Abstract
The recent rapid technological change bears the challenges of introducing and adapting new technologies in organizations. The technological potential is very much dependent on the knowledge and skills of employees who work, adapt and potentially improve the technology. In this paper, we introduce the case of how the digital factory in Amberg of Siemens Germany used an employee-centered, bottom-up workshop concept to derive employee skill demands based on new technologies coming from their technology road map.
Major practical challenges were to choose the right selection and number of participants, developing a common understanding of the technology and further implementation of the results.
Our derived guidelines for technology driven companies include to give workers a major voice in the decision who should participate, start with a common technology understanding and use cases and end the workshop with a clear definition of responsibilities for anchoring the results on changing skill demands in the organization.
1 Introduction
“People want guidance, not rhetoric. They need to know what the plan of action is and how it will be implemented. They want to be given responsibility to help solve the problem and the authority to act on it.” [1]
Today’s organizations are confronted with rapid change and uncertainty. Especially in the context of the industrial Internet of Things (IoT), organizations are challenged to equip their workforce with the right set of technologies and to ensure that the workforce is skilled accordingly. Social embeddedness of technologies is as critical as the new technology itself.
To assimilate novel technologies effectively into existing work environments of highly automated and digitalized factories, both office and production workers (white- and blue-collar employees) thus need to have the right skill set to understand the potentials of fourth industrial revolution (4IR) technologies as well as their impact on existing processes.
One major issue for pioneering multinational corporations such as Siemens is that the number of novel technologies to consider grows continuously. This means for a large part of the workforce that it is often unclear what potential a new technology has and what it means for their individual work processes. Consequently, organizations struggle with understanding and managing the impact of novel technologies on their business as well as on the portfolio of skills their workforce needs to remain future-ready. This lack of understanding poses two main challenges. First, the HR and Industrial Engineering department struggles when it comes to training and development decisions for employees. Second, the exploration of potentials of novel and thus not well-understood technologies in the workplace can create uncertainty and fear among employees, e.g., about their job security, and thus foster resistance towards a 4IR technology before its potentials, limitations and consequences for the workplace are understood in detail [3].
To address the depicted challenges in the context of the industrial IoT, we partnered with a consortium of HR and business excellence experts of Siemens Amberg, Germany to develop a bottom-up workshop concept helping the workforce to better understand the potentials and limitations of 4IR technologies, to analyze its impact on existing work processes and structures and ultimately to derive resulting changes in the skill portfolios of employees, and measure how to transform the existing workforce respectively.
The main idea of the workshop concept was to rely on the technology roadmap of the organization, which was developed in a program called “Lean Digital Factory” (LDF) and to analyze the impact of each technology for a certain part of Siemens Amberg. Furthermore, the main goal of the workshop concept was to include employees that are affected by the results in a bottom-up manner right from the beginning.
This is important, since we know from literature and due to crude facts that the implementation of new technologies in the workplace should not be enforced from the top down by the management but should rather be developed together with the workforce to reduce uncertainty and resistance and foster adoption and acceptance [8]. Another goal of the workshop concept was to include the very diverse knowledge and experience of the skilled workforce when it comes to the structures and processes within their organization.
We relied on insights from the literature on Collaboration Engineering (CE) as a foundation for the development of the workshop concept. CE concentrates on the design and deployment of collaborative work practices for collaborative tasks that are a) recurring in nature and b) of high value for the organization.
Furthermore, the goal of CE is to develop and document the collaborative work practice in a way that empowers organizations to run future workshops on their own without the support of external facilitators or CE researchers [6].
In this paper, we outline the workshop concept we jointly developed and present guidelines for technology driven companies on how to develop and implement similar workshop concepts to address the problem of introducing 4IR technologies in their organizations. We showcase the work of the Siemens digital factory in Amberg, Germany, in which we implemented the workshop concept in three settings with different upcoming technologies and participants to identify challenges and derive guidelines. Each of the workshops tackled a new technology coming from the Siemens Lean Digital Factory technology road map.
The first workshop dealt with the super trend Digital Twin (Simulation) in the cost and pricing department. The second addressed Robotics (Autonomous Robots) and the third Big Data & Analytics, both held in the production departments. Based on this workshop concept, Siemens Amberg conducted five more workshops with CE experts and several more workshops without support in different works with 4IR technologies from the Lean Digital Factory road map to ensure that employees keep up with the new technology implementations.
The remainder of the paper is structured as follows. In section 2, we start with explaining the context of the digital factory of Siemens Germany in Amberg.
Afterwards, we explain shortly their technology road map and CE in more detail before we focus on the different steps of the workshop concept. Next, we provide insights into the workshop implementations and, finally, in section 3 and 4, we derive challenges and guidelines to implement similar workshop concepts in the other factories of Siemens and therefor also in general.
2 The Workshop Concept at Electronic Works Amberg
The Siemens AG is one of the largest industrial manufacturing companies in the world. The headquarters of this worldwide operating company is in Munich. The entity that is in focus of this project is the Electronic Works Amberg (EWA) (Germany), one of Siemens’ most advanced and highly automated electronics factories, specialized on the production of automation technology and the winner of multiple industrial and factory awards. The EWA includes about 1250 employees and produces more than 17 million products per year, which is more than 1 product per second. The factory serves more than 60,000 customers worldwide at a quality level above 99%. The EWA plays a key role for the implementation of the “Lean Digital Factory” for over 30 factories of the Business Digital Industries and therefor it tests new approaches like artificial intelligence and industrial edge computing, only to name two 4IR technologies, directly on the shop floor. With the application of innovative systems and technologies, the EWA is also regularly confronted with the challenges of a successful technology introduction. With the upcoming requirements often comes the question of how new technologies can be successfully transferred into workers’ skill profiles.
As complexity increases and business models change, traditional improvement methods must be augmented with new technologies, which is key for efficiency, flexibility and speed. Successfully implementing technologies is complex and requires high effort. Sharing the burden by bundling the expertise of an international manufacturing network and collaborating cross-disciplinarily and organizationally, representatives of all departments have jointly developed scalable solutions to tap local potentials.
For this target, states were defined, according to the five work streams of “Lean Digital Factory”, in the areas of: a) comprehensive automated production engineering using holistic digital twins, b) autonomous supply chain coordination based on Artificial Intelligence (AI) and real time transparency, c) Manufacturing Data Ecosystem supported by AI, d) Interconnected Autonomous Production Systems and e) digitally guided workers applying new ways of working.
2.1 LDF Technology Roadmap
To reach these target states, The program LDF has created a comprehensive technology road map in which it identifies the relevant 4IR technologies and divides them into different areas so that they can then be integrated into the working environments. LDF developed the roadmap by matching strategic and long-term planning with specific technology solutions.
The different technologies than has been clustered in five work streams in which the 4IR technologies are enablers to reach target states based on reference processes. The five work streams are
1) Digital Twin – Comprehensive automated production engineering & optimization based on consistent usage of holistic digital twin to increase engineering efficiency and speed-up time-to-market.
2) Processes – Autonomous E2E-coordination of supply chain resources based on artificial intelligence and real time transparency to maximize speed and efficiency
3) Big Data & Analytics – A comprehensive Manufacturing Data Ecosystem, using artificial intelligence to increase quality and efficiency in production while laying the foundation for scalability
4) Robotics – Cooperation of digitally guided workers and interlinked autonomous production systems, enabling efficient, flexible and easy to scale-up manufacturing
5) New Ways of Working – Live-long learning employees with digital mindset apply new ways of working, increasing the flexibility and efficiency of digital and sustainable factories of the future
It also includes technology forecasting to identify suitable emerging technologies. From the pure technology point of view, the Lean Digital Factory roadmap contains the following broad areas:
Figure 1. Lean Digital Factory technology roadmap
One main goal of the EWA for LDF is to test these technologies for their staff members and, if it is successful, to roll it out for the other plants in Germany, Europe, Asia and America.
2.2 Collaboration Engineering as Organizational Background
The design of the workshop concept was inspired by the theoretical domain of collaboration engineering (CE). The aim of CE is the design of repeatable collaboration processes for working groups [4, 5]. In this context, collaboration can be explained as a planned and structured co-working of two or more individuals [2].
The focus lies on the processes of working groups to reach their shared goals. It is an approach to design and deploy collaboration processes that can be executed by practitioners for high-value recurring tasks without the need of hiring external facilitators. A collaboration engineer designs collaboration processes and transfers them to practitioners in an organization. To manage the complexity of the design process, they use a modeling technique called facilitation process models (FPM) to capture high-level design decisions that serve different purposes,
such as documenting and communicating a design, etc. [7]. The FPM of the third workshop implementation is depicted in the Appendix and further described in section 2.7. For more information about the FPM notation, see [7].
CE seems to fit well to Siemens’ goals of creating repeatable group processes for high value recurring tasks (translate new technology requirements into skill demands) without the need of external facilitators. The author team took the role as collaboration engineers and created the workshop concept together with the HR and business excellence department of the EWA.
2.3 The “From Technology to Skill” Workshop Concept
Inspired by CE, we created a workshop concept with the goal to identify, together with those concerned, the skills that will be needed to introduce and apply new 4IR technologies. These skills should then be included in the department’s skill profiles and serve as a basis for appraisal interviews and training plans.
The workshop concept can be divided into three main parts: 1. Getting to know the technology, 2. Identify use cases and affected job roles and 3. Identify skill demands. All three parts, the sub-questions and corresponding in- and outputs are depicted in Figure 2.
In the first part, external or internal technology experts explain the why, what and how of the new technology. The workshop participants should understand what the technology is about and how it can add value to their work. After achieving a common understanding of the technology, participants should then think of promising use cases where the technology has the greatest potential. In part 2, after identifying a first set of use cases, participants cluster and rank the use cases according to their potential impact for the overall organizational goals. Then, participants discuss which job roles are affected related to the most important use cases of part 1.
In part 3, participants brainstorm about new activities and corresponding skills that are needed to integrate the new technology into their work processes. Based on these activities, participants think of new skills that are needed and discuss them in a plenum.
Figure 2. Siemens workshop concept „from technology to skill demands”
Finally, for each skill, participants should think about the most appropriate way to acquire the skills based on Siemens’ already existing training measures or even by new ones who can be developed by Siemens Professional Education department.
2.4 Implementing the Workshop Concept
We implemented and iteratively improved the workshop in three settings with different characteristics.
We selected these three settings together with the Siemens HR and Business Excellence Team based on the technology road map. Table 1 shows the different characteristics of the settings.
In step 1, the workshop started with a one-hour presentation on simulations and their potential presented by an external presenter. The presenter showed how simulations can help to make predictions based on past data. Among others, the presenter showed the participants how to predict future demands in a steel company based on a linear regression model.
Based on step 1, in step 2 the participants brainstormed about possible use cases for the technology Simulation in their daily work processes. We asked the participants to form subgroups of three and to discuss it with the whole group afterwards. The participants came up with use cases such as price calculations, model-based forecasting, personnel planning, maintenance, etc. For example, one specific use case was the quarter-based estimation of future demand of electrical circuit boards. Based on the identified use cases, the group thought about relevant job roles. One identified job role was “the purchaser”, who can use simulations in the future to calculate future demands.
In step 3, based on the identified job roles of step 2, the group identified new skill demands such as smart data analytic skills, data management skills, creativity, etc. For example, “the purchaser” needs new skills in data analytic to execute future tasks. This skill was therefore added in their skill profile, which is part of its job description. It is used as a basis for further discussions about employees’ training plans, which is part of its job description.
In step 1, the technology presentation was held by an internal expert. He showed specific Siemens use cases for the technology and also showed examples of the Chinese plant, where they already tested some of the newest lightweight robotics.
Based on step 1, in step 2, the participants discussed together the possible use cases and expanded the use cases that the expert showed. The participants came up with use cases such as maintenance, programming, calibration, personnel training, etc. For example, one use case describes an employee explaining the workflow of how employees can collaboratively work together with the robots. The group identified several related job roles such as “the interactor”, “the user”, “the technical consultant”, “the maintainer”, “the planner”.
In step 3, based on the identified job roles, the group discussed the different skill demands for each of the job roles. Figure 3 shows an exemplary result.
Figure 3. Identified job roles and corresponding skill demands
For example, the technical consultant needs in-depth programming skills, team and mentoring skills, etc. These skills were also added to the skill profile of the technical consultant and further discussed in the annual employee meetings.
Step 1, the technology presentation, lasted a full day and was held by two internal experts on big data analytics. The presenters prepared many slides and also showed some use cases for the technology in the future. For example, they showed how machine maintenance demands can be calculated thanks to sensors.
Figure 4. Subgroups in Big Data Analysis setting
In step 2, based on step 1, the participants discussed the possible use cases and expanded the use cases that the expert showed first in subgroups and then together in the plenum (see Figure 4). Some identified use cases were robotic tear maintenance, transport system screening, light barrier check, etc. The group identified several related job roles such as “the process expert”, “the data analyst”, “the connectivity manager”, etc.
In step 3, based on the identified job roles, the group discussed several skill demands. For example, “the connectivity manager” needs an in-depth understanding of cloud logistic systems. These skills were then further included in the skill profile of “the connectivity master”. Furthermore, the HR discussed the results with the responsible department heads so that these skills are included in staff interviews and development plans.
3 Challenges Faced in Implementing the Workshop Concept
We faced three major challenges in the implementation of the workshop concept depicted in Table 2.
3.1 Challenge 1: Choosing the right selection and number of participants
One of the main challenges was to choose the right selection and number of participants for this workshop concept. The kind and number of participants selected for the workshop is very different in all three settings. In setting Simulation, we had 12 employees and two department leaders. In setting Autonomous Robotics, we had 8 participants on management level and in setting Big data analytics we had 80 and 20 participants coming from all hierarchy levels of production. In setting Simulation and Autonomous Robots, participants were selected top down from the business excellence and HR department. In setting Big Data Analytics, participants could voluntarily participate in the workshop step 1, 2 and 3.
The top-down selection has often made employees feel under pressure and other-determined, as if there is no way around acquiring new skills for the technology. The voluntary participation in the setting Big Data Analytics in the workshop had an impact on their motivation. The discussions were much livelier and mostly free from pressure to act smart in front of the managers. Many of the employees were thankful for the opportunity to get a vote. On the other hand, for the workshop organizer it was much more difficult to plan the workshop with an open call because you never knew exactly how many people would come. Moreover, it was important to invite influential participants to the workshop so that the results would be accepted by the organization. An early management buy-in is highly important to get resources and implement actual change. The management needs to define budgets for future and report to HR which employees with what skills are needed in the future.
3.2 Challenge 2: Developing a common understanding of the new technology
The presentation of the technology in the first step of the workshop concept was very different in all three workshops. In setting Simulation, we had an external presenter, who presented the technology for about an hour. In the Autonomous Robots setting, we had a presenter from the EWA itself for about one hour. In the Big Data Analytics setting, we had two internal presenters with a duration of one full day.
In setting Simulation, where we only had a one-hour presentation from an external presenter, we observed that participants had great difficulty with the transfer to their own work processes. Developing a common understanding of the new technology is an essential building block for further steps. Getting an expert in the field is a highly important factor to successfully derive future skills. While expertise is very important, the expert must often be able to present complex issues in a way that participants with very different levels of knowledge understand them. Imagining the future application of this new technology is a highly abstract task and needs a distinct power of imagination. Therefore, one of the main decisions is to instruct internal or external experts (e.g., from outside the company, such as from the manufacturer, consulting companies and partner companies).
Selecting experts from within the company has the advantage that they are familiar with the company culture, the structure and current projects. However, this can also be a drawback since it is difficult to think outside the box and not be biased related organizational structures. Furthermore, the role, function and personality of the presenter can highly influence the workshop participants’ willingness to participate, if this person is high/or not highly respected or liked within the company.
Selecting experts from outside the company can have the benefit of getting cutting-edge experts/knowledge. Furthermore, these experts have often seen multiple companies using their products. However, the drawback might be that this expert is more interested in selling, e.g., products than really thinking about what kind of skills might be important in the future. Thus, it could happen that a less realistic estimation is carried out in part because the expert might not know the structure and processes of the company. Furthermore, there is the possibility that ideas from the outside are rejected since those ideas are not coming from the specialists within the company.
Additionally, the levels of previous knowledge of the participants were very different in the three settings. In the Simulation setting, it soon became clear that the technology input was not enough for the participants to transfer the knowledge into relevant use cases. This might be caused by the fact that the technology was presented in a more abstract and technical way and in unfamiliar fields of applications. This was much better in the setting Big Data Analytics, where two internal presenters talked about the technology the whole day.
3.3 Challenge 3: Ensuring further implementation of the results
Once the skill demands have been identified, the question of how they will now be further processed is at stake. Siemens decided to include these skills in the job skill profiles to use them as a basis for staff meetings and the creation of development plans. The workshop implementations showed that just adding the skills to the skill profile does not seem to be enough.
In the setting Big Data Analysis, when the workshop was over, the managers mapped the results directly into the existing competence management system and derived necessary trainings together with the employees. This was supported by a prescreening of necessary trainings out of the technology roadmap. The greatest challenge here was the clear transparency and corresponding communication of what happens with the results and the announcement of responsible persons. Most participants wanted to know how results will be processed right from the start.
4 Guidelines
Based on the challenges and insights while conducting the workshops in three settings with different configurations, we developed corresponding guidelines for other factories or tech companies who wish to implement similar workshop concepts to address the problem of introducing new 4IR technologies and to derive future skill demands in their organizations. (see Table 3)
4.1 Guideline Rule 1: Let participants co-decide who participates and create subgroups
In the third workshop setting, participants were able to decide bottom up whether they want to participate or not. On the one hand, this led to the employees being more motivated and contributing more to the individual subtasks. On the other hand, the organization of the workshop was more difficult and ran the risk that influential people in the company were not present. Consequently, we created new recommendations for a bottom-up and top-down participant selection process for upcoming workshops. In a first step, department leaders can nominate who they want to participate in the workshop. The nominated people can then also nominate people that they think are appropriate as participants.
The selection of participants had a great influence on the following collaborative processes. Employees from lower hierarchies were often a little inhibited when they had to present an idea in the plenum, especially if their department leader was also present. In this case, it is useful to create subgroups where participants can first think of the task for themselves. Thus, let the employees and managers work first independently on possible impacts of the technology on future skills. Afterwards, combine both groups and let them share their ideas. This helps to get a more diverse and hierarchically unbiased view of which skills might be needed in the future.
Furthermore, the participation in workshops could be discussed and added to the employees’ annual set objectives (annual employee appraisal) so that employees feel involved in the skill development process at an early stage.
4.2 Guideline Rule 2: Talk about technology use cases, with which employees can identify themselves
We experienced that it is easier for participants to define and alter job roles when the presenter of the new technology was from the internal staff. Internal presenters know their audience and thus prepare use cases with which employees can identify very well. Otherwise, employees have problems to imagine the potentials of the technology. A common understanding and the creation of use cases based on this understanding is a factor not to be underestimated so that the participants understand the impact of the new technology and can consider which skills are needed.
Thus, our suggestion based on the lessons learned from the three conducted workshops is to either have internal experts who receive regular training (e.g., by the manufacturer) or to co-create the technology input with external presenters so that they really know the audience before preparing their material. The benefit of internal experts is that they know the companies’ culture, processes and structures that help to break down often complicated new technologies to use cases.
Additionally, it is also highly important that these experts get incentives and time to prepare the workshop. Otherwise, the experts feel less motivated and do not take enough time to translate complex materials into easy-to-imagine use cases. Thus, beforehand, the expert needs to get a predefined set amount of time to get their training and prepare for the workshop.
4.3 Guideline rule 3: Define responsible persons who implement the skill demands in the company
An initial reason for skepticism among some participants was related to the sustainability of the results generated in the workshop. “Yet another workshop”, was something we heard many times. The major issue here was to understand that it is not enough to derive skill demands and potential measures and then assume that someone in the organization will somehow take this up as an input and move on. Hence, these workshops need to be embedded in the overarching organizational processes of workforce development, and a process owner for this new part needs to be defined. The process owner can then either initiate new workshops based on the technology road map like it exists at Siemens or respond to bottom-up needs, e.g., triggered by the business units. Furthermore, the process owner needs to set up the workshops, serve as a moderator – this role could also be taken by another employee, but in our case it worked well that way – and define the future skills and employee profiles with HR and the responsible person for setting up trainings. Afterwards, the process owner needs to conduct regular assessments with the business units if the employees have the skills which were initially defined.
At the third setting Big Data Analytics, the HR department discussed the workshop results with the heads of the department. This was a crucial step to receive the buy-in of the department heads. These skills were then considered in employee meetings and the department heads cooperatively discussed how to develop the new skill demands within their skill development plans.
The workshops as proposed have been conducted and every job profile has been mapped with specific 4IR competences. Additionally, to the detailed job profile – 4IR competency matrix, dedicated qualification methods according to the target group have been developed and implemented.
Figure 5. Competence – job profile matrix and dedicated qualification methods
5 Conclusion
This paper has presented the case of Siemens Amberg, Germany, which implemented a bottom-up workshop concept to derive much needed 4IR skill demands from technologies of their technology road map. We have implemented this workshop concept in three settings with different characteristics. Based on the implementations, we identified three challenges and derived guidelines from it. Until today, Amberg has conducted several more workshops in different works with technologies from the LDF road map to ensure that employees keep up with new technology implementations.
This case provides an example of how employees can be empowered to co-decide which skills they should acquire in the future.
It shows also, how employees can best be taken along on the technology road map resulting in a more self-determined and motivated workforce.
References
[1] Auster, E.R. and L. Hillenbrand, Stragility: Excelling at Strategic Changes, University of Toronto Press, 2016.
[2] Leimeister, J.M., Collaboration Engineering: IT-gestützte Zusammenarbeitsprozesse systematisch entwickeln und durchführen, Springer Gabler, Berlin, 2014.
[3] Mordini, E., “Technology and fear: is wonder the key?”, TRENDS in Biotechnology, 25(12), 2007, pp. 544–546.
[4] Robert Briggs and Gert-Jan de Vreede, “ThinkLets: Bilding Blocks for Concerted Collaboration”.
[5] Vreede, G.J. de and R.O. Briggs, “Collaboration engineering: designing repeatable processes for high-value collaborative tasks”, in Hawaii International Conference on System Science. IEEE Computer SocietyPress, Hawaii International Conference on System Science. IEEE Computer SocietyPress, Editor, Los Altos. 2005.
[6] Vreede, G.J. de, R.O. Briggs, and A.P. Massey, “Collaboration engineering: foundations and opportunities: editorial to the special issue on the journal of the association of information systems”, Journal of the Association for Information Systems, 10(3), 2009, p. 7.
[7] Winkler, R., R.O. Briggs, G.-J. de Vreede, J.M. Leimeister, S. Oeste-Reiss, and M. Sollner, “Modeling Support for Mass Collaboration in Open Innovation Initiatives—The Facilitation Process Model 2.0”, IEEE Transactions on Engineering Management, 2020, pp. 1–15.
[8] Zhang, X. and K.M. Bartol, “Linking empowering leadership and employee creativity: The influence of psychological empowerment, intrinsic motivation, and creative process engagement”, Academy of management journal, 53(1), 2010, pp. 107–128.
