Digital Readiness Accelerator:
Digital Innovation Fund Research Project
This online synopsis shares key highlights from the Digital Readiness Accelerator project. For a complete view of the analysis, findings, and project insights, access the full report.
Summary
The Digital Readiness Accelerator is a Rapid Demonstrator research project undertaken by EDGE Digital Manufacturing (EDGE) and funded through the Digital Innovation Fund of the Smart Manufacturing Data Hub (SMDH), a Made Smarter Innovation-funded innovation hub that supports small and medium-sized (SME) manufacturers to become more competitive.
The Digital Readiness Accelerator project investigated the correlation between readiness to adopt data and technologies (digital readiness) and business performance in manufacturing. The project team worked alongside SME manufacturers to build an evidence-based improvement framework.
The Core Hypothesis
The project tested the hypothesis that SMEs that implement a holistic digital transformation strategy across multiple dimensions and promote cross-functional collaboration are more likely to achieve superior business performance, innovation, and operational efficiency compared to SMEs which focus on isolated digital initiatives or limited departmental collaboration. The research explored performance indicators to measure activities that can increase SME manufacturer confidence and reduce risks inherent in digital investment and adoption.
The research sought to identify operational activities, interventions and indicators SMEs should focus on; and to quantify the benefits that can be achieved through these actions. Ultimately, research findings informed the development of a prototype Digital Readiness Impact Calculator. The concept of this calculator was to enable manufacturers to evaluate the benefits of digital technologies and readiness activities, helping them to build solid business cases for digital transformation.
Participants invested time and financial resources to understand their digital readiness and business performance, further develop their digital roadmap and implement data and digital projects in their business while benefiting from consulting support, peer learning and networking and industry recognition.
SECTION 1
Digital Readiness
Digital Readiness and Barriers to Adoption
Digital Readiness is a measure of maturity in adopting digital technologies (British Standards' PAS 1040: 2019). Digital Readiness is used to benchmark and track growth in the digital maturity of individuals, teams and businesses over time, with a higher readiness level indicating a capability to achieve business benefits from data and digital technologies.
Digital technologies and their potential to drive productivity improvements are increasingly well defined, for example in the Made Smarter Review 2017 and in British Standards' PAS 1040 on Digital Readiness. However, a range of organisational and personal barriers can hold back manufacturing companies from adopting data and digital technologies and fully realising the business benefits, including those outlined below.
In addition to the above, EDGE’s work with SME leaders revealed common implementation questions to navigating digital adoption, including:
Where do I start?
How do I make the right decision?
How do I choose the right technology?
Where can I find the right people?
Who can I trust to help me?
Will this investment deliver a return?
What about my customers?
How do I fit this in with the day-job?
Digital Readiness Themes
Digital Readiness is a comprehensive framework built around five themes and covers of all aspects of the manufacturing value chain and business dimensions influenced by digital adoption, including leadership vision, culture of innovation, technology integration, processes, systems, collaboration, skills, workforce performance, operational performance and partner performance.
Companies can evaluate their digital readiness via a number of tools. EDGE’s Digital Readiness Assessment Tool is one self-assessment tool available to UK manufacturers. The tool aligns with the dimensions and framework found within PAS1040 and has been used by over 1000 business leaders across all UK regions.
Digital Readiness Analysis and Initial Findings
From the 1000 users, we compiled a dataset of 746 company responses to provide a research dataset for the Digital Readiness Accelerator project. This dataset is representative of the full dataset but excludes incomplete data and partially completed responses. We undertook extensive analysis of the dataset using a range of techniques which including basic linear regression and use of artificial intelligence and machine learning. Our analysis sought to address the following questions:
What correlation exists within the underlying questions?
What patterns or clusters exist within the dataset?
Which aspects of digital readiness (questions or themes) have a greater impact?
Do any specific questions or themes show a greater impact on digital readiness
The probability density from our sample has been used as the basis for establishing a percentile benchmark score for each company based on their Digital Readiness Score.
The distribution of scores across businesses in EDGE’s data set follows a Gaussian distribution. In addition, examination of the average score for each theme (using raw scores based on the Likert scale of 0 to 5) shows operations and partners typically lagging leadership, technology and people.
Distribution of Digital Readiness Scores
Digital Readiness Score - Theme Averages
The data demonstrated clear correlation within each of the diagnostic themes, with correlations generally stronger closer to the diagonal. Further analysis by SMDH data scientists identified inter-correlated groups of questions. These generally aligned with the 10 aspects of digital readiness identified in PAS1040. Where questions showed strong correlation with another group this could be explained by an overlap in the type of technology (e.g., data risk is explored in the Operations theme but correlates with the Partners theme; questions relating to internet of things, connectivity, sensors and tracking are included in the Operations theme but also correlate with the Partners theme).
Other analysis undertaken by the data science team included Clustering (a technique used to find similarities within datasets); Symbolic Regression (a type of regression analysis that searches for mathematical expressions to represent a given dataset); neural networks, decision tree and random forest regression.
One interesting finding was on the importance of the Partners dimension on overall digital readiness. In two separate analyses, questions within this dimension showed the greatest feature importance. Further work would be required to validate these initial findings.
Further data and discussion around the data analysis methods and initial finding are included in the full research report.
SECTION 2
Business Performance
Key Performance Indictors and Data Collection
As a starting point for business performance, we identified a comprehensive list of key performance indicators (KPIs) in a literature review with a focus on manufacturing and operational data.
Following our desktop literature review, we encountered several limitations and research challenges with our proposed approach. Discussions with companies in our research cohort failed to determine any specific trends or consistent methods used to capture and monitor key performance indicators. Our core focus was to build a dataset which might support development of our proposed Digital Readiness Impact Calculator, so we pivoted our approach to address some of our research challenges at this phase, electing to use Winning Moves’ Benchmark Index® to streamline and standardize KPI data collection.
SECTION 3
SME Perspectives
The challenge
We set out to recruit a cohort of businesses to test our core hypothesis and build an initial dataset. We targeted up to 12 SMEs for this cohort to provide a breadth of company sizes and sectors with different levels of digital readiness and business performance.
The planned customer journey built on EDGE’s existing Digital Breakthrough methodology to help businesses understand their needs and develop an actionable digital roadmap. Businesses would be signposted to relevant support including Made Smarter Adoption and regional funding programmes; introductions to catapult centres and research organisations; training and development content including various fully funded platforms delivered by SMDH, SMDH partners including Hartree and Datalabs; and other Made Smarter Innovation hubs/research centres such as the Digital Supply Chain Hub.
Initial Research Cohort Activities
Businesses were encouraged to complete a digital readiness baseline diagnostic and the Benchmark Index® Manufacturing Excellence diagnostic. Data from these two diagnostic tools would provide the basis for our initial research into correlation between digital readiness and business performance.
Engagement of the cohort varied across different activities. Peer to peer engagement was the most popular aspect as demonstrated by the data and anecdotal feedback. Engagement with KPIs was challenging and although nine companies engaged in the process, only one company was able to provide a full set of data to all the sections.
Data and Digital Readiness Projects
Cohort members pursued a variety of digital readiness activities. These activities emerged from the roadmapping workshops. SMEs were signposted and referred to additional support including catapults, SMDH, and technology providers with relevant solutions.
Case Studies
A number of impactful case studies emerged from the data and digital readiness projects. Please refer to the full report for additional examples beyond the two profiled here.
Case Study – Digital Readiness and AI Adoption
BACKGROUND
Manufacturing leaders are under pressure to explore and understand the opportunities from artificial intelligence for their organisation. Fear of missing out is creating a burning platform, but leaders are also wary of making the wrong choices and finding it challenging to navigate a complex landscape of technologies, solution providers and regulatory/legislative requirements.
DIGITAL READINESS ACCELERATOR PARTICIPANT
Growing Industrial Equipment manufacture with 70 FTE and a medium to high digital readiness. Early adopter with strong experience of digital adoption across the business.
ACTIVITIES UNDERTAKEN
Review of existing digital roadmap. Consultation with department sponsors regarding current pain points. Exploration of potential artificial intelligence use cases.
APPROACH
Scored opportunities were scored to enable prioritisation against a clear set of criteria. Digital readiness activities helped the business to develop a project shortlist and communicate this with key stakeholders.
DIGITAL READINESS DIMENSION
Leadership dimension of PAS1040 (leadership vision; culture of innovation).
Case Study – Engaging people in digital transformation
BACKGROUND
Supply chains are experiencing disruption leading to increased lead times, costs and inefficiencies. There is growing pressure to identify cost savings and to improve environmental sustainability. Supply chain digitalisation requires co-operation and collaboration with customers and suppliers.
DIGITAL READINESS ACCELERATOR PARTICIPANT
Growing manufacturer active in the aerospace sector. 20 FTE and a medium to high digital readiness. Early adopter with strong experience of digital adoption across the business.
ACTIVITIES UNDERTAKEN
Review of digital roadmap and digital transformation plans. Identification of employee engagement as a blind spot within the business.
APPROACH
Reviewed different approaches to employee engagement; adopted an employee engagement survey.
DIGITAL READINESS DIMENSION
People dimension of PAS1040 (skills, workforce performance). Leadership dimension of PAS 1040 (culture of innovation)
Peer Group Feedback
Four areas of common interest arose from the cohorts’ projects and business priorities: (1) the practicalities of AI adoption, (2) data-driven business strategy, (3) employee engagement and workforce performance management tools, and (4) data sources and opportunities (digital twins and cobot deployment). These became focus topics for four peer exchange sessions where participating SME leaders shared their experiences, identified challenges and use cases, adoption considerations and potential actions.
In addition, a number of cross-cutting themes emerged from the peer workshop sessions with cohort members, including:
1. Balancing Technology with Human-Centric Approaches:
Importance of integrating AI and digital tools as supplements to, not replacements for, human input.
2. Leadership and Change Management:
Leadership plays a pivotal role in driving adoption and addressing cultural resistance.
3. Skills and Training:
Need for upskilling and hands-on support to build trust and confidence in new technologies.
4. Data Management and Integration:
Challenges with integrating diverse datasets and ensuring data quality. Governance and security concerns, particularly in sensitive sectors.
5. Iterative Experimentation:
Starting with focused trials, learning from small successes, and scaling gradually.
6. Customer-Centric Innovation:
Leveraging data and AI to better understand customer needs and improve service.
7. Cultural Resistance to Digital Change:
Overcoming scepticism with transparent communication, clear benefits, and continuous engagement.
These themes underscore the multifaceted nature of digital readiness, requiring a blend of technological, cultural, and strategic actions to drive meaningful progress in manufacturing.
Validation Cohort
We ran a series of regression analyses looking at the correlation between different business performance variables and digital readiness. Whilst there were no significant correlations observed, several variables demonstrated some correlation.
The KPI Percentile calculation comprised an average of all the qualitative and quantitative business performance questions. This also did not show any correlation with digital readiness, suggesting that our core hypothesis could not be proven from our initial research cohort data.
We ran the same regression analysis on the larger dataset as we did with the initial research cohort. Due to incomplete data, only 22 of the companies were included in this analysis but their average digital readiness and business performance was broadly the same as the full dataset (59th percentile and 62nd percentile respectively).
It is important to note that the dataset used for this analysis was extremely small and therefore the results are not reliable.
The original intent of the validation cohort was for up to 40 SMEs to validate the preliminary findings from the initial cohort. However, as the initial data did not identify a clear measurable link between digital readiness and business performance, the validation research phase evolved to focus on building a larger dataset to enable further analysis and provide a reference benchmark for the calculator. To achieve this data collection objective, EDGE invited SMEs to complete a Digital Impact Survey, a shorter diagnostic combining digital readiness and business performance questions.
Scatter Graph showing Digital Readiness Percentiles and KPI Percentiles
Additional detail on the initial cohort and the validation phase may be accessed in the full report.
Further Reading
Please refer to the full report to read more about highlights from the peer discussions and inspiration from businesses that are tackling these business challenges and opportunities.
You may also download the reflective exercise and roadmap template to start your own data and digital readiness journey.
SECTION 4
Findings and Recommendations
Summary
The project covered a broad scope and proved to be significantly more complex than envisaged at the outset. Looking at the challenges encountered, some (e.g., client engagement) were anticipated with mitigating actions deployed during the project. Others (e.g. access to data) were more pronounced than envisaged and even led to new strategies and approaches for the project.
Throughout the project, we focused on four core objectives:
Developing our understanding of digital readiness and our existing dataset
Providing digital readiness support to the research cohort
Building new datasets with digital readiness and business performance data
Innovation activities to develop a Digital Readiness Impact Calculator
Digital Readiness
Analysis of our existing digital readiness dataset helped to generate new insights about our data and to develop internal data science capabilities. We were able to share outputs as a demonstration of the art of the possible of AI and ML techniques on business data to SME manufacturers in the research and validation cohorts.
From the analysis we were able to draw the following conclusions:
The Gaussian (normal) distribution suggests that although we have adopted a Likert scale of 0-5, there is less nuance within the scale so a Yes/No question format may be just as effective to measure Digital Readiness.
The correlation between questions within a theme suggests that although the additional detail provides valuable insights about the company and specific actions and areas of readiness; it may not be necessary to assess their overall Digital Readiness score.
The diagnostic could be improved by asking questions randomly and through use of control questions. This improvement would need to be balanced with the additional burden on users of the tool.
The analysis has given us confidence to select the 5 identified questions (1 per theme) as the basis for a simplified Digital Readiness survey to incorporate into our Digital Readiness Impact Calculator prototype. Whilst this subset will not provide the level of detail or accuracy as 40 questions, it should be sufficient for its intended purpose.
Further analysis could inform appropriate weightings for each question and may lead to a more complex algorithm than looking at the simple mean.
Business Performance
We observed the importance of this topic through our engagement with SMEs during the project and in our dissemination activities. Businesses are keen to improve in this area and recognise the requirement to improve the quality of current data (e.g. by undertaking data cleansing projects); to capture more relevant data (e.g. by implementing sensors) and to use their data to support decision making (e.g. by using artificial intelligence and machine learning techniques).
Current maturity levels are reflected in our digital readiness data (Figure 5) where we see the Operations theme scoring lowest for average digital readiness. This is the theme where questions relating to real-time data and use of data analytics technologies are explored and assessed.
Core Hypothesis
Central to this project was a hypothesis that digital readiness can impact business performance. Our data was insufficient to demonstrate a conclusive direct correlation and identify an algorithm or formula aligning digital readiness and business performance. However, the data did show evidence that certain factors may be related which could be further tested with a larger dataset.
Factors we would want to explore further included:
Leavers and Absenteeism which could suggest a link between digital readiness and employee wellbeing, engagement and motivation
ICT investment which would intuitively seem to be related to digital readiness
Sales growth demonstrated higher correlation in the initial research cohort but was not included in the validation cohort due to insufficient data
R&D expenditure demonstrated higher correlation in the initial research cohort but did not make the final shortlist for the Digital Impact Survey
We are undertaking further data analysis to explore a more complex algorithm based on our initial data. This work will continue with support of the University of Birmingham and Winning Moves.
Recommendations for Improved Future Research
Here we outline recommendations for how project outputs could be scaled up as part of wider industrial research to develop a market ready impact calculator.
1. Longitudinal study for ongoing monitoring and data collection
A limitation of the study was the focus on a single point in time as it was not possible to undertake a before and after comparison within the compressed project timeline. Companies involved a project such as this could be approached at regular intervals (e.g., annually) to capture longitudinal data and enable monitoring of the impact of digital readiness activities undertaken and enabling before and after data analysis.
Additional questions could be considered to capture further details of specific activities undertaken, digital investment projects implemented, and digital adoption support received to build a more robust profile of successful enabling actions.
It may also be possible to incorporate a longitudinal study as part of monitoring and evaluation activities for existing business support programmes such as Made Smarter Adoption.
2. Budgeting for data acquisition and client management
Additional resource and budget to support more hands-on data collection could increase certainty and comprehensiveness of data collection efforts. Other benefits of a more hands-on approach would include ongoing client relationship management which could lead to identification of data projects and other business support interventions as gaps in existing data and systems are uncovered.
3. Develop a larger business performance dataset
This project was limited in scope with budget to engage with 12 SMEs in depth over 8 months. Given lessons learned around study costs and effective company engagement, we estimate a full project would require 18 months minimum and larger research budget.
Partner with other research institutions to leverage existing and planned activities8. This could include leveraging existing datasets such as Benchmark Index®, Companies House and commercially available data to build a dataset of 1000+ organisations.
4. Randomised control trial
A randomised control trial would enable better identification of which activities and actions lead to the greatest impact on digital readiness. Control groups could be established to investigate the impact of specific interventions, with such a study capturing qualitative and quantitative data points.
5. Digital Readiness Impact Survey
The existing digital impact survey could remain open and be used to support several objectives:
Grow the existing dataset and support more accurate correlation calculations
Lead generation tool for SMDH and other digital adoption and innovation programmes
Benchmarking programme participants
Questions could be refined to align with new insights, e.g., from further analysis of larger business performance datasets.
The survey structure could be enhanced, e.g., using drop-downs, to make it easier for SMEs to respond and provide accurate information.
It may also be possible to enhance the survey with a results and report page so that users receive some immediate feedback and recommendations, similar to the full digital readiness assessment tool. This could be integrated with the data provided by the users and a database capturing all responses.
6. Funding opportunities
Given the scale and complexity of a longitudinal study this would best be delivered as part of a structured research programme. We see strong alignment with the aims and objectives of the Made Smarter Review and the ongoing Made Smarter Adoption programmes in the UK.
Developing a Digital Readiness Impact Calculator
An original aim of the project was to develop a Digital Readiness Impact Calendar prototype. This would help SME manufacturers to develop an evidence-based business case for investment in digital transformation and to explore different digital transformation scenarios. Over the course of the project, we evolved our vision for the calculator to focus on existing data obtainable from the digital readiness diagnostic instead of developing a new set of questions based on various digital readiness activities. This reflected a desire to minimise additional data capture and recognised our learnings regarding correlation within each digital readiness theme. Our ideas were also influenced by the lack of before and after data to track how digital readiness and business performance changes over time. Capturing this data would be a powerful tool for decision makers which we consider in our future recommendations below.
We had hoped to see strong evidence of correlation in our initial research and validation data to enable us to design an initial algorithm for our calculator prototype. Work is still ongoing to explore potential formulae but at the time of writing we do not have sufficient evidence to feed into a working calculator tool.
As an interim development stage, a Digital Impact Matrix may be possible. Results from the Digital Impact Survey could be translated into a four-box matrix with companies allocated to the most relevant corner based on their current scores.
Example Digital Readiness / Business Performance Matrix
Dependent variable
Validation Cohort R-squared
Improvers – low/medium digital readiness and medium/high business performance
Characteristics:
Delivering strong results
Continuous improvement culture
Not yet embraced digitalisation
Some resistance to digital transformation
Potential actions:
Build on existing operational and cultural strengths.
Financial incentives may create a catalyst for change.
Explorers – low/medium digital readiness and low/medium business performance
Characteristics:
Start of digital journey
May demonstrate under performance in current KPIs
Potential actions:
Non-digital interventions (e.g., operational efficiencies)
Accelerate results through digital transformation.
Trailblazers – medium/high digital readiness and medium/high business performance
Characteristics:
Enjoying benefits from existing digital solutions
Excellent reference companies and advocates for smart manufacturing
Potential actions:
Further embed digital transformation (e.g., artificial intelligence, distributed ledger technology, quantum computing).
Pathfinders – medium/high digital readiness and low/medium business performance
Characteristics:
Enjoying benefits from existing digital solutions
Excellent reference companies and advocates for smart manufacturing
Potential actions:
Further embed digital transformation (e.g., artificial intelligence, distributed ledger technology, quantum computing).
Digital Impact Pathways for SME Investment
From the Digital Impact Matrix, companies might consider three investment pathways starting from the Explorer box.
1.
Performance improvement (Improver): Businesses invest in new processes and/or machines as part of continuous improvement and business growth. Whilst this may include use of data and digital technologies, digital readiness is not a consideration. Whilst performance will improve, they are not future-proofing themselves and remain at risk of disruption.
2.
Operations and Technology-led (Pathfinder): Businesses invest in a new technology to solve a specific challenge. They do not take a cross-functional collaborative approach and there may be a temptation to make savings by limiting adoption to a small user group or under-investing in training and development. From a digital readiness perspective improvements may be evidenced in some dimensions only (e.g., Operations, Technology) with little change in others (e.g., People, Partners). Whilst performance may improve in the short-term, they may miss out on longer-term impact.
3.
Balanced investment (Trailblazer): Businesses make investments with consideration of both performance factors and digital readiness. Our core hypothesis set out to demonstrate how this approach is likely to be optimal and should lead to superior longer-term performance and growth.
Figure 20 Digital Impact Pathways
We note that this is a simplistic visualisation. However, in the context of an alternative tool, we can use this representation to propose initial digital readiness actions that a business can take to pursue both digital readiness and business performance, aligned to the British Standard PAS 1040.
Acknowledgments
The Digital Readiness Accelerator project was funded by the Made Smarter Innovation | Smart Manufacturing Data Hub.