PERSPECTIVES

Three Reasons That Industry 4.0 POCs Do Not Achieve Scale

2019-09-11


Although many industrial proofs of concepts (POCs) struggle to reach full production, qualitative and quantitative indexing can provide organizations with an informed way forward. Here is how we helped a Nordics manufacturer improve the scalability of its digital initiatives.

We recently reviewed several digital industrial POCs and pilot projects of a manufacturing client in the Nordics to better understand their scalability. These were innovative industrial digitization projects in areas such as digital twins, inspections with drones, RFID and asset tracking, predictive maintenance, condition monitoring, data exchange with customers, gamification, scenario and processes simulations, shop-floor safety and security, production sustainability, assets localization and tracking, etc.

These projects spanned the operational technology (OT) pyramid consisting of several levels (e.g., hardware, software, platform, use case, etc.). Each of these projects had a different theme that could be in one or multiple levels. For example, one project can cover integration of innovative hardware, while another may require the integration of hardware and cloud, as well as a new user experience.

The results showed that less than 40% of the projects are scalable without requiring major modifications to the program’s construct. This ratio approximates what we have seen in other accounts in the manufacturing and transportation sectors, and also in line with external studies.

We used an evaluation approach that involved absolute and comparative analysis against business and technical criteria. Figure 1 shows our approach, which involved a rigorous review of project documentation, interviews with various project managers and architects, and a probe of relevant technical and organizational key performance indicators (KPIs), resulting in scoring the projects against home-grown indices and best practices.

We developed three different qualitative and quantitative indices to analyze each project:

  • A multi-dimensional scalability index. This quantitative index evaluates the technical and non-technical aspects of the projects to achieve a multi-dimensional scalability score per project. It embeds a seven-dimensional assessment matrix:

    • Business case. What is the added value of the solution to the current business? How will it impact customers? What is the business case?

    • Technology. Where does the solution sit in the technology stack? Is the technology sound and scalable?

    • People. How does it impact the lives of workers on the shop floor? What percentage of workers will benefit from this solution?

    • Governance. What is the project’s governance model over the short and long term? What are the legal and regulatory constraints?

    • Relevance. To what extent can the solution be applied to other factories? Is the solution relevant to multiple factories or only to a specific factory?

    • Simplicity. Is the solution easy to deploy and scale or does it require meeting other dependencies? Does the solution need a large configuration or extended learning curve?

    • Solutions as a service. How independent is the solution to the underlying capabilities? How modular is the architecture? Are the interfaces easy to scale and deploy?

Figure 1

  • Industry 4.0 transformation maturity index. This qualitative maturity index formulates a stepwise journey from computerizing the systems up to full automation and reconfiguration. The result of this index can help the organization to formulate the roadmap, break down the required capabilities to successfully complete projects, prioritize the execution, and reduce the investment and implementation risks. As a result, industrial companies can identify where and when the investment is needed and how the roadmap should be tuned to the business needs.
  • Technology stack spread index. This qualitative index evaluates the technical spread of the project along the operational technology stack.


The results of the evaluations revealed common pitfalls for industrial digitization projects. They included:

1    A majority of the projects were not driven by a strong and clear business case.

Results showed that only 33% of the projects had a strong business goal that was (at least to some extents) quantified before project kick-off. Only three projects revealed a detailed calculation or simulation of the expected business value.

Clear measurable business goals rather than IT outcome and technology should drive a successful transformative industrial project, especially in the digitization domain. Industrial digital transformation projects must be able to address specific business problems such as improving product quality, increasing overall equipment effectiveness (OEE), reducing waste, driving faster improvement cycles, and improving compliance with environmental regulations.

The cost-benefit analysis should be prioritized assessment criteria for digital transformation projects. The lack of such foundation in business metrics will usually result in not receiving follow-up funding and budgets to scale the project and live up to the promise.

2    Data availability and quality have left a visible impact on the outcome and performance of most of the projects.

Results showed that a lack of data or insufficient data quality hindered 57% of the projects. Some projects had to concentrate a large part of the effort on making the required data available and improve its quality in an ad hoc manner, rather than focusing on the application and the POC business case. This indicates that the organization needs to focus on the foundational steps of transformation such as data strategy with a higher priority compared to the application-level POCs.

Industrial digital transformation without access to healthy and granular data will be very slow, painful and expensive. Designing a stepwise data strategy that supports the digitization and executing it in a timely manner can accelerate achieving business goals.

3    A majority of the projects did not have a clear long-term governance and ownership model.

In only 29% of the projects, project managers were able to explain the long-term responsibility assignment matrix model (RACI) and the hand-over from system integration and technology vendor to the business units and corporate. Many industrial projects require complex governance, which involves local IT and engineering, corporate IT and engineering, technology vendors (and startups), a cloud platform provider and a systems integrator. Our assessment shows that many POC owners were not clear about the split of ownership between factory IT and corporate IT when it comes to scaling the POC, ownership and long-term operations.

Clear and consistent communication and definition of ownership is key to a sustainable digital transformation. It is important to define and establish clear roles and responsibilities within the projects while keeping in mind scalability and ownership.

This article was written by Dr. Rouzbeh Amini, a Senior Director in Cognizant’s Industry 4.0 and Digital Strategy Practice.

To learn more, please visit the Industry 4.0 section of our website, or contact us.