How Predictive Analytics Changes the Manufacturing Game

Business needs, market trends, and consumer demand can change in the blink of an eye. The 2020-2022 semiconductor shortage and the following glut are perfect examples. The ramifications of which will last years into the future. Lack of supply chain visibility and traditional ordering methods contributed to mounting sourcing challenges and production stalls.  

For original chip manufacturers (OCMs), their capacity for chips is based on prior year demand and market analytics. Since many original equipment manufacturers (OEMs), contract manufacturers (CMs), and others favor just-in-time (JIT) manufacturing for its cost-efficiency, demand usually sets the pace. When demand becomes unpredictable, it becomes challenging to determine capacity correctly. Throughout the pandemic, demand, despite market shifts, was hard to predict.

Those that suffered the worst were automakers. Automotive OEMs make up a smaller portion of global chips sales than consumer and white goods OEMs. When auto OEMs saw dropping consumer demand alongside historical worldwide lockdowns, OEMs canceled chip orders, and production slowed. Witnessing the sales decline, OCMs cut automotive chips' capacity the following year.  

Automotive demand, fed by EV adoption and relaxing pandemic lockdown protocols, shot skyward the following year. OCMs, who had cut capacity after last year’s flurry of canceled orders, did not have the labor, resources, or capabilities to pick up lost ground quickly. It’s why the automotive shortage is still here in 2023 while the chip shortage is in a downturn.  

In contrast, consumer and white goods OEMs are facing the same issue but in reverse. During the pandemic, the work-from-home (WFH) model rose in use when strict lockdowns kept staff homebound. Rather than completely cease operations, businesses started digitalizing where they hadn’t before, embracing remote work possibilities. Throughout the first year of the pandemic, voracious consumer electronic orders caused OCMs to raise the capacity for advanced chips.  

As the shortage’s duration grew and the war in Ukraine began, inflationary costs started to climb. Raw materials and logistics costs were rising. People began to return to the workplace as lockdown policies relaxed and recession concerns mounted. Demand for consumer electronics collapsed, leaving OEMs and OCMs with excess inventory.  

Lacking total market visibility, significant historical trend data, and the fragile global supply chain weakened by years of disruptions contributed to many OEMs missing the warning signs of upcoming shortages and gluts. As a result, production stalls, delayed product development, lost cost, and time have plagued thousands of OEMs. The traditional method of managing a supply chain needs to be revised. Today's global supply chain is complex and requires hundreds of steps to produce one single chip. To accurately track market trends, current and future disruptions impacting the supply chain, the multiple bill-of-materials (BOM) lists per product and other information takes weeks, if not months, for a team to pour over.  

Predictive analytics and its alerts accomplishes this monumental task with ease. Better yet, you don’t have to wait months to implement it. You can do it now.  

What Predictive Analytics Does

Predictive analytics combines traditional demand forecasting with risk management. Predictive analytics utilizes algorithms and machine learning (ML) to forecast future demand and alert users of possible constraints. Depending on the need, the program considers several factors but primarily includes raw material availability, consumer behavior, sales data, weather conditions, logistics, and historical trends. Predictive analytics determines the likelihood of breakdowns along the supply chain and which components are prone to future risks.  

Implementing predictive analytics awards manufacturers numerous benefits. These include reduced risk and cost, improved operations, and increased revenue. Predictive analytics is so valuable for its utilization because it gives manufacturers greater flexibility and deeper insights to make faster and more accurate decisions. For those that stick with traditional supply chain monitoring, the gap between them and OEMs with predictive analytics is immense. The real-time forecasting capabilities simply outpace any traditional method.  

Artificial intelligence (AI), the greater umbrella that encompasses predictive analytics, will continue to be a primary driver in improving manufacturing processes. A study by Deloitte found that “93% of companies believe that [AI] will be a critical technology to drive growth and innovation in the industry.” Procurement analytics and Internet of Things (IoT) supply chain analytics tools, like predictive analytics, are only expected to grow in use over the coming years. The supply chain analytics market is expected to grow by a CAGR of 20.07% over 2022-2027.

According to a study by Research and Markets, the increase in adoption is driven by the increased need to “improve business processes, rising demand, and increasing needs to improve supply chain visibility.” The strategic insights and alerts given by predictive analytics allow manufacturers to increase their market position by strategizing for all upcoming growth opportunities. Awareness of supply chain pain points and component disruptions can aid engineers during product design phases, making them more resilient to possible shortages.  

Sourceability CEO, Jens Gamperl, spoke of the importance of predictive analytics in mitigating future shortages with Thomas Insights. Gamperl expressed that predictive analytics would only become more accurate with each year. The tool most likely to make such exact predictions is Datalynq.  

Datalynq’s Predictive Analytics and Alerts

Datalynq’s predictive analytics and alerts feature identifies cost-saving opportunities for anyone in the electronic components industry. As a partner of Sourcengine under Sourceability, Datalynq can provide users with visibility of actual market transactions. Since Sourcengine is a global marketplace with over 1 billion part listings, Datalynq can receive accurate component trend data first-hand.  

This also gives Datalynq’s predictive analytics further aid from historical transactional data. The historical transactional data reveals to users the accessibility of a part over time. Products can vary widely in availability due to material shortages, logistics complications, geopolitical conflicts, natural disasters, accelerated obsolescence, and other challenges. Despite the variety of ways, a product’s availability can be affected, Datalynq can provide an accurate forecast of future part availability.  

Datalynq’s overall presentation of pertinent information is simple and streamlined, so users don’t have to sort through the same mountains of data Datalynq analyzes. Price and inventory insights are critical to supply chain momentum, so lead time, inventory, pricing, and shortage patterns are revealed quickly with regular updates on parts and assemblies. Datalynq’s predictive analytics shows demand changes for form-fit-function (FFF) equivalent parts, so users are always aware of possible changes.  

Predictive analytics will only become a faster and more accurate tool as the years go on. The pandemic revealed inefficiencies within the traditional methods of supply chain monitoring. Adapting digital tools, like Datalynq, to sort through loads of market data is necessary for a quickly evolving industry and complex global supply chain.  

Datalynq’s free 7-day trial is available for anyone ready to become a competitive market leader.  

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