A periodic inventory system of intermittent demand items with fixed lifetimes

Balugani, Elia; Lolli, Francesco; Gamberini, Rita; Rimini, Bianca; Babaï, Mohamed Zied

doi:10.1080/00207543.2019.1572935

Cited by 22 publications

(7 citation statements)

References 44 publications

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“…Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity (Aloini et al , 2018; Ammar et al , 2018; Schmidt and Hazır, 2019). Artificial intelligence, as a discipline, initiated by scientist John McCarthy (McCarthy et al , 2006) and formally publicized at Dartmouth Conference in 1956 (Moor, 2006), now occupies a centre stage for many organizations (Balugani et al , 2018; Sreedharan et al , 2018; Balugani et al , 2019; Kumar, 2019). Artificial intelligence forms a part of computer or computing science that generates expert systems, algorithms and programs (Aldasoro et al , 2019; Chen et al , 2019; Colicchia et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Role of artificial intelligence in operations environment: a review and bibliometric analysis

Dhamija

Bag

2020

TQM

214

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Purpose“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.Design/methodology/approachAs rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).FindingsResults obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.Originality/valueThe result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

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Section: Introductionmentioning

confidence: 99%

Role of artificial intelligence in operations environment: a review and bibliometric analysis

Dhamija

Bag

2020

TQM

214

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“…Our proposed method was effective regardless of the number of zeros contained in the DPUT data samples. The simple exponential smoothing and Croston's method approaches have been extensively employed in intermittent demand forecasting (Balugani et al, 2019). However, they lack the properties of a statistical distribution, so they generally show low performance measures when used in stochastic inventory models, such as the one studied in this work.…”

Section: Discussionmentioning

confidence: 99%

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Rojas

Coluccio

Vega-Vargas

et al. 2020

PeerJ Computer Science

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston’s method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

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“…AI is mainly known for its computing abilities, potential and genetic algorithms (Baryannis et al , 2019b). Some of the popular AI applications are speech recognition algorithms, expert systems and machine vision (Nayak et al , 2019; Balugani et al , 2019; Sreedharan et al , 2018; Balugani et al , 2019; Kumar et al , 2019). ML is one of those industry 4.0 revolution techniques related to the application and development of computer algorithms that “learn” from experience (Mitchell, 1997).…”

Section: Literature Reviewmentioning

confidence: 99%

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

Nayal

Raut

Queiroz

et al. 2021

IJLM

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PurposeThis article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.Design/methodology/approach20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.FindingsThe study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.Research limitations/implicationsThis study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.Originality/valueThis study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

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A periodic inventory system of intermittent demand items with fixed lifetimes

Cited by 22 publications

References 44 publications

Role of artificial intelligence in operations environment: a review and bibliometric analysis

Role of artificial intelligence in operations environment: a review and bibliometric analysis

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

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