3D-CUBE readiness model for industry 4.0: technological, organizational, and process maturity enablers

Figures & data

Figure 1. Timeframe of the analyzed MMs. source: authors.

Figure 2. The framework of the proposed readiness model. source: authors.

Figure 3. 3D-CUBE readiness model levels. source: authors.

Figure 4. 3D-CUBE readiness model. source: authors.

Table 1. 3D-CUBE dimensions, sub-dimensions, and elements (source: Author).

DIMENSIONS	SUB-DIMENSIONS	ELEMENTS
ORGANIZATIONAL ENABLERS (X)	Organizational Strategy (X1)	Top-Down Support and Governance (X11)
		Organizational Structure Management (X12)
		Business Model Management (X13)
		Regulatory Compliance and Contractual Relations (X14)
	Human Workforce (X2)	Leadership (X21)
		Communication (X22)
		Training (X23)
		Culture of Innovation (X24)
TECHNOLOGICAL ENABLERS (Y)	Production Technology (Y1)	Anthropomorphic Support Systems (Y11)
		Cognitive Support Systems (Y12)
		Managerial Support Systems (Y13)
		Driving Network Production (Y14)
	Information Technology (Y2)	Data Collection, Analysis, Interconnectivity, and Transparency (Y21)
		Information Security (Y22)
		Decentralized Decisions (Y23)
PROCESSES MATURITY (Z)	Product-Service Development (Z1)	Cross-company Engineering, Research, and Development (Z11)
		Customer-based New Product Development (Z12)
		Supply-Chain Development (Z13)
	Order Fulfilment (Z2)	Customized-based Production System (Z21)
		Sales and Operations Integration (Z22)
		Smart Quality Management System (Z23)

Table 2. Readiness vector matrix (source: Author).

LEVEL	READINESS VALUES MATRIX: R (X,Y,Z)
0	(0,0,0)	(0,0,1)	(0,1,0)	(1,0,0)	(0,1,1)	(1,1,0)	(1,0,1)
1	(1,1,1)	(1,1,2)	(1,2,1)	(2,1,1)	(1,2,2)	(2,2,1)	(2,1,2)
2	(2,2,2)	(2,2,3)	(2,3,2)	(3,2,2)	(2,3,3)	(3,3,2)	(3,2,3)
3	(3,3,3)	(3,3,4)	(3,4,3)	(4,3,3)	(3,4,4)	(4,4,3)	(4,3,4)
4	(4,4,4)	(4,4,5)	(4,5,4)	(5,4,4)	(4,5,5)	(5,5,4)	(5,4,5)
5	(5,5,5)

Figure 5. Analyzing the sub-dimensions of the 3D-CUBE model. source: authors.

Table

N	Author	Country	Year	Enterprise Type	Model Name	Model Type	Input Dimensions	Output Levels	Main Output	Tested?	Critical Analysis
1	R. L. Nolan	EUA	1973	General Enterprises	Computer Resource MM	Computer Resource	(1) Priority setting(2) Budget(3) Computer operations(4) Programming control(5) Project management(6) Mgt. reporting system(7) Data base policies(8) Chg. out/non-chg. out systems(9) Audits(10) Quality control(11) Manual systems and procedures	1: Initiation 2: Contagion 3: Control 4: Integration	Maturity	Yes	Topic not applicable nowadays. This model was important for later models but had old templates and no web application. Similarly, some models use delayed data of the enterprises, so they are no longer up-to-date.
2	K. Lichtblau et al.	Germany	2015	Large and SME Enterprises (Mechanical, Plant Engineering and Manufacturing industries)	IMPULS	Smart Manufacturing	(1) Strategy & organization(2) Smart factory(3) Smart operations(4) Smart products(5) Data-driven services(6) Employees	0: Outsider 1: Beginner 2: Intermediate 3: Experienced 4: Expert 5: Top performer	Readiness	Yes	Lichtblau et al. (2015) state some questions of the online IMPULS form that do not follow a profile of the six levels as the maturity model proposes. It brings a question of why they appear in the form. Moreover, a juxtaposition of dimensions, for example, among smart factories and smart operations, could generate doubts for respondents regarding the clarity of each dimension and their questions to answer. It emphasizes the process view without tracking the common company functions such as engineering, marketing, manufacturing, and finance; however, most companies are structured in these functional units. Therefore, it can be difficult to identify the right person in a company who is familiar with the model’s queried information. The model is only empirically grounded.
											It uses a questionnaire, but it is not transparent and not validated, and it has just an online self-assessment based on a questionnaire. It doesn’t describe an assessment method; it is for readiness only and doesn’t consider all organizational aspects. The model is only empirically grounded. The model does not consider a few key technologies such as AI, AR, VR, smart glasses, and Blockchain Technology. The weights of each dimension are decided but not for the items, and they are decided only with the help of a survey conducted in the firms.
3	M. C. Paulk, B. Curtis, M. B. Chrissis, C. V. Weber	EUA	1993	General Enterprises	CMM (Capability Maturity Model)	General	(1) Commitment to perform (2) Ability to perform (3) Activities performed (4) Measurement and analysis (5) Verifying implementation	1: Initial 2: Repeatable 3: Defined 4: Managed 5: Optimizing	Capability	Yes	Traditional models, like CMM and its variations, were built only for well-established processes, such as software or product development. However, the outcomes of the new industrial revolution are still uncertain, especially when the processes involved are not known. So, the CMM is not a silver bullet and does not address all the important issues for successful projects in Industry 4.0. For example, it does not currently address expertise in particular application domains, advocate specific software technologies, or suggest how to select, hire, motivate, and retain competent people. However, these issues are crucial to a project’s success.
4	A. Schumacher et al.	Austria	2016	General Manufacturing Enterprises (not SME)	I 4.0 Schumacher MM	Smart Manufacturing	(1) Strategy(2) Leadership(3) Customers(4) Products(5) Operations(6) Culture(7) People(8) Governance(9) Technology	1: Complete lack of attributes 2: (no info) 3: (no info) 4: (no info) 5: State-of-the-art	Maturity	Yes	Schumacher et al. (2016) lack a process view connecting the whole supply chain. The model didn’t address the lean aspects or identify improvement opportunities or a roadmap for further developments. Furthermore, an SME perspective is also missing from the model. It uses a validated questionnaire, but it is not a transparent methodology. It doesn’t have a maturity definition or just general recommendations. The model extends existing maturity models and focuses on discrete manufacturing firms. It lacks details regarding maturity items and inadequate information regarding maturity levels.
5	M. B. Chrissis et al.	EUA	2003	General Enterprises	CMMI (Capability Maturity Model Integration)	General	(1) Systems Engineering(2) Software Engineering	0: Incomplete – Doesn´t have any process 1: Performed – No support infrastructure 2: Managed – Not performed across the organization 3: Defined – Doesn’t have way to measure and improve 4: Quantitatively Managed – Can quantitatively maximize business 5: Optimized – Has all of these carachteristics	Capability	Yes	Chrissis et al. (2003) state some clauses that are difficult to map. It is difficult to use and needs professional judgment to interpret the results, and input dimensions are difficult to understand. Output levels differ from Continuous and Staged representation capability levels. Not feasible for a web-based application.
6	De Bruin et al.	Australia	2005	General Enterprises	BPM – MM (Business Process Management Maturity Model)	General	(1) Factor(2) Maturity Stage(3) Scope Organizational Entity(4) Scope Time(5) Coverage(6) Proficiency	1: Initial State 2: Defined 3: Repeated 4: Managed 5: Optimized	Maturity	Yes	This model is incomplete at this stage and the impact of their findings and success in overcoming/minimizing inherent criticism is unknown. At this stage, there is no empirical evidence for the correlation between the factors of the BPMM model and BPM success. Further testing of the relationship between independent and dependent variables will be the core of future work.
7	C. Leyh et al.	Germany	2016	SME	SIMMI 4.0 (System Integration Maturity Model Industry 4.0)	IT and Software (digital integrationtechnologies)	(1) Vertical integration(2) Horizontal Integration(3) Digital product development(4) Intersection of technologies (crosssectional technology criteria)	1: Basic Digitalisation 2: Crossed 3: Horizontal 4: Vertical 5: Total	Maturity	No	Leyh et al. (2016) are rather vague when describing how technology can be used for integration. Organizational enablers are treated as elements of maturity levels. Digital competencies and technologies outside the field of IT are not discussed. The model is simple to understand with a focus on the IT landscape in SMEs, but it doesn’t have a questionnaire, assessment method described, or continuous assessment. It just has general recommendations. The scientific documentation of model development is missing, and it is not empirically developed, validated, and tested. The details about the maturity assessment and readiness level are missing. Derivated from RAMI 4.0 (The Reference Architectural Model Industry 4.0), the model is missing in crucial dimensions, like organizational awareness and cybersecurity.
8	M. Kerrigan	Ireland	2013	General Enterprises	DI-CMM (Digital Investigations Capability MM)	Digital investigations	(1) Processes(2) People(3) Technology	1: Ad-hoc 2: Performed 3: Defined 4: Managed 5: Optimized	Capability	Yes	Kerrigan (2013) proposes the DI-CMM Model. It needs to be tested against more organizations to assess its usefulness fully as a benchmarking tool. While the DI-CMM has been developed with the needs of regulatory bodies and criminal investigations in mind, further refinement could make it applicable to various other sectors; for example, large corporations that maintain internal digital investigations capabilities.
9	R. Geissbauer et al.	USA	2016	Companies with morethan 500 employees, interaction company-customer	PwC SA (Price WaterhouseCoopers)	SCM scope	(1) Processes & Infrastructure(2) Vision, Strategy & Business Model (3) Customer Engagement(4) People, Culture, Governance & Organisation(5) Product and service portfolio(6) Market and customer access(7) Value chain(8) IT architecture(9) Compliance, risk, security, tax	1: Beginner 2: Vertical Integrator 3: Horizontal Collaboration 4: Digital Specialist	Readiness	Yes	Geissbauer et al. (2016) is a consulting-based model, not an applied scientific effort. The maturity dimensions are not elaborated on in detail. Considering it was proposed in 2016 when the effort to propose maturity models was starting, some maturity levels and dimensions lack synergy. It doesn’t use a traditional questionnaire and a transparent methodology. It has just an online self-assessment. The scientific documentation of the model is missing.
10	Rockwell Automation Inc	USA	2014	General Enterprises	ROCKWELL	Smart Manufacturing	(1) Customer-focused innovation(2) Process improvements(3) Supply Chain management & collaboration(4) Human-resource management(5) Sustainability(6) Global engagement	1: Assessment 2: Secure & upgraded network and controls 3: Defined & organized working data capital 4: Analytics 5: Collaboration	Readiness	Yes	It uses a validated questionnaire but is not a transparent methodology with no explicit assessment approach. The model focuses only on the facets of the existing IT/OT network and inadequately addresses the organizational and operations-related dimensions. The model has insufficient details about its structure and maturity items and notably lacks scientific documentation.
11	J. Ganzarain, N. Errasti	Spain	2016	SME	3SMM (3 Stage MM)	Smart Manufacturing	(1) envision(2) enable(3) enact	1: Initial 2: Managed 3: Defined 4: Transformation 5: Detailed Business Model	Maturity	No	Doesn’t have questionnaire.
12	B. Gajsek et al.	Slovenia	2019	General Enterprises	FDMM4.0 (Forrester Digital Maturity Model 4.0)	General I4.0	(1) Culture(2) Organisation(3) Technology(4) Insights	1: Skeptics 2: Adopters 3: Collaborators 4: Differentiators	Capability	Yes	Gajsek et al. (2019) presents a digital maturity model that is useful for general diagnostics but insufficient for more detailed improvement planning.
13	G. Schuh et al.	Germany	2016	General Enterprises	ACATECH Industrie 4.0 Maturity Index	General	(1) Resources(2) Information Systems(3) Corporate Culture(4) Organisational Structure	1: Informatization/Computerisation 2: Connectivity 3: Visibility 4: Transparency 5: Predictive Capacity 6: Adaptability	Maturity	No	Despite its well-integrated concepts, it lacks clarity in its evaluation, with few and incomplete examples provided. Moreover, the suggested improvements are area-specific. Consequently, the model only suggests capability improvements, not maturity improvements, which can strengthen differences in silo-areas. A lack of technology consideration for the proposed process analysis makes it difficult to comprehend the differences between the maturity analysis for Industry 4.0 and a generic improvement analysis for increasing something in the company´s performance. Even though the study is designed specifically for manufacturing companies, a section dedicated to Lean Production Systems (LPS) or existing production systems is missing. The model is also missing the perspectives concerning SMEs. It has a proposal of generic archetypes related to the different digital maturity stages but no explicit indications of activities for enabling maturity stage transition. It has just general recommendations and doesn’t have a continuous assessment. Detailed information regarding the structure of the model is given, but the quantitative assessment process is missing.
14	E.Gökalp et al.	Turkey	2017	General Enterprises	SPICE MM	Smart Manufacturing	(1) Asset Management(2) Data Governance(3) Application Management(4) Process Transformation(5) Organizational Alignment	0: Incomplete 1: Performed 2: Managed 3: Established 4: Predictable 5: Optimizing/Innovating	Maturity	No	Gökalp et al. (2017) don’t have a questionnaire and assessment method described. It is not a transparent methodology, and it has just general recommendations. The readiness evaluation process is not explained in detail, and the model lacks an account of scientific and empirical-based development. There is no testing and validation of the model in real-life applications.
15	A. De Carolis et al.	Italy	2017	General Enterprises	DREAMY (Digital REadiness Assessment MaturitY)	Smart Manufacturing	(1) Processes(2) Control(3) Monitoring(4) Organization(5) Technology	1: Initial 2: Managed 3: Defined 4: Integrated & Interoperated 5: Digitally Oriented	Maturity	Yes	De Carolis et al. (2017) did not explore the technological enablers of its interconnectivity dimension. It has a validated questionnaire, but it is not a transparent methodology. There are no explicit indications for maturity improvement. It doesn’t have an assessment method described and presents just general recommendations.
16	C. Weber et al.	Germany	2017	General Enterprises	M2DDM (Maturity Model for DataDrivenManufacturing)	Smart Manufacturing, IT architecture, IoT	(1) Data storage and compute(2) Service oriented architecture(3) Information integration(4) Digital twins(5) Advanced analytics(6) Real-time capabilities	0: Nonexistent IT integration 1: Data and system integration 2: Integration of Cross-lifecycle data 3: Service orientation 4: Digital twins 5: Self-optimising factory	Capability	No	Doesn’t have questionnaire.
17	O. Agca et al.	United Kingdom	2017	General Enterprises	WMG (The University of Warwick)	General	(1) Strategy & Organization(2) Products & Services(3) Manufacturing & Operations(4) Supply Chain(5) Business Model(6) Legal Considerations	1: Beginner 2: Intermediate 3: Experienced 4: Expert	Maturity	Yes	Agca et al. (2017) present a model that lacks an alignment among maturity levels and technology applications. It doesn’t have an assessment method described and has just general recommendations. The model structure is elaborated along with details about sub-dimensions. However, it has fewer details regarding maturity assessment and identification of maturity level. The model is not scientifically developed, and no empirical validation in a real-life environment is done.
18	E.Pessl et al.	Austria	2017	General Enterprises	Capability Human Roadmap 4.0 MM	Human	(1) Acceptance & Application of new Technologies and Media(2) Professional Competence(3) Learning Competence(4) Corporate Strategy(5) Human Resources Development Strategy(6) Organization & Democratization(7) Flexible Working Models(8) Health & Safety(9) Information & Communication(10) Employer Branding(11) Change Management(12) Process Orientation(13) Knowledge Management	1: I 4.0 is not considered 2: Company has begun to deal with I 4.0 3: Measures are implemented in some areas. 4: Measures are implemented by a majority 5: Measures are consistently implemented and evaluated	Maturity	Yes	Pessl et al. (2017) present a well-described but complex model where people from different divisions should ideally be included in the maturity assessment process. Moreover, the definition of the target requirements and the implementation of the final action plan become especially challenging if industry 4.0 has not yet been an embedded part of the overall strategy. It has a questionnaire not validated and is not a transparent methodology. It doesn’t have an assessment method or continuous assessment and incorporates just general recommendations.
19	SINGAPORE Economic Development Board (EDB)	Singapore	2017	General Enterprises	SINGAPORE READINESS INDEX	Manufacturing Enterprises	(1) Vertical Integration(2) Horizontal Integration(3) Leadership Competency(4) Strategy & Governance(5) Inter- Intra Company Collaboration(6) Integrated Product Life-cycle(7) Workforce Learning & Development	0: Undefined 1: Defined 2: Digital 3: Integrated 4: Automated 5: Intelligent	Readiness	Yes (tested by T. Lin⁠, K. J. Wang, M. L. Sheng)	It is well-done research intended to analyze only manufacturing sites. However, first: the questionnaires were only distributed to Taiwanese enterprises within a specific industry, thus limiting the validity of the findings. Second, the subjects of this study are only manufacturers. Third, this study examines only manufacturing sites (not including executives and senior managers).
20	K. Y. Akdil et al.	Turkey	2018	General Enterprises (Retail Sector)	Akdil MM	SCM scope	(1) Smart products & services(2) Smart business processes(3) Strategy & Organization	0: Absence 1: Existence 2: Survival 3: Maturity	Maturity and Readiness	Yes	The diagnostic maturity form is unclear regarding the four levels for each question. It doesn’t have a traditional questionnaire, just some complex architecture with an index to translate answers to a specific maturity level. The model fails to provide empirical validation of maturity items, and important levels of items and dimensions are not considered.
21	S. Mittal⁠ et al.	USA	2018	SME	SM3E (Smart Manufacturing Maturity Model for SMEs)	General	(1) Finance(2) People(3) Strategy(4) Process(5) Product	1: Novice 2: Beginner 3: Learner 4: Intermediate 5: Expert	Maturity	No	Mittal et al. (2018) present only one (digital) capability: “data-driven decision making”.
22	L. Canetta et al.	Switzerland	2018	General Enterprises	Digitalization Canetta MM	Smart Manufacturing	(1) Strategy(2) Processes(3) Products & Services(4) Human Resources(5) Technologies	1: Absence 2: Beginner 3: Intermediate 4: Experienced	Maturity	No	In Canetta and Et. Al (2018), some steps were not available yet for analysis. It has a questionnaire, but it is not a transparent methodology.
23	S. Rübel et al.	Germany	2018	General Enterprises	Business Model Management MM	Business Model and SCM scope	(1) costumer segment(2) value proposition(3) channels(4) customer relationship(5) source of income(6) key resources(7) key activities(8) key partners(9) cost structure	1: Implicit 2: Defined 3: Validated/Standardized 4: Analyzed 5: Optimized	Maturity	No	Rübel et al. (2018) don’t have a questionnaire. Further development levels need to be reached to achieve satisfactory results.
24	D. R. Sjödin et al.	Sweden	2018	General Enterprises	Sjödin MM	General	(1) Processes(2) People(3) Technologies	1: Connected Technologies 2: Structured Data Collection & Sharing 3: Real-time Process Analysis & Optimization 4: Intelligent & Predictive Manufacturing	Maturity	Yes	Sjödin et al. (2018) did not present the details of its dimensions, and the reported results suggest only a general analysis without the necessary granularity level to define process improvements.
25	F. Facchini et al.	Italy	2018	General Enterprises	Logistics 4.0 Facchini MM	Logistics/SCM scope	(1) management(2) flow of material(3) flow of information	1: Ignoring 2: Defining 3: Adopting 4: Managing 5: Integrated	Maturity	Yes	It is preliminary model. Just for logistics.
26	B. Asdecker⁠, V. Felch, E. Sucky	Germany	2018	General Enterprises	DPMM 4.0 (Delivery Process MM)	Deliver Processes in Supply Chain (SCM)	(1) Plan(2) Source(3) Make(4) Deliver(5) Return(6) Enable	1: Basic digitization 2: Cross-department digitization 3: Horizontal & vertical digitization 4: Full digitization 5: Optimized full digitization	Maturity	Yes	Felch and Asdecker (2018) presented a MM validated through expert opinions and judgments. The model doesn’t have a validated questionnaire, just a verified architecture. Moreover, the population and evaluation process were mostly based on experts from German industrial companies. Therefore, further research should consider a more international perspective.
27	J. Puchan et al.	Germany	2018	General Enterprises	I4-MMM/MUAS MM (Munich University of Applied Sciences)	General	(1) Key Factors(2) Employees(3) Organization(4) Product(5) Production	0: Basic Level1: Novice Level2: Advanced Level3: Expert Level4: Pioneer Level	Maturity	No	It is preliminary model.
28	J. Tavčar et al.	Slovenia	2018	Automotive supply chain enterprises	Engineering Change Management MM	Lean criteria	(1) ECM process flow(2) Set-based CE(3) Chief engineer – technical leadership(4) Customer-defined value(5) Knowledge management(6) Continuous improvement culture	0: The criterion is not implemented at all. 1: There is a modest 2: The criteria are specified in written procedures, but are not practiced strictly in everyday work. 3: The criterion is practiced, but over 40% of employees do not recognize it as very helpful. 4: The criteria are defined and practiced at a good level, and are well accepted by 80% of employees. 5: The implementation of the criterion is at the top level, and can be used as a reference for others.	Maturity	Yes	In Tavčar et al. (2018), the ECM (Engineering Change Management) maturity assessment tool was tested and validated at eight automotive suppliers of different sizes, presenting state-of-the-art on this specific topic. So, there is a strong emphasis only on a reliable supply of automotive enterprises.
29	Y. Jin et al.	China	2018	SME	EMMMs (Energy Management Maturity Models)	Energy	(1) Energy Management System(2) Leadership(3) Planning(4) Support and Operation(5) Performance evaluation(6) Improvement	1: Initial 2: Managed 3: Systematic 4: Improved 5: Optimized	Maturity	Yes	In Jin et al. (2018), the main limitation of their model is that it presents difficulty to apply the SMEs in China, whose EM (Energy Management) practices were seldom summarized or even raised. Another limitation is that there are no standardized EM tools, although the first-stage standardization is moving forward. And this lack increases the difficulty of applying the maturity model.
30	M. Colli et al.	Denmark	2018	General Enterprises (not SME)	360 Digital MM (based on PBL – Problem Based Learning)	Smart Manufacturing	(1) Governance(2) Technology(3) Connectivity(4) Value creation(5) Competence	1: None 2: Basic 3: Transparent 4: Aware 5: Autonomous 6: Integrated	Maturity	Yes	Colli et al. (2018) present no development of toolboxes to operationally address the improvement of each defined digital dimension and quantify the related potential. It has been tested only in large companies, not in SMEs. Further work has to be directed towards an efficiency improvement of the proposed assessment approach.
31	A. P. T. Pacchini et al.	Brazil	2019	General Industry Enterprises	SAE RM	General	(1) Cloud Computing(2) Cyber Physical System(3) Co-bots(4) Additive Manufacturing(5) Augmented Reality(6) Artificial Intelligence(7) IoT(8) Big Data	1: Embryonic 2: Initial 3: Primary 4: Intermediate 5: Advanced 6: Ready	Readiness	Yes	Pacchini et al. (2019) rely on only eight enabling technologies and a few prerequisites for each technology. The enabling technologies cannot have the same impact as far as I 4.0 implementation is concerned. In addition, the inter-relations among enabling technologies possibly affect the degree of readiness. The model is tested and validated only in a Brazilian auto-parts manufacturing organization.
32	L. Gaur et al.	India	2019	General Enterprises	IoT MM	IoT	(1) Strategy(2) IT(3) Data(4) Process(5) People(6) Assets(7) Products(8) Technology(9) Financial	0: No adoption 1: Work in Progress 2: Successful implementation & results	Maturity	No	It is preliminary model.
33	F. Pirola et al.	Italy	2019	SME	Pirola RM	Digital	(1) Strategy(2) People(3) Processes(4) Technology(5) Integration	1: (1 < I ≤1.8) not involved in I 4.0 2: (1.8 < I ≤2.6) intermediate 3: (2.6 < I ≤3.4) I 4.0 strategy & investing 4: (3.4 < I ≤4.2) already implementing 5: (4.2 < I ≤5) already implemented	Readiness	Yes	Pirola, Cimini, Pinto (2019b) do not extend capabilities assessments by defining specific roadmaps and action steps to drive the transition from the current maturity level to the desired one while considering SMEs’ often limited resources.
34	J. Vrchota et al.	Czech Republic	2019	General Enterprises	Human Vrchota RM	Human Resources/Skills	(1) Technical(2) Personal	0: None1: Low 2: Medium 3: High	Readiness	No	Vrchota et al. (2019) analyze the employee competencies in Industry 4.0. However, national data of individual countries are always delayed by approximately 2 years, so it is always an analysis of data that is no longer up-to-date. Research does not include the economic situation of individual countries and market developments.
35	D. Trotta et al.	Italy	2019	SME	I 4.0 Trotta MM	General	(1) Strategy(2) Technology(3) Production(4) Products(5) People	1: not implemented/not present 2: (no info) 3: (no info) 4: (no info) 5: completely implemented/ present	Maturity	No	Trotta and Garengo (2019) present a preliminary model. Further research could improve the goodness of the proposed model, expanding the list of items emerging from the on-site feedback collection.
36	A. Toboła et al.	Poland	2019	General Enterprises	AI MM	Artificial Intelligence	(1) Management(2) Flow of material(3) Flow of information	1: AI Novice 2: AI Ready 3: AI Proficient 4: AI Advanced	Maturity	No	It is preliminary model.
37	F. Odważny et al.	Poland	2019	General Enterprises	ISO 9004:2000 MM	General	(1) Work environment(2) People(3) Communication(4) Mission, vision, values, culture(5) Natural resources(6) Infrastructure(7) Technology(8) Resource management(9) Organizational knowledge(10) Performance analysis(11) Selfassessment	1: Ignoring 2: Defining 3: Adapting 4: Managing 5: Integrating	Maturity	No	Odważny et al. (2019) propose a preliminary model with many lacks, such as a clear definition and specific value for each maturity level. This model will be ready for validation across companies with all values identified.
38	R. C. Santos⁠ et al.	Portugal	2020	General Enterprises (automotive industries)	I 4.0 Santos MM	General	(1) Organizational strategy, structure & culture(2) Workforce(3) Smart factories(4) Smart processes(5) Smart products & services	0: low or none degree of implementation1: pilot actions 2: actions initiated, with some benefits 3: partial implementation 4: advanced implementation 5: reference in applying Industry 4.0	Maturity	Yes	Santos and Martinho (2020) developed a maturity model for Industry 4.0 to collaborate with companies to implement main related concepts and technologies and academics to understand the phenomenon better. The proposed model was adapted from three existing maturity models (Schuh et al., 2016), (Lichtblau et al., 2015) and (Schumacher et al., 2016), and a pilot test was performed on two Brazilian companies, both from the automotive industry. The main limitation of this research is the small number of professionals in the industry that participated in the validation phase of the model.
39	O. K. K. Bandara et al.	Sri Lanka	2019	General Enterprises	Banking MM	Bank processes	(1) Products & services(2) Technology & Resources(3) Strategy & organisation(4) Operations(5) Customers(6) Governance(7) Employees	1: Initial 2: Managed 3: Defined 4: Established 5: Digital Oriented	Maturity	Yes	The model of Bandara et al. (2019) can be further optimized by studying the importance of each dimension separately when calculating the overall maturity from a future research perspective.
40	G. Nick et al.	Hungary	2020	SME	CCMS (Company CoMpaSs)	Manufacturing	(1) Strategy & organization(2) Smart factory(3) Intelligent processes(4) Smart products(5) Services(6) Employees	Not considered	Readiness	No	It does not have information about output levels and hasn’t been tested.
41	A. Azevedo et al.	Brazil	2019	General Enterprises	PIM 4.0 MM (Industrial Pole of Manaus)	Manufacturing	(1) Products and Services(2) Manufacturing(3) Business Model(4) Strategy(5) Supply Chain(6) Interoperability	Not considered	Maturity	Yes	Tested just in a pilot company. Doesn’t have output levels.
42	R. Rojas et al.	Peru	2019	Security of Web Enterprises	Security of Web Attac MM	Security of web attac	Doesn’t have, only has tests matrix	1: Incipient 2: Basic 3: Intermediate 4: Strategic 5: Optimized	Maturity	Yes	Just applicable for security of web. Doesn’t have input dimensions.
43	J. Basl	Czech Republic	2018	General Enterprises	Metamodel Basl	General	(1) ERP applications(2) Production planning(3) Workplace ergonomics(4) Security(5) Maintenance(6) Connectivity(7) Data & processes	Not considered	Readiness	No	Doesn’t have output levels.
44	D. Li et al.	Sweden	2019	SME	Li CM	General I4.0	(1) Resources(2) Information systems(3) Organizational structure(4) Culture	1: ‘World of Mouth’2: ‘Pen and Paper’3: Computerization 4: Connectivity 5: Visibility 6: Transparency 7: Predictability 8: Adaptability	Capability	Yes	Li et al. (2019) have qualitatively assessed the Industry 4.0 capabilities of two case companies based on their employees’ understanding of their working conditions. While previous research requires company managers to possess a certain amount of knowledge about Industry 4.0, this paper exhibits the possibility of exploring the subject based on individuals’ situational awareness.
45	L. Joblot et al.	France	2019	General Enterprises	BIM2FR (Building Information Modeling Maturity Model For Renovation)	Renovation work	(1) Strategy(2) Foundations(3) Collaboration(4) Process(5) People(6) Technology(7) Standards(8) Enabling Tools(9) Resources	1: Pre-BIM 2: Modelling 3: Collaboration 4: Integration 5: Post-BIM	Maturity	Yes	Joblot et al. (2019) is just a preliminary model. In agreement with the work presented by De Bruin and Rosemann (2005), this BiM2FR will next be tested at a larger scale as a way to adjust the first version.
46	S. Elnagar et al.	USA	2019	General Enterprises	ARE-MMI4.0 (Agile Requirement Engineering Maturity Model for Industry 4.0)	Agile	(1) Technology(2) Governance(3) Products(4) Customers(5) Operations(6) Leadership(7) Strategy(8) Culture(9) People	1: Basic 2: Cross-departmental 3: Horizontal and vertical 4: Full digitization 5: Optimized	Maturity	No	Elnagar et al. (2019) presented the first version of ARE-MMI4.0, which is currently still at phase three, “populate”, according to the general model (De Bruin & Rosemann, 2005). So, the next phases are the “evaluation” in developing the framework and the “action and observation” in integrating the maturity models, and the model still needs to be evaluated for user perception and technical excellence.
47	D. Ifenthaler et al.	Germany	2020	General Enterprises	EOMM (Educational Organizations MM)	Educational Organizations	(1) Equipment and technology(2) Strategy and leadership (3) Organization(4) Employees(5) Culture(6) Digital learning and teaching	Not considered	Maturity	Yes	Ifenthaler and Egloffstein (2020) present practical concerns about organizational culture. Its model doesn’t have output levels. An executive survey could provide valuable information on organizational conditions and cultural factors – ‘digital leadership’ (i.e. leadership that is in line with the affordances of digital transformation) is likely to play a crucial role. Doesn’t have output levels.
48	R. G. G. Caiado⁠ et al.	Brazil	2020	General Enterprises	3M4.0 (Maturity Model for Manufacturing 4.0)	General	(1) Supply Chain Management(2) Technology(3) Sales & Operations Management(4) Knowledge, Skills & Attitude	1: Conceptual 2: Managed 3: Advanced 4: Self-optimized	Maturity	No	Caiado et al. (2020), through a systematic literature review, could demonstrate that no maturity model currently exists that meets the needs of manufacturing 4.0 in terms of socio-technical skills, production operations management, and supply-chain, considering the context of an emergent country. Therefore, as in (Leyh et al., 2016), 3M4.0 development is not yet complete.
49	A. A. Wagire et al.	India	2020	General Enterprises	Digital Novice MM	Manufacturing	(1) People and culture (0.12)(2) Industry 4.0 awareness (0.06)(3) Organisational strategy (0.18)(4) Value chain and processes (0.17)(5) Smart manufacturing technology (0.13)(6) Product and services oriented technology (0.15)(7) Industry 4.0 base technology (0.18)	1.00 ≤ Mo < 2.00 Level 1: Outsider2.00 ≤ Mo < 3.00 Level 2: Digital Novice3.00 ≤ Mo < 4.00 Level 3: Experienced4.00 ≤ Mo ≤ 5.00 Level 4: Expert	Maturity	Yes	The maturity model of Wagire et al. (2020) is supported by prior literature, expert consultation and case study, and experts’ judgment and opinion. Further efforts may be directed to measure the degree of readiness of the organization for Industry 4.0 as suggested by Pacchini et al. (2019), which is based on the assessment of prerequisites adopted by an organization for each maturity item mentioned in this study. Next, the important weight identification of each maturity item is based on opinions from a comparatively limited number of experts. Additionally, the existing model could introduce more maturity items related to legal and regulatory issues.
50	V. M. Harokopio et al.	Greece	2020	Public Sevice	Organizational Interoperability MM	Organizational interoperability maturity	(A1) Procurement criteria(A2) Specification Process(A3) Design methodology(A4) Collaboration(B1) Compatibility (intergovernmental legislation)(B2) Certification(B3) Business Process Modelling(C1) Compatibility with EIF(C2) Compatibility with GDPR(D1) Procedural transparency(D2) User Feedback(D3) Service level Agreements(E1) Service Consumption(F1) Reuse and sharing(G1) Once-Only Principle(G2) Cross border service delivery(H1) Staff restructuring(H2) Training	1: Ad hoc 2: Opportunistic 3: Essential 4: Sustainable 5: Seamless	Maturity	No	The model of Harokopio et al. (2020) must be applied to test it and turn it into a standard for the objective sector.
51	M. A. O. Sanabria et al.	Colombia	2020	Public institutions	Open Government Data RM	Open government data	(1) Normativity(2) Organizational(3) Technology(4) Strategies	Not considered	Readiness	No	In Sanabria et al. (2020), the model must be applied to test it and turn it into a standard for the sector. It doesn’t have output levels.
52	J. Merkus et al.	Netherlands	2020	General Enterprises	GCR MM (Generic Capabilities Reference)	Organizational interoperability maturity	(1) Strategy(2) Governance & Control(3) Organisation & Processes(4) Information Technology(5) Human Resources(6) Leadership(7) Communication (8) Culture(9) Value chain (10) Legislation(11) Environment	Not considered	Maturity	No	The model of Merkus et al. (2020) is limited to the selection made in its Literature Research (LR). Further research is necessary to validate the outcomes of this research, and the model doesn’t have output levels.
53	L. D. Rafael⁠ et al.	Spain	2020	General Enterprises	Machine Tool MM	Machine Tool	(1) Strategy & organization(2) Smart factory(3) Smart operations(4) Smart products(5) Data-driven services	0: Outsider1: Beginner 2: Intermediate 3: Experienced 4: Expert 5: Top performers	Maturity	No	Rafael⁠ et al. (2020) present a model that must be applied to test it and turn it into a standard for the sector. A future improvement for the model could lie around using a more scientific and rigorous method for weighing the dimensions.
54	S. Gamache et al.	Canada	2020	QuebecManufacturing SMEs	Gamache MM	General	(1) Leadership;(2) Culture and organization;(3) Technology management;(4) Data management;(5) Measurement system (decision-making process);(6) Customer experience.	0: Non-existant: absence of the business practice.1: Handwork: a worker works for this activity in a traditional way.2: Disciplined: the use of several non-integrated tools 3: Integrated: the use of an integrated, but not automated, management software package4: Predictable: is the maximum achievable and means that a respondent functions for the activity in an integrated, connected and real-time manner, ie uses up-to-date and real-time data to make effective decisions.	Maturity	Yes	Gamache et al. (2020) present a sound study, but it needs a more comprehensive assessment of the SMEs’ tools to target those that offer the most benefits. A longitudinal study with the sample would also be interesting to validate the impact of the approach, the number of projects and the progress of the projects that have been put in place, and the gains made by the digital tools implemented.
55	N. Chonsawat et al.	Thailand	2020	SME	SMEs Readiness Indicators	General	(1) Organizational Resilience(2) Infrastructure System(3) Manufacturing System(4) Data Transformation(5) Digital Technology	1: Not Achieved 2: Partially Achieved 3: Achieved 4: Fully Achieved	Readiness	Yes	Chonsawat et al. (2020) have a limitation: this research has tested only a simple example. Future research will develop decision‐making in selecting the priority of improvement implementation. Then, the researcher will develop indicators to cover more SME cases.
56	H. T. Maier et al.	Austria	2020	General Enterprises	LSM MM (Lean Smart Maintenance Maturity Model)	General	(1) Philosophy & Target System(2) Corporate Culture(3) Business Model & Service Strategy(4) Asset Strategy(5) Controlling & Budget(6) Organizational Structure(7) Process Organization(8) Knowledge Management(9) Data & Technology	0: Incomplete1: Initial 2: Managed 3: Defined 4: Quantitatively Managed 5: Optimizing	Maturity	Yes	In Maier et al. (2020), a more intensive investigation of the category ‘Business & Service Strategy’ is needed. Besides, only the data from Austrian companies were analyzed.
57	M. I. Sanchez-Segura et al.	Spain	2020	SME	ALTUS MM	Software engineers and IT professionals	(1) Configuration(2) Training(3) Operational(4) Proactive(5) Transactive memory(6) SP3 valuation(7) Social	1: covers the capabilities that allow company employees to improve their knowledge individually 2: covers the capabilities that allow company employees to generate and improve collective knowledge 3: covers the capabilities that allow the company knowledge to be sustainable 4: covers the capabilities that allow the company knowledge to evolve	Maturity	Yes	Sanchez-Segura et al. (2020) discuss the tools that support the ALTUS model (excel sheet and interview templates). They will improve the tool to be used as a web application and to be applied in different sizes of companies to see the results.
58	R. B. Unny et al.	India	2020	General Enterprises	Blockchain in Supply Chain Management	Blockchain Technology	(1) Network(2) Information Systems(3) Computing Methodologies(4) Security and Privacy(5) Skills	1: Initial 2: Repeatable 3: Defined 4: Managed 5: Optimizing	Maturity	No	It is preliminary model.
59	P. J. Hsieh et al.	Taiwan	2020	General Enterprises	KNM 2.0 (Knowledge Navigator Model)	General	(1) Strategy(2) KM Promotion(3) Knowledge Sharing(4) Data and Knowledge Acquire(5) Knowledge Store(6) Knowledge and Intelligent Application(7) Knowledge Creation and Innovation(8) Knowledge Protection(9) Knowledge Learning	1: Knowledge Chaotic Stage 2: Knowledge Conscientious Stage 3: Knowledge Management Stage 4: KM Advanced Stage 5: KM Integration Stage	Maturity	Yes	In Hsieh et al. (2020), further research may focus on analyzing data collected from their model to reveal the difference in maturity status across the production/service industry and large/small-medium companies. In addition, the relationship among KM (Knowledge Management) culture, process, technology, and KM performance is worthy of being observed, as also the interplay between knowledge and intelligent application.
60	R. G. G. Caiado et al.	Brazil	2021	General Enterprises	OSCM4.0 (Smart Operations and Supply Chain Management)	Operations and Supply Chain	(1) Customer(2) Logistics(3) Supplier(4) Integration(5) Planning & control of production(6) Quality(7) Maintenance	0: Non-existent 1: Conceptual 2: Managed 3: Advanced 4: Self-optimized	Maturity	Yes	Caiado et al. (2021) have a well-done and complete model with a fuzzy rule-based industry 4.0 maturity model. However, it focuses only on operations and supply chain management. For future works, this research suggests is to conduct a longitudinal survey and evaluating maturity at different times by applying a roadmap with periodic goals.
61	A. Amaral, P. Peças	Portugal	2021	SME	Framework SME 4.0	General	(1) People(2) Production Processes(3) Technology(4) Smart Products(5) Organization(6) Changes	0: Depends on the dimension (not informed) 1: Depends on the dimension (not informed) 2: Depends on the dimension (not informed) 3: Depends on the dimension (not informed) 4: Depends on the dimension (not informed) 5: Depends on the dimension (not informed)	Maturity	No	Outputs not transparent.
62	L. F. P. Ramos et al.	Brazil	2021	General Enterprises	I 4.0 Ramos RM	General	(1) Innovation(2) Culture(3) Strategy and Leadership(4) Smart Factory(5) Agile and Modular Management(6) Governance and processes(7) Digital Infrastructure(8) Smart Logistics(9) Smart Product and Services	Not considered	Readiness	Yes	In Ramos et al. (2021), the main contribution of this research is the need to align the theoretical concepts with those seen by specialists daily. Such a point allows a better understanding of what is intended to be delivered. All professionals stressed that the evaluation models aimed at I4.0 are essential to avoid too many trials and errors. Nonetheless, there is still a gap between a theoretical conception and a realistic view. Doesn’t have output levels.
63	M. Zoubek, M. Simon	Czech Republic	2021	General Enterprises	Logistics 4.0 Zoubek MM	Logistics Internal Processes	(1) Manipulation(2) Storage(3) Supply(4) Packaging(5) Material identification.	Level 0: Processes are not explicitly defined. Information systems and simple software are not used.Level 1: Certified process management takes place here, which is controlled by the human factor. It uses simple software and basic information systems.Level 2: The use of automated elements in standardized processes is beginning. Data collection is partially digitized and data are processed by information systems only within the company.Level 3: Most processes are automated with partial human cooperation. Digitized technologies and information systems are used for data collection, which are also connected to external sources.Level 4: Processes are digitized and automated, with limited human intervention. It uses smart information systems that connect all areas, including external sources.Level 5: Processes are fully automated and human-controlled. The control of all systems is autonomous. Online communication thanks to sophisticated information systems that connect all company areas, including external sources.	Readiness	No	Especific just for logistics processes.

Table

Initial Information
Email:
Full Name:
Cell Phone Number:
Age:
Level of Educational Qualification: () ELEMENTARY SCHOOL () HIGH SCHOOL () UNDERGRADUATE () MASTER () DOCTORATE () POST-DOCTORATE
Name of your Company:
Company Full Address:
Years of Experience in the Company:
Option	How big is your Company?
(1)	From 1 to 5 employees
(2)	From 6 to 10 employees
(3)	From 11 to 49 employees
(4)	From 50 to 249 employees
(5)	More than 250 employees
Option	What is your Company’s Economic Sector? (choose just one answer)
Primary sector: extraction of raw materials
(1)	Agribusiness
(2)	Animal husbandry
(3)	Forestry Base Materials
(4)	Mining
(5)	Other:
Secondary Sector: Industry
(6)	Ceramics (Ex: Glass)
(7)	Chemical Materials
(8)	Composite Materials
(9)	Electrical, Electronic, and/or Smart Materials
(10)	Energy
(11)	Food and Beverages
(12)	Leather
(13)	Paper
(14)	Pharmaceutical Materials
(15)	Polymers (Ex: Plastic, Rubber)
(16)	Steel and/or Other Metals
(17)	Textiles
(18)	Tobacco
(19)	Wood
(20)	Other:
Tertiary Sector: sale of services and material goods
(20)	Bank
(21)	Building
(22)	Education
(23)	Entertainment
(24)	Gastronomy
(25)	Health
(26)	Logistics
(27)	Trading
(28)	Transportation
(29)	Water Supply and Sanitation
(30)	Other:
Quaternary sector: goods and services related to Industry 4.0 (I4.0)
(31)	3D Print
(32)	Artificial Intelligence
(33)	Augmented Reality
(34)	Big Data
(35)	Biotechnology
(36)	Bitcoin e Blockchain
(37)	Cloud Computing
(38)	Cyber security
(39)	Cyberphysical Systems (ex: Autonomous Robotics)
(40)	Digital Twin
(41)	Gamification
(42)	Internet of Things
(43)	Nanotechnology
(44)	Virtual Reality
(45)	Other:
Option	Which of the options below better characterizes your current position in your Company?
(1)	Technical level occupations - dealing with equipment or assembly lines
(2)	Technical level occupations - dealing with administrative support and sales
(3)	Analyst and/or higher-level occupations
(4)	Business Manager
(5)	Company Director or Chairman
FIRST QUESTION
Score	How do you describe the status of the I4.0 implementation process in your Company?
(0)	NOT INITIATED: my Company does not comply with at least one of the three readiness dimensions (organizational, technological, or processes maturity). My Company has its internal processes but cannot use the concepts of I4.0, so it does not have the satisfactory organizational structure, technology, and/or process maturity that make it ready for this new reality.
(1)	INITIATED: my Company complies with the three readiness dimensions in an unstructured way. My Company shows interest and recognizes the importance of I4.0 but does not know how to make these changes.
(2)	MANAGED: my Company knows about I4.0 and plans to implement it. My Company allocates resources, people, and infrastructure, but with an individual solution adjusted separately for each department.
(3)	DEFINED: my Company defines goals, methods, and performance indicators for I4.0 implementation with an established, defined, and implemented process in which all departments are included.
(4)	OPTIMIZED: a process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. I4.0 improvements are re-evaluated regularly, and improvements are derived manually.
(5)	SELF-ADAPTED: my Company has a set of autonomous systems and adaptable organizational structures, technologies, and processes that allow self-optimization (without previous approval).
ORGANIZATIONAL STRATEGY
Score	How is the TOP-DOWN SUPPORT AND GOVERNANCE (executive and senior management) for I4.0 in your Company?
(0)	My Company doesn’t have it.
(1)	Top managers show interest and recognize the opportunities of I4.0 but do not know how to make the necessary changes in the Company. There is a well-communicated guideline for employees to propose improvements in top-down support and governance for I4.0. Top managers take sporadic and random initiatives to include I4.0 in the Company, but still in an unstructured way.
(2)	Top managers know about I4.0 and plan to implement it with investments in resources, people, and infrastructure. However, it’s an individual solution adjusted separately for each department.
(3)	Top managers define goals, methods, and performance indicators for I4.0 implementing projects at the Company. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Top-down support and governance are re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt top-down support and governance to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How is the ORGANIZATIONAL STRUCTURE management’s continuous improvement to conform to I4.0 in your Company?
(0)	My Company doesn’t plan to make any changes yet.
(1)	My Company shows interest and recognizes that the organizational structure management needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve organizational structure, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to implement organizational structure management continuous improvement. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for organizational structure management continuous improvement. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Organizational structure management is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt organizational structure management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How is the BUSINESS MODEL management to conform to I4.0 in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the business model management needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for business people to propose new business models (IT/CLOUD-BASED, SERVICE-BASED, SPIN-OFFS-BASED AND/OR PARTNERS-BASED) or some changes to existing models to conform to I4.0. The employee is congratulated when it happens. My Company takes sporadic and random initiatives to improve business model management, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to implement business model management improvements. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for business model management improvements. A companywide process is established, defined, and implemented, which all business departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Business model management is re-evaluated regularly, and improvements are derived manually. Business model management is beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems to self-adapt business model management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Business model management already uses 4.0 technologies fully, with all trained staff. The business models are autonomously evaluated and adapted partially without human interference on the self-adapted level, like exception messages in MRP II systems. The machine will identify data patterns and suggest adaptation strategies.
Score	How is the REGULATORY COMPLIANCE AND CONTRACTUAL RELATIONS management to conform to I4.0 in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that regulatory compliance and contractual relations need to be updated continuously to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve regulatory compliance and contractual relations, but still in an unstructured way.
(2)	My Company knows I4.0 and plans to implement regulatory compliance and contractual relations improvements. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators to improve regulatory compliance and contractual relations. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Regulatory compliance and contractual relations are re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt regulatory compliance and contractual relations to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
HUMAN WORKFORCE
Score	How is the LEADERSHIP preparation for I4.0 in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the leadership preparation needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for employees to propose improvements in leadership preparation. My Company takes sporadic and random initiatives to improve leadership preparation, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to implement leadership preparation improvements. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for leadership preparation improvements. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Leadership preparation is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt leadership preparation to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How is the Vertical and Horizontal INTERNAL COMMUNICATION (Information Transparency) management in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the internal communication management needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for employees to propose improvements in internal communication management. My Company takes sporadic and random initiatives to improve internal communication management, but still in an unstructured way. My Company has no technological solutions provided to enhance information transparency within the Company, like a digitally certified signature, for instance.
(2)	My Company knows about I4.0 and plans to implement internal communication management improvements. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for internal communication management improvements. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Internal communication management is re-evaluated regularly, and improvements are derived manually. Internal communication management is beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems to self-adapt internal communication management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Internal communication management already uses 4.0 technologies fully, with all trained staff.
Score	How is the employee TRAINING management in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the employees’ training management needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve employee training management, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to implement employee training management improvements. However, it’s an individual solution adjusted separately for each department. Different departments have a qualification process to address the requirements within an I4.0 environment.
(3)	My Company defines goals, methods, and performance indicators for employee training management improvements. All training departments adhere to a companywide established, defined, and implemented process. A process to systematically empower employees is established in all departments and tracked with a qualification matrix.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Employee training management is re-evaluated regularly, and improvements are derived manually. Employee training is beginning to include 4.0 technologies.
(5)	My Company has autonomous systems to self-adapt employee training management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Employees training already uses 4.0 technologies fully.
Score	How is the CULTURE OF INNOVATION (Willingness to Change and Agility) management in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that a culture of innovation needs to be updated continuously to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for employees to propose a culture of innovation improvement methods to conform to I4.0. The employee is congratulated when it happens. My Company takes sporadic and random initiatives to improve the innovation culture, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to implement a culture of innovation improvements. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for the culture of innovation improvements. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. The culture of innovation is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt a culture of innovation to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
PRODUCTION TECHNOLOGY
Score	How are the ANTHROPOMORPHIC SUPPORT systems (as Robots) in your Company?
(0)	My Company doesn’t have it.
(1)	The Company addresses only a few areas of physical support for working, but in an incipient (without technology) and individual way. My Company takes sporadic and random initiatives to improve anthropomorphic support, but still in an unstructured way.
(2)	Different departments offer automated anthropomorphic support technologies and plan to improve anthropomorphic support systems. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for anthropomorphic support systems improvements. A companywide process is established, defined, and implemented, which all engineering departments adhere to and integrate these technologies into practice.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Anthropomorphic support systems are re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt anthropomorphic support systems to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How are your Company’s COGNITIVE SUPPORT systems (as AI Systems)?
(0)	My Company doesn’t have it.
(1)	The Company addresses only a few areas of cognitive support for working, but in an incipient (without technology) and individual way. My Company takes sporadic and random initiatives to improve cognitive support, but still in an unstructured way.
(2)	Different departments offer automated cognitive support technologies and plan to improve cognitive support systems. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for cognitive support systems improvements. A companywide process is established, defined, and implemented, which all engineering departments adhere to and integrate these technologies in practice.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Cognitive support systems are re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt cognitive support systems to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How are the MANAGERIAL SUPPORT systems (as BIG DATA reports) in your Company?
(0)	My Company doesn’t have it.
(1)	The Company addresses only a few areas of managerial support for working, but in an incipient (without technology) and individual way. My Company takes sporadic and random initiatives to improve managerial support, but still in an unstructured way.
(2)	Different departments offer automated managerial support technologies and plan to improve managerial support systems. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for managerial support systems improvements. A companywide process is established, defined, and implemented, which all engineering departments adhere to and integrate these technologies in practice.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Managerial support systems are re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt managerial support systems to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How is the DRIVING NETWORK-BASED PRODUCTION (integrated production based on horizontal activities and employed by its value chain partners) management in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the driving network-based production management needs to be updated to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve driving network-based production management, but still in an unstructured way. My Company is focused on internal company manufacturing and traditional value networks for material flow. It has no technologies for collaborating with unusual manufacturing value chains systematically.
(2)	My Company knows about I4.0 and plans to improve driving network-based production management. Different business areas have technologies in place that allow this horizontal integration, but network partners are not systematically integrated to apply these technologies (which could be, for example, social networks: Facebook, Instagram, WhatsApp, Telegram, WeChat, and others, that can be used to connect the Company directly with the end consumer and not just as advertising).
(3)	My Company defines goals, methods, and performance indicators for driving network-based production management improvements. All manufacturing departments adhere to a companywide established, defined, and implemented process. For example, a manufacturer of plastic pots (that can be made on a 3D printer) could make available, in its social network, the updates of the products, besides having a contact team with the final customer who wants to perform customizations.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Driving network-based production management is re-evaluated regularly, and improvements are derived manually. Driving network-based production is beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems to self-adapt and drive network-based production management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Driving network-based production already uses 4.0 technologies fully, with all trained staff.
INFORMATION TECHNOLOGY
Score	How is your Company’s DATA COLLECTION, ANALYSIS, INTERCONNECTIVITY, AND TRANSPARENCY?
(0)	Data are not collected, analyzed, interconnected and/or transparent.
(1)	Data are collected and analyzed (manually or automatically), but my Company doesn’t have a designated interconnectivity and transparency procedure employed. Objects and people within the Company are hardly connected, and silo-thinking is prevalent. My Company shows interest and recognizes that data interconnectivity and transparency need to be updated to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve data interconnectivity and transparency, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve data interconnectivity and transparency. However, it’s an individual solution adjusted separately for each department. Different people and/or objects deploy different structures and processes to exchange data.
(3)	My Company defines goals, methods, and performance indicators for data interconnectivity and transparency improvements. A companywide process to use the same relevant structures and processes for exchanging data is established, defined, and implemented, which all departments adhere to. Data are automatically collected, analyzed, and interconnected with transparency, with a single source of information and in real time by sensors, providing predictions and possible causes.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Data interconnectivity and transparency are re-evaluated regularly, and improvements are derived manually. Data interconnectivity and transparency are beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems to self-adapt data interconnectivity and transparency to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Data interconnectivity and transparency already use 4.0 technologies fully, with all trained staff. My Company integrates horizontally and vertically to exchange data and information continuously. It has an adequate data management system to support integration and allow all users access to the same data set. The following features of I4.0 should be implemented: 1) horizontal integration through value networks, 2) end-to-end digital integration of engineering across the entire value chain, and 3) vertical integration and networked manufacturing systems. So, vertical and horizontal company integration with transparency continuously exchanges information between all the value chain IT systems, so that order information is always linked to the product, work, process instructions, and customer information.
Score	How is the INFORMATION SECURITY management in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that information security management needs to be updated to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve information security management, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve information security management. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for information security management improvements. A companywide process is established, defined, and implemented, which all IT departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Information security management is re-evaluated regularly, and improvements are derived manually. Information security is beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems to self-adapt information security management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). Information security already uses 4.0 technologies fully, with all trained staff.
Score	How is the DECENTRALIZED DECISIONS (with tools for data-driven decisions: IoT, big data, analytics, augmented and virtual reality, cloud computing, and/or artificial intelligence) management in your Company?
(0)	My Company doesn’t have it. Decisions of employees are not data-driven but based on experience. Some arrays, tables, or forms are used to facilitate the decision.
(1)	My Company shows interest and recognizes that decentralized decision management needs to be updated to adapt to I4.0, but it does not know how to make these changes. My Company takes sporadic and random initiatives to improve decentralized decision management, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve decentralized decision management. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for decentralized decision management improvements. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Decentralized decision management is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt decentralized decision management to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
PRODUCT-SERVICE DEVELOPMENT
Score	How are CROSS-COMPANY ENGINEERING, RESEARCH, AND DEVELOPMENT in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the cross-company engineering, research, and development need to be updated to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for business people, engineering, or top-down management to propose cross-company engineering, research, and development throughout engineering. The employee is congratulated by NDP (New Product Development) managers when it happens. My Company takes sporadic and random initiatives to improve cross-company engineering, research, and development, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve cross-company engineering, research, and development. However, it’s an individual solution adjusted separately for each department, which reports data in mobile systems and is non-necessarily integrated into other company systems.
(3)	My Company defines goals, methods, and performance indicators for cross-company engineering, research, and development. All engineering departments adhere to a companywide established, defined, and implemented process. A procedure for organizing project teams in multidepartmental groups is running. Employees on teams go beyond engineering, research, and development and involve manufacturing, marketing, sales, financial, accounting, and post-sales personnel.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Cross-company engineering, research, and development are re-evaluated regularly, and improvements are derived manually. Interdepartmental committees are in place to analyze current and possible NPD efforts. Integrated PLM (product lifecycle management) solutions are in place. Several collaborative tools (big data, problem-solving, and analytics techniques) are used to track project progress to enable fast decision-making and ensure that the NPD process has been carried out on a multidepartmental basis, with integrated solutions.
(5)	My Company has autonomous systems to self-adapt cross-company engineering, research, and development to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). All collaborative data gathering and data processing technologies are integrated into this self-adaptation.
Score	How is the CUSTOMER-BASED NEW PRODUCT DEVELOPMENT (NPD) in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the customer-based NPD needs to be updated to adapt to I4.0, but it does not know how to make these changes. There is a well-communicated guideline for an employee to propose customer-based NPD improvements. The employee is congratulated when they happen. My Company takes sporadic and random initiatives to improve customer-based NPD, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve customer-based NPD, with clear policies to drive NPD based on technological-enabled customer relations and inter-departmental approaches. However, it’s an individual solution adjusted separately for each department. The process is based on marketing and sales personnel information, which report data in mobile systems is non-necessarily integrated.
(3)	My Company defines goals, methods, and performance indicators for customer-based NPD improvements. All engineering departments adhere to a companywide established, defined, and implemented process. Gathering customer needs is based on the life cycle of customer experience. People involved in customer interfaces are multidepartmental, meaning gathering customer data in mobile systems are integrated and validated in collaborative tools. All procedures and technologies imply customer requirements indicators.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Customer-based NPD is re-evaluated regularly, and improvements are derived manually. Several tools are used to gather customer needs, assuring that a customer-based procedure has driven the NPD process. Strict partnerships are in place with current and potential customers, IoT sensors are gathering customer data, collaborative tools are in place, and big data and analytics techniques are used to discover unusual requirements.
(5)	My Company has autonomous systems to self-adapt customer-based NPD to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). All collaborative data gathering and data processing technologies are integrated into this self-adaptation.
Score	How is the SMART SUPPLY CHAIN integration in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that smart supply-chain integration needs to be updated to adapt to I4.0, but it does not know how to make these changes. Logistics are partially integrated. A large inventory is kept to ensure flexibility. My Company takes sporadic and random initiatives to improve smart supply chain integration, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve smart supply chain integration. However, it’s an individual solution adjusted separately for each department. For some parts of the value chain, supply and on-demand order fulfillment solutions have been established.
(3)	My Company defines goals, methods, and performance indicators for smart supply chain integration improvements. A companywide process is established, defined, and implemented, which all departments adhere to. On-demand order, fulfillment, and supply solutions are applied throughout the Company. However, these solutions are not optimized in terms of flow and inventory.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Smart supply-chain integration is re-evaluated regularly, and improvements are derived manually. On-demand order fulfillment and supply solutions are applied throughout the Company and optimized in terms of flow and inventory.
(5)	My Company has autonomous systems to self-adapt smart supply-chain integration to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
ORDER FULFILLMENT
Score	How is the CUSTOMIZED-BASED PRODUCTION system in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the customized-based production system needs to be updated to adapt to I4.0, but it does not know how to make these changes. Only some manufacturing departments would allow a single lot without any technology. My Company takes sporadic and random initiatives to improve its customized-based production, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve the customized-based production systems. However, it’s an individual solution adjusted separately for each department. Different parts of the production system (such as machining, assembly, storage, picking, and/or transportation) allow a single lot with some technology. However, customization requires extensive setup time and transition efforts. For these parts, manufacturing and supply chain solutions have been established.
(3)	My Company defines goals, methods, and performance indicators for customized-based production system improvements. All engineering departments adhere to a companywide established, defined, and implemented process. The production system is adjusted to “lot with size one” but without data-driven optimization.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. The customized-based production system is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt the customized-based production system to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization).
Score	How is the SALES AND OPERATIONS (commercial, marketing, technical assistance, customer feedback department) integration in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that sales and operations integration needs to be updated to adapt to I4.0, but has no knowledge of how to make these changes. My Company makes ad-hoc (temporary) integration between sales and operations. Sometimes they are integrated, and sometimes they have different goals and objectives. My Company takes sporadic and random initiatives to improve sales and operations integration, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve sales and operations integration. However, it’s an individual solution adjusted separately for each department. My Company integrates sales and operations when new endeavors are starting, as well as all levels of production planning.
(3)	My Company defines goals, methods, and performance indicators for sales and operations integration improvements. A companywide process is established, defined, and implemented, followed by every operational department.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. Sales and operations integration is re-evaluated regularly, and improvements are derived manually.
(5)	My Company has autonomous systems to self-adapt sales and operations integration to market and I4.0 contextual changes. They redefine goals, methods, and performance indicators without previous approval (self-optimization), enabled by artificial intelligence systems that use multiple customer integration channels.
Score	How is the SMART QUALITY MANAGEMENT system in your Company?
(0)	My Company doesn’t have it.
(1)	My Company shows interest and recognizes that the smart quality management system needs to be updated to adapt to I4.0, but has no knowledge of how to make these changes. My Company takes sporadic and random initiatives to improve smart quality management systems, but still in an unstructured way.
(2)	My Company knows about I4.0 and plans to improve its smart quality management system. However, it’s an individual solution adjusted separately for each department.
(3)	My Company defines goals, methods, and performance indicators for smart quality management system improvements. A companywide process is established, defined, and implemented, which all departments adhere to.
(4)	A process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. The smart quality management system is re-evaluated regularly, and improvements are derived manually. The quality management system is beginning to include 4.0 technologies, but still with teams in training.
(5)	My Company has autonomous systems for the self-adapt smart quality management system to market and I4.0 contextual changes, redefining goals, methods, and performance indicators without previous approval (self-optimization). The quality management system already uses 4.0 technologies fully, with all trained staff.
FINAL QUESTION
Score	How do you describe the status of the I4.0 implementation process in your Company?
(0)	NOT INITIATED: my Company does not comply with at least one of the three readiness dimensions (organizational, technological, or process maturity). My Company has its internal processes but cannot use the concepts of I4.0, so it does not have a good organizational structure, technology, and/or process maturity that make it ready for this new reality.
(1)	INITIATED: my Company complies with the three readiness dimensions in an unstructured way. My Company shows interest and recognizes the importance of I4.0 but does not know how to make these changes.
(2)	MANAGED: my Company knows about I4.0 and plans to implement it. My Company allocates resources, people, and infrastructure, but with an individual solution adjusted separately for each department.
(3)	DEFINED: my Company defines goals, methods, and performance indicators for I4.0 implementation with an established, defined, and implemented process in which all departments are included.
(4)	OPTIMIZED: a process of data-driven optimization (of goals, methods, and performance indicators) runs with manager approval. I4.0 improvements are re-evaluated regularly, and improvements are derived manually.
(5)	SELF-ADAPTED: my Company has a set of autonomous systems and adaptable organizational structures, technologies, and processes that allow self-optimization (without previous approval).
Option	How would you evaluate this questionnaire?
(1)	Difficult to fill in and ineffective
(2)	Difficult to fill in and effective
(3)	Easy to fill in and ineffective
(4)	Easy to fill in and effective
OPEN QUESTION
	What can your Company do to accelerate the development of the processes, skills, and attitudes needed for I4.0?
	Other Comments:

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