Modelling wholesale distribution operations: an artificial intelligence framework

Bottani, Eleonora; Centobelli, Piera; Gallo, Mosè; Kaviani, Mohamad Amin; Jain, Vipul; Murino, Teresa

doi:10.1108/imds-04-2018-0164

Cited by 31 publications

(17 citation statements)

References 58 publications

(45 reference statements)

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“…Moreover, Klumpp ( 2018 ) demonstrated that AI-driven SCI, such as self-driving systems, have great potential to positively stimulate the performance of logistics and transportation functions in the supply chain. Bottani et al ( 2019 ) implemented an AI-based framework to support decision-making in wholesale distribution. Accordingly, the authors claimed the potential to decrease economic loss due to out-of-stock occurrences by more than 56%.…”

Section: Research Framework and Hypotheses Developmentmentioning

confidence: 99%

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

et al. 2021

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Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

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Section: Research Framework and Hypotheses Developmentmentioning

confidence: 99%

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

et al. 2021

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“…Fear is a natural and comprehensible response of humans. Fear about the unknown is human’s ancient and powerful emotion ( Bottani et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Fear is a natural and comprehensible response of humans. Fear about the unknown is human's ancient and powerful emotion (Bottani et al, 2019). For a quick understanding, the fear can circulate distance between the job and employees to get the salary.…”

Section: What Is the Connection Between Operational Excellence And Artificialmentioning

confidence: 99%

Achieving Operational Excellence Through Artificial Intelligence: Driving Forces and Barriers

2021

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This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence (AI). Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence.

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“…MSMEs can obtain business information on which decisions can be taken quickly. Research Bottani et al (2019) found the application of ANN (Artificial Neural Networks) has the potential to reduce economic losses due to out of stock up to 56 percent for wholesale distribution companies. AI's contributions to business activities are reducing losses, cost efficiency and accuracy of business decisions.…”

Section: The Integration Model Of Rbv and Artificial Intelligencementioning

confidence: 99%

An Integrated Artificial Intelligence and Resource Base View Model for Creating Competitive Advantage

Ristyawan

2020

J. Bus. Econ. Review

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Objective – The performance of MSMEs (Micro, Small, and Medium Enterprises) is one of the main indicators to increase Indonesia's economic growth. The contribution of Indonesia's MSMEs is low in the global market and the lowest in ASEAN. One of the ways to increase the contribution is to create the value of competitiveness product. Management of resources is needed to provide high-quality products and competitive advantage of the company. This study aims to examine the combination of resources of the company through the Resource Base-View (RBV) approach. Methodology/Technique – A mix research method was employed, utilizing multiple criteria decision making as the analysis method and the Analytical Hierarchy process (AHP) as the instrument tool of the analysis. The respondents were 100 MSMEs in Pontianak. Findings – The criteria were determined by RBV Concept and the characteristic of company resources. The combination was the sequence level of the weight of the resources. The study also formulated the integration of the Artificial Intelligence and RBV model. The results of the study indicated that human resource is as the highest priority of company attention and the following sequences are marketing and distribution, finance and funding, operational and production, and the internet usage and technology. Novelty – The study also resulted in the creation of the RBV-AI Framework model to obtain strategic resources maintaining the competitive advantage of the company. Type of Paper: Empirical. Keywords: Artificial Intelligence (AI), Resource Base-View (RBV), MSMEs. Reference to this paper should be made as follows: Ristyawan; M.R. 2020. An Integrated Artificial Intelligence and Resource Base View Model for Creating Competitive Advantage, J. Bus. Econ. Review, 5(1) 28– 37 https://doi.org/10.35609/jber.2020.5.1(4) JEL Classification: M15, O32

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Modelling wholesale distribution operations: an artificial intelligence framework

Cited by 31 publications

References 58 publications

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Achieving Operational Excellence Through Artificial Intelligence: Driving Forces and Barriers

An Integrated Artificial Intelligence and Resource Base View Model for Creating Competitive Advantage

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