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# From pain points to scale points: Exploring barriers to lean digital transformation

Sagar Shinde and Kathryn Cormican

Enterprise Research Centre, School of Engineering, University of Galway

s.shinde1@universityofgalway.ie

## Abstract

Lean digital transformation (LDT) has gained momentum recently, with small and medium-sized enterprises (SMEs) eager to implement innovative digital solutions to enable scale and growth. However, LDT is not an easy endeavour and is fraught with challenges. Many organisations struggle to convert to a digitally-enabled business, finding it difficult to identify right-sized solutions that align with their needs or implement good management practices. This exploratory study aims to better understand the challenges and barriers to LDT in small and medium-sized enterprises. First, a comprehensive review of the extant literature was conducted to identify and classify these barriers. Second, empirical data was collected from key opinion leaders in the industry using structured interviews to capture real-world insights. Third, thematic analysis was conducted to isolate and categorise the constructs. These constructs then helped to design a survey which was circulated to relevant people in industry to elicit their opinions. The findings from this study add value to the SMEs by expanding the debate on lean digital transformation and will help organisations to identify barriers and levers toward more efficient business practices.

Keywords: Digital Transformation, Lean, Strategy, People, Process, Technology, SMEs

## 1. Introduction

In the era of industry 4.0, small and medium enterprises (SMEs) are leaning towards the use of new technologies and digital products to increase optimisation, drive profits and grow. However, the move to a digitally enabled business leads to several challenges. For example, a lack of technology infrastructure [1, 2], a lack of relevant skills [3, 4] and complexity in integrating systems [5].

Kieviet [6] asserts that organisations must incorporate lean thinking into their operations to integrate digital technology effectively. LDT is based on lean concepts and business process improvement through the use of technology [7]. While lean enables organisations to achieve better customer value by reducing waste, lean digital focuses on maximising data usage and reducing digital waste. The aim is to achieve higher customer satisfaction and user engagement [5, 8]. Implementing lean digital transformation (LDT) as a mechanism to adopt digital tools effectively and efficiently is expected to give organisations a better chance of successful transformation towards efficiency.

Although LDT is lauded to benefit organisations, the process is complex. Not all projects are successful, and many organisations face challenges in implementing LDT initiatives [9]. Organisations, especially SMEs, find it difficult to implement digital solutions that help them to achieve customer value, operational efficiency and waste reduction [10]. Prior research suggests that SMEs face several barriers during their LDT. For example, Vogelsang et al. [1] conducted interviews with forty-six industry experts, which resulted in a list of common barriers in LDT, the greatest of which was a lack of relevant skills. According to Buvat et al. [11], more than 60% of companies lack the digital competencies to transform successfully into a digitally enabled organisation. In addition to technological barriers, Stentoft et al. [12] identified people and processes as major barriers. These categories strongly resonate with manufacturing SMEs, where the rate of adoption and LDT initiatives is slow [13]. In addition, cybersecurity threats and organisational processes are the foremost barriers related to LDT projects [13, 14]. Thus, previous analysis reveals that while there are many factors that impede LDT, they mainly comprise people, processes and technology-related issues.
Researchers are calling for a deeper analysis of this domain [15, 16]. A better understanding of these challenges can help design and take effective steps that address LDT challenges, thus improving SMEs' ability to transform to lean digital. Therefore, this research aims to better understand and classify SMEs' barriers in their journey toward lean digital transformation. The results are important as they provide a deeper understanding of the barriers to transformation and bring valuable insights from the real world to address critical challenges and offer practical suggestions for SMEs.

## 2. Literature Review

It is evident that three key constructs affect the LDT journey: people, process and technology-related issues. While these categories are by no means exhaustive or indeed mutually exclusive, they are factors that must be addressed as challenges and barriers to enabling LDT and deserve further scrutiny. The following sections synthesise the current literature relating to these factors.

### 2.1 People-related barriers

LDT projects centre around people. For example, Ashrafian et al. [7] emphasise that transformation always starts with senior business leaders agreeing on the LDT projects' goals. However, these goals are often poorly communicated within an organisation [5, 17]. In contrast, Verina and Titko [4] point out that an organisation can sometimes have a clear goal, but other 'people' factors are involved, for example, unmotivated employees who are not engaged in the process [18]. Other studies have identified the lack of relevant skills as a key challenge in LDT, which can lead to increased resistance to implementing modern technology [2, 3, 7, 15]. The summary of the studies mentioning people-related barriers is shown in table 1.

<table><caption>**Table 1.** Overview of people-related barriers</caption><thead><tr><td>Authors</td><td>Poor Leadership</td><td>Lack of Employee Motivation</td><td>Lack of skills</td></tr></thead><tbody><tr><td>Kutnjak [17]</td><td>X</td><td></td><td></td></tr><tr><td>Verina and Titko [4]</td><td></td><td>X</td><td>X</td></tr><tr><td>Erol et al. [18]</td><td></td><td>X</td><td></td></tr><tr><td>Ashrafian et al. [7]</td><td>X</td><td></td><td>X</td></tr><tr><td>Karre et al. [19]</td><td>X</td><td>X</td><td></td></tr></tbody></table>

### 2.2 Process-related barriers

LDT is considered a socio-technical phenomenon where people drive change by creating new processes [20, 21]. New technological introductions result in redefining business processes and operational structures [5]. However, prior research suggests that it is difficult to redefine processes. For example, Lichtenthaler [22] highlights that organisations often design processes around technology instead of customer behaviour. It leads to fragmented processes impeding LDT. To address this, Brozzi [23] points out that lean implementation requires an experienced expert to improve the flow of an organisation's processes, and consequently, the cost of hiring a professional in this domain can be a barrier for SMEs. In addition, small enterprises adopting technological transformation expose themselves to stiff competition from more prominent institutions using advanced technology. This results in a significant challenge to out-compete large organisations due to poor resource allocation [1]. Other studies suggest that inaccurate and disconnected utilisation of data leads to poor data collection and fragmented processes within and between the organisation, which is another key barrier to LDT [5, 20, 23, 24]. The summary of the studies discussing process-related barriers is shown in table 2.
<table><caption>**Table 2.** Overview of process-related barriers</caption><thead><tr><th>Authors</th><th>Poor integration of organisations process</th><th>Poor resource allocation</th><th>Poor data collection</th></tr></thead><tbody><tr><td>Zezulka et al. [21]</td><td>X</td><td>X</td><td></td></tr><tr><td>Romero et al. [5]</td><td>X</td><td></td><td>X</td></tr><tr><td>Lichtenthaler [22]</td><td>X</td><td></td><td></td></tr><tr><td>Brozzi et al. [23]</td><td></td><td>X</td><td>X</td></tr><tr><td>Vogelsang et al. [1]</td><td>X</td><td>X</td><td></td></tr><tr><td>Chan [24]</td><td></td><td>X</td><td>X</td></tr></tbody></table>

**2.3 Technology-related barriers**

Chan [24] asserts that technology transformations require an effective system that embraces large amounts of data. However, the lack of digital infrastructure in some SMEs required for managing and storing these large amounts of data hampers technological transformation. To implement new technologies organisations therefore require a roadmap for migrating from traditional technologies to modern technologies [25]. According to Borovkov et al. [2], organisations that are overly dependent on their old technologies, which have minimal automation of processes, are likely to find it very difficult to transform effectively towards digital as the latter relies on advancing and changing modern technologies. In addition, Julião and Gaspar [26] argue that traditional technologies can result in security breaches of confidential information. Furthermore, research suggests that old technologies cannot be relied on in a competitive market where advanced firms use modern technology [22, 24]. Thus, an organisation that does not-invest and upgrade existing technologies faces many challenges in their lean digital transformation journey [2, 18, 26, 27]. The summary of studies mentioning technology-related barriers is shown in table 3.

<table><caption>**Table 3.** Overview of technology-related barriers</caption><thead><tr><th>Authors</th><th>Lack of technology infrastructure</th><th>Lack of cybersecurity</th><th>Dependencies on legacy systems</th></tr></thead><tbody><tr><td>Chan [24]</td><td>X</td><td></td><td></td></tr><tr><td>Julião and Gaspar [26]</td><td></td><td>X</td><td>X</td></tr><tr><td>Borovkov et al. [2]</td><td></td><td></td><td>X</td></tr><tr><td>Weber & Studer [27]</td><td></td><td>X</td><td>X</td></tr></tbody></table>

**3. Research Methods**

This exploratory study aims to uncover emerging real-world challenges in lean digital transformation projects. This section begins by presenting a description of the research process used in the research.

**3.1 The research process**

Figure 1 presents an overview of the research process, followed by a description of the four stages of the research project.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Overview of the research process</figcaption></figure>

**3.2 Stage 1. Synthesis of the Literature**

The first stage comprised a synthesis of the literature, which involved exploring prior work focusing on the barriers to LDT. The most relevant barriers to the implementation of lean digital projects were identified from the literature and classified into people, process and technology, forming the basis for the initial conceptual framework.

**3.3 Stage 2. Develop instrument**

The second stage in the study involved developing an instrument to capture empirical data for this research. This involved conducting interviews with relevant industry experts who were associated with LDT projects. Five interviews were conducted to gain practitioners' insight into barriers to LDT implementation. The interview texts were transcribed using NVivo. The study used a three-phase coding system advocated by Strauss and Corbin [28]. First, open coding was used to understand and identify key items. The second phase involved axial coding, where similar themes were grouped to form a higher category. The third phase involved linking categories and similar themes and removing irrelevant data to highlight relationships between items. The final themes were then used to develop the questionnaire in the following step. The details of the interviewees are shown in table 5.
<table><caption>**Table 4.** Profile of interviewees</caption><thead><tr><td>Interviewee ID</td><td>Role</td><td>Company-size</td><td>Industry sector</td></tr></thead><tbody><tr><td>INT1</td><td>Senior Leadership</td><td>Large</td><td>Industrial Automation</td></tr><tr><td>INT2</td><td>Senior Executive</td><td>Medium-sized</td><td>Electronics</td></tr><tr><td>INT3</td><td>Director</td><td>Medium-sized</td><td>Engineering</td></tr><tr><td>INT4</td><td>CEO</td><td>Small</td><td>Technology</td></tr><tr><td>INT5</td><td>Partner</td><td>Small</td><td>Technology</td></tr></tbody></table>

**3.4 Stage 3. Collect and analyse data**

The third stage involved the collection and analysis of empirical data. This step consisted of a questionnaire designed to measure relevant variables [29]. The questionnaire focused on capturing critical information regarding organisational barriers to lean digital transformation. Survey items were derived based on the themes from the literature and analysis of the interviews in the previous step. Every item was carefully designed (and refined through an iterative process) to measure specific elements of each parent factor in regard to LDT challenges [30, 31]. These factors comprised people-related barriers, process-related barriers, and technology-related barriers. Last, the survey was pre-tested with four industry professionals. Based on their feedback, ambiguous questions were amended. The survey was then pilot tested with a sample size of eight relevant industry people to test the structure and the coherence of the questions. Finally, the survey was distributed to 165 professionals through emails, LinkedIn contacts, industry networks and in-person events of which 58 responded.

The final stage involved the quantitative analysis of the data collected from the surveys. Pareto analysis was conducted using python ( matplotlib and seaborn to visualise) to summarise the results into sections and subsections [32]. Pareto Analysis is used to identify the principle barriers to LDT. To perform this analysis, a bar chart was made of two main variables: the x-axis (barrier factors) and y-axis (count). The x-axis is used for plotting the different categories into which the data was broken down into (people, process and technology). The y-axis shows the number of the count for each specific category. The bars are ordered from the highest frequency to the lowest frequency, starting from left to right. Following the previous step, a line graph is used to depict the cumulative percentage of the total count number. Following the logic of the Pareto analysis, the analysis reveals that 80% of the challenges are due to 20% of the factors (x axis).

**3.5 Stage 4. Report findings**

The last stage of the research design is to summarise and discuss findings, suggesting areas for future work and research limitations. The following section will present this fourth stage.

**4. Findings and Discussion**

This research and analysis suggest that people, processes, and technology are the key barriers to lean digital transformation. This section details participants' characteristics, identifies barriers and details how each barrier affects digital transformation based on the survey results.

**4.1 Participant characteristics**

The survey targeted experienced professionals in Ireland. Of the 58 survey respondents, 79.3% were male, while 20.7% were female. Regarding age, 37.9% of respondents were between 31-40 years of age, 31% were between 21-30, 27.6% were between 41-50, and 3.4% were above 51 years of age. Regarding working experience, 43.1% of participants had worked for more than five years, 24.1% had been working for less than five years, and 32.8% had worked for more than ten years. In terms of their organisations, more than half of the respondents claimed their organisations were still at the start of their transformational journey (36.2%), while 36.2% said they were in the middle in comparison, 19% of respondents stated that their organisation had yet started the transformative process, while 8.6%
stated they were already at the final stage of the transformation. The overview of the participant characteristics is shown in table 5.

<table><caption>**Table 5.** Characteristics of research participants</caption><thead><tr><th>Characteristics</th><th>Frequency</th><th>Percentage</th><th>Characteristics</th><th>Frequency</th><th>Percentage</th></tr></thead><tbody><tr><td><strong>Age (years old)</strong></td><td></td><td></td><td><strong>Highest education</strong></td><td></td><td></td></tr><tr><td>21-30</td><td>18</td><td>31.0%</td><td>Bachelors</td><td>29</td><td>50%</td></tr><tr><td>31-40</td><td>22</td><td>37.9%</td><td>Masters</td><td>27</td><td>46.6%</td></tr><tr><td>41-50</td><td>16</td><td>27.6%</td><td>Others</td><td>2</td><td>3.4%</td></tr><tr><td>More than 50</td><td>2</td><td>3.4%</td><td></td><td></td><td></td></tr><tr><td><strong>Gender</strong></td><td></td><td></td><td><strong>Role</strong></td><td></td><td></td></tr><tr><td>Male</td><td>46</td><td>79.3%</td><td>Engineer</td><td>20</td><td>34.5%</td></tr><tr><td>Female</td><td>12</td><td>20.7%</td><td>Management</td><td>24</td><td>41.4%</td></tr><tr><td></td><td></td><td></td><td>Consultant</td><td>14</td><td>24.1%</td></tr><tr><td><strong>Working experience (years)</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 5 years</td><td>14</td><td>24.1%</td><td></td><td></td><td></td></tr><tr><td>More than 5 years</td><td>25</td><td>43.1%</td><td></td><td></td><td></td></tr><tr><td>More than 10 years</td><td>19</td><td>32.8%</td><td></td><td></td><td></td></tr><tr><td>Total</td><td>58</td><td>100%</td><td></td><td>58</td><td>100%</td></tr></tbody></table>

## 4.2 Overview of barriers to lean digital transformation

According to figure 2, the first two factors on the cumulative line contribute to 68% of barriers. Overall, 32.8% of respondents considered process-related barriers to be the most difficult challenges to address, followed by people-related barriers (30.5%) and technology-related barriers (27.6%). These findings suggest that most respondents acknowledged the significance of having appropriate systems and infrastructure that support lean digital transformation. Meanwhile, 9.2% of respondents suggested that there are other barriers such as fear of the unknown and lack of experts than the three mentioned above. The following subsections explore each type of barrier in more detail.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Overview of barriers to lean digital transformation</figcaption></figure>
### 4.3 People-related barriers

In this section, as depicted by the cumulative line in figure 3 below, the first two factors contribute to 68% of barriers. The survey results show that 35% of respondents claimed the lack of relevant skills as the top barrier to lean digital transformation, followed by poor leadership (33.2%), the lack of employee motivation (24.5%), and others (8.8%). The lack of relevant skills would impede any transformation since employees do not know what they must do during such a process [24]. In addition, Kutnjak [17] and Romero et al. [5] argue that leaders often communicate LDT goals poorly to their subordinates, indicating poor leadership. This result suggests that leadership is crucial in the success of LDT because leaders determine how communication within their team works.

This result is complemented by interview data, where INT1 respondents stated that “digital transformation starts at the top. The management at the top is responsible for disseminating the company's transformation vision. The message is sometimes lost when it reaches ground employees, creating a challenge for management”. When the leadership fails to communicate digital transformation and its importance to their subordinates, these subordinates may not have the necessary motivation to engage in the process, thus helping to explain why 24.5% of respondents regarded the lack of motivation as one of the key barriers that impede LDT. When they lack such motivation, it would be hard to expect them to be willing to develop relevant skills or adapt to new technologies. Participant INT4 stated that “knowing your existing employees’ capability and training them is important. Firms often include experienced professionals in management teams who need to change to have an innovative approach to projects.” Participants INT3 and INT5 further confirmed this argument by pointing out potential knowledge gaps in the lean digital transformation process.

<figure><img src="image_4.png" /><figcaption>Figure 3. Overview of people-related barriers</figcaption></figure>

### 4.4 Process-related barriers

Regarding process-related barriers, depicted by the cumulative line in figure 4 below, the first two causes contribute to 75% of barriers. Overall, 45.5% of respondents argued that poor integration of their organisation's process is the main barrier to LDT initiatives. As Lichtenthaler [22] suggests, many organisations design their transformation procedure around technology rather than people, meaning they fail to integrate the process into their system. Meanwhile, 31.5% of respondents addressed poor resource allocation, and 22.8% regarded poor data collection as two key process-related barriers to LDT, while the remaining 1.8% identified other barrier forms such as unsure of the organisation process.
Brozzi et al. [23] suggest that organisations hire experienced professionals to help them implement their lean digital transformation initiatives. Without adequate experience, organisations will likely experience poor resource allocation and data collection. However, hiring qualified experts is expensive, which is why only organisations with a strong financial stature do so. That said, organisations are encouraged to focus on investing in the other two areas of people and technology. In this way, organisations can ensure they have the expected level of process that supports the LDT. The survey results also suggest that data collection and resource allocation are central to LDT. An adequate data collection capacity facilitates the transformation from manual data entry to an automated system. Meanwhile, an organisation's capacity to allocate its physical, material, and people resources determine how well it can embrace digital transformation initiatives. In this context, INT4 argued that start-ups have a better chance at embracing the transformation because "their flexibility leads to improved processes".

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Overview of process-related barriers</figcaption></figure>

**4.5 Technology-related barriers**

In technology-related barriers, the first two causes contributed to 68% of barriers. Of respondents, 42.5% stated that the difficulty of moving from traditional systems towards automation impedes the implementation of lean digital transformation the most. Meanwhile, 23.4% of respondents chose a lack of IT infrastructure, 21.2% complained of the lack of security, and 14.9% identified other barrier forms such as lack of awareness of technology. Moving from manual practices to automated systems requires significant adjustments to how people do their day-to-day activities. For instance, they may no longer need to input data individually into the database. However, outdated and insufficient hardware and software may hamper such adjustments because no technology allows people to automate the system. Moreover, Borovkov et al. [2] claim that organisations that rely on old technologies would find it difficult to implement effective digital transformation. The interview data support the survey results with this. Participant INT5 also argued that "technology is the biggest factor for SMEs because organisations do not want to move from legacy systems".

Organisations need to consider building their people, processes, and technologies to succeed in adopting LDT initiatives. Moreover, these three development areas influence each other. For example, the lack of IT infrastructure may lead to the lack of qualified human resources since employees do not have adequate tools and facilities to develop their skills. Consequently, companies are incapable of
delivering the required transformation process. An effective lean digital transformation requires organisations to build an adequate capacity in all those three areas to ensure they have the required infrastructure and resources for the process.

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Overview of technology-related barriers</figcaption></figure>

**5. Conclusions, recommendations,**

In conclusion, the main challenges for LDT are related to people and processes, such as lack of skills, poor leadership, and poor integration of organisations' processes. Based on the findings from this study, the following recommendations are made for companies to develop their roadmap for LDT based on three steps.

**5.1 People**

Developing people and their skill sets can be achieved by training internal employees about lean concepts and digital transformation processes. Organisations can be encouraged to improve employee motivation and engagement internally by company experts or hire external experts to train and guide the teams through the entire process. The leadership team can enrol on courses related to lean digital transformation to familiarise themselves with the concepts.

**5.2 Process**

Organisations are encouraged to focus on assessing their current process gaps and start fixing those issues. For example, focussing on developing sustainable processes that can be easily integrated with other systems and online tools. In today's digital world, it is imperative to concentrate on generating, organising and analysing data. Hence, it is important to plan how data will flow through organisations' processes. There are several methods to remove challenges related to the process, such as fishbone analysis, root-cause analysis and creative thinking (design thinking) to develop innovative process designs.

**5.3 Technology**

Most respondents consider technology to be the easy part of the LDT journey. However, it depends from organisation to organisation and their planning to utilise different forms of technology. It is crucial to plan a roadmap for the organisation and pick technologies to help an organisation reach their goal. Sometimes, organisations choose old technologies due to cost factors, which can affect them negatively in the long term. In contrast, some organisations plan the roadmap and choose more
expensive but advanced technologies at the start of their journey. This helps an organisation to pivot to other modern technologies if necessary. Organisations are encouraged to view technology infrastructure and cyber security as an investment.

**6. Limitations and future research**

The study's goal was to uncover insights into barriers to lean digital transformation in the specific context of SME organisations. Because of the importance placed on lived experiences of participants, a phenomenological approach was employed. The advantage of this method is its applicability to understanding social-technical systems, both from the qualitative and quantitative data generated. While this approach provides a broader spectrum for understanding the complex problem of a limited number of barriers regarding lean digital transformation, it is limited in the depth and generalisability of the results.

**6.1 Research limitations**

This study examined complex social constructs by reducing them to several quantitative scale items. The scope of this study, limited to LDT and SMEs in Irish companies, therefore cannot take into account all possible antecedents and determinants of LDT adoption. Moreover, the findings indicate that SMEs do not approach people and process factors equally. Therefore, further in-depth analysis of each factor is required to understand how they impact SMEs and how they could overcome challenges and barriers while adopting LDT.

**6.2 Future research**

Finally, the results of this study demonstrate a lack of relevant skills, poor integration of the organisation's process, and difficulty in moving to automation as top barriers to lean digital transformation by executives and senior management. It would be interesting and potentially quite valuable to further study people and process-related factors to understand the possible barriers to lean digital transformation philosophy. The results of this study offer a clear understanding of the barriers to lean digital transformation projects. Unfortunately, only employees from Ireland participated in the study. A multilevel analysis comprising various hierarchical samples with multi-national companies might yield further intriguing results.

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