**3.2 Development of the model**

To build up knowledge on the area we started performing a critical literature review on SMEs digital transformation, frameworks and methods, digital technologies and applications, linked to managerial aspects, performance, and deviation management. Research indicates that analytical models for managerial capabilities exist [7, 10, 24] but still there is a practical approach missing in terms of what structure or sequence to apply and guidance on how to integrate specific performance objectives. For our theoretical model, we adopted the framework proposed by [5] as a foundation, due to the components it envisions i.e. capabilities, means of implementation, and operational performance objectives.

According to [22] intelligence and connectivity enable an entirely new set of product functions and capabilities, which can be grouped into four areas: monitoring, control, optimization, and autonomy. The four capabilities defined initially are linked together, where each capability builds on the next one. For instance, monitoring capabilities are the base for product control, optimization, and autonomy. In that sense, a company will not be able to control and optimize without first having a monitoring system in place. Such a model does not include concrete metrics on the operational performance objectives that SMEs can utilize to track improvement through the development of the capabilities. Our contribution includes the overall equipment effectiveness "OEE" as the initial metric to integrate. The model consists of three main levels 1. Managerial capabilities, 2. Means of implementation and 3. Operational performance objectives. The capabilities are arranged in a proposed deployment order from left to right. Each level and its elements are represented in **Figure 1**.

## **Figure 1.**

*Capability deployment model in SMEs industrial processes based on [5].*

*A Conceptual Model for Deploying E-Service in SMEs through Capability Building… DOI: http://dx.doi.org/10.5772/intechopen.97245*

According to our research, SMEs are sometimes lacking formal systems to control and measure their performance. That is the reason why we considered the operational performance to be initially measured by the three elements of OEE as key performance indicator "KPI" i.e. availability, performance, and quality. To the best of our knowledge, most SMEs (independent of industry and production strategy), have an initial understanding of the three elements of OEE. Although the OEE data is not always analyzed, the data itself is often generated. Our model aims to change the common reactive practice among SMEs, by proposing a gradual longterm approach that builds on developing managerial capabilities in the production processes.

The introduction of OEE is envisioned as an initial step of the working procedure for SMEs, it is expected to progressively connect to production disturbances improvement and likewise generate positive effects in performance.

## **3.3 Model validation: practical application on building the managerial capabilities**

For validation of the model, we utilized a comparative multiple-study case research approach. This approach was adopted given that case study research is a comprehensive method that incorporates multiple sources of data to provide detailed accounts of complex research phenomena in real-life contexts [31, 32]. The cases in our study are SMEs that decided to take the challenge of internally codevelop a production digitalization system as part of their digital transformation. The context is unique given the innovative approach the company cases have taken i.e. designing and implementing instead of purchasing and implementing an existing system or digital solution. Our data collection consisted of multiple sources such as interviews, observation, notes from physical meetings, and records from disturbances logging. We performed both semi-structured and unstructured interviews along the whole development process. The multiple sources of data with alignment to the results and having multiple researchers (referred to as advisors in the digital system development), who worked in the data analysis guarantee triangulation. Triangulation of data sources, data types, or researchers is a primary strategy that can be used and would support the principle in case study research that the phenomena be viewed and explored from multiple perspectives [32, 33]. The interviews included plant manager, production manager, developers, and operators at both Case A and Case B; this guarantees the elimination of single informant bias.

#### *3.3.1 Practical industrial cases*

The cases selected as testbeds for the model are a Swedish SME and a Latvian SME, for confidentiality purposes they will be called case A and case B. Case A has more than 75 years of experience in manufacturing fasteners and industrial components for the automotive and engineering industries. Case B has around 15 years of experience in manufacturing similar components for the Latvian industry and has been considered the largest conical pin manufacturer in Europe. Both cases share experience in manufacturing and belong to the same German-owned corporate group. Their production processes include high-speed cutting, centerless grinding, length turning, tumbling, cylindrical grinding, and centerless grinding. Their production strategy comprises processes from prototyping to serial production.

In 2019, to improve their disturbance handling and become more autonomous, the companies started to join efforts on their digital transformation by collaborating in the development and implementation of a low-cost and tailor-made digital tool, which they call "production process digitalization system". The project was

#### *Digital Service Platforms*

interlinked with a research team (advisors) and a couple of students (developers). Before starting this journey both companies had the same Enterprise resource planning system (ERP) in place, representing the only digital solution implemented in their production sites. The system was mostly utilized for production planning purposes. The two cases shared the need for monitoring their production processes more efficiently and integrating real-time data in their ERP-system. Features such as production order completion level, equipment status, and equipment performance were examples of desired information to improve both decision-making and disturbance handling.
