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Understanding Modern Manufacturing Optimization
Manufacturers often adopt new technologies to reduce waste, increase margins, and produce better products.
The pandemic emphasized and accelerated these aspects of operations as companies turned to technology to establish digital threads of production to better understand and alleviate staffing and supply chain challenges. As a result, many solutions not only became battle-proven, but are now here to stay. This includes cloud software that enables remote collaboration among distributed teams, and a more modern approach to Manufacturing Optimization (MO).
The MO ecosystem exists between slow-moving, expensive one-time audits and rapid, real-time business intelligence tools. It encompasses many industries to address the different problems that need to be solved, as well as the different kinds of data used to solve those problems.
Reducing Manufacturing Inefficiencies with Data
The lack of widespread automation in the manufacturing industry results in inefficiencies and waste. Scrapping defective parts or products, product returns, and brand damage are just a few examples of costly mistakes. In fact, according to research from Bain & Company, roughly US$0.20 of every dollar spent in electronics manufacturing is wasted, and roughly 5% to 10% of all products need rework. Putting the right data in front of the right people at the right time can significantly reduce this waste, especially in bringing new products to market. It is only recently that new manufacturing optimization tools have been available to address these challenges.
There has been an accelerated need for digital manufacturing data, which has been a core driver for adopting MO systems in the last 2 years; and now, more than ever, is not the time to fall behind.
This paper elaborates on the core facets and opportunities of manufacturing optimization, including:
- The four categories of modern manufacturing optimization platforms (MOPs)
- The trade-offs of DIY business intelligence tools in manufacturing
- How to move up the Data Maturity Scale
- Failure analysis case studies with Fortune 50 and Fortune 100 electronics brands
Download the full whitepaper to read more.