Maturity and Readiness Model for Industry 4.0 Strategy

Abstract

Companies that transform their businesses and operations regarding to Industry 4.0 principles face complex processes and high budgets due to dependent technologies that effect process inputs and outputs. In addition, since Industry 4.0 transformation creates a change in a business manner and value proposition , it becomes highly important concept that requires support of top management for the projects and investments. Therefore, it requires a broad perspective on the company’s strategy, organization, operations and products. So, the maturity model is suitable for companies planning to transform their businesses and operations for Industry 4.0. It is a very important technique for Industry 4.0 in terms of companies seeking for assessing their processes, products and organizations and understanding their maturity level. In this chapter, existing maturity models for Industry 4.0 transformation are reviewed and a new Industry 4.0 maturity model is proposed.

1 Introduction

In today’s world, economic challenges driven by technological and societal developments force industrial enterprises improve their agility and responsiveness in order to gain ability to manage whole value-chain. Hence, enterprises require assistance of virtual and physical technologies which provide collaboration and rapid adaption for their businesses and operations (Ganzarain and Errasti 2016). Implementation of Industry 4.0 strategies require wide applications in companies since executives from several industries are uncertain about outcomes of the Industry 4.0 projects and investment costs and they have lack of knowledge in the concept of Industry 4.0. Maturity models provide large scale of knowledge about companies’ current state and a path to pursue for implementation of Industry 4.0 strategies.

Nikkhou et al. (2016) stated that maturity term refers to being in a perfect condition; also, it is an evidence of an achievement and it provides a guidance to correct or prevent problems. Mettler (2009) defined maturity as a development of a specific ability or reaching to a targeted success from an initial to an anticipated stage. Proença and Borbinha (2016) reported that maturity can be used as evaluation criteria and described as being complete, perfect or ready and also used for progression from a basic stage to more advanced final stage.

According to Tarhan et al. (2016) a maturity model represents desired logical path for processes in several business fields which include discrete levels of maturity. In addition, maturity models are defined as a valuable technique in order to assess processes or organization from different perspectives (Proença and Borbinha 2016). Backlund et al. (2014) also noted that maturity model frameworks are becoming extremely important to assess organizations. Nikkhou et al. (2016) described maturity models as a tool that can be used to describe perfect progression to wanted change utilizing a few progressive phases or levels. Maturity models enable organizations to audit and benchmark regarding to assessment results, to track progress towards to desired level and to evaluate elements of organizations such as strengths, weaknesses and opportunities by sequencing maturity levels in an order from basic to advanced stage: Initial, Managed, Defined, Quantitatively Managed and Optimizing (Proença and Borbinha 2016). According to Schumacher et al. (2016) maturity models are positioned as a tool for comparing current level of an organization or process to desired level in terms of maturity by conceptualizing and measuring. In the article, the difference between readiness and maturity models is explained in such a way that readiness models clarify whether organization is ready to start development process or not; however, maturity models target to demonstrate which maturity level the organization is in. In brief, Duffy (2001) concluded that maturity models help organizations to decide when and why they need to take an action to progress; in addition, teach organizations which actions should be considered in order to achieve advanced maturity level. According to author, organization must need the information obtained from maturity models to compare its current state to the best-practices in related business fields. Therefore, a value of a maturity model is measured by its usability on analysis and positioning.

In study of IBM in 2015, it is stated that Industry 4.0 transformation has many difficulties because of the inadequacy of current IT technologies, lack of knowledge and high investment costs (Erol et al. 2016). Digital transformation of companies’ businesses and operations is driven by investments in information and telecommunication technologies and new machineries. In addition, the need of the integration in current technologies, new machines and automated work processes restrain horizontal and vertical integration along the value chain (Erol et al. 2016). According to industry-wide interviews, when implementing Industry 4.0 in practice, following problems come in view (Schumacher et al. 2016):

• Lack of strategic guidance and the problem of perception about highly complex Industry 4.0 concept.

• Uncertainty about outcomes of Industry 4.0 projects in the matter of benefits and costs.

• Failure of assessing Industry 4.0 capability of company.

With regard to third problem, maturity models and assessment of Industry 4.0 maturity become highly important, since a lot of companies seem to struggle to initialize Industry 4.0 transformation.

The purpose of this chapter is to explain “maturity models”; discuss the encountered problems when implementing Industry 4.0 strategies; to explain reasons of utilization of these models and their benefits to Industry 4.0 strategies. The rest of the chapter is organized as follows. Section 4.2 includes detailed explanation of four maturity models in the literature. Then in Sect. 4.3, the comparison chart of analyzed Industry 4.0 maturity and readiness models is presented. Section 4.4 is organized to propose Industry 4.0 maturity model and an application in retail sector. The conclusion and future work are presented in final section.

2 Existing Industry 4.0 Maturity and Readiness Models

In this section, we performed analysis of several Industry 4.0 maturity and readiness models and assessment surveys. From these works, we derived concepts relevant for the structure of our model. Models and assessments are given below:

• IMPULS—Industrie 4.0 Readiness (2015)

• Industry 4.0/Digital Operations Self-Assessment (2016)

• The Connected Enterprise Maturity Model (2016)

• Industry 4.0 Maturity Model (2016).

2.1 IMPULS—Industrie 4.0 Readiness (2015)

Lichtblau et al. and other project partners performed Industry 4.0 workshops and literature researches to propose an Industry 4.0 readiness model. This model contains six levels of Industry 4.0 readiness given below:

• Level 0: Outsider

• Level 1: Beginner

• Level 2: Intermediate

• Level 3: Experienced

• Level 4: Expert

• Level 5: Top performer.

In this study, existing readiness model redesigned with contributions from workshops and formed in six Industry 4.0 dimensions by adding two dimensions to previous model. These dimensions (Lichtblau et al. 2015) are “Strategy and organization”, “Smart factory”, “Smart operations”, “Smart products”, “Data-driven services” and “Employees”. Dimensions and associated fields of Industry 4.0 related with this model are given in Table 4.1.

Table 4.1 Dimensions and associated fields of Industry 4.0 (Lichtblau et al. 2015)

Proposed readiness model is used in order to measure companies’ Industry 4.0 readiness levels from 0 to 5 containing minimum requirements that companies should possess. Questionnaire of this readiness model measures structural characteristics of companies, their Industry 4.0 knowledge, their motivations and obstacles through Industry 4.0 journey. Assessment survey has 24 questions in total for related dimensions and a few questions about industry, size of domestic workforce and annual revenue. In order to measure and define Industry 4.0 readiness, five point Likert scale is used.

Company profiles are grouped under three titles to summarize results better (Lichtblau et al. 2015) such as Newcomers (level 0 and 1), Learners (level 2) and Leaders (level 3 and up). Newcomers consist of companies that have never initialized any projects or have studied a few projects. Learners consists a group of companies which initialized first projects related to Industry 4.0. Leaders is a group that contains level 3, 4 or 5 companies which are way ahead of other companies about Industry 4.0 implementation.

Companies’ readiness levels are determined based on lowest level of associated field in the dimensions. Industry 4.0 dimensions are weighted on 100-point-scale. “Strategy and organization” has 25 point overall, “Smart factories” has 14 points overall, “Smart products” has 19 points overall, “Data-Driven services” has 14 points overall, “Smart operations” has 10 points overall and finally “Employees” has 18 points overall.

As a result of measurements, company profiles are identified and main hurdles in the dimensions are listed. In the final stage, action plans are created for companies to help them reach level 5 Industry 4.0 readiness.

2.2 Industry 4.0/Digital Operations Self-Assessment (2016)

PwC published a report entitled “Industry 4.0: Building the digital enterprise” to provide companies comprehensive perspective on Industry 4.0 by representing its own maturity model and “Blueprint for Digital Success” given in Table 4.2. 

In the first step of “Blueprint for Digital Success”, PwC provides companies a maturity model to assess their capabilities. This maturity model is formed in four stages and seven dimensions. Stages are determined as below:

• Digital novice

• Vertical integrator

• Horizontal collaborator

• Digital champion.

PwC assess companies’ maturity levels with seven dimensions such as “Digital business models and customer access”, “Digitisation of product and service offerings”, “Digitisation and integration of vertical and horizontal value chains”, “Data and Analytics as core capability”, “Agile IT architecture”, “Compliance, security, legal and tax”, “Organisation, employees and digital culture”.

PwC enables companies to assess their Industry 4.0 maturity and map their results by online self-assessment tool. In the final stage of assessment, PwC provides companies an action plan to make them successfully reach high level of Industry 4.0 maturity.

Online self-assessment tool (PwC, 2016) has 33 questions in total for related dimensions and a few questions about industry, region, country and annual revenue to classify companies. In questionnaire, five point Likert scale is used for each question and radar graphic is provided at the end of the assessment.

2.3 The Connected Enterprise Maturity Model (2016)

The Connected Enterprise Maturity Model was developed by Rockwell Automation in 2014 and this model contains five stages and four technology focused dimensions. Stages in this model are given below (Rockwell Automation 2016):

• Stage 1: Assessment

• Stage 2: Secure and upgraded network and controls

• Stage 3: Defined and organized working data capital

• Stage 4: Analytics

• Stage 5: Collaboration.

The Assessment Stage of the Connected Enterprise Maturity Model evaluates all facets of an organization’s existing OT/IT (Operational Technologies/Information Technologies) network with four dimensions such as “Information infrastructure (hardware and software)”, “Controls and devices (sensors, actuators, motor controls, switches, etc.) that feed and receive data”, “Networks that move all of this information” and “Security policies (understanding, organization, enforcement)”. It is stated that a major challenge during assessment stage is potential hesitations on investing time for questioning practices that they have relied upon for years.

In Stage 2, OT/IT organization is being formed to deliver secure, adaptable connectivity between plant-floor operations and enterprise business systems after an assessment stage. Long-term upgrades begin and gaps and weaknesses of current operations are identified. In large-scale companies, outdated control and networks create a challenge for transformation as well as hesitations from executives and engineers who feel that current systems remain viable.

Stage 3, which the improvements made with the current data is in progress with Stage 2. At this stage, it is defined how the collected data will be processed and how to obtain optimum outcome from these data. Organized team ensures that new workflows, charts and responsibilities are set so that they are not overwhelmed by the company data pool thanks to Working Data Capital.

In Stage 4, focal point shifts towards continuous development with data. Analytics utilizing Working Data Capital will assist to pinpoint the greatest needs for real-time information and ensure the continuity of standardized protocols triggered by the data. In addition, these analytics provide information transfer about asset management for leadership team. Challenges during Stage 4 will be use of lots of unnecessary data and distrust of analytics.

In Stage 5, main idea is to provide collaboration between company and environment with the help of analytics and data sharing.

2.4 Industry 4.0 Maturity Model (2016)

Schumacher et al. (2016) used nine dimensions and sixty-two maturity items in order to assess companies Industry 4.0 maturity levels. Nine dimensions and maturity items are given in Table 4.3. Maturity levels are examined under five levels. According to this model, level 1 companies have lack of attributes the supporting concepts of Industry 4.0 and level 5 companies can meet all requirements of Industry 4.0.

Table 4.2 Blueprint for digital success (Geissbauer et al. 2016)

In this maturity model , assessment surveys are made by using five point Likert scale for each closed ended question. After survey results weighted points are calculated and maturity levels of companies are determined. To determine maturity level of a company, an equation (Eq. 4.1) is used. In this equation, “M” corresponds to “Maturity”, “D” corresponds to “Dimension”, “I” corresponds to “Item”, “g” corresponds to “Weighting Factor” and “n” corresponds to “Number of Maturity Item” (Schumacher et al. 2016).

Table 4.3 Dimensions and maturity items of Industry 4.0 Maturity Model (Schumacher et al. 2016)

$$M_{D} = \frac{{\sum\limits_{i = 1}^{n} {} M_{DIi} *g_{DIi} }}{{\sum\limits_{i = 1}^{n} {} g_{DIi} }}$$

(4.1)

3 Comparison of Existing Industry 4.0 Maturity and Readiness Models

In this section, maturity/readiness levels, dimensions and industry scope are compared between existing Industry 4.0 maturity and readiness models. In order to provide easy understanding, comparison table is given in Table 4.4.

Table 4.4 Comparison of existing industry 4.0 maturity and readiness models

4 Proposed Industry 4.0 Maturity Model

In order to facilitate different analyses of Industry 4.0 maturity , the proposed model includes a total of 13 associated fields which are grouped into 3 dimensions. Table 4.5 provides an overview on the dimensions together associated fields, related Industry 4.0 to support understanding. An assessment criterion of the maturity model is based on Industry 4.0 principles and technologies given in Table 4.6 for each associated field.

Table 4.5 Proposed industry 4.0 maturity model

Smart products can do computations, store data and be involved in an interaction with their environment as well as they can give information about their identity, properties, status and history (Schmidt et al. 2015). These features create a chance to obtain data from products and interpret data to offer services. Smart Products and Services dimension is formed to measure these features of companies’ products and their service offerings driven by product data.

Smart Business Processes is formed as a dimension containing functional operations of companies to assess their maturity level regarding to Industry 4.0 principles and triggering technologies.

Strategy and Organization can be defined as an “input” for Industry 4.0 transformation where it is important to shape business and organization. Development of new smart products, data-driven services and smart business operations depend on generating suitable business models or transforming current one for Industry 4.0, investments in triggering technologies, collaboration with strategic partners which provides fast progression and organizational structure and leadership.

Table 4.6 Industry 4.0 principles and technologies

To identify Industry 4.0 maturity level of a company, four stages are used and answers of the assessment survey is evaluated regarding to these stages such as “Absence”, “Existence”, “Survival” and “Maturity”. Each associated field’s questions weighted between 0—“Absence” and 3—“Maturity” to determine a maturity level.

Level 0: Absence identifies a level of a company that does not meet any of the requirements for Industry 4.0. Some of the requirements are at low level.

Level 1: Existence is a maturity level where company has some pilot initiatives in its functional departments. Company provides products, but these products are not capable of being fully smart. Integration and automation levels are low and data collection/use levels are not enough to realize Industry 4.0 transformation. Digital technologies and cloud has not been implemented to all operations. Equipment infrastructure readiness is also at low level. Top management is considering implementing Industry 4.0 strategy with investments in a few areas. There are pilot initiatives to generate business models or transform current one. Organizational structure is not suitable enough.

Level 2: Survival is a maturity level where company’s products are capable of real time data management and being tracked through different sites; in addition, data-driven service offerings are at medium level. Company’s business processes at medium level in terms of integration, data sharing/collection/use and agility. Processes are ready for decentralization and interoperability principle is implemented a few areas in company with support of digital technologies. Leadership is developing plans for Industry 4.0 and has made investments in a few areas. Company is considering new business opportunities at medium level and creating partnerships with other companies or academics. Organizational structure is suitable for initial Industry 4.0 projects and new business models are being generated.

Level 3: Maturity is a maturity level where company’s products are defined as smart and data-driven services are provided high level. Company’s business processes at high level in terms of integration, data sharing/collection/use and agility . Nearly all processes are capable of being decentralized and interoperability principle is implemented lots of areas in company with support of advanced digital technologies. Leadership team provides widespread support for Industry 4.0 and has made investments for nearly all departments. Organizational structure is suitable for managing transformation across the company. Company is creating lots of partnerships with companies, academics, suppliers and technology providers. Digital business models are integrated to company’s current business models and company is generating revenue from these models.

Each associated field in this maturity model is graded with related survey questions by 0–3 points. After all, calculated points of associated fields are grouped under dimensions and sub-dimensions in order to identify maturity levels individually and overall. Equations to calculate maturity levels are given in Eqs. 4.1–4.3.

M:

Maturity

D:

Dimension

A:

Associated Field

Q:

Question Number

O:

Overall

n:

Number of Total Questions

m:

Number of Associated Fields

$$M_{DAi} = \frac{{\sum\limits_{j = 1}^{n} {} Q_{Aij} }}{n}$$

(4.2)

$$M_{D} = \frac{{\sum\limits_{i = 1}^{m} {} M_{DAi} }}{m}$$

(4.3)

$$M_{O} = \hbox{min} \left( {M_{1} ,M_{2} ,M_{3} } \right)$$

(4.4)

To determine overall maturity level, limit values for each level is given in Table 4.7. Maturity levels of “smart products and services”, “smart business” and “strategy and organization” are explained in Table 4.8, 4.9 and 4.10 respectively. 

Table 4.7 Limit values to determine maturity level

Table 4.8 Smart products and services maturity level requirements

Table 4.9 Smart business processes maturity level requirements

Table 4.10 Strategy and organization maturity levels requirements

5 An Application in Retail Sector

The study was conducted in a retail company operating in Turkey. In this study, the questionnaires in Appendix were answered and according to the answers given, scores related to the relevant fields and dimensions were calculated according Eqs. 4.2–4.4.

The scores corresponding to the answers given to the questions are shown on the Tables 4.11, 4.12 and 4.13. Since the firm operates in the retail sector, it has decided not to answer the questions about the field of “R&D—Product Development”. So this field did not participate in the scoring account according to Eqs. 4.2–4.4.

Table 4.11 Smart products and services maturity score

Table 4.12 Smart business processes maturity score

Table 4.13 Strategy and organization maturity score

According to an equation (Eq. 4.4), overall maturity level of a company is determined by minimum maturity level of dimensions. As we can see in Table 4.11, Table 4.12 and Table 4.13, minimum maturity level score is 1.14 which is calculated for Strategy and Organization dimension. Therefore, a retail company is at “Level 1: Existence” regarding to Industry 4.0 maturity. Summary of maturity scores is given in Table 4.14. Radar chart is provided in Fig. 4.1.

Table 4.14 Maturity score and level table for each dimension/sub-dimension of a company

Fig. 4.1

Maturity levels of dimensions of company in a radar chart

6 Conclusion

The study presented here aimed to develop of an Industry 4.0 maturity model and an assessment survey to provide companies a tool to help them understand their current state regarding to Industry 4.0. Different application areas were proposed for Industry 4.0 such as smart finance, smart marketing and human resources in order to differentiate the model and increase companies’ perspective for Industry 4.0 applications. Future studies will aim at diversifying Industry 4.0 maturity model to enhance the industry scope with weighted associated fields for each industry and create activity plans according to companies’ current maturity level.

Appendix: Survey Questionnaire

Smart Products and Services

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Interoperability Decentralized Service oriented	Data analytics and Artificial intelligence Embedded systems Communication and Networking Cybersecurity Sensors and Actuators Cloud RFID and RTLS Technologies

Questionnaire

1. Which functions can your company’s products fulfill the following options?

Communicating with other products/platforms, machines and external systems

Collecting data from environment and other systems

Keeping the data they collect on their system or in the cloud

Having a platform on which the product or cloud applications are working

2. What stages of the data analysis can the product perform? (Porter and Heppelmann 2015)

Descriptive—Capture products’ condition, environment and operation

Diagnostic—Examine the causes of reduced product performance or failure

Predictive—Detect patterns that signal impending events

Prescriptive—Identify measures to improve outcomes or correct problems

3. To what extent can products be tracked throughout their lifecycle? (The University of Warwick Maturity Model)

No or limited product tracking

Products can be tracked as they move between manufacturing and internal distribution sites

Products can be tracked through manufacturing and distribution until they reach the customers DC

Products can be tracked along their complete lifecycle

4. Who do you offer service/insights for according to the user data obtained from the product?

None

Business

Customers

Partners

Smart Business Processes

Production, Logistics and Procurement

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Interoperability Virtualization Decentralized Agility Integrated business processes	Data analytics and Artificial Intelligence Adaptive robotics Simulation Communication and Networking Cybersecurity Additive manufacturing Virtualization technologies Sensors and Actuators Cloud RFID and RTLS Technologies Mobile Technologies

Questionnaire

1. Which of the following systems do you use? Does the system have an interface to the leading system? (Lichtblau et al. 2015)

	Interface to leading system
	No	Yes
MES—manufacturing execution system
ERP—enterprise resource planning
PDM—product data management
PPS—production planning system
PDA—production data acquisition
MDC—machine data collection
CAD—computer-aided design
SCM—supply chain management

2. To what extent is the current supply chain integrated? (The University of Warwick Maturity Model)

Ad hoc reactive communication with suppliers and customers

Basic communication and data sharing where required with suppliers and customers

Data transfer between key strategic suppliers/customers (for example customer inventory levels)

Fully integrated systems with suppliers/customers for appropriate processes (for example real time integrated planning)

3. To what extent are the production equipment and systems automated?

Machine level: Partial

Machine level: Exact (Loading/Unloading + Operation)

Production line/cell level: Partial

Production line/cell level: Exact (Loading/Unloading + Operation +Transportation)

Factory level: Partial

4. Express the level of personalization in production.

Low—10,000 + batch size

Medium

High—1 batch size

5. Which data about your machinery, processes, and products as well as malfunctions and their causes is collected during production, and how is it collected? (Lichtblau et al. 2015)

	Manually	Automatically
Inventory data
Manufacturing throughput times
Equipment capacity utilization
Production residues
Error quota
Employee utilization
Data on remaining processing
Overall equipment effectiveness (OEE)
Other:

6. How is the data you collect used in production? (Lichtblau et al. 2015)

Predictive maintenance

Optimization of logistics and production processes

Creation of transparency across production process

Quality management

Automatic production control through use of real-time data

Optimization of resource consumption (material, energy)

Other:

7. How is the data you collect used in logistics and procurement? (Schreiber et al. 2016)

Predictive supplier risk management (to detect supplier failures early on)

Digital supplier scorecards, objectives and improvement tracking.

Automated tracking of target achievement and bonus payments

Digital claim management system with integrated automatic warning system

Big data analytics to detect new suppliers globally

8. To what extent does your supply chain an end-to-end visibility? (The University of Warwick Maturity Model)

No integration with suppliers or customers

Site location, capacity, inventory and operations are visible between first tier suppliers and customers

Site location, capacity, inventory and operations are visible throughout supply chain

Site location, capacity, inventory and operations are visible in real time throughout supply chain and used for monitoring and optimization

9. What is the level of real-time traceability of the operation in the digital environment? (Digital-twin concept)

None
Machine level
Production line/cell level
Factory level

10. What is the use level of technologies in production, logistics and procurement?

	Mobile and virtual technologies	3D Printers	Adaptive and collaborative robots
None
Low
Medium
High

Smart Business Processes

R&D— Product Development

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Virtualization Agility	Data analytics and Artificial intelligence simulation communication and Networking Cybersecurity additive manufacturing virtualization technologies cloud RFID and RTLS technologies

Questionnaire

1. To what extent are the manufacturability and terms of use of the product simulated during product development?

None

Low

Medium

High

2. To what extent is the data obtained from the product used in the new product development?

None

Low

Medium

High

3. Do you use 3D printers in the production/prototyping processes?

No

Yes

4. Is product design information automatically transferred with the CAD/CAM systems to the machine?

No

Yes

5. Can your customers customize your products before production according to their preferences?

No

Yes

Smart Business Processes

After Sales Services

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Virtualization Agility Service oriented	Data analytics and Artificial intelligence Embedded systems Communication and Networking Cybersecurity Virtualization technologies Cloud RFID and RTLS technologies Mobile technologies

Questionnaire

1. How do you benefit from data you collect in after-sales services?

Early detection of product quality issues and focused recalls

Improved product design

Advanced supplier recovery

Optimized spare parts planning

Minimized suspect and fraudulent claims

Reduced “remorse returns” and no trouble found rates

Increased reserves forecast accuracy

Enhanced service quality and service information

Intensified customer intimacy and next best action

2. Which services do you provide by using data analytics and other technologies in after-sales services?

Remote maintenance

Assistance with problems or faults in real time

IT-assisted claim management

Order management (CRM, order history, delivery tracking, etc.)

Display of product history

Delivery forecast

3. Do you utilize from digital technologies (mobile and virtualization technologies) in after-sales service processes?

No

Yes

Smart Business Processes

Pricing/Promotion

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Decentralized Service oriented Integrated business processes	Data analytics and Artificial intelligence Communication and Networking Cybersecurity Cloud

Questionnaire

1. Which of the following studies are conducted within customer analytics?

Customer segmentation

Customer lifetime value

Cross selling

Campaign management

Market basket analysis/product bundling

Product recommendation

Customer churn analysis

Product portfolio management

2. Do you utilize from data obtained from environment/other platforms in product pricing or dynamic pricing?

	Product pricing	Dynamic pricing
No
Yes

3. Do you generate new campaigns from purchasing and product usage data?

No

Yes

4. Do campaign management systems work integrated with other systems?

No

Yes

5. Do you analyze campaign performance to use these analyses in new campaigns?

No

Yes

Smart Business Processes Sales and Distribution Channels

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Agility Service oriented	Data analytics and Artificial Intelligence Communication and Networking Cloud Mobile technologies

Questionnaire

1. What is the level of sales team support with digital products and services and real-time access to systems?

None

Low

Medium

High

2. Do you conduct real-time profitability analysis?

No

Yes

3. Do you use real-time and automated performance management systems for local sales force?

No

Yes

4. To what extent are your sales channels integrated?

None

Low

Medium

High

5. To what extent do you use integrated channels to communicate with customers and to manage customer interaction?

None

Low

Medium

High

6. To what extent do you collaborate with partners to reach customers (i.e. exchange of customer insight, etc.)?

None

Low

Medium

High

7. Which content analyses are performed on social media?

None

Sentiment analysis

Trend analysis

Smart Business Processes

Human Resources

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Agility	Data analytics and Artificial intelligence Cloud Mobile technologies

Questionnaire

1. In what areas is the data collected and data analytics is used?

	Data collected	Data analytics used
Capability analytics—(a talent management process that allows you to identify the capabilities or core competencies you want and need in your business.)
Capacity analytics—(seeks to establish how operationally efficient people are in a business.)
Competency acquisition analytics—(the process of assessing how well or otherwise your business acquires the desired competencies.)
Employee churn analytics—(the process of assessing your staff turnover rates in an attempt to predict the future and reduce employee churn.)
Corporate culture analysis—the process of assessing and understanding more about your corporate culture or the different cultures that exists across your organization.)
Recruitment channel analytics—(the process of working out where your best employees come from and what recruitment channels are most effective.)
Leadership analytics—(unpacks the various dimensions of leadership performance via data gained through the use of surveys, focus groups, employee interviews or ethnography.)
Employee performance analytics—(seeks to assess individual employee performance.)

2. Can your company share real-time data with employees in the field?

No

Yes

3. Can employee training be carried out in a virtual environment?

No

Yes

Smart Business Processes Information Technology

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Interoperability Virtualization Decentralized Integrated business processes	Data analytics and Artificial intelligence Communication and Networking Cybersecurtiy Cloud Mobile technologies

Questionnaire

1. How far along are you with your IT security solutions? (Lichtblau et al. 2015)

	Solution planned	Solution in progress	Solution implemented
Security in internal data storage
Security of data through cloud services
Security of communications for in-house data exchange
Security of communications for data exchange with business partners

2. Are you already using cloud services? (Lichtblau et al. 2015)

	Cloud-based software	For data analysis	For data storage
Production, Logistics and Procurement
R&D—Product development
After sales services
Sales and Distribution channels
Pricing/Promotion
Human resources
Information technology
Finance

3. Do IT dashboards be used for traceability of company processes?

No

Yes

4. How would you evaluate your equipment infrastructure when it comes to the following functionalities? (Lichtblau et al. 2015)

	No, not available	Yes, to some extent	Yes, completely
Machines/systems can be controlled through IT
M2 M: machine-to-machine communications
Interoperability: integration and collaboration with other machines/systems possible

Smart Business Processes

Smart Finance

Principles	Technologies
Real time data management (Collection/Processing/Analysis/Inference) Decentralized	Data analytics and Artificial intelligence Cloud

Questionnaire

1. Do you perform real-time cost calculations with data obtained from production?

No

Yes

2. Do you analyze company’s cash flow and investments on a historical basis?

No

Yes

3. To what extent do you utilize from financial data when make investment decision?

None

Low

Medium

High

4. To what extent are your financial systems automated?

None

Low

Medium

High

5. How do you perform financial risk measurement?

None

Historical basis

Real-time

Strategy and Organization

Business Models

Questionnaire

1. Do your existing products and services comply with innovative digital business models?

No

Yes

2. To what extent are you aware of the “As-a-service” business model? (The University of Warwick Maturity Model)

No awareness.

Aware of concept with some initial plans for development

High awareness and implementation plans are in development

“As-a-service” has been implemented and is being offered to the customer

3. Which degree of resource is allocated to digital business models?

None

Low

Medium

High

4. Is the current business model of the company evaluated and updated during the interim period in the matter of digitization?

No

Yes

5. To what extent do you monetize your new data-driven services?

None

0–2.5%

2.5–10%

Over 10%

Strategy and Organization

Strategic Partnerships

Questionnaire

1. Does your company have partnerships for Industry 4.0 projects with following options?

None

Academics

Technology providers

Suppliers

Customers

2. How would you describe the implementation status of your Industry 4.0 strategy? (Lichtblau et al. 2015)

No strategy exists

Pilot initiatives launched

Strategy in development

Strategy formulated

Strategy in implementation

Strategy implemented

3. Do you use indicators to track the implementation status of your Industry 4.0 strategy?

No, our approach is not yet that clearly defined

Yes, we have a system of indicators that gives us some orientation

Yes, we have a system of indicators that we consider appropriate

Strategy and Organization

Technology Investments

Questionnaire

1. Which technologies in your company are driving Industry 4.0?

None

Data analytics and Artificial intelligence

Adaptive robotics

Simulation

Embedded systems

Communication and Networking

Cybersecurity

Cloud

Additive manufacturing

Virtualization technologies (VR & AR)

Sensors and Actuators

RFID and RTLS technologies

Mobile technologies

2. To what extent do you allocate sufficient budget to investments in Industry 4.0?

None

Low

Medium

High

3. How often do you conduct a cost/benefit analysis for Industry 4.0 investment? (The University of Warwick Maturity Model)

No measurable Industry 4.0 investment yet

No ongoing review of cost/benefit analysis for Industry 4.0 investment yet

Annual cost/benefit analysis of Industry 4.0 investment

Quarterly cost/benefit analysis of Industry 4.0 investment

4. In which parts of your company have you invested in the implementation of Industry 4.0? (Lichtblau et al. 2015)

	Planning investment	Investment done
Production, Logistics and Procurement
R&D—Product development
After sales services
Pricing/Promotion
Sales and Distribution channels
Human resources
Information technology
Finance

Strategy and Organization

Organizational Structure and Leadership

Questionnaire

1. Are business units/project teams structured in interdisciplinary in the company?

No

Yes

2. Is there any business unit to maintain relationship or communicate with customers?

No

Customer service

Customer relationship management

3. Is there any data-driven organizational structure? (Data scientists, analytics team, digital transformation director, etc.)

No

Yes

4. To what extent are employees equipped with relevant skills for Industry 4.0? (The University of Warwick Maturity Model)

Employees have little or no experience with digital technologies

Technology focused areas of the business have employees with some digital skills

Most areas of the business have well developed digital and data analysis capability

All across the business, cutting edge digital and analytical skills are prevalent

5. Do you have training for the digital transformation in the company?

No

Yes

6. How is your IT organized? (Lichtblau et al. 2015)

No in-house IT department (service provider used)

Central IT department

Local IT departments in each area (production, product development, etc.)

IT experts attached to each department

7. To what extent do departments collaborate with each other? (The University of Warwick Maturity Model)

The business operates in functional silos

There is limited interaction between departments (i.e. S&OP process)

Departments are open to cross-functional collaboration

Departments are open to cross-company collaboration to drive improvements

8. To what extent does the leadership team support Industry 4.0? (The University of Warwick Maturity Model)

Leadership team does not recognize the value of the Industry 4.0 investments

Leadership team is investigating potential Industry 4.0 benefits

Leadership team recognizes the financial benefits to be obtained through Industry 4.0 and is developing plans to invest

Widespread support for the Industry 4.0 within both the leadership team and across the wider business

9. How is your Industry 4.0 team organized to execute innovative projects?

There is no employee for Industry 4.0 projects

There are employees for Industry 4.0 project; but in different business units

There are employees for Industry 4.0 project in the same business unit

10. Is there any working environment where OT/IT units work together?

No

Yes