A Digital Transformation Strategy For Manufacturers


Digital transformation, Industry 4.0 – with lofty names like these it’s easy to forget that all of this new promise and innovation centers on one thing: data, and the vital importance of making it work for you rather than against you.

The onslaught of big and bigger data is inevitable, thanks to miniaturized electronics that can place RFID chips and sensors in just about any physical package. What you do with this data will have an immense impact on you transition into the all-digital realm.

On the positive side, big data can be optimized by manufacturers in a variety of ways. A report by ScienceSoft” noted some examples:

  • Production process optimization led to a greater than $10 million yearly revenue boost for a precious-metals maker, while yield improvements helped a chemicals manufacturer cut raw materials waste by 20 percent, and a pharmaceuticals maker boosted vaccine yields by 50 percent.
  • Preventive maintenance, using pattern recognition, fault detection and visualization, helped Intel predict $100 million in savings in 2017.
  • After-sales examples include Caterpillar Marine, which used analytics to help a customer optimize hull-cleaning processes, and General Electric, which uses analytics in a number of ways to monitor and optimize performance of wind farms.

The growing diversity of applications also hints at a significant challenge, however, where the seemingly limitless possibilities of big data and analytics can work against an ability to focus on the most pressing, or most valuable, priorities.

Every manufacturer is different, of course, and will have its own big-data/analytics priorities. But some fundamentals are likely to apply to a majority of businesses, and these can be useful to keep in mind during the strategic planning process.

Instrumenting The Factory Floor

Digital transformation begins, for most, in production and the factory floor. That’s where data usefulness is already well established, thanks to CAD, CAM and related software systems for design, virtual prototyping and systems automation. Also, predictive and preventive maintenance analytics are evolving rapidly as newer machinery becomes more fully instrumented and capable of self-diagnostics.

Upgrading big data analytics in production is thus an obvious and ideal strategic starting point because production is the heart of the manufacturer’s business.

Bringing Visibility To Operations

The next step – bringing visibility to operations – will set the stage for all following steps. It will also require a company-wide commitment to an end-to-end analytics fabric, one that encompasses not just the factory floor but all operational assets, from networks and IT to logistics, purchasing and billing.

The analytics fabric should be able to take in the data from a wide variety of sources, including network components, machine sensors, and application log files – all in real time. It should be able to access databases and other company stores for the historical information necessary to create contextual models. By using machine learning algorithms to combine the fresh data with contextual intelligence, the analytics can then discover patterns that will deliver a wealth of information via graphics dashboards.

These patterns are somewhat analogous to the preventive maintenance processing that goes on in production analytics. The difference: the analytics now cover the entire organization with a single visual plane that’s able to oversee and overcome departmental silos and other operational disconnects to show the end-to-end workings of the organization’s operations.

Connecting To The Customer

The next likely step is to bring the customer into view by adding more sophisticated customer-centric analytics. These can show how events in operations may affect customer relations, how a slowdown in shipments, for example, might create a bottleneck with a large customer. This information can then give the customer relations team an opportunity to work around the issue proactively.

Customer analytics can work in other ways, too. For example, the analytics might spot a sudden change in customer behavior, then signal production or logistics to quickly adapt.

Adding Supply Chain Analytics

All manufacturers have ways of analyzing their supply chain dynamics. But not all have embarked on real-time analytics, on using predictive models, for instance, to re-route or revise delivery options in the event of a sudden storm or other event. Real-time supply chain analytics will become critical as manufacturers grow more capable of assessing customer behaviors in real time. Also, today’s faster product-development and time-to-market cycles will dictate greater competitive urgency in responding to customer demands, and this will create demand for faster responses in supply chains as well as production capabilities.

That’s why manufacturers should consider extending their real-time analytics infrastructure to the supply chain as an important step in fully implementing their digital transformation strategies.

The Final Step: Honing Skills, Accepting Change

Finding employees with data-handling and analytics skills is high on every executive’s to-do list, but best-practice managers will also pay attention to their employees’ abilities in adapting to the inevitable changes that will take place as Industry 4.0 moves ahead.

The people who make up future analytics teams – data scientists, data engineers and business analysts – will have to call on the historical knowledge and domain expertise of colleagues, some of whom may not be enthusiastic about adapting to technology change. Managers should commit to bringing these people into new projects earlier rather than later, and to communicating with them fully about likely technical and organizational changes.

Thanks to real-time analytics, data is becoming more valuable to manufacturers by the day. But managers should keep in mind that successful transformations will require valuing the people as highly as the data they’ll be working with.

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