The manufacturing supply chain is only as strong as its weakest link. But how does a firm know where its weakest link lies?
To that end, the philosophy of predictive analytics is taking root. 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.
The trend is clear. According to recent data from MHI, a North Carolina-based supply chain trade association, almost 25 percent of companies have adopted predictive analytics. That number is expected to skyrocket to 70 percent over the next three to five years.
The public expects goods to be delivered when promised and at competitive prices. As a result, manufacturers are being held to higher 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 — and adapt soon — may struggle to remain competitive and deliver orders accurately and on time.
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,
- Time of production,
- Number of engineering changes,
- Consumer usage patterns, and
Most Common Benefits
Predictive analytics is most often associated with benefits in four main areas:
- Quality improvement. This might be job number one. 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). That hasn’t changed, but 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. Similarly, 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.
The Positive Side-Effects
Predictive analytics is proactive rather than reactive. This can enable:
- Parts analysis. Software can show which parts will fail first and which will last the longest. Management can then adjust inventory, stockpiling certain parts that are likely to wear out and bulk-ordering replacements ahead of time.
- Cost-benefit analyses. By conducting enhanced cost-benefit analyses, manufacturing teams can better understand the risks of not performing maintenance at any given time.
- Warranty claims. Companies can assess warranty offerings based on the insights gleaned from the analysis.
- Risk mitigation. Manufacturers may be able to avoid penalty fees by fixing issues before they escalate.
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.
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