Five ways digital transformation metrics give manufacturers more flexibility

Digital transformation (DX) provides manufacturers with more flexibility and transform industrial processes and operations. See five ways metrics cover the DX solution lifecycle.


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Courtesy: Industrial Internet Consortium (IIC)

Digital transformation (DX) enables more efficiency, new business, operational opportunities and flexibility for manufacturers. DX leverages emerging technologies such as the Industrial Internet of Things (IIoT) to transform industrial processes and operations to produce better outcomes.


However, the business case for undertaking such transformations is rarely clear from the start. How much value can be gained and are these gains worth the investment costs and process changes? Are companies creating more risks and complexity, are there unexpected adverse effects and how can companies evaluate and mitigate the risks and the complexity? Will a digital transformation solutions (applied hardware, software and services) withstand operational changes over time and still show value?


The role of metrics goes beyond known business key performance indicators (KPIs) and operational performance indicators.

The trustworthiness space as defined by its metrics. Courtesy: Industrial Internet Consortium (IIC)



Five metrics for industrial digital transformation

The investigation phase of a solution: Measurements keep track of operational performance and its correlation with the context of operations, revealing improvement opportunities (such as how to enhance a product, a process, or how to increase sub-optimal service availability).

  1. Agreements and contracts such as a service-level agreement (SLA): What do we expect and what is defined as a success. A precise definition of metrics and agreed targets for these brings everyone on the same page: customers, end users operational personnel, service providers, solution developers, technology vendors, regulators and external experts. DX solutions often involve several partner, beyond the understood customer-vendor relationship.

  2. Solution assessment (outcome evaluation): This is not a one-time activity. DX solutions evolve, objectives and constraints change and measures are needed to validate performance. In addition, many DX developments are incremental and involve cycles of trials and errors that require a prompt assessment. Non-functional properties such as safety, security, reliability, or resilience have their own objectives that must be part of a well-rounded set of evaluation metrics.

  3. Managing trade-offs:  The operational expectations that motivate DX are easy to formulate: performance, throughput, productivity, cost reduction, response and lead times, defect or error rates.Understanding adverse effects is key to long-term viability. These include undesirable side effects, unexpected costs, overhead, disruption, the rigidity and fragility of a process and other risks. Establishing the right metrics to capture both positive and negative aspects is crucial to controlling the impact of DX choices in a complex environment.

  4. Establishing good practices for DX technologies. DX solutions involve a growing set of technologies (such as AI, digital twins, real-time analytics, time-sensitive networks (TSNs)), the deployment of which in industrial context needs to be adapted to specific conditions. Measuring the value of these technologies for digital transformation technologies and their operational contexts is critical to developing best practices for applications. The other facet of associating digital transformation best practices with a solution is the capture of its requirements such as the type and volume of data, characteristics of physical assets to be connected and networking constraints, which require their own measurements.


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