yet confusing in application of VSM in warehousing context. The suggested methods enable fundamental statistical/mathematical analysis, which leverage VSM to a more dynamic evaluation tool. Application of the introduced approach will facilitate the decision making process for warehouse systems evaluation and improvement. The resultant methodology is applied to a factual case and this serves to demonstrate its practical application. It is worth mentioning that the findings applications, which can be termed ‘dynamic VSM’, are not limited to warehouses but can also be applied to any dynamic environment with non-deterministic processes.