Why Are Plastic Manufacturers Embracing Digital Transformation?

As I sit through a digital transformation and technology oriented conference, there are some recurring themes that are relevant for plastic manufacturing.

“Focus on the outcome”…

”Start with the end in mind”…

While a tad cliché-ish, they remain true.

By the way, each presentation is being automatically converted via voice to text, so every single presentation has English sub titles.  Could easily be in Spanish, French or Japanese… Whether it’s a 3D Mixed Reality HoloLens or voice activated and driven user interface, the world is changing around us.

Some other major themes include:

1. For years, plastic manufacturers, by virtue of machine data being produced every second, have already had foundational data for operational improvement. What is changing is the way in which this data is being ingested, analyzed and transformed in real-time to automate action to avoid costly rework, trigger recalibration of machines or just-in-time preventive maintenance, or early detection of anomalies to avoid other costly events.
2. Signals, in Kanban terms, has been a meaningful replenishment term – now, automatically detecting deeply embedded signals (and, sometimes multiple connected and embedded data points) is an important key to identifying problems, to automate alerts and minimize latency normally realized after the fact. Unfortunately, most of the visual signals used today are simply too late to avoid an unfortunate event.
3. Disruption – after years of doing things “because that’s the way they’ve always been done”, manufacturers have tools and resources to make significant impact on mass customization, days sales outstanding, or new business models normally resulting in new revenue streams or higher margin offerings. Latency avoidance or self-service (internal or for customers) visibility to these key signals and in a form that is easily digested (we’ve come a far way from the “exploding pie chart”) are simple ways to positively disrupt the user experience.
4. IoT offerings impacting core OEE metrics are getting easier and easier to implement and with better root cause analysis.
5. Device/machine connectivity is becoming more and more prevalent and will not only be retroactively supported, but new devices will produce more and more relevant information related to performance, quality, and breakdown avoidance.
6. Role based dashboards with relevant graphs (they are now routinely called visualizations) are actually really cool, relevant, and helpful.
7. Machine Learning and Artificial Intelligence will become omnipresent. AI and Machine Learning as a service will literally be seen everywhere… just saw a session where it used stop lighting (green = good, red = bad, yellow = meh) to signal a potential issue based upon a projected outcome…
8. While the terms, Software as a Service, Platform as a Service, and Infrastructure as a Service (I could use some Quality of Life as a Service (QoLaaS)) sometimes seem hard to grasp, one of the primary benefits is time to value. Which brings to us to the last point…
9. Many of the Digital Transformation projects won’t cost a fortune, won’t take months or years to implement, and generally do not require much assistance from IT. Quick time to value and lower upfront costs are a nice combination when weighing all the possible projects and uses of money and operating expenses (versus depreciated capital costs).

While all this change is occurring, revenue and margin pressure will remain. New business models, while difficult to create, will still be expected. Customer, quality, and production output expectations will remain unnaturally high. Continuing to operate and execute the same way may work okay for some niche segments, but definitely not in general.

Will Digital Transformation automatically provide all the answers? Of course, not.  But, they will help intelligent and driven companies succeed.