Software is a multi-billion dollar industry, creating new jobs and driving in-demand skills that we didn’t see coming. And now we have found new ways to consume these applications of software, commonly referred to as ‘software as a service’.
This revolution in how software technology is delivered enables businesses to deploy rapidly. It also allows them to innovate at a pace and risk profile we once could only dream of, even as recently as five years ago. Today, this system is as mainstream as having food delivered to your door via an app. Businesses have never had a stronger case and mandate for change.
There are many ways to deploy advanced technologies when embracing Industry 4.0 opportunities and smart manufacturing.
Customer focus as part of your Industry 4.0 strategy
Having a customer focus is a much larger priority today than ever before—and that’s what Industry 4.0 is. It’s about streamlining your operations around this customer-focused goal in order to meet it. One of the best ways to get started is by digitizing.
Digitizing is not about outplacing people, but rather is about increasing your productivity and competitiveness by growing workers' skills and connecting them in a digital value chain.
Industry 4.0 initiatives don’t ask manufacturing facility workers to leave their brains at the door, but rather engages their minds more fully with greater analytical and critical thinking responsibilities.
To remain competitive, your company needs a culture of continuous improvement through innovation and employees who are ready to challenge the norm. Your business is setting the compass and commencing the journey to a digital destination—one you will never stop seeking. The energy you put into this transformation is as important as the technologies you will utilize to realize your vision.
First, you need to lay the foundations for your digital future by getting control of your business data. This groundwork creates the opportunity to apply smart solutions, such as artificial intelligence (AI) and machine learning.
Opportunities for AI and machine learning
Gartner believes that, in the next two years, 50% of midsize enterprises will improve their productivity and growth by adopting artificial intelligence into their core business operations.
Adopting AI can lead to positive impacts in:
- Product quality;
- Maintenance and machinery life cycle;
- Speed of design;
- Waste reduction; and
- Customer service
For companies that operate across multiple production sites, or with different shifts, the ability to compare operational conditions and draw insights from the comparison is hugely valuable. You need business intelligence solutions in place to gain this insight, as they capture performance data that AI technologies use to identify patterns. These solutions capture a wider business picture—allowing insight not just into equipment, but the efficiency of the production line. You can derive comprehensive insight into product quality metrics and begin combining other sources of data such as customer feedback and supply chain efficiency.
We’ve used this approach in food and beverage projects through the application of smart models. With one of Nukon’s clients in this industry sector, we used this approach to improve their quality process. We implemented TilliT, a digital tool that orchestrates the entire quality process from generating work orders to assigning tasks, while generating digital quality records.
After implementing TilliT, our client saw their labor input decrease and efficiency increase. Some of the results the client achieved include:
- Vastly improved process adherence, as important tasks are claimed and confirmed done in a timely manner.
- Identified where skip rates went up on particular lines, indicating additional training is required.
- Reduced the number of pre-start and in-process quality checks needing to be completed by more than 25%, thereby reducing the average time to completion.
The client also gained valuable data around labor input, enabling them to build a case for future areas of improvement.
Advancements in machine learning, pattern recognition, and Big Data analysis tools are now making digitization more accessible, cheaper, easier to configure, and more powerful.
This capability crosses many industries and is now becoming commonplace among manufacturing businesses for root cause analysis, anomaly detection, asset performance, predictive maintenance, and other areas. The ability to detect quality issues through image recognition tools can dramatically reduce the risk of a reputation damaging (and costly) recall.
The capability to continuously improve and optimize operations is more than enough reason to investigate whether it makes sense for your organization to trial these technologies.
Alec Konynenburg is Managing Director of Nukon, a SAGE Group. SAGE is a certified member of the Control System Integrators Association (CSIA). For more information about SAGE, visit its profile on the Industrial Automation Exchange.