What You Need to Know About the Optimization Process to Achieve Your Lean Manufacturing Goals


Read Time: 3 min

Ensuring that assets and processes are running at their most efficient has always been a prime concern for manufacturers. Trimming an extra second from a production cycle or using 1cm less steel for a car door makes an enormous difference when scaled up to multiple plants. As lead times shorten, iteration cycles tighten, and customization becomes the norm—necessitating faster than ever switchovers—optimizations like these have never been more important. Digital technology has made the optimization process easier and more effective.


Why optimization matters

Lean manufacturing began as the Toyota Production System (TPS) and attained the ‘lean’ moniker in the 1990s. It’s the systematic process of cutting waste while retaining—or even boosting—productivity. Since its inception, it has been a key differentiator for manufacturers. The leaner your processes, the cheaper—or faster—you can offer your service; the lower your costs, the more capital you have to invest elsewhere in the enterprise. With the current acceleration of production, and the proliferation of digital lean manufacturing processes, it has become a case of keeping up as much as getting ahead.


The optimization process

The optimization process can be broken down into four major stages:

  1. Collecting data on current production assets and processes
  2. Analyzing the data to identify inefficiencies and areas for improvement
  3. Formulating solutions to address scrap, rework, and waste
  4. Implementing the solution

You might, for example, identify a skills gap in one team of workers by analyzing how production rates differ between shifts, and cross-referencing who is working at those times. This skills gap can then be addressed with extra training. Or you might identify a machine performing suboptimally. Further investigation may reveal a component wearing out. Replacing that part can return the machine to full working order. Equally, comparison to wider industry benchmarks may suggest the need for an upgrade. The optimization process should be undertaken on a continual basis, identifying and resolving inefficiencies in ever-tightening circles.


The business impact of optimization

The most immediate benefit of the optimization process is a reduction in scrap, rework and waste. As discussed above, incremental decreases in lost time and materials can, over time, add up to significant amounts, and are probably the loss factors most under a business’ control. Optimized businesses will also improve their Overall Equipment Effectiveness (OEE) as each machine produces more per hour. Perhaps most significantly, the productive life of equipment will be extended.

Keeping machines in optimal condition keeps them running longer—reducing maintenance costs as issues are prevented rather than reactively resolved and improving return on investment. Budgeting and forecasting accuracy significantly improve as a result of optimization. Maintaining a close eye on costs at every level of the production process means having more accurate data from which to project. Needless to say, better forecasting can have far-reaching effects on the business as a whole. And of course, the more effective your optimization efforts, the greater your competitive edge.


What digital technologies are involved in the optimization process?

Recent digital technologies have massively amplified the capabilities and subsequent impact of the optimization process, allowing far more granular analysis than previously possible. This is primarily a result of the industrial internet of things (IIoT). Machines can be fitted with an array of sensors for startlingly-detailed insights into their operation. The real-time data feeds both eliminate errors in data collection and form a much larger dataset from which to work—enabling a greater number of deeper, more accurate insights.

All this data requires bleeding edge processing to handle it—on-site (at the ‘edge of the network’) and in the cloud. Many sensors are equipped with the ability to pre-process data before it’s transmitted to central servers over the internet. Following AI-assisted, machine learning analysis, the data becomes available in intuitive, role-based analytics dashboards, providing the right information to the right people to make sense of it.

Augmented reality (AR) can play a significant role in implementation. Intuitive, step-by-step digital work instructions can be rolled out centrally, leaving little room for error. New SOPs can be deployed immediately. And remote experts can provide over-the-shoulder walkthroughs, sharing the same view as on-site operatives, annotating the display as necessary.

Digital technologies are enabling manufacturers to achieve lean manufacturing goals at greater rates than ever before. To find out how you can do the same, explore our Digital Manufacturing solutions or learn more about our Intelligent Asset Optimization use case.


Tags: Augmented Reality Industrial Connectivity Industrial Internet of Things Aerospace and Defense Automotive Electronics and High Tech Industrial Equipment Oil and Gas Connected Devices Digital Transformation

About the Author

Prema Srinivasan, Digital Content Marketing Manager

As a Digital Content Marketing Manager, I bring the latest technology stories to the forefront. I'm passionate about engaging readers and empowering decision makers with relevant, up-to-date content.