Chrysalis to butterfly digital transformation

Digitalisation Value Creation

Digitalisation offers businesses new opportunities to create additional value for customers. While hardware becomes increasingly commoditised, software provides companies with an opportunity to move higher up the value chain, build customer intimacy and generate additional revenues from new business models. Lakana was recently engaged by an Eindhoven based mechatronics manufacturing company to provide advice and consultancy on the use of software to help them achieve these benefits.

Predictive Maintenance

The customer’s engineering department had created a long list of innovative ideas for software solutions that could add value to the systems they design and manufacture. Initial assessment of the list by Lakana led to two observations: firstly, while most of the ideas had merit it became clear that one, namely Predictive Maintenance, could be directly linked to a current business opportunity; secondly, many of the customer’s ideas had in common the use of IIOT, Big Data, Business Analytics and, ultimately, the application of A.I. or M.L. software technology.

OEE Poster

Consequently, the customer’s steering committee decided to focus on investigating cloud based Predictive Maintenance (PM) applied to a particular business opportunity. They also recognised that the software technologies that would need to be mastered for PM were common to many of the other ideas on their long list. Thus, by focussing on cloud based PM, they could build competences that could be reused for other business opportunities.

Realising a PM application is conceptually a two stage process. The first stage is to establish a pool of data in the cloud suitable for performing PM. The second stage involves turning that data into valuable information. These two steps involve the use of entirely different software technologies, the first focussing on implementing IIoT and Big Data technologies, the second on A.I./M.L. technologies.

Attempting to adopt all of these technologies in one go would clearly be a huge challenge. By splitting the program into two stages with an intermediate goal, a more agile and focussed approach could be adopted. Lakana therefore proposed that the goal for stage one should be the measurement of Overall Equipment Effectiveness (OEE) metrics using straight forward business analytics, leaving stage two to focus on the adoption and application of A.I./M.L. technologies.

Sourcing Data

Access to a suitable source of data is a prerequisite to building a Predictive Maintenance capability. Since the customer’s engineering department did not have a suitable data source available, creating one was clearly a priority given that accumulating sufficient data would potentially take some time. Additionally, creating a source of data requires access to suitable machines that are in constant use, representative of the real world scenarios to which PM would be applied. Such machines were not available in the engineering department.

OEE Poster

However, the customer has factories containing numerous numerically controlled (NC) milling machines and lathes. These machines potentially provide a useful source for collecting maintenance related data, the nature of which is similar to that of the commercial PM application envisaged by the customer. This data could be used for building PM knowledge and competences transferable to the development of commercial PM. A further advantage of this approach is that the customer’s own operations would directly benefit from OEE metrics derived from the NC machines during stage one of the program, strengthening the overall value proposition of a Predictive Maintenance program.

Building Competences

Lakana therefore proposed a roadmap for building PM competences, the first phase of which would be the establishment of OEE metrics based on NC machine data. This would result in the engineering team developing the competences necessary to collect, manage and analyse cloud based data. The second phase of the roadmap would be the development of an experimental intelligent PM application based on the growing data pool of NC machine data. The third stage would be the application of the knowledge, competences, IOT/Cloud framework and PM expertise developed in the first two stages to the original business opportunity.

Result

Lakana made the following recommendations to the customer:

  1. The customer should establish a “Digital Competence Centre” (DCC) within its organisation. The purpose of the DCC is to build up knowledge of and experience in the application of IOT, Big Data and Machine Learning technologies.
  2. The first goal of the DCC should be to build a cloud based Predictive Maintenance application based on data collected from a number of the customer’s NC machines. Additional, early value can be obtained from using the data to produce OEE metrics related to the NC machines.
  3. The focus of the DCC should be to make use of commercial IOT/Big Data/ML platforms and tools wherever possible. It should restrict bespoke software development to an absolute minimum. Further, a “fail fast”, rapid, agile approach to building a PM solution should be adopted.
  4. There is a significant opportunity for the customer to benefit from PM within its own operations and that, consequently, the customer should consider setting up a wider digitalisation program.

The customer accepted Lakana’s recommendations and has since allocated resources to creating a Digital Competence Centre. The customer is also taking steps to plan and execute a wider digitalisation program.