Abstract
Goal: Industry 4.0 has the main purpose to turn the company into an agile organization, able to adapt continuously to the dynamicity of the market. Thus, to reach this target, the business should be aware of its current level of readiness and it needs to establish digital strategies to reach higher levels. The goal of this research is 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 in 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 companies indicators.
 Results: The conduct of the action research revealed the current status of the company 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 further elements about the perception of improvements that each functional group had at the end of the implementation process.
 Practical implications: The case study suggests that applications of maturity models 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.
 Keywords: Industry 4.0; maturity model; logistics; inventory; storage management systems; RFID.
Highlights
In recent years, factors such as the emergence of new technologies coming from different areas, increasing international competition, changes in consumer purchasing habits, and the reduction of product life cycles have significantly increased the complexity of the manufacturing industry (Seidelmann, 2018; Taliaferro et al, 2016, p. 7; Schumacher et al, 2016, p. 162)
According to our literature review, five maturity models in Industry 4.0 were taken into account: Model 1- Industrie 4.0 Maturity Index – Acatech Study - (Schuh et al, 2017), Model 2- A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises – (Schumacher et al, 2016) Model 3- Industrie 4.0 readiness – IMPULS – (Lichtblau et al, 2015) Model 4- An Industry 4 readiness assessment tool - WMG; Crimson & Co; Pinsent Masons
The steps of Analytical Hierarchy Process (AHP) were performed, according to: 1. Definition of the general objective The general objective of applying the AHP method is to choose the best maturity model in Industry 4.0 to be applied in this research
Summary
Factors such as the emergence of new technologies coming from different areas, increasing international competition, changes in consumer purchasing habits, and the reduction of product life cycles have significantly increased the complexity of the manufacturing industry (Seidelmann, 2018; Taliaferro et al, 2016, p. 7; Schumacher et al, 2016, p. 162). Factors such as the emergence of new technologies coming from different areas, increasing international competition, changes in consumer purchasing habits, and the reduction of product life cycles have significantly increased the complexity of the manufacturing industry Digitalization and interconnectivity, and new technologies drive a transformation never seen before in the industry They must understand the current maturity level concerning Industry 4.0 and what concrete actions are identified to help them reach higher stages Logistics will be affected in two aspects to i4.0: the physical dimension, with the use of collection robots for stock movements, even at the transportation and um-packing steps, and the digital dimension with the collection of machinery data through the internet of things (IoT) sensors, the transmission of this data to cloud and utilization of them for optimizing the process itself (Schuh et al, 2017; Hofmann and Rüsch, 2017, p. 25)
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