Part 2 of a 3-part “Manufacturing Fitness” series
In the previous blog in this series, we used the human body as an analogy to talk about the idea of manufacturing fitness – where the manufacturing function acts like the heart of the business by pumping out products, the supply chain acts like its veins and arteries by moving things around, and predictive analytics serve as the brain by helping teams to see ahead and plan accordingly.
Manufacturing fitness, of course, is of primary concern for all companies that make and deliver physical products. The competitive pressure to produce faster, cheaper, and better is relentless. Faster and cheaper is hard enough, but producing better – which is all about quality assurance (QA) – is often the most difficult challenge.
But quality cannot be ignored for companies that want to do more than survive. After all, if you increase your yield at the cost of producing inferior products, then you’ll only succeed at doing damage to your brand. It’s a bit like exercising your body to the point of exhaustion or injury. Optimal fitness is the trick of striking the right balance.
Which brings us to a discussion of robotics.
Robotics in manufacturing, of course, is hardly new. But what companies are finding is that in today’s competitive global manufacturing environment, there’s robotics and then there’s robotics. Companies that use robotics in intelligent ways to strike the right balance between productivity and quality are those that will differentiate themselves in the market.
The productivity advantages of robots in manufacturing are obvious and intuitive. Robots can run on a 24×7 basis at speeds that human teams simply cannot match. For highly routinized aspects of manufacturing operations, robots are ideally suited. They don’t get tired, they can be programmed to do whatever is required, and – crucially – the safety issues that would be of concern for human teams are essentially eliminated for robots.
Robots help manufacturers increase production volume, throughput, and yield. Humans, meanwhile, can be put to work in areas of production that present fewer safety risks, involve less physical exertion, and require thinking and dexterity beyond what any machine can match. Leading companies, furthermore, are increasingly putting humans and robots together thorough the idea of “cobots” – where robots are designed to collaborate safely and efficiently with humans on the shop floor or in the warehouse. Such an approach allows for synergies where humans and robots each focus on the tasks for which they are best suited – the result being greater productivity overall.
As mentioned, efficiency without quality is a recipe for brand destruction. Thus, automating production with robotics calls for an automated approach to QA. This requires the ability to track data regarding material variables and characteristics – with variable data focusing on measurables such as weight or length and characteristic data focusing more on aesthetic attributes such as bubbling or blotchiness.
Detecting quality issues is one thing. Leading manufacturers go a step further by incorporating this data into the manufacturing process flow so that robots can take action automatically. If automated manufacturing means automating the efficient path (the path leading to the perfectly made product), then automated QA means also automating the undesirable path (the path for what’s done when poor quality is detected).
What sort of escapes do your robots have when they get a message that materials are subpar? Do you move the defective product to recycling? Do you push it on for further refinement? Do you call for human intervention?
Companies that automate these decision trees are able to automate more of their manufacturing activities and, thus, gain an efficiency advantage. But this is only the first step – because even greater advantage lies in the data gathered from automated QA processes.
Intelligent technologies such as predictive analytics and machine learning now enable you to analyze historical QA data in the context of incoming data from live production runs. This makes it possible to identify anomalies, detect trends, and predict what’s about to happen.
The result is the difference between the efficient path (which leads to executing on plan) and the undesired path (which leads to cost overruns and drops in efficiency). Earlier detection of quality issues means earlier (and less expensive) intervention – which leads to fewer products entering the undesired path. Efficient operations is the key to success.
In this sense, then, intelligence like predictive analytics – the brain of manufacturing operations – is foundational for any approach that uses robots to automate production. It’s the intelligence of your technology, systems, and processes that gives you insight into what goes onto the shop floor.
So, yes, as most manufacturers know, robotics is a good answer to the competitive pressures for doing everything faster, cheaper, and better. But, in the end, it’s the companies that integrate robotics more deeply into manufacturing processes – especially those for managing quality – that can achieve optimal manufacturing fitness. Or, as a fitness guru might say, by using robotics to increase efficiency and quality at the same time, you can have your best business body ever.
For more information, download the latest IDC report on Digital Manufacturing.