Input/Process/Output. I think this is a great way to evaluate new ideas or to simply process a unique or emerging trend. As we view Industry 4.0, let’s first define it and then evaluate how this might impact Plastics Manufacturing. Part of the excitement around Industry 4.0 is the fact that the Input and Process spectrum is exploding. This means that the Output (the good stuff, as in the results) is also getting really good in that it will lower costs, drive revenue and potentially unleash new disruptive business models that will differentiate you.
What is Industry 4.0?
According to Wikipedia, “Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of Things and cloud computing.” It goes on to discuss the Internet of Things and the Internet of Services to create the “Smart Factory.” We’ll get into what a Smart Factory really is and why you should care in a moment.
Industry 4.0 is impossible without Digital Transformation. Remember, how we started with “Input” above? Well, the volume of inputs, both objective (think of on/off bits and bytes) and subjective (think of emotion interpretation, sarcasm, etc. and placing objective values on sentiment) is growing exponentially. While this is exciting, there must be filters and analytics in place to accurately interpret the vast amount of data becoming readily available. Then, there’s also the element of time, or really, timing. There’s a shelf life for some decisions, so taking advantage sometimes involves doing so within a finite window of opportunity. Digital transformation covers the gamut of exploding data sources and all the processes and services that make this useful to a company.
What will be the impact of Industry 4.0?
Let’s talk about the results. As Steven Covey says, “let’s start with the end in mind.” In this case, it’s about saving and generating money. Affecting both the top and bottom lines. And, this is the goal of Industry 4.0. There are Customer Relationship Management (CRM) systems which are not only capturing relevant data, but also guiding the sales process through timely alerts and suggestions.
Mass customization. Sounds like an oxymoron – like “Jumbo shrimp.” Smaller, more frequent orders means shorter production runs. The world is turning in favor of a highly personalized customer experience and preferences. Larger manufacturers already have an economy of scales advantage. If mass customization occurs, it may drive out slow to change small to mid-sized manufacturers that cater to a higher touch clients. All other things being equal, a customer will absolutely favor those that provide a superior customer experience.
What about “Smart Factory?” This is the operational changes brought on by digitally receiving millions of data inputs and then driving automated responses to improve the products and services made. Layer in the possibilities of Artificial Intelligence and Machine Learning and now you have a system that adapts to changes through constant monitoring, testing, and retesting. It becomes a feedback loop that is automated and with continuous improvement in mind even as the landscape changes.
On the production side alone, consider the productivity, quality and uptime benefits across the many components in a traditional extrusion line with sensors monitoring temperature, pressure, vibration, sound, and cycle times for:
- Water flow regulators
- Flow and Temperature controllers
- Screw Drive Motors
- Extruders & Dies
Now, think of the “Output” Possibilities:
- What if you could build models to predict resin cost accurately using various indices and also including external factors and weighting to your specific industry, customer set and risk factors?
- What does a 1% improvement in OEE mean financially to your business?
- Would more customers be drawn to your company’s products if you could drive customer experience initiatives that could personalize, customize and improve visibility to important metrics and information?
- What if you could avoid costly outages or downtime nearly all together and perform maintenance based upon metrics indicating that the machinery was beginning to produce less effectively or at a point where maintenance made sense financially?
- What if you could accurately predict the Remaining Useful Life of a machine and better yet, use this and depreciation, capital and operating expenses to ensure that machines were being replaced when all variables were being considered?
Microsoft is building a platform for Industry 4.0 around Azure. It provides application, infrastructure and platform services that will take the exploding data, filter and transform it, and then drive insight and automated action through a myriad of business intelligence and artificial intelligence/machine learning services.
To prove a point, using a low cost sensor on a Raspberry Pi (costing about $50), and a multitude of Azure Services, we built a real-time temperature sensor application that was integrated into Microsoft’s ERP called Dynamics 365…