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Manufacturing: How to Use Predictive Analytics to Reduce Losses

Posted by Concannon Miller on Tue, Oct 12, 2021

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Manufacturing: How to Use Predictive Analytics to Reduce LossesThe manufacturing supply chain is only as strong as its weakest link. But how does a firm know where its weakest link lies? The answer may be found with predictive analytics.

As the name suggests, this branch of advanced analytics uses many techniques to predict where a system can break down, so it can be fixed beforehand.

Many companies have already adopted predictive analytics. And many more are expected to jump on the bandwagon over the next year.

Higher Standards

The public expects goods to be delivered when promised and at competitive prices. As a result, manufacturers are being held to increasingly high standards. They need to know more than when to expect delivery from suppliers so they can adjust production and delivery schedules. This sets the stage for predictive analytics.

By applying advanced statistical analysis of big data to identify patterns and predict events, manufacturers are better able to anticipate the current and forecasted needs of customers. Thanks to advances in technology, the data that is now available to supply chain manufacturers is so huge and complex that some manufacturers are discarding historical planning methods.

In fact, innovations in supply chain management are taking place at a lightning-fast pace. Firms that don't adapt soon may struggle to remain competitive and deliver orders accurately and on time.

READ MORE: 4 Strategies to Improve Productivity in Manufacturing

The Question of Price

Of course, the shift to predictive analytics comes at a price in terms of software and related costs, and at a time when manufacturers are being forced to cope with ever-rising costs of materials and goods.

This isn't the first time that promises about forecasting capabilities have been pitched to the manufacturing sector. Companies have been encouraged to spend on many technological innovations over the past few decades, with varying degrees of success, and may be gun-shy about further financial commitments in this area.

So what makes predictive analytics different?

Some industry experts point to the cloud. Storing vast amounts of data over the Internet makes it possible for companies to access information that helps them make better decisions and gain valuable insights into potential problems.

For example, say a company makes refrigerators and ovens. The components may be produced by outside contractors, which can make it difficult to pinpoint where a failure in production may occur. The company may determine when the process is most likely to break down using predictive analysis and such factors as:

  • Production line,
  • Quantities,
  • Time of production,
  • Number of engineering changes,
  • Consumer usage patterns, and
  • Demographics

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Most Common Benefits

Predictive analytics is most often associated with benefits in four main areas:

Quality improvement: Improvement in databases and storage, supplemented by easy-to-use, analytical software, is a major factor in improving the quality of a company's products. Manufacturers can store more data about their products and processes, which lets them analyze more factors to help improve quality. This, in turn, aids in forming a definitive plan of action.

Demand forecast: Forecasting is critical to success. Manufacturers must project the products desired, quantity needed and required delivery time. Traditional demand forecasts were based mainly on experience. A company could predict with reasonable certainty that some products would sell faster or in greater bulk during particular seasons (for example patio furniture for the summer or ski equipment for the winter). Predictive analytics adds another layer by allowing companies to consider more factors.

Predictive analytics paints a comprehensive picture that identifies likely trends and other events based on historical data collection and analysis. It combines demand forecasting with risk management using fewer resources.

Equipment improvement: Manufacturing firms that provide quality goods generally use quality machines. However, even the best equipment breaks down or experiences wear and tear over its useful life. Replacing parts or updating the equipment can cost thousands of dollars.

Predictive analytics can anticipate equipment failures. By automating the analysis of data from sensors within equipment — as well as the actual operation of these machines — a firm may determine when machines should be replaced before any damage occurs. This saves both time and money.

Preventive maintenance: Firms can reduce operational issues by triggering alerts from machines, based on data they provide internally. For instance, automatic signals could be sent when a belt or gear is torn or broken, reducing the burden on a particular machine or identifying patterns for certain types of equipment.

This is a critical step for ensuring equipment continues to operate at maximum efficiency. At other times, predictive analytics could be used to identify manufacturer defects in machines.

READ MORE: Why Manufacturers Are Looking Forward to Growth

Focal Point

Competition in manufacturing is fierce and advances in technology only up the ante. Use of predictive analytics in manufacturing is expected to increasingly become a focal point. If you want your company to be among the leaders of the manufacturing renaissance, consider hopping on the predictive analytics bandwagon sooner rather than later.

© 2021

Topics: Manufacturing

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This communication is designed to provide accurate and authoritative information in regard to the subject matter covered at the time it was published. However, the general information herein is not intended to be nor should it be treated as tax, legal, or accounting advice. Additional issues could exist that would affect the tax treatment of a specific transaction and, therefore, taxpayers should seek advice from an independent tax advisor based on their particular circumstances before acting on any information presented. This information is not intended to be nor can it be used by any taxpayer for the purposes of avoiding tax penalties.

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