The advent of digital transformation has provided a unique opportunity for manufacturers to embrace new technologies that will allow them to take their lean manufacturing initiatives to the next level. Of course, the goal of lean manufacturing is continuous improvement of production processes, while eliminating waste and cutting costs. Given the increasing complexity of operations, many companies have found that lean management by itself is not sufficient to address their operational challenges, and setting the stage for a lean system in reality is just the first step in the lean journey. Thus, the implementation of a digital system that allows you to record and optimize your manufacturing productivity accurately and efficiently is what will transform your lean model to the next level.
In a time when the bottom line has never been more meaningful for companies, waste elimination is one of the most effective ways to increase the profitability of any business. Understanding what waste is and where it exists is essential to reduce or eliminate its effect on productivity, overall performance and quality. Many manufacturing software systems (otherwise known as MES) that either schedule jobs and/or track shop-floor metrics like overall equipment effectiveness (OEE) in real time can provide manufacturers the necessary tools to help achieve these objectives. Without collecting and analyzing factory data, manufacturing managers are blind to problems occurring on the plant floor. Machine monitoring systems can contain a wealth of information about the health of the factory and opportunities for waste reduction, but many manufacturers simply haven’t invested in them due to a hesitancy to embrace the technology and modernization that solves these new challenges presented by digital transformation (discussed in our previous article).
By monitoring machine performance, not only can they correct existing inefficiencies, they can identify potential issues allowing them to effectively execute preventative or predictive maintenance measures. All that said, at MachineMetrics we are constantly learning from our customers and looking for ways to provide them opportunities to identify bottlenecks, wastes, and optimize their processes. Below are some of the top areas of waste that we’ve found data collection can help dramatically improve:
Quality/Defects (10%-20% average loss in efficiency)
One of the most easily recognizable wastes in lean manufacturing is the production of Defects. Examples include waste such as scrap parts, products that require rework, or assemblies that are missing details. Defects are often considered to be one of the most significant manufacturing wastes because they can actually lead to the generation of additional wastes such as Overproduction, Transportation, and Excess Processing.
Overproduction (10%-20% average loss in efficiency)
Of all the wastes in manufacturing, Overproduction has, by far, the most negative impact on success. Overproduction occurs any time more parts or products are produced than the customer is willing to purchase. Like the production of Defects and subsequent Excess Processing, Overproduction can also lead to the generation of additional lean manufacturing wastes such as Waiting, Inventory, and Motion, consuming vast amounts of time and resources.
Planned Downtime/Setup (40-50% average loss in efficiency)
A planned downtime (such as set up) is any event where the process is unavailable to run due to a pre-planned activity, such as a changeover or scheduled maintenance. Shops will often changeover jobs frequently, and this can be the biggest source of lost production time for a business. Setup times for the same job can vary wildly by operator, or by shift. Only by tracking planned downtimes like setup can this be improved.
Unplanned Downtime/Waiting (40-50% average loss in efficiency)
Unplanned downtime is downtime that occurs unexpectedly or as a result of a failure (for example, a hardware failure or waiting on appropriate materials to complete a task). When product waits, no value is being produced but the cost of overhead operations continues to grow, which strips potential profit from the sale. Waiting not only destroys material and information flow, but also generates excess Inventory.
Asset Productivity (people and machines) (15%-25% average loss in efficiency)
While it’s essential for any manufacturer to optimize the productivity of their existing assets (people and machines), many have no way to measure this productivity effectively. Thus, opportunities to improve asset performance are constantly lost. OEE measures how well an asset is performing by building availability, performance and quality into one key performance measure. Understanding how OEE relates to waste helps pinpoint waste generated by the 6 Big Losses (Breakdowns, Setup and Adjustments, Small Stops, Reduced Speed, Startup Rejects, Production Rejects) trouble spots that undermine the goal of lean manufacturing, with the goal of constantly identifying opportunities for improvement.
The only lean manufacturing waste that is not manufacturing-process specific, but rather manufacturing management related, is Non-Utilized Talent. This type of manufacturing waste occurs when management in a manufacturing environment fails to ensure that all of their potential employee talent is being utilized. In relationship to Motion waste, if an employee is aimlessly moving material around the production area without adding value their efforts are being wasted where they could be performing value-added activities instead. Non-Utilized Talent also refers to management’s ability to utilize the critical thinking and continuous improvement feedback from employees to improve a lean manufacturing process. If management does not engage with manufacturing employees on topics of continuous improvement and allow employees to influence change for the better, it is considered manufacturing waste.
Understanding your waste is at the heart of meeting the new standard for getting and keeping business: combining Big Data with lean manufacturing tools and strategies to maximize productivity.
Powerful process analytics tools like MachineMetrics allow manufacturers to “see inside” their production models and identify how energy is currently being used in their operations. Not only can you quickly identify and eliminate productivity drains in your existing production process, but you also gain access to additional data that can help you uncover “hidden” issues that could be negatively impacting your shop. In the end, gathering data -- and acting upon the derived insights -- is a necessary catalyst for improvement.
To achieve these standards of proactive (and eventual preventative) problem solving requires a full commitment that calls for the creation and implementation of integrated, highly efficient manufacturing systems that use less energy, increase productivity, and reduce maintenance costs across the plant. Harnessing the combined benefits of Big Data and intelligent modernization of manufacturing processes can revolutionize plant operations and business practices. The ability to capture, analyze and act on this data isn’t the future measuring stick for manufacturing success: it’s here now.
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