Industry 4.0 and Lean Management – Synergy or Contradiction?

Abstract

Considering the ongoing trend of digitalization of the manufacturing industry to Industry 4.0, this paper assists in the transformation. The research work is focused on studying the possible impacts of Industry 4.0 on lean management (LM) tools which play a vital role to foster quality and reliability of products and services that are delivered to the customers. The LM tools which are impacted by the advent of Industry 4.0 and assisting in successful implementation of future smart factory will be investigated in particular focus. An interaction plot matrix is established to quantify the influence of LM tools on Industry 4.0. Interaction between these Industry 4.0 design principles and LM tools reveal several opportunities for achieving synergies thus leading to successful implementation of future interconnected smart factories. Overall, the research work serve as a guideline for industries that are under the transformation phase towards future smart factory and offers space for further scientific discussion.

The complementary effect of lean manufacturing and digitalisation on operational performance

“…Since research on this relationship accelerated in 2016, studies have moved slowly from purely conceptual studies towards more empirical-based studies (Buer, Strandhagen, and Chan 2018). Discussing this interaction on a conceptual level, Sanders et al (2017) argue that the concept of lean manufacturing will not fade away but rather will become more important for a successful Industry 4.0 implementation. They claim that most lean manufacturing tools will benefit from the introduction of Industry 4.0, while some lean manufacturing tools can also be facilitators or even prerequisites for a move towards Industry 4.0.…”

Speeding Up the Implementation of Industry 4.0 with Management Tools: Empirical Investigations in Manufacturing Organizations

“…We can argue that those tools will importantly contribute to the implementation of Industry 4.0 principles in organizations. Lean manufacturing, however, is an exception, as it has not been proven to be significant in readiness for Industry 4.0 implementation, although it has been considered as an important vehicle for Industry 4.0 implementation [ 39 , 40 , 41 ]. As observed, Slovenian organizations do not use lean manufacturing as often as other tools, which is signaling that they are more focused on the processes [ 29 ].…”

“…On the other hand, some management tools that are frequently used, such as outsourcing, are understood not to have a beneficial influence on Industry 4.0 implementation [ 37 ]. For other tools, such as lean production and management, opinions among researchers are more biased about their effectiveness, because lean production can present a fundamental building block of Industry 4.0 [ 38 , 39 ], or an insufficient base to build on that cannot be upgraded [ 40 , 41 ]. Management tools’ role in Industry 4.0 implementation, however, is only suggested in the sense that the content of the specific tool can be aimed at helping to cope with the challenges of implementing and supporting Industry 4.0.…”

Evaluación de las sinergias entre lean manufacturing y la Industria 4.0

“…• I4.0 could support Single-Minute-exchange-of-Die (SMED) to allow flexible, modular production Sanders et al (2017) present a comprehensive table that shows to what extent each one of the principles of Industry 4.0 (namely: real-time capability, decentralization, modularity, interoperability, service orientation and virtualization) support each LM tool according to a scale that ranges from -10 to +10. The average beneficiary coefficien is only negative for takt time because Sanders et al believe that the application of takt time is too rigid to benefit from principles based on flexibilit .…”

“…Other lean tools and practices that may be enhanced are the 5S (only 2.5 marks according to Sanders et al, 2017), standardized work (only 2.8 marks in Sanders et al, 2017), and cellular manufacturing (seru in Japanese, to avoid confusion with group technology cells) -ignored in Sanders et al (2017)-. Since Six Sigma needs data to optimize processes and improve quality, it can benefit from the huge amount of data captured by sensors, and methods like big data analysis (Dogan & Gurcan, 2018).…”