A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era

Colli, Michele; Berger, Ulrich; Bockholt, Markus Thomas; Madsen, Ole; Møller, Charles; Wæhrens, Brian Vejrum

doi:10.1016/j.arcontrol.2019.06.001

Cited by 138 publications

(107 citation statements)

References 18 publications

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“…Moreover, is it not necessarily be specific to the company's demands for describing maturity improvements (Colli et al, 2019)? As mentioned, all areas of the maturity model approached could reap results from the technologies implemented, but the company needs to be integrated to achieve these results.…”

Section: Discussionmentioning

confidence: 99%

The effect of islands of improvement on the maturity models for industry 4.0: the implementation of an inventory management system in a beverage factory

Dantas

Barbalho

2021

BJO&PM

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Goal: Industry 4.0 has the main purpose of turning the company into an agile organization and adapting continuously to the market's dynamicity. Thus, to reach this target, the business should be aware of its current readiness level and establish digital strategies to reach higher levels. This research aims to explore how improvements in industry 4.0 can improve companies' business processes.Design / Methodology / Approach: The present study is action research that initially identified the most suitable maturity model in Industry 4.0 in the scientific and practitioner literature. Furthermore, the chosen model was applied to a beverage manufacturer to evaluate its current maturity level. Some Industry 4.0 improvements were implemented on the warehouse as part of the company's digital strategy into which it was integrated the inventory management system with new digital solutions. Finally, interviews were conducted, and data was gathered from company indicators. Results:The action research's conduct revealed its current status and how storage management contributed to its digital transformation process. A phenomenon of islands of improvement is discussed at the end. Limitations of the investigation:Our main limitation is our case study approach. Additionally, more interviews crossing functional areas could bring additional elements about the perception of improvements that each functional group had at the implementation process. Practical implications: The case study suggests that maturity models' applications can generate the phenomena of islands of improvements. Managers must take this situation into account to plan digital transformation in the whole company.Originality / Value: The phenomena of the island of improvement is suggested to be a side effect of implementing industry 4.0 based on maturity models.

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Section: Discussionmentioning

confidence: 99%

The effect of islands of improvement on the maturity models for industry 4.0: the implementation of an inventory management system in a beverage factory

Dantas

Barbalho

2021

BJO&PM

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“…Stefan et al (2018) proposed organization, people and technology aspects in their Industry 4.0 migration model to enable organizations to changeover toward I4.0. Colli et al (2019) proposed that technology, governance, connectivity, competence and value are key factors of I4.0. It is quite evident that there exist diverging perception and very little common ground as far as critical factors for I4.0 implementation from the perspective of manufacturing activities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is great disagreement among experts on what can be the critical factors, technologies, tools and methods that can change their business forever. Moreover, in the literature, several studies have presented set of related factors of I4.0 and associated terms (Ganzarain and Errasti, 2016; Lichtblau et al , 2015; Jones and Pimdee, 2017; Saucedo et al , 2018; Colli et al , 2019; Trotta and Garengo, 2019). The factors responsible for the I4.0 application should be empirically tested for creating a wide framework or adoption model (Leyh et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 adoption key factors: an empirical study on manufacturing industry

Narula

Prakash

Dwivedy

et al. 2020

JAMR

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PurposeThis research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.Design/methodology/approachThis article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.FindingsThis study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.Research limitations/implicationsThe proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.Originality/valueThe existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

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“…Nowadays, Industry 4.0 is transforming the physical, digital, and virtual systems through numerous connections with different entities [1]. Supply chain (SC) digitalization emanates from the communication and coordination [2] in a real-time situation to increase organizational competencies [3] by embracing digital tools [4]. Moreover, digitalization has caused significant economic shifts in supply chain management (SCM) as the demand for products and services is growing exponentially due to globalization.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

et al. 2021

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Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.

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A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era

Cited by 138 publications

References 18 publications

The effect of islands of improvement on the maturity models for industry 4.0: the implementation of an inventory management system in a beverage factory

The effect of islands of improvement on the maturity models for industry 4.0: the implementation of an inventory management system in a beverage factory

Industry 4.0 adoption key factors: an empirical study on manufacturing industry

Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

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