The Impact of Supply Chain Integration on Performance: Evidence from the UK Food Sector

Kumar, Vikas; Chibuzo, Esinaulo Nwakama; Garza‐Reyes, Jose Arturo; Kumari, Archana; Rocha-Lona, Luis; López-Torres, Gabriela Citlalli

doi:10.1016/j.promfg.2017.07.183

Cited by 86 publications

(73 citation statements)

References 40 publications

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“…An RBV perspective suggests companies can establish a competitive advantage by developing distinctive capabilities, which is reflected in its effectiveness and business results (Barney, 2014). When a company develops characteristic supply chain capabilities through supply chain integration, it is likely to improve operational competencies (Halley and Beaulieu, 2009), and achieve competitive advantage in the market (Kumar et al, 2017). Customer-oriented performance can directly reduce costs, increase sales, and improve market share.…”

Section: Supply Chain Performance and Firm Performancementioning

confidence: 99%

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

Delić

Eyers

Mikulić

2019

SCM

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Purpose Additive Manufacturing offers much potential for industry, but at the same time is likely to have a significant impact on supply chain theory and practice. To-date there has been limited empirical work exploring the effect of Additive Manufacturing, and this study aims to provide a detailed appraisal of how supply chain integration, supply chain performance and firm performance may be affected by the adoption of Additive Manufacturing. These are critical factors for supply chain management, but have received little quantified attention to date. Design/methodology/approach A theoretical model is developed from a detailed review of the literature, from which a quantitative analysis is performed. Using data collected from 124 automotive manufacturers in European Union and the partial least square- structural equation modeling method, the research examines the relationships among different dimensions of supply chain integration, supply chain performance and firm performance from the perspective of Additive Manufacturing adoption. Findings The findings indicate that Additive Manufacturing adoption positively influences supply chain performance and as a consequence, firm performance. In addition, supporting the resource-based view perspective, the results show a positive indirect effect of supply chain integration on the supply chain and firm performance improvements, enabled by the Additive Manufacturing adoption. This helps to explain some inconsistent findings in previous research regarding the impacts of supply chain integration on performance. Research limitations/implications The results of this study support the view that Additive Manufacturing can make a positive contribution to the supply chain, but this is not achieved solely by the technologies alone. Many of the traditional activities of supply chain management (i.e. integration) are still needed when using Additive Manufacturing, and research needs to understand whether Additive Manufacturing adoption will necessitate changes to the way these traditional activities are undertaken. Building on the findings of the current study, much more work is therefore needed to understand how operations within the supply chain may be changed, and how this may affect the integration and performance of the supply chain. Practical implications This study provides quantitative evidence to show that the adoption of Additive Manufacturing has the potential to affect both firm and supply chain performance. This is significant for those companies considering the adoption of Additive Manufacturing, and may serve as a valuable insight in the strategic decision-making process. For those already using Additive Manufacturing, this study serves to underline the potential for firm performance to be influenced, by focusing on improvements to their production strategies and policies. Originality/value This study provides an initial insight into some fundamental supply chain concepts within an Additive Manufacturing context, which have received very little research attention. It develops a novel conceptual model, and through a large-scale industry survey provides quantified evidence of the impact of Additive Manufacturing on the supply chain. To date, much of the supply chain research is exploratory and qualitative; the quantitative evidence presented in this work, therefore, makes an important and original contribution to both research and practice.

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Section: Supply Chain Performance and Firm Performancementioning

confidence: 99%

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

Delić

Eyers

Mikulić

2019

SCM

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“…their capability to exchange information, thus also jeopardizing the cooperation of the involved resources within the overall network [9]. In addition, it is still open so far the debate on which kind of data integration can enhance performance of the supply chain [10]. In order to overcome the issue of data heterogeneity, this paper aims at the development of an experimental platform for integration between traceability systems and management tools and optimization tools within the inbound logistics of automotive assembly plant.…”

Section: Managing Logistics In Collaborative Manufacturing: the Integmentioning

confidence: 99%

Managing Logistics in Collaborative Manufacturing: The Integration Services for an Automotive Application

Mincuzzi¹,

Falsafi

Modoni³

et al. 2019

Collaborative Networks and Digital Transformation

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The critical success factor of the supply chain management process in a modern manufacturing company consists in the company's capability to exploit the data produced by a growing number of different sources. The latter include a network of collaborative sensors, digital tools, and services, made available to suppliers and other involved supply chain actors by the recent advancements in digitalization. The collected data can be processed and analyzed in near real time to extract significant information useful for the company to take some relevant decisions. However, these data are typically produced under the form of heterogeneous formats, as they arrive from different types of sources. This is the reason why the real challenge is finding valid solutions that support the data integration. In this regard, this paper investigates the potential of a solution for data integration that allows supporting a set of interacting decision-support tools within the inbound logistics of the automotive manufacturing. This solution is based on a messageoriented middleware which enables a collaborative approach where suppliers, trucks, dock managers and production plants can share information about their own status for the optimization of the overall system.

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“…Actively integrating partnership of suppliers and partners effectively increases available resources and information, and will jointly accomplish goals to meet customer needs [24]. Supply chain partnership integration can develop a strategic collaboration between suppliers [25] and create a win-win situation by helping enterprises look for the best ways to raise the ability of making a profit of every link partner in the supply chain [26].…”

Section: Supply Chain Partnership Integrationmentioning

confidence: 99%

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

Lü

Wang

2019

Applied Sciences

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Integrating a partnership with potentially stronger suppliers is widely acknowledged as a contributor to the organizational competitiveness of a supply chain. This paper proposes an event-based model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network in order to understand factors that affect supply chain partnership integration. We develop a multi-objective supply chain partnership integration problem by maximizing trustworthiness, supplier service, qualified products rate and minimizing cost, and then, apply a hybrid algorithm (PSACO) with particle swarm optimization (PSO) and ant colony optimization (ACO) that aims to efficiently solve the problem. It combines the advantages of PSO with reliable global searching capability and ACO with great evolutionary ability and positive feedback. By using the actual data from 1688.com, experimental studies are carried out. The parameter optimizing of the hybrid algorithm is firstly deployed and then we compare the problem solution results of PSACO with the original PSO, ACO. By studying the partnership integration results and implementing analysis of variance (ANOVA) analysis, it shows that the event based model with PSACO approach has validity and superiority over PSO and ACO, and can be served as a tool of decision making for supply chain coordination management in business.

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The Impact of Supply Chain Integration on Performance: Evidence from the UK Food Sector

Cited by 86 publications

References 40 publications

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

Managing Logistics in Collaborative Manufacturing: The Integration Services for an Automotive Application

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

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