Read our experts' newest digital factory optimization insightsRecent economic uncertainty and volatility in global supply chains are enough to put even the strongest manufacturing companies on edge. But where there is a challenge, there is also significant opportunity! Through this series of articles, we offer insights how you can maximize your production with digital factory optimization. A manufacturer that can rapidly adapt to new circumstances and optimize production will grow and expand its market share - even as market forces shift.
The manufacturing industry is poised for significant growth - but organizations still face a range of complex challenges.
Between the pandemic, geopolitical turmoil, and talent shortages, the road ahead for manufacturers is anything but easy. These challenges emphasize the importance of each manufacturer’s ability to optimize their production cycle, and nowhere is this more true than on the production line itself.
If you work in manufacturing, you know better than anyone that it’s getting more and more complicated to optimize your production line. Let’s take a look at some of the complex challenges you may be facing and why they are so difficult to address using traditional methods.
Maximizing Output While Maintaining Quality
Of course, the biggest challenge facing manufacturers is maximizing output while maintaining or ideally improving product quality. Your team may be focused on reducing downtime, especially when it’s unplanned. Alternatively, your team may struggle more with eliminating bottlenecks and alleviating their impact on production and revenue.
Unfortunately, anticipating the causes of downtime on each production line is an extremely difficult task. If you’re like most organizations, you have no trouble identifying a bottleneck. The real challenge lies in rapidly identifying a bottleneck before it cuts into production quotas.
Reducing Scrap to Mitigate the Impacts of Supply Chain Disruptions
Recent disruption and volatility in the supply chain has made it more important than ever for manufacturers to minimize scrap and waste. According to the American Productivity and Quality Center, scrap and rework costs you up to 2.2% of your annual revenue. That figure doesn’t even include the impacts from reduced production capacity, scrap handling and disposal costs, and other indirect effects on your business.
Similar to maximizing output, you may be struggling with identifying and anticipating the causes of scrap in advance. Whether it’s faulty machine operation, adjustment errors, or another issue, few companies are able to spot these issues early enough to address the problem before costly scrap generation.
Optimizing Material Handling & Material Flow
Ideally, you’ll always have the right parts in the right place just-in-time. However, material shortages and supply chain disruptions have made this more difficult than ever. On the production line level, elements like automated guided vehicles (AGVs) pose challenges as well. You must determine not only how many AGVs are needed, but how they can best handle pathfinding and how these vehicles can be seamlessly integrated into the rest of your production cycle.
If you manage larger facilities, simply tracking materials and where they are placed in storage is another problem. There’s no doubt manufacturers are eager for solutions that optimize material handling and material flow to support efficient production of quality goods.
The Solution - Leverage Data Analytics and AI
The good news is that manufacturers have two powerful tools at their disposal to address all of the above challenges - and more. Most every manufacturer collects and analyzes data, but few organizations use data analytics to its fullest potential. If you’re serious about increasing output, reducing scrap and optimizing production lines, you have to make sure you’re getting the most from your data.
Firstly, you must shift from simply collecting data to drawing meaningful conclusions and creating actionable insights. Oftentimes, the data collected is either too siloed or too complex for manufacturers to rapidly draw insights. This means that many manufacturers overlook tremendous opportunities for greater quality, production, and efficiency.
Secondly, you need to shift from manual data analysis to AI data analysis. In today’s fast-paced, ever-changing manufacturing environment, manual data analysis is simply too time-consuming. AI is able to provide rapid insights on how to avert bottlenecks, avoid machine failures, and implement optimization opportunities.
Unlocking the Full Potential of Production Line Optimization
In the last few years, we’ve seen a vast increase in the adoption of digital technologies in the manufacturing industry. Manufacturers like yourself are overcoming the complex challenges of today’s marketplace with cutting-edge technologies that would have been impossible just a decade prior. It’s an exciting time to be in manufacturing, and the best part is that we are just scratching the surface.
About the author
Christoph Inauen is Head of Siemens Digital Enterprise and Digital Services Group in the USA. Prior, he was working at SAP as the VP of IoT Business Incubation. He has over 30 years of hands-on experience in digitalization across various industries in Europe, Asia and North America. Christoph’s passion is to help customers achieve their business goals by leveraging digitalization. He has lived in Dallas, TX for the past 19 years.