business model and business plan di era 4.0

Business Model And Business Plan di Era 4.0

• INDRA MAHARDIKA PUTRA, S.E., AK., M.AK.

Pilih Format Buku Yang Tersedia

Deskripsi buku.

9786237324584

Rekomendasi Untukmu

Tim Redaksi Redaksi

Han Yu Rang

Io Sakisaka

Takao Koyano

Tim Redaksi Bip

Nozomi Yanahara

Yukimo Hoshino

Tomine Naruse, Takasato Shiina

Yukimo Hoshimori

Produk Menarik Lainnya

Alexander Osterwalder

Oliver Gassmann, Karolin Frankenberger, Michaela Csik

Tim PPM Manajemen

Dicky Sumarsono

Alex Osterwalder, Yves Pigneur, Greg Bernada, Alan Smith

Tri Siwi Agustina

Douglas Walker dan Stephen Sorkin

Mohon pilih format terlebih dahulu

Koneksi Terputus

Kamu tidak terhubung internet. Mohon periksa kembali jaringan internet kamu.

Atur jumlah dan catatan

Stok Total : Sisa 7

BUKU BUSINESS MODEL AND BUSINESS PLAN DI ERA 4.0

bintang 4.8 ( 10 rating)

Diskusi ( 1 )

• Kondisi : Baru
• Min. Pemesanan : 1 Buah
• Etalase : Buku Manajemen Bisnis

ULASAN PEMBELI

10 rating • 5 ulasan

Foto & Video Pembeli

Ulasan Pilihan

Menampilkan 5 dari 5 ulasan

Lebih dari 1 tahun lalu

Layanan cepat tepat

Cetakan bukunya menggunakan warna Cyan / biru muda sehingga menyulitkan untuk fokus saat membacanya

buku sudah sampai, penjualnya bertanggung jawab sekali. walaupun awalnya paket yang Dateng salah. trima kasih buku happy🙏 sukses selalu

Super cepat sampai dan kemasan paketnya super aman, senang berbelanja disini

Lainnya di toko ini

Pilihan lainnya untukmu

Digital Business Models for Industry 4.0

How Innovation and Technology Shape the Future of Companies

• © 2022
• Carlo Bagnoli 0 ,
• Andrea Albarelli 1 ,
• Stefano Biazzo   ORCID: https://orcid.org/0000-0003-3373-2964 2 ,
• Gianluca Biotto 3 ,
• Giuseppe Roberto Marseglia 4 ,
• Maurizio Massaro   ORCID: https://orcid.org/0000-0001-6461-2709 5 ,
• Matilde Messina 6 ,
• Antonella Muraro 7 ,
• Luca Troiano 8

Department of Management, Ca’ Foscari University of Venice, Venice, Italy

You can also search for this author in PubMed   Google Scholar

Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Mestre, Venice, Italy

Department of management and engineering, university of padua, padua, italy, strategy innovation s.r.l., venice, italy, university of pavia, pavia, italy, avanade italy s.r.l., milan, italy, zeb consulting s.r.l., milan, italy.

• Offers a detailed classification of emerging business models in the digital era
• Includes real case studies to help readers to understand how technologies affect business operations
• Provides an assessment framework for creating competitive digital strategies

Part of the book series: Future of Business and Finance (FBF)

6442 Accesses

7 Citations

8 Altmetric

This is a preview of subscription content, log in via an institution to check access.

Access this book

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (5 chapters)

Front matter, strategic innovation driven by digital transformation.

• Carlo Bagnoli, Andrea Albarelli, Stefano Biazzo, Gianluca Biotto, Giuseppe Roberto Marseglia, Maurizio Massaro et al.

Industry 4.0 and Its Digital Paradigms

Industry 4.0 and the emergent business models, the integration of digital business models: the amazon case study, conclusions.

• Digital Transformation
• Digital Innovation

About this book

Authors and affiliations.

Carlo Bagnoli

Andrea Albarelli

Stefano Biazzo

Gianluca Biotto, Matilde Messina

Giuseppe Roberto Marseglia

Maurizio Massaro

Antonella Muraro

Luca Troiano

About the authors

Carlo Bagnoli is Professor of Strategic Innovation at Ca’ Foscari University of Venice (Italy). He is the founder, partner, scientific director, and Board member of Strategy Innovation S.r.l., a university spin-off located in Venice dealing with innovation and focusing on action research. His research focuses on topics such as digitalization, strategic innovation, business model innovation and knowledge management.   

Andrea Albarelli is Associate Professor of Computer Science at Ca’ Foscari University of Venice, (Italy). His research focuses on computer vision, pattern recognition, optimization, and Artificial Intelligence.

Stefano Biazzo is Professor of Innovation Management at the University of Padua (Italy). He has over 20 years of experience in organizational change management, focusing on developing and disseminating concepts, methodologies, and tools to enhance firm performance through the adoption of innovation and fostercontinuous improvement.

Gianluca Biotto is the Chief Research Officer at Strategy Innovation S.r.l., a university spin-off and consulting firm based in Venice (Italy) and Business and Innovation Manager at Veeco, a management consulting firm located in Milan (Italy). His research interests include strategic innovation, business modelling and business model innovation. 

Giuseppe Roberto Ciro Bruno Marseglia is the Chief Executive Officer at Daat Consulting, a digital consulting firm based in Milan (Italy) and the Chief Technology Officer at Italica Turismo S.p.A., a leisure and travel company located in Trento (Italy). Marseglia is also research fellow at the Department of Civil Engineering and Architecture at the University of Pavia.

Maurizio Massaro is Associate Professor of Digital Management at Ca’ Foscari University of Venice  (Italy). His research interests include knowledge management, intellectual capital, business sustainability, business models and innovation. Massaro is the Chief Scientific Officer of the MIKE – Most Innovative Knowledge Enterprise Award for Italy.

Matilde Messina is Research Assistant and Junior Consultant at Strategy Innovation S.r.l., a university spin-off and consulting firm located in Venice (Italy). Her research interests include digitalization, digital business models, strategic innovation, business model innovation and digital technologies for business.

Antonella Muraro is an ERP Business Analyst at Avanade Italy S.r.l., a global professional services company located in Milan (Italy) and providing IT consulting and services. Her research focuses on topics such as Artificial Intelligence, Blockchain and digital business models.

Luca Troiano is Consultant at ZEB Consulting, a leading strategy and management consulting firm located in Milan (Italy). His research focused on topics such as digital business models, strategic innovation and business model innovation.

Bibliographic Information

Book Title : Digital Business Models for Industry 4.0

Book Subtitle : How Innovation and Technology Shape the Future of Companies

Authors : Carlo Bagnoli, Andrea Albarelli, Stefano Biazzo, Gianluca Biotto, Giuseppe Roberto Marseglia, Maurizio Massaro, Matilde Messina, Antonella Muraro, … Luca Troiano

Series Title : Future of Business and Finance

DOI : https://doi.org/10.1007/978-3-030-97284-4

Publisher : Springer Cham

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Hardcover ISBN : 978-3-030-97283-7 Published: 21 May 2022

Softcover ISBN : 978-3-030-97286-8 Published: 21 May 2023

eBook ISBN : 978-3-030-97284-4 Published: 20 May 2022

Series ISSN : 2662-2467

Series E-ISSN : 2662-2475

Edition Number : 1

Number of Pages : XIII, 268

Number of Illustrations : 6 b/w illustrations, 92 illustrations in colour

Topics : Business and Management, general , IT in Business , Business Information Systems , e-Commerce/e-business , Organization

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Masih Kosong

• Track Order
• Login Register

• # Agama Islam
• # Agrikultur
• # Budaya Jawa
• # Buku Anak
• # Ilmu Sosial & Sejarah
• # Kesehatan
• # Komunikasi
• # Parenting & MPASI
• # Penunjang Pendidikan
• # Programming
• # Self Help

BUSINESS MODEL AND BUSINESS PLAN DI ERA 4.0

Di tengah perkembangan zaman 4.0 yang semakin maju ini, teknologi informasi memiliki peran penting dalam perkembangan bisnis dan usaha. Hal ini pun membuka peluang lebar bagi para calon pebisnis dan pengusaha muda untuk pertama kalinya mengepakkan sayap mereka di dunia bisnis… Selain itu, buku yang bertajuk Business Model and Business Plan di Era 4.0 ini juga dapat menambah pengetahuan serta wawasan kepada Anda (para calon pebisnis sukses) dalam bidang usaha ekonomi dan metode-metode tepat untuk mengelola suatu usaha di Indonesia. Sebab, menjadi seorang pebisnis dapat membantu perkembangan ekonomi negara tercinta ini, sehingga bisa menghadapi persaingan industri 4.0. Semoga, buku ini bisa hadir di tangan Anda dan memberikan ilmu yang bermanfaat, khususnya di ranah bisnis dan entrepreneurship.    

KEUNGGULAN :

1.Buku ini hadir dengan macam ide model bisnis yang bisa langsung Anda terapkan ketika ingin membuka usaha baru. 2.Setiap model dan perencanaan bisnis dalam buku ini disusun dengan sistematis dan praktis. Sehingga, ketika Anda berniat membuka bisnis baru, buku ini akan menjadi pembimbing Anda dalam mendesain, menjalankan, serta mengelola bisnis baru. 3.Selain itu, dengan penataan aksara yang menarik dan bahasa yang ringan, setiap bab pembahasan model dan perencanaan bisnis dalam buku ini senantiasa bisa dengan mudah dipahami serta dipraktikkan oleh semua kalangan pembaca. 4.Yang paling penting, buku ini akan langsung membuat Anda ingin MEMBUKA BISNIS BARU. 

Terakhir Dilihat

• 085878766347
• penerbitahi_official
• penerbitAHI

You are using an outdated version of Internet Explorer, please upgrade to any of the following web browsers to access this website.

Sorry for the inconvenience!

• E-Publications
• Gallery Kampus Utama
• Gallery Kampus Geger Kalong
• Gallery Kampus Jakarta
• Tel-U Press
• Tel-U Open Courseware
• Tel-U Open Educational Resources (OER)
• Tel-U Document Selection Information

Bussiness Model and Bussiness Plan di Era 4.0 Indra Mahardika Putra

Informasi Umum

Klasifikasi.

658.401 - Planning and Strategic Management

Buku - Elektronik (E-Book)

Business Planning

Informasi Lainnya

Di tengah perkembangan zaman 4.0 yang semakin maju ini, teknologi informasi memiliki peran penting dalam perkembangan bisnis dan usaha. Hal ini pun membuka peluang lebar bagi para calon pebisnis dan pengusaha muda untuk pertama kalinya mengepakkan sayap mereka di dunia bisnis… Selain itu, buku yang bertajuk Business Model and Business Plan di Era 4.0 ini juga dapat menambah pengetahuan serta wawasan kepada Anda (para calon pebisnis sukses) dalam bidang usaha ekonomi dan metode-metode tepat untuk mengelola suatu usaha di Indonesia. Sebab, menjadi seorang pebisnis dapat membantu perkembangan ekonomi negara tercinta ini, sehingga bisa menghadapi persaingan industri 4.0. Semoga, buku ini bisa hadir di tangan Anda dan memberikan ilmu yang bermanfaat, khususnya di ranah bisnis dan entrepreneurship.

• KBI2K3 - MODEL BISNIS
• BAH4A3 - PEMODELAN BISNIS
• UEI4F4 - VALIDASI MODEL BISNIS
• KBI4W3 - W. ENTRE - VALIDASI MODEL BISNIS

Koleksi & Sirkulasi

Seluruh 1 koleksi sedang dipinjam

Anda harus log in untuk mengakses flippingbook

Download / flippingbook.

Lokasi Perpustakaan

Tel-U Gedung Manterawu Lantai 5 Jl. Telekomunikasi - Ters. Buah Batu Bandung 40257 Indonesia 081280000110 +6222 756 5929 [email protected]; bebas pustaka : [email protected]

Waktu operasional

Tel-U Jakarta - Kampus A Jalan Daan Mogot KM.11, RT.1/RW.4, Kedaung Kali Angke, Cengkareng, RT.1/RW.4, Kedaung Kali Angke, Kecamatan Cengkareng, Kota Jakarta Barat, DKI Jakarta (021)-545-1697 (021)-545-1697 [email protected]

Tel-U Jakarta - Kampus B Jalan Halimun Raya Nomor 2A, RT 010, RW 006, Kelurahan Guntur, Kecamatan Setiabudi, Kota Jakarta Selatan, Provinsi DKI Jakarta (021)-545-1697 (021)-545-1697 [email protected]

Tel-U Kampus Geger Kalong Jl. Geger Kalong No. 1 Bandung Indonesia 081280000110 +6222 756 5929 [email protected]; bebas pustaka : [email protected]

Tel-U Surabaya Jl. Ketintang No.156, Ketintang, Kec. Gayungan, Surabaya, Jawa Timur 60231 0811-3278-005

Telkom University Library

Copyright © 2011 - Telkom Open Library NPP : 3204122D0000002

Telkom Open Library

Daftar Sebagai Anggota

Daftar Sebagai anggota untuk dapat mengakses koleksi flippingbook Tugas Akhir kami

Pencarian berdasarkan :

0" class="label mt-4">Pencarian terakhir:

Business Model and Business Plan di era 4.0 Arif Ishartadi (Penyuting) - Nama Orang;

Ditengah perkembangan zaman 4.0 yang semakain maju ini teknologi informasi memiliki peran penting dalam perkembangan bisnis dan usaha. Hal ini membuka peluang lebar bagi para calon pebisnis dan pengusaha muda untuk peryama kalinya mengepakkan sayap mereka didunia bisnis. Mereka harus mampu mengaktualisasikan kreatifitas dan kemampuan belajar agar bisa bersaing dengan para pelaku bisnis lainya. melalui buki ini akan membimbing untuk menentukan , merencanakan dan mengelola model model bisnis yang tak membutuhkan modal besar . Buku ini juga menambah pengetahuan serta wawasan kepada para calon bisnis sukses dalam bidang usaha ekonomi dan model-model tepat untuk mengelola suatu usaha di indonesia

Ketersediaan

Informasi detail, versi lain/terkait.

Tidak tersedia versi lain

Lampiran Berkas

Pilih subjek yang menarik bagi anda.

Pencarian Spesifik

Business model innovation in small- and medium-sized enterprises: Strategies for industry 4.0 providers and users

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 7 March 2019

Industry 4.0 is expected to significantly transform industrial value creation. However, research on business models affected through Industry 4.0, and on small- and medium-sized enterprises (SMEs), remains scarce. In response, the purpose of this paper is to address both aspects, further elaborating on the role that SMEs can take toward Industry 4.0 as provider or user.

Design/methodology/approach

The paper used an exploratory research design based on 43 in-depth expert interviews within the three most important German industry sectors, mechanical and plant engineering, electrical engineering and automotive suppliers. Interviews were conducted with leading personnel of the respective enterprises, including 22 CEOs. They assign business model implications through Industry 4.0, referring to the Business Model Canvas, while the paper delineates between Industry 4.0 providers and users.

The paper finds that key resources and value proposition are among the most affected elements of the business model, whereas channels are the least affected. Furthermore, distinct characteristics between Industry 4.0 providers and users can be delineated. In general, Industry 4.0 providers’ business models are significantly more affected than users, except for key partners and customer relationships.

Research limitations/implications

Industry 4.0 remains at its early stages of implementation. As a result, many interviewees’ answers remain at a rather general level.

Practical implications

Strategies for the further alignment of the business models are provided for Industry 4.0 providers and users.

Originality/value

The paper is among the few that investigate Industry 4.0 in the context of SMEs and business models.

• Manufacturing industry
• Case studies
• Small- and medium-sized enterprises
• Industry 4.0

Müller, J.M. (2019), "Business model innovation in small- and medium-sized enterprises: Strategies for industry 4.0 providers and users", Journal of Manufacturing Technology Management , Vol. 30 No. 8, pp. 1127-1142. https://doi.org/10.1108/JMTM-01-2018-0008

Emerald Publishing Limited

Copyright © 2019, Julian Marius Müller

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Industry 4.0 is a concept initiated by the German government that intends to introduce a paradigm shift toward a digital future in industrial production. It is an attempt to ensure future competitiveness for German industry ( Kagermann et al. , 2013 ; Lasi et al. , 2014 ). Industry 4.0 attempts to address two developments for German industry within a common program, changing environmental conditions and relevant technological developments ( Lasi et al. , 2014 ). Examples of changing environmental conditions include globalization, increased market volatility, abbreviated innovation cycles, intensified competition and increasing complexity ( Lasi et al. , 2014 ). Relevant technological developments in industrial production include increasing automation, digitalization and interconnection between machines, products and users. These developments are based on the concept of the Internet of Things, which will be introduced into the German manufacturing industry ( Kagermann et al. , 2013 ; Lasi et al. , 2014 ).

In addition to securing Germany’s industrial position in the world through efficient value creation, Industry 4.0 intends to provide flexibility and customization of products and services ( Kagermann et al. , 2013 ). Ecological and social benefits, such as reduced energy consumption, waste reduction and new, adaptive work environments, also will be achieved through Industry 4.0 ( Kagermann et al. , 2013 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Müller, Kiel and Voigt, 2018 ). Along with technical challenges, organizational conversions must be faced regarding Industry 4.0 ( Loebbecke and Picot, 2015 ). One of the main elements within these conversions is the business model, where highly profitable new or changed models are predicted ( Arnold et al. , 2016 ; Kagermann et al. , 2013 ; Loebbecke and Picot, 2015 ). In contrast to research in technical fields regarding Industry 4.0, economic challenges and potential, especially relating to business models, thus far have been examined less ( Arnold et al. , 2016 ; Brettel et al. , 2014 ; Loebbecke and Picot, 2015 ). Therefore, this paper attempts to support research in the field of Industry 4.0 and correlated effects on business models.

Which specific characteristics regarding user and provider perspectives of Industry 4.0 exist in SMEs toward Industry 4.0-triggered business models?

In response to rare research in this field, this paper applies an exploratory research design that is based on 43 in-depth expert interviews. These were conducted within the three most important German industry sectors: mechanical and plant engineering, electrical engineering and the automotive industry. Interviews were conducted with leading officials of the respective enterprises, including 22 chief executive officers (CEOs). These interviews were used as primary sources, whereas archival data were used as secondary sources. By analyzing the interviews, obtained statements were allocated to the elements of the Business Model Canvas by Osterwalder and Pigneur (2010) . Subsequently, those were analyzed regarding the user and provider perspectives of Industry 4.0.

In essence, this paper addresses the research topic of the appropriate design of companies’ business models in order to extract value from new technologies ( Chesbrough, 2010 ). Technologically triggered business model innovation remains an acknowledged but comparatively less researched stream in business model research ( Baden-Fuller and Haefliger, 2013 ). In response to technological developments, such as Industry 4.0, companies need to correspondingly adapt their business model, fostering opportunities and meeting challenges that arise ( Saebi et al. , 2016 ). The topic of new or changed business models that are enabled or challenged through Industry 4.0 remains a relatively new topic that few studies have investigated so far ( Arnold et al. , 2016 ; Ehret and Wirtz, 2017 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ). Especially addressing their unique characteristics regarding Industry 4.0, SMEs require research that supports them on redeveloping or extending their existing business models. However, SMEs’ business models considering Industry 4.0 have been neglected thus far ( Müller, Buliga and Voigt, 2018 ). Therefore, this paper addresses the topic of business model innovation through Industry 4.0 in SMEs. It delineates the user and provider perspectives toward Industry 4.0 that play a vital role in how companies approach the concept ( Kagermann et al. , 2013 ).

2. Theoretical background

2.1 industry 4.0.

The term Industry 4.0 encompasses the expectations of politics and corporate practice that industrial manufacturing is heading toward a fourth Industrial Revolution ( Kagermann et al. , 2013 ; Liao et al. , 2017 ; Maynard, 2015 ). The previous three Industrial Revolutions have achieved high productivity increases driven by a few fast-spreading general-purpose technologies, such as mechanization, electricity and information technology ( Veza et al. , 2015 ). These general-purpose technologies resulted in strong technical improvements and initiated complementary developments ( Bresnahan and Trajtenberg, 1995 ). For Industry 4.0, the underlying technology is represented by cyber-physical systems, whose technological infrastructure is based on the concept of the Internet of Things ( Kagermann et al. , 2013 ; Lasi et al. , 2014 ; Xu, 2012 ). Together with cloud computing, cyber-physical systems and the Internet of Things are regarded as the central technological foundations of Industry 4.0 ( Zhong et al. , 2017 ).

In Industry 4.0, cyber-physical systems establish an interconnection between the physical world and cyberspace ( He and Xu, 2015 ; Ren et al. , 2013 ). The systems create mechanisms for human-to-human, human-to-object and object-to-object interactions along the entire value-added chain ( Kagermann et al. , 2013 ). The task of integrating humans into this concept remains a challenge as it faces employees’ resistance, including fear of being replaced or having inadequate skills ( Frazzon et al. , 2013 ; Gorecky et al. , 2014 ; Hirsch-Kreinsen, 2016 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ).

The integration of cyber-physical systems into industrial production leads to the creation of cyber-physical production systems ( Schlechtendahl et al. , 2015 ). These can fulfill their potential when interconnected across the entire supply chain ( Haddud et al. , 2017 ). Cyber-physical production systems enable several data-based services, such as predictive condition monitoring and balancing and reducing energy consumption within production ( Shin et al. , 2014 ; Tao et al. , 2011 ). Those features are established along the entire lifecycle of machinery and products ( Lennartson et al. , 2010 ).

Aside from cyber-physical production systems, Industry 4.0 is driven by technological developments such as service-oriented architectures ( Guinard et al. , 2010 ; Mikusz, 2016 ; Raja et al. , 2013 ; Vogel-Heuser et al. , 2015 ). These enable the creation of new services and product-service utilities ( Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Ehret and Wirtz, 2017 ). Those developments result in the concept of smart production, also termed smart manufacturing ( Davis et al. , 2012 ; Radziwon et al. , 2014 ; Wang et al. , 2016 ; Zuehlke, 2010 ). Smart production is characterized by manufacturing of intelligent, personalized products and by high levels of collaboration through production networks ( Lasi et al. , 2014 ; Veza et al. , 2015 ; Xu et al. , 2014 ).

Besides the German Industry 4.0 initiative, similar initiatives have been developed worldwide ( Liao et al. , 2017 ). The European Union has started a public-private partnership under the title “Factories of the Future” to achieve sustainable and competitive production ( European Commission, 2016 ). In the USA, similar efforts are underway through the Industrial Internet Consortium. In China, the “Internet Plus initiative” and “Made in China 2025” represent programs comparable to Industry 4.0, and are among several approaches worldwide ( Liao et al. , 2017 ; Müller and Voigt, 2018 ).

2.2 Small- and medium-sized enterprises

In Germany, the term SME refers to companies with less than €50m in sales and fewer than 500 employees ( German Federal Ministry of Economic Affairs and Energy, 2014 ). For this paper, SMEs represent a fruitful research context for various reasons.

First, prospects for Industry 4.0 can primarily be expected because of the horizontal and vertical interconnection of the value chain. In German industrial value creation, SMEs represent an essential part, as they represent 99.6 percent of all enterprises, generating more than 50 percent of the GDP. In turn, integrating SMEs is perceived as a key success factor of Industry 4.0 ( Müller, Buliga and Voigt, 2018 ).

Second, existing studies reveal that SMEs’ specific challenges differ from those of large companies regarding Industry 4.0. These challenges include resource limitations, low bargaining power and concerns that existing business models might be unsuitable for Industry 4.0 ( Müller and Voigt, 2016 ; Müller, Kiel and Voigt, 2018 ). SMEs also tend to have distinct characteristics regarding the introduction of information technologies in general ( Sharma and Bhagwat, 2006 ). Therefore, SMEs require solutions tailored to meet their specific challenges, but research has mainly focused on large enterprises rather than on SMEs ( Müller, Buliga and Voigt, 2018 ).

Third, the upper management of SMEs is able to supervise the whole enterprise. The managers’ knowledge reveals information about Industry 4.0 that affects the whole enterprise and the entire business model. They may possess knowledge of key aspects within their enterprise and can therefore provide both an external and internal perspective ( Müller, Buliga and Voigt, 2018 ).

2.3 Business model innovation

Companies can extract value from new technologies only through suitable business models ( Chesbrough, 2010 ). Technological innovation therefore is a key driver for business model innovation ( Baden-Fuller and Haefliger, 2013 ). In response, companies must adapt their business models to external threats and opportunities ( Saebi et al. , 2016 ). By introducing Industry 4.0, manufacturers are able to develop new customer value ( Arnold et al. , 2016 ; Ehret and Wirtz, 2017 ; Müller, Buliga and Voigt, 2018 ). This is expected through new services and product-service systems. Manufacturers can take two roles, the user or the provider perspective of Industry 4.0 ( Kagermann et al. , 2013 ).

Whereas different approaches describe business models, most of the current literature agrees on central aspects: creating and capturing value by providing a value proposition to customers ( Casadesus-Masanell and Ricart, 2010 ; Zott et al. , 2011 ; Zott and Amit, 2013 ). This paper used the Business Model Canvas by Osterwalder and Pigneur (2010) . It originates from the Business Model Ontology by Osterwalder et al. (2005) . The Business Model Canvas is used in current literature to analyze business models from a practitioner’s perspective. It has proven to be a comprehensive approach to business models, as its nine building blocks assist in generating a holistic and nuanced view on business models ( Wirtz et al. , 2016 ).

The following description offers a brief summary of the building blocks within the Business Model Canvas. The value proposition provides an overview of a company’s bundle of products and services. Customer segments describes the groups of customers that a company wants to offer value. Channels describe the various means that the company utilizes to contact its customers. Customer relationships explains the types of links that a company establishes between itself and its different customer segments. Key activities describes the arrangement of activities and resources. Key resources outlines the competencies necessary to execute the company’s business model. Key partners portrays the network of cooperative agreements with other companies necessary to efficiently offer and commercialize value. Cost structure summarizes the monetary consequences of the means employed in the business model. Revenue streams describes the ways that a company makes money through a variety of revenue flows ( Osterwalder and Pigneur, 2010 ). Figure 1 illustrates the nine building blocks within the Business Model Canvas.

Several authors in business model research address the capabilities and impacts of digital technologies on business models. However, the specific impact of Industry 4.0 on business models remains a field scarcely investigated ( Arnold et al. , 2016 ). Furthermore, empirical investigations of business models in context of the Industrial Internet of Things mostly have not focused on the special requirements of SMEs ( Arnold et al. , 2016 ; Echterfeld et al. , 2016 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Müller, Buliga and Voigt, 2018 ).

3. Research design

This qualitative empirical study aims to present the impacts of Industry 4.0 on business models of manufacturing SMEs. First, the study generates an overview of business model changes brought from Industry 4.0 for manufacturing SMEs. Changes in the nine building blocks are highlighted, supported by exemplary expert statements. Further, the paper attempts to differentiate between several business model changes that are specific for the user or provider perspective upon Industry 4.0. Therefore, relative frequencies of affected building blocks are used to show differences and similarities. The perspective of providers encompasses manufacturing cyber-physical systems and selling them to customers. Users of Industry 4.0 apply cyber-physical systems in production and services. This delineation presents an important categorization that companies can take regarding Industry 4.0 ( Kagermann et al. , 2013 ). The delineation between providers and users of Industry 4.0 investigation is especially suitable in the context of SMEs. SMEs often have a focused and often single business model that can be overseen by top managers, in contrast to larger enterprises ( Müller, Buliga and Voigt, 2018 ). The paper thereby also attempts to overcome the limitation of single key informants not being able to report complex organizational phenomena ( Glick et al. , 1990 ; Hughes and Preski, 1997 ).

Because of scarce prior research on Industry 4.0-related effects on business models of manufacturing SMEs, an exploratory qualitative research design was chosen. This is appropriate in exploratory research as rich data can be obtained, allowing the investigation of concrete managerial problems and extending the existing state of research ( Yin, 2009 ; Eisenhardt and Graebner, 2007 ). Except for single-case studies, empirical research on business models in the context of emerging technologies remains scarce ( Demil et al. , 2015 ). Multiple-case studies enable contextualization, allowing the comparison of distinct findings and increasing the reliability of obtained data ( Eisenhardt and Graebner, 2007 ). Furthermore, case study research has been used successfully in information systems research ( Dubé and Paré, 2003 ).

To conduct the qualitative survey, a representative sample was chosen ( Yin, 2009 ; Eisenhardt and Graebner, 2007 ; Demil et al. , 2015 ). According to Kagermann et al. (2013) , mechanical and plant engineering, the automotive industry and the electrical industry would be primarily affected by Industry 4.0. Therefore, those three industry sectors were chosen for the study. Case firms were selected to have one predominant business model, as competing business models within a single enterprise are difficult to include in research design ( Markides and Charitou, 2004 ). To ensure these aspects, SMEs were preselected using publicly available data for classification before requesting an interview. Furthermore, a competent interview partner in a leading position, preferably the CEO, was required ( Kumar et al. , 1993 ).

Using semi-structured interviews, an interview guideline representing the research question was developed. Publicly available data and internal data provided by the interviewees for triangulation to validate the research were used as secondary sources ( Yin, 2009 ; Gibbert et al. , 2008 ; Huber and Power, 1985 ). The interview guideline consists of two parts, obtaining information about the interviewee and the enterprise, and then about business model changes from Industry 4.0. The interviews were conducted via telephone and recorded on audio files in accordance with the interview partners. Subsequently, the interviews were transcribed from the audio files, and then subjected to a qualitative content analysis ( Miles and Hubermann, 1994 ). During this process, the initial categories were defined inductively and then aligned to existing research, intending to develop theoretical knowledge ( Edmondson and McManus, 2007 ; Holsti, 1968 ). In the next step, the final categories were condensed from the initial categories using a frequency analysis ( Holsti, 1968 ). This process was conducted independently by two researchers. Subsequently, the categories were checked for consistency and compared to derive inter-coder reliability ( Holsti, 1969 ), thereby validating the coding process.

All surveyed enterprises fulfill the criteria of SMEs according to the definition of the German institute of SME research: a maximum number of 500 employees and a maximum annual turnover of €50m ( German Federal Ministry of Economic Affairs and Energy, 2014 ). In all, 43 interviews with lengths between 25 and 60 min were conducted and 22 of the 43 interview partners are CEO of their respective enterprises. The others are among the top officials of their enterprises, and all are members of their management board. Within the 43 enterprises, 24 are mechanical and plant engineering enterprises, 13 are automotive suppliers and 6 are from the electrical industry sector. The average number of employees is 91.02, whereas the average annual turnover is €16.09m. Figure 2 reveals the detailed distribution within the empirical sample.

4. Findings

4.1 changes of business model elements through industry 4.0.

Table I provides an overview for the nine building blocks within the Business Model Canvas, including exemplary changes of the respective building blocks through Industry 4.0.

Key resources is the most named building block within the sample (58.14 percent of interviewees). The interviewees mainly mention two aspects. First, production facilities and equipment need to be altered or especially purchased according to the specifications of Industry 4.0. As one interviewee mentions: “Industry 4.0 will require [us] to either adapt existing machinery, also known as “retrofitting,” or to purchase new machines. Especially the second will inflict high costs on SMEs” (Interviewee No. 21). Second, the interviewees describe the need for new personnel or required retraining of existing personnel. An SME representative states: “Industry 4.0 technologies require especially trained experts in the field of IT. SMEs cannot afford those easily, as such competencies are rare on the market. Currently, such experts mainly go to larger enterprises” (Interviewee No. 2). Another expert says “[…] it will be a challenge that existing personnel might become obsolete because we can’t train them accordingly” (Interviewee No. 30). This is confirmed by another interviewee, who states: “An SME has a limited workforce. The retraining required for Industry 4.0 is manageable for a larger enterprise. But who runs the daily business in an SME if the workforce is being trained? No SME can easily acquire adequate backup here” (Interviewee No. 14).

Value proposition is the second most named building block (53.49 percent of interviewees). The interviewees mainly describe new products, services or a combination of both to be offered to the customer. As one interviewee explains: “Our machines can produce more individually and tailored to customer demands. The ultimate goal here is often described as ‘batch size one.’ However, it remains questionable if this is really required for each and every product” (Interviewee No. 23). A manufacturer of machinery and equipment states: “We can offer new features with our machines, e.g., predictive maintenance, self-optimization, or reduction of energy consumption” (Interviewee No. 8). New services through Industry 4.0, which are often mentioned together with new products, are mostly described as “data-driven.” As another representative explains: “It is our goal to not only produce the machine, but set it up and connect it with other machines in the factory. We want to produce ‘turnkey’ solutions for our customer, especially regarding the integration in existing production plants” (Interviewee No. 39). Another interviewee describes the aforementioned retrofitting of machines: “As an SME, we can hardly keep up to build the newest equipment everywhere. But our strength is individual service. And this is something that you need if you want to make a 30-year-old machine ready for Industry 4.0” (Interviewee No. 6).

Key partners is tied for the third most named building block (48.84 percent of interviewees). In this context, the SMEs’ representatives primarily name partners that are experts in IT-related aspects. One interviewee states: “An SME cannot be an expert in machines and in IT easily. But it can look for the right partner, for me preferably another SME, that it can work together with” (Interviewee No. 11). Another interviewee mentions that “data security is a task that requires the right partners for us, but those partners are rare” (Interviewee No. 27).

Customer relationships is also tied for the third most named building block (48.84 percent of interviewees). Most interviewees agree that Industry 4.0 will lead to long-term relationships that include working closer together: “I experience that customers and suppliers are trying to solve these issues regarding Industry 4.0 together. You cannot achieve new technological developments in a short time, or if you only worry about costs in the short run” (Interviewee No. 27).

Customer segments is tied for the fifth most named building block (41.86 percent of interviewees). Here, the interviewees explain that new data-based products and services can lead to new customers being addressed. As one expert mentions: “Industry 4.0 rewrites the rules of the game to a certain extent. Also, the smaller [SMEs] are trying to address new customers, but also other players from abroad or from the IT industry try to enter the market” (Interviewee No. 28).

Revenue streams is also tied for the fifth most named building block (41.86 percent of interviewees). In this regard, all experts state that new pricing and revenue models will be available through Industry 4.0: “Pay per use, pay per feature, or other payment methods like this become possible. But before, this would just have been too hard to supervise. In Industry 4.0, this can be done automatically to a large extent” (Interviewee No. 13).

The Cost structure (37.21 percent of interviewees) is third least named by the interviewees, but still significantly more than key activities and channels. Most statements in this regard relate to investments required for Industry 4.0. As one interviewee mentions: “SMEs have a hard time facing the costs that come with Industry 4.0 implementation” (Interviewee No. 5). Cost savings through increases in productivity are also mentioned: “We will get more flexible and efficient in value creation, decreasing our variable costs through Industry 4.0” (Interviewee No. 26).

Key activities is second least named from all building blocks within the Business Model Canvas (23.26 percent of interviewees). Most interviewees name data-based activities through Industry 4.0: “We will be able to do things like data analysis and thereupon optimize our processes” (Interviewee No. 12). Another SME representative offers a possible explanation for the low number of SMEs naming key activities in the context of Industry 4.0: “SMEs tend to stick to what they did. They just want to do it better and especially more efficiently. For me, Industry 4.0 also includes the chance to do something completely new, even for an SME” (Interviewee No 40).

Channels is the least named building block (17.65 percent of interviewees). The few interviewees who mention this element of the Business Model Canvas relate to digital channels to communicate with their customers. One interviewee explains: “Industry 4.0, from my point of view, is able to save a lot of time in communication. One can now communicate digitally, or even the machines take over that job as they communicate among themselves” (Interviewee No. 17). Another interviewee offers a possible explanation for the lowest naming of this building block: “SMEs’ key asset, at least for many of them, is talking personally to each other and knowing the person you talk to. As such, they won’t easily turn to new ways of communication, although they should find a form to enter these new ways of communication” (Interviewee No. 40).

4.2 User and provider perspectives on Industry 4.0

In general, the present study finds that SMEs regarding Industry 4.0 from a provider perspective name the highest ratio of building blocks affected within their enterprise. Five out of nine building blocks are predominantly mentioned from a provider perspective. Two building blocks are stated comparably often from the user perspective and the provider perspective. The building block that both perspectives agree to be least affected by Industry 4.0 is channels, whereas key resources is mentioned by the majority of both perspectives as most affected by Industry 4.0. Key activities is named comparably seldom by the providers and the users, indicating a small impact on this building block for both perspectives on Industry 4.0. Only two building blocks are named by a higher percentage of Industry 4.0 users than from Industry 4.0 providers: key partners and customer relationships. These findings are summarized in Table II .

Figure 3 illustrates the ratio of building blocks affected from the perspectives of Industry 4.0 users and Industry 4.0 providers.

Within the sample of this study, most SMEs perceive themselves as users rather than as providers in the context of Industry 4.0. Only three enterprises regard themselves as both users and providers regarding Industry 4.0. Overall, the findings for both perspectives on Industry 4.0 can be summarized as follows: the providers of Industry 4.0 generally expect a significantly higher impact on their respective business models through Industry 4.0, except for the building blocks of key partners and customer relationships. However, this group constitutes the smaller portion of SMEs surveyed. The users of Industry 4.0 expect less impact on their business models through Industry 4.0 than providers, except for key partners and customer relationships.

5. Interpretation

Key resources is named predominantly by the providers of Industry 4.0-based solutions. This can be explained as manufacturing firms having to obtain competencies regarding IT. This in particular affects human resources, as data analysis experts and associated hardware are required ( Müller, Buliga and Voigt, 2018 ; Rachinger et al. , 2018 ). Fewer changes are expected for employees in production itself, but “creative problem-solvers” are increasingly required ( Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ). Both developments are logical in respect to the providers of Industry 4.0-based products and services, as the new value propositions especially will require new key resources. Still, the task of integrating existing workers into Industry 4.0 remains a challenge that requires further investigation. This encompasses the need for training and qualification of the existing workforce that could be especially challenging for SMEs.

Value proposition is named predominantly by the providers of Industry 4.0-based solutions. This seems reasonable, as new value propositions, such as individualized products meeting customer requirements specifically with batch size one products, are expected through Industry 4.0 ( Kagermann et al. , 2013 ; Oesterreich and Teuteberg, 2016 ; Rachinger et al. , 2018 ). Furthermore, individual and customer-oriented services, making use of digital technologies and data analysis, are predicted through Industry 4.0. These include condition-based monitoring and predictive maintenance along with optimization of production systems and value chains ( Kagermann et al. , 2013 ; Müller, Buliga and Voigt, 2018 ). Conclusively, the providers of Industry 4.0-based predominantly mention value proposition, as providers of those solutions and products will primarily generate new value propositions.

Key partners is named predominantly by the users of Industry 4.0. This is confirmed in current literature, which states that key partners is considered to be especially important for operation and control of Industry 4.0-based systems ( Kagermann et al. , 2013 ; Porter and Heppelmann, 2014 ). Due to their limited size, SMEs as providers will most likely require key partners in order to develop new Industry 4.0-based solutions ( Müller, Buliga and Voigt, 2018 ).

Customer relationships is named predominantly by the users of Industry 4.0. Long-term, intensified relationships with manufacturers and providers of products and services are predicted, especially for usage of products and services ( Kagermann et al. , 2013 ; Müller, Buliga and Voigt, 2018 ). The findings are therefore in line with current research but should be extended with further investigation regarding the customer relationships of users and providers in Industry 4.0.

Customer segments is named comparably often by the providers and the users of Industry 4.0. This finding questions the assumption that providers would address new customer segments to a greater extent. Also in literature, no differentiation between provider and user perspectives of Industry 4.0 can be found thus far. However, new customer segments are predicted through Industry 4.0 ( Arnold et al. , 2016 ; Kagermann et al. , 2013 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Müller, Buliga and Voigt, 2018 ). This differentiation should be addressed in detail in further research.

Revenue streams is named predominantly by the providers of Industry 4.0-based solutions. New revenue models, such as dynamic pricing and pay-per-use payment models, are expected through Industry 4.0 ( Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Müller, Buliga and Voigt, 2018 ). This can be explained as providers might foresee these new possibilities, as they will provide new revenue streams.

Cost structure is named predominantly by the providers of Industry 4.0-based solutions. Large investments are expected for Industry 4.0 to obtain required key resources. However, reductions in fixed and variable costs are predicted through increased quality and productivity ( Arnold et al. , 2016 ; Kagermann et al. , 2013 ; Müller, Buliga and Voigt, 2018 ). For obtaining new key resources, this evaluation seems logical, as providers will require large investments in this respect. For possible savings, those might not yet be seen for users in industry, especially in SMEs ( Müller, Buliga and Voigt, 2018 ).

Key activities is named predominantly by the providers of Industry 4.0-based products and solutions. In general, new key activities are expected through data management, data analysis and data mining to enable knowledge-based decision making ( Brettel et al. , 2014 ; Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ). The low estimation for key activities from SMEs can be reasoned as manufacturing SMEs are not as far developed in terms of data management and handling so far. Therefore, they might not regard this topic as important yet ( Müller, Buliga and Voigt, 2018 ). However, the estimation of the providers of Industry 4.0-based solutions is in line with the state of research, as the provision of smart products and services will especially require new key activities.

Channels is the least named for all perspectives (17.65 percent of interviewees), and no differentiation between providers or users of Industry 4.0 can be found. Collaborative ways of value creation are predicted regarding Industry 4.0 that include co-creation between the providers and the users of Industry 4.0-based solutions, based on new communication technologies ( Kiel, Arnold and Voigt, 2017 ; Kiel, Müller and Voigt, 2017 ; Müller, Buliga and Voigt, 2018 ). As a result of this co-creation process, individual products can be created, understanding customer needs in detail ( Kagermann et al. , 2013 ; Müller, Pommeranz, Weisser and Voigt, 2018 ). However, the results do not reveal a differentiation between provider and supplier perspectives and offer a low evaluation of channels in general. This can be explained as direct, personal and highly individual Channels are already present in SMEs and provide a highly valuable characteristic of SMEs ( Müller and Voigt, 2016 ).

The finding that providers consider more changes in terms of impacts on their respective business model through Industry 4.0 seems logical. The provision of products and services from design to market affects more elements within a business model than using them. This becomes especially obvious for the low evaluation of key activities from a user perspective. The evaluation of key resources regarded as most important and channels regarded as least important from both perspectives is mostly in line with current research for larger enterprises ( Arnold et al. , 2016 ).

6. Managerial implications

Besides presenting the key findings, this paper separates managerial implications for the perspectives of both providers and users of Industry 4.0. Providers in respect to Industry 4.0 are recommended to investigate necessary key activities and required key partners regarding Industry 4.0. Especially for the latter, SMEs as providers will definitely require adequate partners for the provision of Industry 4.0-based solutions due to their limited size. Addressing new customer segments could provide new Industry 4.0-based solutions to a broader group of potential customers. New customer relationships should also be considered, also in respect to the mentioned new relationships that could emerge through digital channels. Furthermore, this paper recommends the possibilities of new channels in terms of Industry 4.0. This includes new technologies for communication in collaboration with existing and well-established channels. For the users of Industry 4.0, it can be suggested to especially regard the value of key partners and customer relationships, thereby finding suitable key partners for the provision of Industry 4.0-based solutions. In addition, the users of Industry 4.0 are advised to foster customer relationships in terms of Industry 4.0.

Extending these recommendations, special challenges of SMEs toward Industry 4.0 must be regarded: lack of financial resources, low production numbers, low degrees of standardization and a lack of understanding of integration, which are major concerns for German SMEs ( Müller and Voigt, 2016 ). Furthermore, access to skilled employees and concerns regarding data security and privacy concerns are among major challenges for German SMEs that plan to implement Industry 4.0. Conjointly with these findings, this paper presents the following recommendations for users and providers of Industry 4.0.

For users, the reduction of capital commitment regarding Industry 4.0-based solutions that are cost-intensive is essential. Pay-per-use contracts and leasing of machinery could provide ways to reduce capital commitment. This approach could also include financing by customers, especially larger enterprises possessing larger financial resources, relating to the high importance of new customer relationships. Furthermore, finding compound effects with other enterprises regarding Industry 4.0 is recommended, relating to the high importance of new key partners for users regarding Industry 4.0. This approach could assist in reducing costs, establishing standards and gaining access to trained personnel and expertise. Potential solutions could be platforms among SMEs or partnerships with larger enterprises.

For providers, this paper proposes business models building on new value propositions, key activities and revenue streams, such as provision of services, complete product-service systems and payment methods. Thereby, providers of Industry 4.0-based solutions could address the concerns of SME users of Industry 4.0, such as lacking expertise toward Industry 4.0 or insufficient financial resources. Offering complete solutions, such as service provision and hardware conjointly, could assist in generating compound effects and offering new value propositions to customers. Addressing the requirement of new key resources in terms of Industry 4.0, compound effects could be found within platforms among SMEs or partnerships with larger enterprises. These could also assist in developing new cost structures required in terms of Industry 4.0, relating to new key resources required.

Overall, it can be concluded that a provider role toward Industry 4.0 creates larger impacts on SMEs’ business models. Therefore, companies need to decide if this poses a threat toward their established business models, and whether they should therefore avoid the provider role toward Industry 4.0. However, taking a provider role toward Industry 4.0 could also enable SMEs to fully grasp the potentials of Industry 4.0, which remain out of reach of Industry 4.0 users. SMEs are therefore advised to consider both roles but encouraged to take the risks to become an Industry 4.0 provider.

7. Conclusion

This paper attempts to provide insights for manufacturing SMEs and how Industry 4.0 affects their business model, leading to specific integration strategies. Thereby, this study contributes to the current body of knowledge regarding business models and Industry 4.0. It presents findings that are especially focused on SMEs as one of the few studies in this context. The detailed differentiation among the nine building blocks for Industry 4.0 providers and users is unique and unseen in literature thus far. This approach is especially suitable regarding SMEs due to their focused and mostly singular business model.

Furthermore, the approach of differentiating between the perspectives of a provider and a user of a technology has scarcely been conducted thus far. It is expandable to other technologically triggered business model innovations and provides an insightful field for future research. The results show that distinct characteristics between these two perspectives exist, whereas providers of Industry 4.0-based solutions expect the most changes in their business models in regard to Industry 4.0. Notably, a significantly larger proportion of SMEs in this study can be characterized as Industry 4.0 users, with Industry 4.0 providers only constituting about one quarter of the sample. This information can be extended for the Industrial Internet of Things and comparable concepts to Industry 4.0 outside of Germany.

Because of the exploratory qualitative research design, the study also inherits several limitations. Exploratory research in this case has the difficulty that Industry 4.0 might be at its start of implementation for many of the surveyed SMEs. A generalization to other research settings without verification using different research methods is considered critical. However, as the study follows a replication sampling logic ( Yin, 2009 ) and encompasses 43 interviews, the results can present general patterns about impacts on business models regarding Industry 4.0. As a further limitation, this study mainly relies on qualitative results and uses case materials exclusively for validation. Future research could include case material in order to extend the empirical results obtained.

Further recommendations for future research include quantitative research methods in order to verify the qualitative research results. A comparison to larger enterprises, if those can be condensed to a single business model referring to Industry 4.0, would present further valuable insights for this topic. The interdependence between different elements within a business model is of particular interest, also in connection with reasons for the adoption of new or changed business models and their implementation processes. An investigation regarding the implementation of business model adaptions over time should be considered, investigating the interplay with partners, suppliers and customers. The managerial implications proposed must be verified in practice, whereas their effectiveness needs to be addressed in future research.

Business model Canvas

Annual turnover and number of employees within sample

Ratio of building blocks affected for provider and user perspectives of Industry 4.0

Business model building blocks and exemplary changes through Industry 4.0

Total ratio of perspectives and their predominant naming of building blocks

Arnold , C. , Kiel , D. and Voigt , K.I. ( 2016 ), “ How the industrial internet of things changes business models in different manufacturing industries ”, International Journal of Innovation Management , Vol. 20 No. 8 , pp. 1 - 25 .

Baden-Fuller , C. and Haefliger , S. ( 2013 ), “ Business models and technological innovation ”, Long Range Planning , Vol. 46 No. 6 , pp. 419 - 426 .

Bresnahan , T.F. and Trajtenberg , M. ( 1995 ), “ General purpose technologies: engines of growth? ”, Journal of Econometrics , Vol. 65 No. 1 , pp. 83 - 108 .

Brettel , M. , Friederichsen , N. , Keller , M. and Rosenberg , M. ( 2014 ), “ How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective ”, International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering , Vol. 8 No. 1 , pp. 37 - 44 .

Casadesus-Masanell , R. and Ricart , J.E. ( 2010 ), “ Competitiveness: business model reconfiguration for innovation and internationalization ”, Management Research: The Journal of the Iberoamerican Academy of Management , Vol. 35 No. 6 , pp. 123 - 149 .

Chesbrough , H. ( 2010 ), “ Business model innovation: opportunities and barriers ”, Long Range Planning , Vol. 43 Nos 2/3 , pp. 354 - 363 .

Davis , J. , Edgar , T. , Porter , J. , Bernaden , J. and Sarli , M. ( 2012 ), “ Smart manufacturing, manufacturing intelligence and demand-dynamic performance ”, Computers & Chemical Engineering , Vol. 47 , pp. 145 - 156 .

Demil , B. , Lecocq , X. , Ricart , J.E. and Zott , C. ( 2015 ), “ Introduction to the SEJ special issue on business models: business models within the domain of strategic entrepreneurship ”, Strategic Entrepreneurship Journal , Vol. 9 No. 1 , pp. 1 - 11 .

Dubé , L. and Paré , G. ( 2003 ), “ Rigor in information systems positivist case research: current practices, trends, and recommendations ”, MIS Quarterly , Vol. 27 No. 4 , pp. 597 - 636 .

Echterfeld , J. , Amshoff , B. and Gausemeier , J. ( 2016 ), “ How to use business model patterns for exploiting disruptive technologies ”, Proceedings of the 24th IAMOT Conference , Cape Town .

Edmondson , A.C. and McManus , S.E. ( 2007 ), “ Methodological fit in management field research ”, Academy of Management Review , Vol. 32 No. 4 , pp. 1246 - 1264 .

Ehret , M. and Wirtz , J. ( 2017 ), “ Unlocking value from machines: business models and the industrial internet of things ”, Journal of Marketing Management , Vol. 30 Nos 1/2 , pp. 111 - 130 .

Eisenhardt , K.M. and Graebner , M.E. ( 2007 ), “ Theory building from cases: opportunities and challenges ”, Academy of Management Journal , Vol. 50 No. 1 , pp. 25 - 32 .

European Commission ( 2016 ), “ Factories of the future ”, available at: http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html (accessed November 20, 2018 ).

Frazzon , E.M. , Hartmann , J. , Makuschewitz , T. and Scholz-Reiter , B. ( 2013 ), “ Towards socio-cyber-physical systems in production networks ”, Procedia CIRP , Vol. 7 , pp. 49 - 54 .

German Federal Ministry of Economic Affairs and Energy ( 2014 ), “ German Mittelstand: Motor der deutschen Wirtschaft ”, German Federal Ministry of Economic Affairs and Energy , Berlin .

Gibbert , M. , Ruigrok , W. and Wicki , B. ( 2008 ), “ What passes as a rigorous case study? ”, Strategic Management Journal , Vol. 29 No. 13 , pp. 1465 - 1474 .

Glick , W.H. , Huber , G.P. , Miller , C.C. , Doty , D.H. and Sutcliffe , K.M. ( 1990 ), “ Studying changes inorganizational design and effectiveness: retrospective event histories and periodic assessments ”, Organization Science , Vol. 1 No. 3 , pp. 293 - 312 .

Gorecky , D. , Schmitt , M. , Loskyll , M. and Zuhlke , D. ( 2014 ), “ Human-machine-interaction in the industry 4.0 era ”, Proceedings of IEEE International Conference on Industrial Informatics (INDIN) , Porto Alegre .

Guinard , D. , Trifa , V. , Karnouskos , S. , Spiess , P. and Savio , D. ( 2010 ), “ Interacting with the SOA-based internet of things: discovery, query, selection, and on-demand provisioning of web services ”, IEEE Transactions on Services Computing , Vol. 3 No. 3 , pp. 223 - 235 .

Haddud , A. , DeSouza , A. , Khare , A. and Lee , H. ( 2017 ), “ Examining potential benefits and challenges associated with the internet of things integration in supply chains ”, Journal of Manufacturing Technology Management , Vol. 28 No. 8 , pp. 1055 - 1085 .

He , W. and Xu , L. ( 2015 ), “ A state-of-the-art survey of cloud manufacturing ”, International Journal of Computer Integrated Manufacturing , Vol. 28 No. 3 , pp. 239 - 250 .

Hirsch-Kreinsen , H. ( 2016 ), “ Digitization of industrial work: development paths and prospects ”, Journal for Labour Market Research , Vol. 49 No. 1 , pp. 1 - 14 .

Holsti , O.R. ( 1968 ), “ Content analysis ”, in Lindzey , G. and Aronson , E. (Eds), The Handbook of Social Psychology , McGraw-Hill , New York, NY , pp. 596 - 692 .

Holsti , O.R. ( 1969 ), Content Analysis for The Social Sciences and Humanities , Addison-Wesley , Reading, MA .

Huber , G.P. and Power , D.J. ( 1985 ), “ Retrospective reports of strategic-level managers: guidelines for increasing their accuracy ”, Strategic Management Journal , Vol. 6 No. 2 , pp. 171 - 180 .

Hughes , L.C. and Preski , S. ( 1997 ), “ Using key informant methods in organizational survey research: assessing for informant bias ”, Research in Nursing and Health , Vol. 20 No. 1 , pp. 81 - 92 .

Kagermann , H. , Wahlster , W. and Helbig , J. ( 2013 ), “ Recommendations for implementing the strategic initiative Industrie 4.0 – final report of the Industrie 4.0 working group ”, Communication Promoters Group of the Industry-Science Research, Frankfurt .

Kiel , D. , Arnold , C. and Voigt , K.I. ( 2017 ), “ The influence of the industrial internet of things on business models of established manufacturing companies – a business level perspective ”, Technovation , Vol. 68 , pp. 4 - 19 .

Kiel , D. , Müller , J.M. and Voigt , K.I. ( 2017 ), “ Sustainable industrial value creation: benefits and challenges of industry 4.0 ”, International Journal of Innovation Management , Vol. 21 No. 8 , pp. 1 - 34 .

Kumar , N. , Stern , L.W. and Anderson , J.C. ( 1993 ), “ Conducting interorganizational research using key informants ”, Academy of Management Journal , Vol. 36 No. 6 , pp. 1633 - 1651 .

Lasi , H. , Kemper , H.G. , Fettke , P. , Feld , T. and Hoffmann , M. ( 2014 ), “ Industry 4.0 ”, Business & Information Systems Engineering , Vol. 6 No. 4 , pp. 239 - 242 .

Lennartson , B. , Bengtsson , K. , Yuan , C. , Andersson , K. , Fabian , M. , Falkman , P. and Akesson , K. ( 2010 ), “ Sequence planning for integrated product, process and automation design ”, IEEE Transactions on Automation Science and Engineering , Vol. 7 No. 4 , pp. 791 - 802 .

Liao , Y. , Deschamps , F. , Loures , E.F.R. and Ramos , L.F.P. ( 2017 ), “ Past, present and future of industry 4.0 – a systematic literature review and research agenda proposal ”, International Journal of Production Research , Vol. 55 No. 12 , pp. 3609 - 3629 .

Loebbecke , C. and Picot , A. ( 2015 ), “ Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda ”, The Journal of Strategic Information Systems , Vol. 24 No. 3 , pp. 149 - 157 .

Markides , C. and Charitou , C.D. ( 2004 ), “ Competing with dual business models: a contingency approach ”, The Academy Management Executive , Vol. 183 No. 3 , pp. 22 - 36 .

Maynard , A.D. ( 2015 ), “ Navigating the fourth industrial revolution ”, Nature Nanotechnology , Vol. 10 No. 12 , pp. 1005 - 1006 .

Mikusz , M. ( 2016 ), “ Towards an understanding of cyber-physical systems as industrial software-product-service systems ”, Procedia CIRP , Vol. 16 , pp. 385 - 389 .

Miles , M.B. and Huberman , M.A. ( 1994 ), Qualitative Data Analysis , Sage , Thousand Oaks, CA .

Müller , J. and Voigt , K.I. ( 2016 ), “ Industrie 4.0 für kleine und mittlere Unternehmen ”, Productivity Management , Vol. 3 , pp. 28 - 30 .

Müller , J.M. and Voigt , K.I. ( 2018 ), “ Sustainable industrial value creation in SMEs: a comparison between industry 4.0 and Made in China 2025 ”, International Journal of Precision Engineering and Manufacturing – Green Technology , Vol. 5 No. 5 , pp. 659 - 670 .

Müller , J.M. , Buliga , O. and Voigt , K.I. ( 2018 ), “ Fortune favors the prepared: how SMEs approach business model innovations in Industry 4.0 ”, Technological Forecasting and Social Change , Vol. 132 , pp. 2 - 17 .

Müller , J.M. , Kiel , D. and Voigt , K.I. ( 2018 ), “ What drives the implementation of industry 4.0? The role of opportunities and challenges in the context of sustainability ”, Sustainability , Vol. 10 No. 1 , pp. 247 - 271 .

Müller , J.M. , Pommeranz , B. , Weisser , J. and Voigt , K.I. ( 2018 ), “ Digital, social media, and mobile marketing in industrial buying: still in need of customer segmentation? Empirical evidence from Poland and Germany ”, Industrial Marketing Management , Vol. 73 , pp. 70 - 83 .

Oesterreich , T.D. and Teuteberg , F. ( 2016 ), “ Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry ”, Computers in Industry , Vol. 83 , pp. 121 - 139 .

Osterwalder , A. and Pigneur , Y. ( 2010 ), Business Model Generation. A Handbook for Visionaries, Game Changers, and Challengers , Wiley , Hoboken, NJ .

Osterwalder , A. , Pigneur , Y. and Tucci , C.L. ( 2005 ), “ Clarifying business models: origins, present, and future of the concept ”, Communications of the AIS , Vol. 16 No. 1 , pp. 1 - 25 .

Porter , M.E. and Heppelmann , J.E. ( 2014 ), “ How smart connected products are transforming competition ”, Harvard Business Review , Vol. 92 No. 11 , pp. 64 - 88 .

Rachinger , M. , Rauter , R. , Müller , C. , Vorraber , W. and Schirgi , E. ( 2018 ), “ Digitalization and its influence on business model innovation ”, Journal of Manufacturing Technology Management (in press) .

Radziwon , A. , Bilberg , A. , Bogers , M. and Madsen , E.S. ( 2014 ), “ The smart factory: exploring adaptive and flexible manufacturing solutions ”, Procedia Engineering , Vol. 69 , pp. 1184 - 1190 .

Raja , J.Z. , Bourne , D. , Goffin , K. , Çakkol , M. and Martinez , V. ( 2013 ), “ Achieving customer satisfaction through integrated products and services: an exploratory study ”, Journal of Product Innovation Management , Vol. 30 No. 6 , pp. 1128 - 1144 .

Ren , L. , Zhang , L. , Tao , F. , Zhao , C. , Chai , X. and Zhao , X. ( 2013 ), “ Cloud manufacturing: from concept to practice ”, Enterprise Information Systems , Vol. 9 No. 2 , pp. 186 - 209 .

Saebi , T. , Lien , L. and Foss , N.J. ( 2016 ), “ What drives business model adaptation? The impact of opportunities, threats and strategic orientation ”, Long Range Planning , Vol. 50 No. 5 , pp. 567 - 581 .

Schlechtendahl , J. , Keinert , M. , Kretschmer , F. , Lechler , A. and Verl , A. ( 2015 ), “ Making existing production systems Industry 4.0-ready ”, Production Engineering , Vol. 9 No. 1 , pp. 143 - 148 .

Sharma , M.K. and Bhagwat , R. ( 2006 ), “ Practice of information systems: evidence from select Indian SMEs ”, Journal of Manufacturing Technology Management , Vol. 17 No. 2 , pp. 199 - 223 .

Shin , S.-J. , Woo , J. and Rachuri , S. ( 2014 ), “ Predictive analytics model for power consumption in manufacturing ”, Procedia CIRP , Vol. 15 , pp. 153 - 158 .

Tao , F. , Zhang , L. , Venkatesh , V.C. , Luo , Y. and Cheng , Y. ( 2011 ), “ Cloud manufacturing: a computing and service-oriented manufacturing model ”, Proceedings of the Institution of Mechanical Engineering, Part B: Journal of Engineering Manufacture, Vol. 225 , pp. 1969 - 1976 .

Veza , I. , Mladineo , M. and Gjeldum , N. ( 2015 ), “ Managing innovative production network of smart factories ”, IFAC-PapersOnLine , Vol. 48 No. 3 , pp. 555 - 560 .

Vogel-Heuser , B. , Fay , A. , Schaefer , I. and Tichy , M. ( 2015 ), “ Evolution of software in automated production systems: challenges and research directions ”, Journal of Systems and Software , Vol. 110 , pp. 54 - 84 .

Wang , S. , Wan , J. , Zhang , D. , Li , D. and Zhang , C. ( 2016 ), “ Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feed-back and coordination ”, Computer Networks , Vol. 101 , pp. 158 - 168 .

Wirtz , B.W. , Pistoia , A. , Ullrich , S. and Göttel , V. ( 2016 ), “ Business models: origin, development and future research perspectives ”, Long Range Planning , Vol. 49 No. 1 , pp. 36 - 54 .

Xu , L.D. , He , W. and Li , S. ( 2014 ), “ Internet of things in industries: a survey ”, IEEE Transactions on Industrial Informatics , Vol. 10 No. 4 , pp. 2233 - 2243 .

Xu , X. ( 2012 ), “ From cloud computing to cloud manufacturing: robotics and computer-integrated manufacturing ”, Robotics and Computer-Integrated Manufacturing , Vol. 28 No. 1 , pp. 75 - 86 .

Yin , R.K. ( 2009 ), Case Study Research: Design and Methods , Sage , Thousand Oaks, CA .

Zhong , R.Y. , Xu , X. , Klotz , E. and Newman , S.T. ( 2017 ), “ Intelligent manufacturing in the context of industry 4.0: a review ”, Engineering , Vol. 3 No. 5 , pp. 616 - 630 .

Zott , C. and Amit , R. ( 2013 ), “ The business model: a theoretically anchored robust construct for strategic analysis ”, Strategic Organization , Vol. 11 No. 4 , pp. 403 - 411 .

Zott , C. , Amit , R. and Massa , L. ( 2011 ), “ The business model: recent developments and future research ”, Journal of Management , Vol. 37 No. 4 , pp. 1019 - 1042 .

Zuehlke , D. ( 2010 ), “ Smart factory: towards a factory-of-things ”, Annual Reviews in Control , Vol. 34 No. 1 , pp. 129 - 138 .

Further reading

Kothman , I. and Faber , N. ( 2016 ), “ How 3D printing technology changes the rules of the game: insights from the construction sector ”, Journal of Manufacturing Technology Management , Vol. 27 No. 7 , pp. 932 - 943 .

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Telecommunications

Business 4.0: A New Business Model for Industry 4.0 

• Digitization
• Published 17 Feb 2021
• 4 min reading

It’s the beginning of 2021, and we’re right at the brink of the Fourth Industrial Revolution, which we like to call “Industry 4.0”. With increased automation, utilization of artificial intelligence (AI) and machine learning (ML), the emergence of the Internet of Things (IoT) and expectations of 5G services, Industry 4.0 has become the new reality. How has this affected business? How are these technological advances influencing industries such as telco and IT, and their ways of working?

Business as usual?

With every industrial revolution come business changes, and Industry 4.0 will be no different. In 2021, it will bring a new business model – Business 4.0 – which will most likely be the hot topic of the year. What does business 4.0 include and what opportunities will bring?

“Business 4.0 will be a new business model that emerges from Industry 4.0. In this concept, where everything is connected, businesses will have to be more united as well. It is not only about utilizing new technologies, such as the IoT, 5G, AI, ML, etc. – it’s about bringing a new business model to life. This business model will be all about cooperation and togetherness.” source: 2021 Telecom Predictions Ebook 

In Business 4.0, connectivity is the key

The new business model will revolutionize the way digital/communication service providers work as it will dramatically change their services. Complex, highly distributed infrastructure, multiple devices and networks, new disruptive technologies and innovative services – these all drive change in business operations.

Industry 4.0 business model will impose a new way of working , one that is remote, distributed, automated, and, above all, connected. Organizations from different industries and sectors will need to cooperate more closely than ever. As their services will change, so will their cooperation. Everyone will be dependent on everyone else.

Security and separation will be crucial

In all this connectiveness, it will be important to keep businesses secure and separate. Secure communication and cooperation will be the key to succeeding. As there will no more be isolated businesses that can depend only on themselves, security will safeguard the chain reaction.

Business 4.0 use case example

This may sound a bit complicated. So, to put it simply, let’s think about an example from our telco field. In Business 4.0, one telecom (T1) may provide a network for many others. Another one (T2) provides optical fiber. Many other telcos (TX) use the network and the infrastructure in order to provide different services to their customers. All of those CSPs – T1, T2, TX - are connected. But at the same time, they all need to stay separated and secure in order to protect their customers and their business. Their commercial value and intellectual property must stay secure.

As you can see, Industry 4.0 and the new business model that it entails provide communication/digital service providers with fresh challenges, but also many benefits. And it is in this year that operators should take a hard look at this concept.

Would you like to discover more expert predictions for 2021? Download our free ebook here and learn what the future holds.

Having gained solid team-building and management skills in previous roles, Rajmund Zieliński brought his holistic approach to business analysis to Comarch. With a firm grounding in project implementation and transitioning and a sound understanding of the agile management philosophy, his economic and telecommunications industry expertise allow him to strike the best strategic balance that delivers on the aspirations of clients and the interests of his own organization.

 Learn how telecoms can prepare to deploy network slicing in the first episode of this year’s Comarch campaign, entitled “Telecoms Journey Towards 5G Monetization”.

How can AI-powered maintenance solutions help telecoms in monetizing 5G networks? 

How can communication and digital service providers make the #SmartFactory concept smarter? 

Learn more about developing a successful 5G monetization business strategy, by viewing the latest episode in our campaign.

Please wait

Related articles

Optical network technologies ensure lightning-fast data transfer while significantly reducing the energy infrastructure’s energy consumption. Find out how the photonics-electronics convergence technology enables near-real-time connections.

In the face of growing demands and the complexity of network infrastructure, telecom operators seek ways to improve efficiency and reliability of processes. Learn how artificial intelligence-powered tools can aid their mission.

Telcos are constantly expanding their portfolios, going beyond traditional connectivity services. Learn how techco evolution and unified applications will allow for the merging of various aspects of our digital lives.

Empowering Telecoms with AI