As the world grapples with the disruptions caused by the COVID-induced effects, businesses find themselves in a unique position to reimagine their purpose. Efficacy matters more than anything else to organizations that consider manufacturing their bread-and-butter. Among the many phenomena that reshaped the global economy last year, digitization was a profound one with impact witnessed at far-reaching levels.
The North Star
Effective cost-cutting measures, access to improved quality assurance methods, and last-mile manufacturing should be the main focus for manufacturers today. However, given the recent developments around the COVID-19 pandemic, executives should not only prioritize their “survival mode” plans but also utilize this pivotal moment as an opportunity to build back stronger. Visualizing a healthy, resilient, and end-to-end supply chain would help sustain the momentum. This is also the time to balance risk v/s rewards. An even focus on health and safety v/s innovation and sustainability will help manufacturers visualize stability in such uncertain times. Finally, rethinking contingency planning would help companies, at least this time, stay ahead of the curve, and balance top-line revenue with bottom-line profit.
What are current leaders thinking?
“Predli has offered four executive education programs on the topic of Manufacturing 2.0 and Industry 4.0 for industry groups in Bologna, Italy, and Baden-Wurttemberg, Germany. During these events, I have had lengthy conversations with senior management at some of the most innovative and forward-thinking manufacturing companies in Europe. One thing is certain: the time has come for disruption of old practices in the manufacturing space, and the early adopters who today utilize and understand the benefits of AI coupled with Robotics, Computer Vision, as well as predictive models will be among the winners in this exciting race that just has started.”
- Alexander Fred-Ojala, Founding Partner, Predli
Manufacturing firms have already started deploying and testing their next-gen AI strategy. Our analysis shed light on three popular use-cases:
1. Predictive Maintenance
As the name suggests, the goal is to predict when a machine or equipment might fail and thereby require maintenance. With an advance-warning system, unplanned shutdowns and expensive supply-chain disruptions can be avoided. In the current Industry 4.0 era, we see machines being increasingly interconnected. Thus, a single fault can bring a global value chain to its knees.
Schneider Electric, a global industrial automation company, is at the forefront of bringing a workable system to life. In 2019, they partnered with Microsoft’s Azure Machine Learning & IoT Edge service to deploy an open-architecture-based predictive analytics solution for their Oil & Gas customers. Given that Oil & Gas producers operate in some of the most remote locations of the world, deployment of human capital was proving to be expensive. Thus, it was prudent for the company to figure out a viable solution. With Microsoft, they were able to limit in-person visits, minimize downtime by increasing pump efficiency by 10-20%, and extending pump lifetime by 3-10 years.
2. Product Quality Control
The idea with Product Quality Control is to replace and automate manual, repetitive tasks like quality-check with the assistance of AI. Heavily-trained Computer Vision AI systems can help drastically cut down the cost of quality assurance. Thus, manufacturers can predict end-product quality, reduce human intervention, and achieve a higher production scale in a short amount of time.
BMW Group, a global automotive manufacturer, uses Product Quality Control at their production facility. At the assembly line, stringent quality standards are set in place to ensure uniformity across the same car models. Thus, naturally, error-spotting turns out to be a time-intensive process as employees are performing monotonous tasks. But, with Automated Image Recognition, infrared cameras can check for deviations in real-time and achieve a near 100% reliability. This fast, easy-to-use solution can also be used for moving objects and now, helps the company maintain the highest quality of production.
3. Demand Planning
Given the rapid scale of digital adoption in the previous year, demand planning is more important than ever. In a time when consumers are increasingly reliant on their devices, maintaining inventory as close to the demand as possible is critical to cash-in-on lifeline revenue. Agility, resilience, and speed matter more than anything else to manufacturers at this point.
Danone Group, a food, and beverage global company, is using the AI-based demand forecasting system at their planning stage. Previously, they were unable to achieve their target-service levels and demand from product promotions. Additionally, poor cross-functional coordination between marketing, sales, and finance teams led to a high number of lost sales. Now, via leveraging time-series-based ML models for demand forecasting, they were able to better predict accuracy, variability, and planning. Ultimately, among others, a 50% saving in demand planners’ workload was realized.
While we see manufacturers fiddling with AI and machine learning, Industry 4.0 is still a moonshot for many, including top Fortune 500 companies. The reasoning is simple, too many companies are stuck in the “pilot purgatory” phase. This is the state where companies have an idea that has moved to the proof of concept (PoC) phase, but instead of reaching customers, it ends up at the infamous PoC graveyard. A 2017 report found that less than 30% of pilots have moved forward from that phase to scale.
Predli sees successful AI adoption and utilization in the manufacturing industry as a three-step process:
- Identify and understand the opportunities and risks
- Ensure you solve a real-world problem end-to-end
- Adopt a scale-driven approach in the implementation phase
Predli’s Masterclasses and the AI Use-case canvas are especially useful resources in this journey
One thing from the early days of the COVID-19 crisis was clear: companies that have invested in end-to-end technology-enabled value chains were able to display resiliency and bounce back faster than the industry. Embracing a change mindset for the “new normal” and fostering innovation at scale by avoiding the “pilot purgatory” would be the ultimate way of building a winning culture for a “race that has just started.”
About the Authors
Alexander Fred-Ojala, Founding Partner, Stockholm
Yash Agrawal, Technology Business Analyst, New York