Development of an information fractal to optimise inventory in the supply network

PurposeThis study proposes the Smart SME Technology Readiness Assessment (SSTRA) methodology which aims to enable practitioners to assess the SMEs Industry 4.0 technology readiness throughout the end-to-end engineering across the entire value chain; the smart product design phase is the focus in this paper.Design/methodology/approachThe proposed SSTRA utilises the analytic hierarchy process to prioritise smart SME requirements, a graphical interface which tracks technologies' benchmarks under Industry 4.0 Technology Readiness Levels (TRLs); a mathematical model used to determine the technology readiness and visual representation to understand the relative readiness of each smart main area. The validity of the SSTRA is confirmed by testing it in a real industrial environment. In addition, the conceptual model for Smart product design development is proposed and validated.FindingsThe proposed SSTRA offers decision-makers the facility to identify requirements and rank them to reflect the current priorities of the enterprise. It allows SMEs to assess their current capabilities in a range of technologies of high relevance to the Industry 4.0 area. The SSTRA assembles a readiness profile allowing decision-makers to not only perceive the overall score of technology readiness but also the distribution of technology readiness across the main smart areas. It helps to visualise strengths and weaknesses; whilst emphasising the fundamental gaps that require serious action to assist the program with a well-balanced effort towards a successful transition to Industry 4.0.Originality/valueThe SSTRA provides a step-by-step approach for decision-making based on data collection, analysis, visualisation and documentation. Hence, it greatly mitigates the risk of further Industry 4.0 technology investment and implementation.

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“… Horizontal integration across the value creation network (Saad and Bahadori, 2018).  Vertical integration in the manufacturing plant.…”

Section: Autonomous Robotsmentioning

confidence: 99%

The smart SME technology readiness assessment methodology in the context of industry 4.0

Saad

Bahadori

Jafarnejad

2021

JMTM

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“…Moreover, these studies appear at different levels of analysis, including internal analysis, dyadic level, and chain level. Despite the diversity of methods and levels of analysis, and due to the nature of research in this cluster, the majority of studies develop an analytical model for developing a response to achieve local improvements and optimums (Saad and Bahadori 2018).…”

Section: Distribution and Operations Systemsmentioning

confidence: 99%

Information systems for supply chain management: a systematic literature analysis

Kakhki

Gargeya

2019

International Journal of Production Research

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Information systems (IS) impact supply chain management (SCM) on processes such as planning, sourcing, and delivering, and at levels ranging from tactical operations to organisational strategy. The vast scope of IS and SCM relationships have resulted in diverse and disintegrated research on the topic. This paper offers a systematic literature analysis at the intersection of supply chain and information systems (SCIS), aiming to provide a classification for existing areas of research. This research is based on an analysis of more than 1500 articles published in peer-reviewed journals over the past four decades to classify topics and methods and to identify major trends and distinguish important research themes. The classification of the literature has identified major clusters of research in SCIS, and suggestions are made for future research in each of the identified clusters. In general, the findings point out that there is a dearth of research on topics such as 'impact of IT on vertical disintegration of supply chains,' 'implications of new technologies for supply chains,' and 'concerns related to trust, governance, ownership, privacy, and security of data in supply chains.' This work provides both researchers and practitioners with an insightful description of the current state of research in SCIS and related future trends in research and practice.

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“…Kleinikkink and Noori (2013) introduced and implemented a model based on the fractal concept to develop and increase manufacturing agility attributes and to quicken responses to uncertainty. Saad and Bahadori (2018) developed a new conceptual framework for an information fractal to optimise inventory including safety stock, cycle stock and prevent stock out at lowest logistics cost and further enhance integration within the network.…”

Section: Introductionmentioning

confidence: 99%

Logistics capabilities measurement in the fractal supply network

Saad

Bahadori

2020

IJLSM

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Measurement of logistics capabilities will enable firms to provide order winners by adding value for products and services during the different stage of supply chain to win the competition and enhance firm's performance and customer's satisfaction.Therefore, the purpose of this research is to develop a Fuzzy-AHP multi-criteria decision-making model to measure logistics capabilities in the fractal supply network.The key areas of measurement within a fractal supply network are identified and a hierarchical model is proposed with a set of generic measures. In addition, a questionnaire is developed for pair-wise comparison and to collect opinions from practitioners, researchers and managers to validate the proposed model. The relative importance of the measurement criteria is assessed using analytical hierarchy process (AHP) and Fuzzy-AHP. Hence, the validity of the model is confirmed with the results obtained.

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Development of an information fractal to optimise inventory in the supply network

Cited by 5 publications

References 19 publications

The smart SME technology readiness assessment methodology in the context of industry 4.0

The smart SME technology readiness assessment methodology in the context of industry 4.0

Information systems for supply chain management: a systematic literature analysis

Logistics capabilities measurement in the fractal supply network

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