Manufacturing AUTOMATION

Features IoT/IIoT Technology
Predicting patterns: data-driven analytics for supplier management


September 16, 2019
By Bo Hagler

Topics

September 16, 2019 – Businessman and author Robert Kiyosaki wisely said, “The best way to predict the future is to study the past.”

To succeed in a highly competitive and frequently changing landscape, manufacturers must look ahead and determine the best course of action related to the supply chain. But without automation, combing through past and current data to project what’s best for tomorrow is likely easier said than done.

For instance, predictive analytics – the advanced form of analysis used to predict future events – allows organizations to study both new and historical data to identify patterns and forecast potential activity, behaviour and trends. For manufacturers, this translates to aggregating real-time supplier data to identify hidden risks in their supply chain.

However, because digital automation hasn’t caught on with many manufacturing companies, the use of predictive analytics is not as widespread as it should be.

To compete in an increasingly digital world, manufacturers must be able to capture and analyze data for faster and larger-scale decision making that can not only drive disruptive change but also reduce risk and provide considerable return on investment.

Catching up with other industries
Only six per cent of Canadian industrial companies surveyed by PwC in its Digital Factories 2020 report say they are fully digitized. While some sectors such as IT, media and financial services are surging ahead with digitization, manufacturing is among those still in the early stages of adopting digital technologies.

For instance, many manufacturers that have yet to adopt digital solutions instead collaborate with hundreds or even thousands of suppliers using spreadsheets and emails. These manual processes are inefficient, error-prone and often lead to costly mistakes.

Furthermore, manual processes do not help to identify hidden risks that have the potential to cause major supply chain disruptions and even production shutdowns.

However, by adopting data-driven automation solutions, forward-thinking manufacturers are increasing efficiency, improving visibility and reducing risk – completely transforming their businesses.

Using data to drive disruptive change
An example of one of these progressive manufacturers is a global transportation technology supplier.

Before adopting a data-driven automation solution, this leading manufacturer juggled multiple ad hoc purchasing, quality and enterprise resource planning (ERP) systems.

The 20,000-plus employee enterprise that was conducting business worldwide lacked a master data solution. Its supplier management system was siloed across the globe and buyers were trying to manage their day-to-day supplier, risk and financial data with manual spreadsheets.

The company took the technology plunge by initially finding a solution to help it manage thousands of globally dispersed product suppliers on one platform, and eventually reduce that number by more than 75 per cent. The new solution also allowed the company to eliminate more than a dozen disparate systems.

In doing so, the manufacturer was able to improve risk mitigation and compliance both internally and externally. It now uses data to create supplier scorecards, which helps its buyers know which suppliers to give business to and which to avoid, based on past performance.

Avoiding risk with predictive analytics
In order to take the next step in technology adoption, manufacturers must consider adding predictive analytics capabilities to their supplier management solutions. A configurable analytics platform allows manufacturers to pull pertinent information such as quality, quoting and supplier risk data, all from one place.

For instance, before entering into a new contract with a supplier, predictive analytics would allow a manufacturer to easily look into the company’s history, set by any parameters they choose. How often has the supplier had late shipments? How regularly is it out of compliance? How often does its prices vary?

By combining machine learning and a variety of metrics, predictive analytics can help warn against conducting business with higher-risk companies, such as one that has had an uptick in defects over the last six months.

If manufacturers don’t have this insight, the ramifications can be devastating. A shipment of damaged parts could mean having to pay to replace the items, working overtime to get new items produced and transporting to the customer at a great expense. Given the disruption, the manufacturer could potentially lose that customer’s future business.

Predictive analytics eliminates guesswork and empowers manufacturers to make more insightful decisions using clear-cut data. It also lowers the potential for costly mistakes, which, in turn, saves money and helps protect reputations.

Getting off the fence and on with transformation
Although predictive analytics can drive transformation by alerting to risks involved in working with under-performing companies, many manufacturers are still only in the early stages of technology adoption.

By sitting on the fence, however, these companies could eventually be overtaken by competitors willing to invest in innovative technologies like multi-enterprise supply chain business networks that show everything from suppliers’ certification status to last minute updates to the bill of materials – making them far more efficient and reliable.

Transforming from a tactical manufacturer to a strategic one simplifies processes, increases supplier collaboration and greatly reduces risk.

Ultimately, investing in a data-driven solution with predictive analytics capabilities will allow manufacturers to distil data into easy-to-understand dashboards and charts, and will legitimize actionable intelligence to enable them to make bold, rewarding decisions.

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Bo Hagler is CEO of LiveSource.

This article originally appeared in the September 2019 issue of Manufacturing AUTOMATION.