# Discovering Artificial Intelligence Implementation and Insights for Lean Production

Bassel Kassem, Federica Costa, Alberto Portioli Staudacher

► To cite this version:

Bassel Kassem, Federica Costa, Alberto Portioli Staudacher. Discovering Artificial Intelligence Implementation and Insights for Lean Production. 7th European Lean Educator Conference (ELEC), Oct 2021, Trondheim, Norway. pp.172-181, 10.1007/978-3-030-92934-3_18. hal-03771935

HAL Id: hal-03771935

https://inria.hal.science/hal-03771935v1

Submitted on 8 Sep 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# Discovering Artificial Intelligence implementation and insights for Lean Production

Bassel Kassem, Federica Costa, Alberto Portioli Staudacher

Politecnico di Milano, via lambruschini 4/b, Milano, Italy

## Abstract

The research aims to understand how the implementation of Artificial Intelligence AI in Manufacturing Operations takes place. This paper will feed wider research on the interaction between Lean Production and AI, after understanding the implementation process of AI. A Systematic Literature Review (SLR) has been performed. A set of more than 2300 documents has been extracted and screened to produce a list of 90 highly selected and classified articles and conference papers dealing with the research question. After a first meta-level analysis, a structured discussion has been presented over the documents. Three macro use-cases for implementing AI into manufacturing systems have been identified. The first two use cases have been deeply analyzed by the SLR, while the third one has been left for further researches. For the first two use cases, the main applications have been presented through a comprehensive categorization (for stand-alone solution) and a clear explanation of the different paradigms (for I4.0 related implementation). Furthermore, for each case, the available frameworks have been presented. The main challenges and issues that managers should consider while implementing this kind of technology were presented. Possible consequences that AI innovations might have were also indicated. The article ends with insights for Lean production and future research.

**Keywords:** Artificial Intelligence, Lean, implementation

## 1 Introduction

The concept of “Artificial Intelligence” (AI), later defined as “The science and engineering of making intelligent machines” was firstly introduced in 1956 at the famous Dartmouth College conference. Unfortunately, for many years AI has been considered academic, unreliable, or just a part of science fiction, causing the development of this subject experiencing different “raises and falls” in history. Only recently, this misconception has been completely reverted; the development of low-cost AI chips, the production of massive data, and the development of algorithms based upon deep learning have led to the current evolution that started in 2010 and continues to evolve.

The current interest in the topic of AI is undoubtedly linked to another major revolution that is happening nowadays, namely the fourth industrial revolution or Industry 4.0 (I4.0). Introduced by the German government in 2011, I4.0 symbolizes a new era of
industrial processes characterized by the implementation of new disruptive technologies in the manufacturing systems such as Service Automation, Robotics, the Internet of Things, and Additive Manufacturing and AI. This emerging phenomenon is reshaping how companies work from the roots, and AI is one of the main enabling technologies of this revolution.

There is no doubt that organizations gain many benefits by implementing AI [1], and most of them are using AI-powered systems to handle customers request or to cluster and target prospects for marketing reasons. According to a study published by McKinsey in November 2020 (Global survey: The state of AI in 2020, 2021), fifty percent of the respondents stated that their organizations had adopted AI. The business functions in which organizations adopted this kind of technologies were mainly service operations, product or service development, and marketing and sales, with manufacturing left behind (for the majority of the industries [2]). In addition, even if AI was born years ago, implementing these systems is still a complex and hard job for managers and professionals, and, unfortunately, implementation steps have not been largely standardized. The current research is trying to answer a simple but not trivial question related to the role of AI within manufacturing:

"How to approach the task of implementing AI in Manufacturing Operations?"

This research is a preliminary step in wider research aiming to explain the interaction between AI and Lean Production and how they could benefit from each other when applied in manufacturing. It was essential to first understand the implementation process of AI and deduce some possible outlook and space for Lean to fit in.

## 2 Research Methodology

The research addresses the main use cases and application of AI in manufacturing operations (a), explaining different frameworks to implement these systems and to understand the role of AI in I4.0 and Smart Manufacturing (b) and highlighting which are the most common issues, challenges, and pitfalls a manager usually encounters in the implementation process (c)

A Systematic Literature Review (SLR) of the articles and conference papers has been chosen since it is characterized by a replicable, scientific, and transparent process for the selection and analysis of the different papers according to the table below. This methodology allows to identification, evaluation, and integration of the findings of all relevant, high-quality individual studies addressing the research questions and the steps described are in line and inspired by previous studies [3, 4].

<table><caption>Table 1: SLR steps</caption><thead><tr><td>STEP</td><td>SUBSTEPS</td></tr></thead><tbody><tr><td>Papers Selection and Classification</td><td>1. Creation of the article DB<br>2. Article Selection Process<br>3. Article Classification Process</td></tr><tr><td>Meta-Level Analysis of the papers</td><td>1. General Descriptive Analysis<br>2. Further Analysis of the "Directly Correlated" papers</td></tr></tbody></table>
<table><tr><td>Qualitative Review</td><td>1. Assessment of each paper and appraisal<br>2. Key concepts mapping<br>3. Comparison and evaluation</td></tr></table>

The keywords are: "AI" and "Artificial Intelligence", "Manufacturing" and "Production" and "Implementing" and "Investing" have been used in the following query (TITLE-ABS-KEY ( "AI" OR "artificial Intelligence" ) AND TITLE-ABS-KEY ( "Implement*" OR "Invest*" ) AND TITLE-ABS-KEY ( "Manufacturing" OR "Production" )).

In this way, 2344 documents have been selected. In the first step of the analysis, only the abstract has been considered. In the second one, the full article with a final list of 90 selected articles and conference papers. Classifying the items of the obtained list into three categories: 40 "Directly Correlated" articles, used to build the "backbone" of the research and to try to answer the research question, 25 "Partially Correlated" and 25 "With limited Correlation" papers used to enrich the discussion. Refer to Sankey diagram in fig. 1.

"Machine Learning" is not included to ensure finding articles related to the general topic of Artificial Intelligence and not only to this sub-group of its technologies.

<figure><img src="image_3.png" /><figcaption>Figure 1: Figure 2 Sankey Diagram</figcaption></figure>

The main statistical insights that have been obtained from the meta-level analysis have regarded the time distribution, the geographical pertinence, and the document type. The vast majority of the "Directly Correlated" articles are published after 2010.

<figure><img src="image_4.png" /><figcaption>Figure 2: Publications' timeline</figcaption></figure>
To answer the research question and its subproblems, after the meta-level analysis an in-depth content review has been performed.

# 3 Findings and Discussion

The literature review helped to identify 4 macro areas corresponding to 4 questions that would allow answering the research question:

a. Which are the main use cases of AI in Manufacturing Operations?

b. How should the implementation process be designed in different cases?

c. Which are the most common issues, challenges, and pitfalls a manager usually encounters in the implementation process?

d. Which are the consequences of implementing AI and I4.0 in general that managers should consider when implementing these technologies?

## 3.1 The main use cases of AI in Manufacturing Operations

From the analytical review and the first evaluation of papers, it was clear that in the literature, it was possible to identify three main use cases for the implementation of AI within the context of Manufacturing Operations. These three macro-categories were:

1. AI or Machine Learning ML applications conceived as stand-alone solutions to be implemented within the manufacturing context.

2. AI or ML applications are seen as an enabling factor for I4.0 or enabling technology for Digital Twins and Cyber-Physical Systems

3. AI or ML applications not directly used in Manufacturing operations but to design manufacturing operations within a company

The first class of articles was focused on simple AI and ML stand-alone tools applied within the manufacturing environment. In these cases, since AI can significantly improve operational effectiveness by bridging business and manufacturing models[5], companies started to use this kind of application for manufacturing Process control, Quality control, Monitoring and diagnosis, Safety evaluation, Process planning, and scheduling, Optimization of manufacturing yield, Material requirement planning, Preventive machine maintenance [6], Assistance and learning systems [7]. These applications can be considered as the simplest if compared with the ones included in the other groups, but managers willing to onboard AI in this way should consider the high variability associated with them. Indeed, within the literature, different possible classifications have been found: Classification by application type [8], by manufacturing process, by industry [9], by impact or application stage [10], and other classifications [11]. These classifications can drive managers and give them a comprehensive overview of the different AI applications available today.

The second use case presented by the literature is related to the concept of I4.0. Managers should evaluate that considering a smart factory as IT system, it is possible to implement AI at different levels [12]. Indeed, AI is one of the technologies included in the paradigm of I4.0, and it is an enabling factor for many innovations, such as for Digital Twins and Cyber-Physical Systems [5]. According to [13], AI as an I4.0
habilitating technology will significantly impact how businesses are planned and con-
ducted. In this case, AI and ML applications are not implemented as stand-alone solu-
tions but interact with other innovative technologies such as Big Data technologies and
the Cloud, which provide the source of the information (data) and a platform necessary
to Industrial AI. The literature review highlights how AI applications (such as deep
learning Image and Video processing, text and speech processing) are used to enable
the intelligent functioning of Smart Manufacturing, Cyber-Physical Systems, and Dig-
ital twins [14].

The third use case, supported by a limited number of papers, was related to the use of
AI in a “meta-level” way. Here, AI is used to design the system [15] or to perform
technology selection [16]. Since this third use case was not sufficiently supported by
the literature an in-depth analysis was not performed.

## 3.2 The design of the implementation process

The second question was addressed in a similar but different way in the case of AI stand-alone application and the case of AI as an enabling factor for I4.0. However, in the first case, the literature presented different frameworks useful to start an effective and efficient implementation process (Stand-alone applications have limited interaction with other innovative technologies). Both for stand-alone application and I4.0 related frameworks, an incremental approach was adopted, starting from the simplest to the most complex but comprehensive one.

Referring to stand-alone applications, managers willing to onboard AI can consider different frameworks to guide their process. In particular, CRISP-DM [9], Four analytic capabilities stage models [17], and Analytic Canvas [18]. The first two frameworks are very simple and can be used as a starting point for managers, while the last one is very comprehensive and, unfortunately, complex. The Analytic Canvas is a very effective visual tool that can help managers in performing their job. Each model has a different degree of specificity and a different complexity to be managed. Indeed, professionals should decide which one (or the combination) can be the most suitable in their specific case considering the experience, the complexity, and the resources available.

Following the same approach, four different frameworks were presented to help man-
agers to understand the role of AI within Smart Manufacturing and Cyber-Physical
Systems. Three frameworks for AI in Digital Twin/Cyber-Physical Systems and one
for more general implementation of AI in Smart Manufacturing. General implementa-
tion guidelines are explained by [19] or by [20]. To understand why AI is so important
for Cyber-Physical Systems, [21] explained in their framework that AI has the goal of
allowing managers to make more informed decisions in a shorter amount of time by
processing a large amount of data coming from the production system. The same con-
cept is presented and expanded by the data and knowledge-driven framework for digital
twin manufacturing cell (DTMC) proposed by [22] and in the "framework for DT-
driven industrial AI" [23]. In conclusion, managers using frameworks related to Smart
manufacturing and I4.0 should be aware of the role of AI as the key enabler for Intelli-
gent Capabilities of these systems.
## 3.3 Challenges and pitfalls in the implementation process

After understanding which are the use cases and applications for AI within the manufacturing context and the frameworks that can be used to implement those applications, the third step each professional should go through is related to the possible issues and challenges that might arise during this kind of project.

Data Issues, since Data availability and quality represent a crucial prerequisite for even being able to use ML and AI systems, and Skills issues [6] should be carefully evaluated at the beginning of the project while the challenges related to the implementation might come out during the process.

AI implementations in I4.0 are more complex than the stand-alone applications, interacting with other technologies and with the physical environment, will most probably involve more challenges to face. Indeed, Data Issues [24], Skills Issues, Model Development Issues [22] are also frequent in the Smart Manufacturing implementation of AI. Some challenges relate to Investments Issues, Cybersecurity Issues [25] Network, and Machine-machine interaction issues [22].

In conclusion, implementing AI applications is complex, and different issues and challenges may arise. Managers and professionals willing to start these projects must carefully evaluate all these possible sources of unforeseen events. Furthermore, it is important to consider that developing AI solutions within the context of Smart Manufacturing can be even more difficult due to the double nature of this kind of innovation.

## 3.4 Further consequences of implementing AI

The final point related to AI or ML applications that any manager should always consider is linked to the consequences that this kind of innovation could bring. AI as a stand-alone application, like any other disruptive innovations, will drive companies, processes, and workflow to evolve, but, unfortunately, it is very complex to identify the consequences of this technology outside of the context of I4.0, as highlighted by the limited number of papers in the Systematic Literature Review.

For sure, AI Applications will allow companies to improve efficiency and productivity through consistent changes that will overcome some current limitations companies have by reducing cost, clarity in the production, or shortening the change-over-time [26].

The most interesting and impactful consequences that will derive from the adoption of AI and ML applications are related to Industry4.0 and Smart Manufacturing. In the literature, it was possible to identify two main groups of consequences that the implementation of AI will cause: Technical Impacts, Social Impact.

AI, together with the other I4.0 technologies, can indeed completely reshape how some processes are performed [25], creating new ways of extracting value from data [27]. Furthermore, the most drastic changes driven by AI will regard the Social sphere. The implementation of SM systems will increase demand for IT highly qualified specialists [25], causing a decreasing need for unskilled workers [28], causing a major social disruption.
The connection between people, AI, and the physical sphere [29] through the CPSs, DTs, and SMSs paradigms, but also stand-alone applications, can cause major changes, both positives and negative, that should be evaluated.

## 4 AI and Insights for Lean Production

Finally, within the selected articles it has been possible to find a subset of papers dealing with the outcomes coming from the combination of AI and ML techniques [30], Lean practices, knowing that the interaction of Lean with I4.0 has been the focus of the research world [31–33], In this context, AI might play a supportive role to human experts by enhancing the background to streamline the entire process and making information more accessible. According to [34], this technology can improve production control and process, help to monitor continuous flow and pull production, support early equipment failure detection while minimizing inefficiencies through the overall supply chain. Lean has in its core the customer and its satisfaction. Though it is based on Pull production, forecasting is often used to have an idea of the trend of the customer orders. Putting customer behavior into formulas is much needed in the lean culture, and AI could help with this aspect. AI and machine learning construct patterns among the various parameters governing the relationships between the customer and the supplier. Managers could use this to build a stronger link with their customers and learn from it. Vice versa the customer who used to spend time going through the various options in the product catalog trying to personalize it or customize it according to its needs, could be assisted by AI that would learn from the customer behavior and speed up the selection process. Existing and potential integration between AI and these management practices has been a topic of discussion since the 1980s [35] and is deeply discussed by [36].

## 5 Conclusion and future research

In conclusion, implementing AI in manufacturing operations will have a long list of (mainly unpredictable) consequences on the company. It would be naïve to think that those consequences and outcomes will only be positive [37]. As described by [38], AI can also harm the value of a company and, for this reason, it should be carefully managed. Nevertheless, managers should consider all these possible consequences before starting a project of this kind.

The research has the main limitation concerning the methodology as one database is used (Scopus) and others could be included. We call for investigating “The meta-level application of AI” as future research.

As mentioned in the introduction, this paper constitutes the initial step for investigating the interaction between Lean Production and AI. This was intended to understand the implementation process of AI, and our future research will expand on the results to have systematic literature analyzing the interaction followed by real case application in manufacturing companies applying AI in their processes and adopt the lean culture as
well. Future research will try to contextualize the various areas of research in this article in the lean environment. Which are the main use cases of AI in Lean Operations? Is the implementation process any different in a lean culture? Which are the consequences of implementing AI that managers should consider when implementing these technologies in a lean environment?

## References

1. Ilieva RY, Nikolov MA (2019) The Impact of AI & ML in Agile Production. 10th Natl Conf with Int Particip Electron 2019 - Proc 1-3. https://doi.org/10.1109/ELECTRONICA.2019.8825615

2. Stanford (2021) Artificial Intelligence Index Report 2021

3. Dada OL (2018) A Model of Entrepreneurial Autonomy in Franchised Outlets: A Systematic Review of the Empirical Evidence. Int J Manag Rev 20:206-226. https://doi.org/10.1111/ijmr.12123

4. Liao Y, Deschamps F, Loures E de FR, Ramos LFP (2017) Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. Int J Prod Res 55:3609-3629. https://doi.org/10.1080/00207543.2017.1308576

5. Ghahramani M, Qiao Y, Zhou MC, Hagan AO, Sweeney J (2020) AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA J Autom Sin 7:1026-1037. https://doi.org/10.1109/JAS.2020.1003114

6. Wang K (2007) Applying data mining to manufacturing: The nature and implications. J Intell Manuf 18:487-495. https://doi.org/10.1007/s10845-007-0053-5

7. Fahle S, Prinz C, Kuhlenkötter B (2020) Systematic review on machine learning (ML) methods for manufacturing processes - Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 93:413-418. https://doi.org/10.1016/j.procir.2020.04.109

8. Sharp M, Ak R, Hedberg T (2018) A survey of the advancing use and development of machine learning in smart manufacturing. J Manuf Syst 48:170-179. https://doi.org/10.1016/j.jmsy.2018.02.004

9. Mayr A, Kißkalt D, Meiners M, Lutz B, Schäfer F, Seidel R, Selmaier A, Fuchs J, Metzner M, Blank A, Franke J (2020) Machine learning in production - Potentials, challenges and exemplary applications. Procedia CIRP 86:49-54. https://doi.org/10.1016/j.procir.2020.01.035

10. Lwakatare LE, Raj A, Bosch J, Olsson HH, Crnkovic I (2019) A taxonomy of software engineering challenges for machine learning systems: An empirical investigation. Lect Notes Bus Inf Process 355:227-243. https://doi.org/10.1007/978-3-030-19034-7_14

11. Li R, Wei S, Li J (2019) Study on the Application Framework and Standardization Demands of AI in Intelligent Manufacturing. Proc - 2019 Int Conf Artif Intell Adv Manuf AIAM 2019 604-607. https://doi.org/10.1109/AIAM48774.2019.00125

12. Wan J, Li X, Dai HN, Kusiak A, Martinez-Garcia M, Li D (2021) Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and
Challenges. Proc IEEE 109:377-398. https://doi.org/10.1109/JPROC.2020.3034808

13. Ahuett-Garza H, Kurfess T (2018) A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing. Manuf Lett 15:60-63. https://doi.org/10.1016/j.mfglet.2018.02.011

14. Engelsberger M, Greiner T (2015) Software architecture for cyber-physical control systems with flexible application of the software-as-a-service and on-premises model. In: Proceedings of the IEEE International Conference on Industrial Technology. pp 1544-1549

15. Renzi C, Leali F, Cavazzuti M, Andrisano AO (2014) A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int J Adv Manuf Technol 72:403-418. https://doi.org/10.1007/s00170-014-5674-1

16. Evans L, Lohse N, Summers M (2013) A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Syst Appl 40:6412-6426. https://doi.org/10.1016/j.eswa.2013.05.047

17. Eriksen L (2021) Industrial Analytics Revolutionizes Big Data in the Digital Business

18. Kühn A, Joppen R, Reinhart F, Röltgen D, Von Enzberg S, Dumitrescu R (2018) Analytics Canvas - A Framework for the Design and Specification of Data Analytics Projects. Procedia CIRP 70:162-167. https://doi.org/10.1016/j.procir.2018.02.031

19. Dossou PE (2019) Development of a new framework for implementing industry 4.0 in companies. Procedia Manuf 38:573-580. https://doi.org/10.1016/j.promfg.2020.01.072

20. Sjödin DR, Parida V, Leksell M, Petrovic A (2018) Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing. Int J Comput Integr Manuf 33:22-43. https://doi.org/10.1080/0951192X.2018.1471277

21. Kharchenko V, Illiashenko O, Morozova O, Sokolov S (2020) Combination of Digital Twin and Artificial Intelligence in Manufacturing Using Industrial IoT. Proc - 2020 IEEE 11th Int Conf Dependable Syst Serv Technol DESSERT 2020 196-201. https://doi.org/10.1109/DESSERT50317.2020.9125038

22. Zhang C, Zhou G, He J, Li Z, Cheng W (2019) A data- And knowledge-driven framework for digital twin manufacturing cell. Procedia CIRP 83:345-350. https://doi.org/10.1016/j.procir.2019.04.084

23. Alexopoulos K, Nikolakis N, Chryssolouris G (2020) Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int J Comput Integr Manuf 33:429-439. https://doi.org/10.1080/0951192X.2020.1747642

24. Lee J, Davari H, Singh J, Pandhare V (2018) Industrial Artificial Intelligence for Industry 4.0-based manufacturing systems. Manuf Lett 18:20-23. https://doi.org/10.1016/j.mfglet.2018.09.002

25. Waibel MW, Steenkamp LP, Moloko N, Oosthuizen GA (2017) Investigating the Effects of Smart Production Systems on Sustainability Elements. Procedia Manuf 8:731-737. https://doi.org/10.1016/j.promfg.2017.02.094

26. Hansen EB, Bøgh S (2021) Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. J Manuf Syst 58:362-372.
https://doi.org/10.1016/j.jmsy.2020.08.009

27. Kusiak A (2018) Smart manufacturing. Int J Prod Res 56:508–517. https://doi.org/10.1080/00207543.2017.1351644

28. Rymarczyk J (2020) Technologies, opportunities and challenges of the industrial revolution 4.0: Theoretical considerations. Entrep Bus Econ Rev 8:185–198. https://doi.org/10.15678/EBER.2020.080110

29. Pathak P, Pal PR, Shrivastava M, Ora P (2019) Fifth revolution: Applied AI & human intelligence with cyber physical systems. Int J Eng Adv Technol 8:23–27

30. Yadav N, Shankar R, Singh SP (2020) Impact of Industry4.0/ICTs, Lean Six Sigma and quality management systems on organisational performance. TQM J 32:815–835. https://doi.org/10.1108/TQM-10-2019-0251

31. Rossini M, Cifone FD, Kassem B, Costa F, Portioli-Staudacher A (2021) Being lean: how to shape digital transformation in the manufacturing sector. J Manuf Technol Manag 32:239–259. https://doi.org/10.1108/jmtm-12-2020-0467

32. Kassem B, Portioli A (2019) The interaction between lean production and industry 4.0: Mapping the current state of literature and highlighting gaps. In: Proceedings of the Summer School Francesco Turco. pp 123–128

33. Rossini M, Costa F, Tortorella GL, Portioli-Staudacher A (2019) The interrelation between Industry 4.0 and lean production: an empirical study on European manufacturers. Int J Adv Manuf Technol 102:3963–3976. https://doi.org/10.1007/s00170-019-03441-7

34. Perico P, Mattioli J (2020) Empowering Process and Control in Lean 4.0 with Artificial Intelligence. Proc - 2020 3rd Int Conf Artif Intell Ind AI4I 2020 6–9. https://doi.org/10.1109/AI4I49448.2020.00008

35. Manivannan S (1989) INTELLIGENCE

36. Shahin M, Chen FF, Bouzary H, Krishnaiyer K (2020) Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. Int J Adv Manuf Technol 107:2927–2936. https://doi.org/10.1007/s00170-020-05124-0

37. Di Vaio A, Palladino R, Hassan R, Escobar O (2020) Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. J Bus Res 121:283–314. https://doi.org/10.1016/j.jbusres.2020.08.019

38. Castillo D, Canhoto AI, Said E (2020) The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective. Serv Ind J 0:1–26. https://doi.org/10.1080/02642069.2020.1787993