This is a guest post from my colleague, Andreas Graesser, a national thought leader on Digital Transformation concepts and strategies for business. His consultancy is Innovad.
Businesses capture day by day data in amounts that are sometimes overwhelming, mostly when you think of the Internet of Things (IoT) that provides countless data points from its machines, sensors, cameras, and other access points. According to IDC, more than 41.6 billion connected IoT devices, or things, will exist in 2025, generating alone 79.4 Zettabytes (ZB). One ZB is circa a billion Terabytes or a trillion Gigabytes.
Fig. 1.1 Annual Size of the Global Datasphere in Zettabyte
Still, many production lines continue to use outdated manufacturing processes with little integration of modern data analytics and decision-support tools. However, as we are in the midst of the smart transformation era, some enterprises invest heavily to make their machinery and their business processes intelligent. Now, the machines are not becoming intelligent per se. While gathering data from sensors and providing it to a data management platform, the algorithms itself let the machines look intelligent, consuming all their data points for calculations and monitoring. So, what are the intelligent layers within Manufacturing?
Smart machinery uses real-time data to get the status of operation at any time. Within Fig. 1.2 you can recognize several Smart Layers of machinery the operator, together with its smart machinery management platform, can use.
On the lowest Smart Layer, the real-time supervision level, the goal is to recognize immediate stoppages or transient issues that hold and impact the production process. This scenario only provides machine status with just-in-time alerting. The data volume of this usage scenario is low. It does not require high sophisticated algorithms to retrieve alarming situations and notifications.
On the middle Smart Layer, the combination of real-time data and the historical records of information provides more profound insights. The algorithms predict near-future job outcomes and, as such, the operators can keep the machines running in its optimal state.
The upper Smart Layer, adding the predictive capabilities, uses sensor data combined with statistical analysis and trend monitoring to predict and forecast when a part of the machine will fail. This predictive maintenance scenario avoids future failures of machinery. The operator uses scheduled production down timeslots to replace parts before they break. The data volume of this smart scenario is high, as all real-time data points must be collected, stored, and analyzed. Additionally, specific predictive analytical software algorithms must be developed and used to identify the pre-breakage-situations of parts, specific to the given runtime scenario of the machine. For example, in a particular production scenario, any temperature above a defined threshold measured by sensors could point to an over-usage or attrition of a specific machinery part and would trigger a maintenance work order.
Fig. 1.2 The Stack of Smart Machine Layers
Key Questions Driving “Digitization” for Manufacturing
While many of the readers might agree on the fundamental need and importance of ‘Digital,’ the road to the perfect world seems to be fuzzy and unclear, not only within the manufacturing space. Too many discussions and presentations happen about features and functions of tools and technological artifacts, all clouding the understanding. To arrive with a much-needed vision, the transformation leaders have to shy away from ‘feature and function’ pitches and to obtain their own knowledge of the needs and the expected value out of the digitization. As the digital revolution is only beginning to take shape, here are the ten critical questions that the leaders need to address and answer to form their digital vision and thinking.
- How will Digital disrupt my business operation in the next five to ten years? Think about:
- Flexible production processes, resilient supply chains
- Manufacturing operations are complicated with tight schedules and numerous resource constraints
- Complex and connected supply chain where data/information and speed are key
- Do I expect a new digital ecosystem to emerge? Consider:
- Services based on intelligent products
- Consumption-based business models
- What are the value drivers of my company today, and what do I have to change in the near-term to stay competitive? Contemplate:
- Maximizing margins
- Outreach to customers, sales channels
- How close is the digital wave to my factories, and how digitized are my competitors already? Ponder:
- Covid-19: standstill – or overload
- What is my ‘Digital Vision’ for my company? Evaluate:
- Investment building blocks, such as digital platform infrastructure, cybersecurity, and partnerships
- Addressing any budget and resource limitations
- What new digital capabilities and open mind-sets will you need in your organization? Assess your employees:
- Demands on the IT department’s technology stack and development structure
- New release cycles, processes, APIs, digital performance
- How will you identify and recruit the right talent? Review:
- Digitalization requires change management for the entire workforce
- Employee reluctance and communication issues
- Technology and innovation centricity (data, industrial IoT, IT runs the business, new business models)
- Are you willing to start small, using a pilot or prototyping approach to evaluate the digital value generation? Innovate:
- Diversify revenue sources, leveraging new ecosystems and connected data
- Are you calculating uncertain macro-economic and geo-political context (i.e., COVID-19, trade wars) into your strategic plans? Examine:
- Managing risks, reducing cost, enhancing efficiencies
- Do you understand the technological enablers of digital opportunities? Learn about:
- Digital twins, robotics, artificial intelligence, 3D printing
- Benefit and use case, holistic business context
Answering these key questions certainly provides a significant insight into the digital future of your business. In my experience, the answers always offer a valuable starting point, even before jumping into any Digital Business Transformation project. Paul Mosenson and I prepared a survey about the Ten Key Areas of Digitization within Manufacturing, and we would love to learn more about your take on “Digital.”
 “The Digitization of the World,” IDC Whitepaper, November 2018, Doc# US44413318
 The Transformers, Andreas Graesser (innovad, 2020)
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Ten critical questions about the Digitization of Manufacturing
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