Search results

The purpose of this paper is to examine the impact of strategic supplier partnership and customer relationship on supply chain responsiveness and then to analyse the influence of supply chain responsiveness on operational performance in the manufacturing industry in India. In addition to that, this study also investigates the moderating effects of demand uncertainty on the relationship between strategic supplier partnership–supply chain responsiveness and customer relationship–supply chain responsiveness.

A structured self-administered questionnaire was developed to collect data from manufacturing companies in India. This study performed the structural equation modelling and moderated regression for testing the hypotheses after ensuring the validity and reliability of identified constructs.

Strategic supplier partnership and customer relationship positively influence supply chain responsiveness, and supply chain responsiveness also positively impacts operational performance. In addition to that, demand uncertainty negatively moderates the relationship between strategic supplier partnership and supply chain responsiveness. However, demand uncertainty does not significantly affect the relationship between customer relationship and supply chain responsiveness.

Strategic supplier partnership and customer relationship are firstly investigated as antecedents of supply chain responsiveness. To the best of the author’s knowledge, this study is one of the first to examine the moderating effect of demand uncertainty on the relationship between supply chain practices (strategic supplier partnership and customer relationship) and supply chain responsiveness.

This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands.

This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model.

The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain.

This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.

The purpose of this paper is to test the moderating effects of technological and demand uncertainties on the relationship between supply chain integration and customer delivery performance.

Based on a survey questionnaire with 151 participants in the Thai automotive industry supply chain, hierarchical regressions are used to test the moderating effects.

Internal and supplier integration, but not customer integration, were positively associated with customer delivery performance. Technological and demand uncertainties were found to moderate the relationships between internal integration and customer delivery performance, and supplier integration and customer delivery performance.

The moderating effects of technological and demand uncertainties in the Thai automotive just‐in‐time (JIT) environment are explained. This research contributes to the development of a contingency theory of supply chain integration suggesting that the impacts of supply chain integration on customer delivery performance vary under different levels of technological and demand uncertainties.

Managers recognize the diminishing effects of internal integration and supplier integration under demand uncertainty, and the increasing effect of supplier integration under high technological uncertainty.

This study contributes to the supply chain management literature by clarifying the moderating effects of technological and demand uncertainties on the relationship between supply chain integration and customer delivery performance.

Managing demand and supply uncertainties is critical for all manufacturers, but it has added importance for companies that intend to achieve mass customization (MC) ability because these uncertainties are an intrinsic characteristic of MC. The purpose of this paper is to investigate how managing uncertainties in a firm's demand and supply affects its MC ability.

Regression analysis and analysis of variance (ANOVA) are conducted on data collected from 189 manufacturing plants to empirically test two hypotheses.

Both demand and supply uncertainty management have a positive impact on a company's MC ability. However, managing either demand or supply uncertainties independently of the other is not enough to achieve MC ability; instead, a company needs to concurrently manage both demand and supply uncertainties to achieve MC ability.

The current literature lacks a sound theoretical basis to link demand and supply uncertainty management with MC ability. The paper provides such a theoretical foundation, and systematically identifies several demand and supply uncertainty management mechanisms that enable firms to achieve superior MC ability. In addition, it is one of the first large‐scale empirical studies to address the impact of managing both demand and supply uncertainties on MC ability.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

The purpose of this paper is to investigate the impact of demand uncertainty on inventory turnover performance through empirical modeling. In particular the authors use the inaccuracy of quarterly sales forecasts as a proxy for demand uncertainty and study its impact on firm-level inventory turnover ratios.

The authors use regression analysis to study the effect of various measures on inventory performance. The authors use a sample financial data for 304 publicly listed US retail firms for the 25-year period from 1985 to 2009.

Controlling for the effects of retail segments and year, it is found that inventory turnover is negatively correlated with mean absolute percentage error of quarterly sales forecasts and gross margin and positively correlated with capital intensity and sales surprise. These four variables explain 73.7 percent of the variation across firms and over time and 93.4 percent of the within-firm variation in the data.

In addition to conducting an empirical investigation for the sources of variation in a major operational metric, the results in this study can also be used to benchmark a retailer’s inventory performance against its competitors.

The authors develop a new proxy to measure the demand uncertainty that a firm faces and show that this measure may help to explain the variation in inventory performance.

The purpose of the paper is to address money demand instability and investigate the impact of economic uncertainty, stock market uncertainty and monetary uncertainty on money demand in India over the period 2003Q1–2019Q4.

The study checks the stationarity of the variables through standard unit root tests. Based on the mixed order of variables' integration, the authors adopt the autoregressive distributed lag (ARDL) model to confirm the cointegration and check the stability of the money demand function (MDF).

The findings confirm the presence of cointegration and reveal a well-specified MDF, which exhibits stable parameters. Besides the conventional variables, all forms of uncertainties emerge as the essential long-term determinants of money demand. Long-run findings show that people demand more money to avoid the future financial crunch amid high economic, monetary and stock market uncertainties.

The paper recommends, based on the findings, incorporating the monetary aggregates in the monetary policy framework as one of the essential information variables to control the fluctuation in the price level under the current flexible inflation targeting (FIT) regime.

The findings also add to the knowledge of economic agents in terms of the overall response of individuals to changes in different forms of uncertainties, thereby helping to formulate their portfolios more diligently.

The current work is the first of its kind in the Indian context. The incorporation of uncertainty measures in the MDF adds to the existing knowledge on money demand.

This paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.

In this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.

Authors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.

Authors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.

The major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.

The purpose of this paper is to investigate whether the inclusion of uncertainty variables in the demand for money function can produce a stable relationship in Turkey.

The stability of a money demand function is studied by testing parameter constancy of long‐run money demand function. To this end, the authors perform Nymblom type tests in the context of the Coingtegrated VAR methodology.

The findings show that inclusion of appropriate measure of uncertainty is necessary to estimate a stable and consistent money demand function for Turkey.

The empirical application of Nymblom type stability tests on cointegrated VAR money demand systems is very recent and to the authors' knowledge there has been no application of this methodology on emerging market economies. Therefore, this paper extends the literature to the emerging market economies.

Building social capital within buyer‐supplier relationships is often associated with high performing supply chains. However, little research has examined the mechanisms by which social capital is formed. The purpose of this paper is to examine the effects of relational and contractual governance mechanisms on the formation of social capital under varying levels of demand and supply uncertainty.

A conceptual framework is developed, grounded in the literature on supply chain management and social capital theory (SCT).

A series of propositions showed that relational governance leads to the formation of social capital under conditions of supply uncertainty, but is subject to opportunism when customer product demand is uncertain. By contrast, in conditions of high demand uncertainty, contractual governance is associated with social capital formation.

The paper illustrates the need for managers to consider both the way in which their choice of governance mechanisms (contractual and relational) contributes to social capital, as well as highlighting the contingent nature of these mechanisms depending on the environmental context.

This paper is a novel contribution, applying SCT to the literature on supply chain management.