Supply chain volatility, stemming from demand and yield uncertainty and generating shifting lead times and product obsolescence, is something the electronics manufacturing industry must deal with on a daily basis. Because of its fast pace, the electronics sector could benefit from the application of predictive analytics to identify supply chain risks and increase agility.
A number of companies are introducing products claiming to apply predictive analytics to supply and demand risk problems and to logistics management. These tools can provide early warnings of shortages and overstocks, and, optimally, assign manufacturing tasks and shipments routing in order to reduce stock levels, increase revenue, and decrease manufacturing costs.
Predictive analytics represents the next generation of business intelligence tools. Traditional business intelligence looks backward. It provides visibility into the historical supply chain and can provide explanations of where a company is doing well or poorly. But in a fast-moving and ever-shifting landscape, relying on historical data alone is like driving a car while only looking into the rear-view mirror. Predictive analytics promises to uncover challenges and opportunities that are coming down the pipeline, providing the chance to act on them proactively.
“Predictive analytics enhance the quantitative value of raw data, allowing logistics managers to analyze current and historical facts to make predictions about future events,” according to Freightgate, a company that applies predictive analytics to logistics problems
Precogs, a Paris-based startup, recently launched its cog-Watch solution, which claims to apply predictive analytics to the electronics supply chain space. The company attributes its predictive algorithm to the historical data of a corporations enterprise resource planning (ERP) system, as well as to logistics data picked up from suppliers and distributors to identify early warning signs of component supply chain problems. This, the company says, enables manufacturing teams to focus on those components most likely to present supply problems.
Freightgate tackles the supply chain challenge with predictive analytics, but from a different angle.
When multiple orders come in simultaneously, suppliers must determine where to most effectively manufacture the components and deliver them.
Freightgate's cloud-based Freightgate Universe Platform (also known as Freightgate's Logistics Cloud Platform), which benefited from the introduction of enhanced predictive analytics earlier this year, aims to make sense of the huge volumes and diverse sources and configurations of logistics data that every company collects.
Freightgate Universe includes several tools which enable “what-if” scenario analysis to satisfy multiple competing constraints. This allows companies to manage suppliers by exception, collaborate across the supply chain, and procure global transportation services.
Freightgate's innovations build on new advancements in data aggregation and pattern recognition, and in the investments the company has made in its cloud-computing infrastructure. Freightgate claims its predictive modeling helps logistics professionals “see the future” by “providing opportunities to create better service performance while reducing operational costs and mitigating risks.”
Of course, Freightgate is one example of many companies in this space. What are your thoughts on predictive analytics? Share them below.