Data analytics has been making waves across dozens of market sectors and industries. The electronic component supply chain is no exception. The global supply chain stands the most to gain from incorporating data-driven insights in decision-making. Using data analytics in supply chain management decisions improves efficiency, optimizes processes, and increases competitiveness in the market. Most notably, data-driven analytics could be the difference between success and failure in the face of supply chain disruptions on par with the Covid-19 pandemic.
The global Covid-19 pandemic turned previous supply chain management practices on their head. Worse still, damage done throughout the pandemic continues to linger and negatively impact companies on all ends of the supply chain, from original component manufacturers (OCMs) to small and specialized contract manufacturers (CMs).
During the pandemic, previous supply chain and inventory management methods were found to hinder more than help. Just-in-time (JIT) inventory management, double-ordering to bypass long lead times to obtain the necessary stock. Unfortunately, the tendency to overcorrect problems led to a rise in inventory, and when market demand suddenly dropped, that safety surplus evolved into challenging excess inventory.
This isn’t an issue that exists during extreme global catastrophes, either. The Covid-19 pandemic presented the perfect storm, but over the last several years, the pandemic has shown market volatility. Consumer confidence can vary widely from month to month, making it difficult to predict future market trends and plan accordingly. In many industries, especially the electronic component market, orders by CMs, original equipment manufacturers (OEMs), or electronic manufacturing service (EMS) providers often happen months in advance, long before actual market conditions fully materialize.
A perfect example of this phenomenon was seen in 2022 and 2023, as many consumer electronics manufacturers ordered six months of part stock only for consumer demand, shaken by the rising inflation prices and concerns of a recession, to drop during the peak of seasonal holiday shopping. OEMs, CMs, and EMS providers were left with months of inventory and no product demand to consume the surplus. For OCMs, strategic production cuts had to commence once orders dropped to avoid further contributing to the problem.
This has a secondary effect as well, for when demand picks back up, it is likely to happen rapidly, and for semiconductor manufacturers, production capacity does not increase suddenly overnight. Increasing production to meet growing demand will take weeks, which could lead to another electronic component shortage.
The global semiconductor shortage during the Covid-19 pandemic perfectly illustrated the need to invest in building efficient and effective supply chains. Likewise, the only way to solidify their resiliency is through maintenance and evolving with the changing market landscape. Merely relying on old processes as the be-all-end-all is insufficient or safe. Supply chains, especially those in the electronic component industry, need the capability to respond to real-time market shifts and predict future areas of constraints for mitigation of disruptions.
5 Ways Data Optimizes Processes and Saves Time
Moving forward, electronic component supply chain managers must prioritize data-driven insights over preferred manual processes that cannot cope with sudden and unforeseen disruptions. A supply chain that is responsive to unpredictable and constantly shifting demand signals. Data analytics gathered from market intelligence generated in real-time can increase the supply chain’s flexibility to better adapt to the fast-paced and complex environment in the semiconductor market.
Through systematic analysis, digital tools can deliver valuable insights from large amounts of data to help key decision-makers elect more informed strategies regarding upcoming points of constraint or surplus. With predictive analytics, supply chain managers can use digital data tools to uncover market demand patterns, seasonal fluctuations, and component risk to optimize inventory management levels. The benefits of data-supported supply chains are immense, but the five most important aspects are:
1. Cost Reduction Through Improved Efficiency
Data analytics can analyze historical trend data to pinpoint components that are shortage-prone or facing obsolescence. With this information, OEMs, CMs, and EMS providers can either remove these parts from a product’s design, replace them with more resilient and multi-source available components, or form mitigation strategies to mitigate upcoming diminishing availability. This lowers the risk of desperate decision-making actions, such as over-ordering or increased counterfeit risk.
2. Risk Mitigation
The global supply chain is excessively fragile, primarily due to the steps required to manufacture a single semiconductor. As a result, it is prone to disruptions from natural disasters, political instability, demand shifts, and macroeconomic challenges. Manufacturing lines for electronic components is not a simple matter of turning off and on when consumer demand grows or declines. It takes time for production capacity to increase and inventory to arrive. Data analytics can derive insights from historical trends, pinpointing components susceptible to disruptions before disruptions occur. Based on this data, organizations can prepare contingency plans for different scenarios to minimize the effects of possible problems or avoid them altogether.
3. Enhanced Supply Chain Visibility
One of the aggravating factors contributing to the widespread damage of the global semiconductor shortage was the lack of visibility across the entire supply chain. Without digital tools or accurate data analytics, supply chain managers and procurement teams often track occurrences of specific partners rather than the whole supply chain at large. Unfortunately, it is hard to record disruptions that could impact any supplier, especially if the manually entered data is inaccurate. Human error is an unfortunate side effect of manual processes.
Digital data analytics removes the risk and presents a wide swath of real-time information so supply chain managers can strengthen areas of inefficiency and maintain strong ones. Greater visibility into the far-reaching shifts in the supply chain can help organizations strategically prepare for upcoming disruptions.
4. Process Optimization
With data analytics, manual data-gathering processes can be consolidated into a singular, streamlined process, especially with digital data collection tools. Large teams can be reduced, giving human staff more time to commit to innovative and productive tasks while efficiently utilizing the gathered data. This can increase performance across an organization, leading to growth in productivity, time and cost optimization, and intelligent strategies that make a company more competitive within the market sector.
5. Leveraging AI to Uncover Better Data
The growing incorporation of artificial intelligence (AI) and machine learning reveals new opportunities for companies to utilize it. According to research by McKinsey, “effective use of AI in inventory control can achieve up to a 20% reduction in inventory carrying costs and a 50% decrease in stockouts.”
Combining advanced AI algorithms with data analytics, AI technologies can increase demand forecasting accuracy, improving predictive analytics that aid companies in anticipating market shifts and customer trends. These insights can help optimize inventory management, reduce excess stock, and streamline logistics.
Datalynq’s Data Analytics and Market Intelligence
Using data analytics to capitalize on its benefits is easy with Datalynq’s market intelligence. Gathering real-time market data from the leading e-commerce site for electronic components, Sourcengine, Datalynq’s analytics provide unrivaled accuracy in the global supply chain. Furthermore, Datalynq’s risk scores dive into vast component market data to illuminate unknown challenges associated with any one component, such as limited availability, not recommended for new design (NRND) notifications, and upcoming obsolescence.
Datalynq is a digital tool that is easy to use and can help replace tedious, manual processes to improve the productivity of both design engineers and supply chain management. Likewise, with Datalynq’s vast coverage of the electronic component market, visibility and transparency into the global supply chain help users identify upcoming constraints and how to mitigate such challenges.
Data analytics is quickly transforming the supply chains of all industries. The electronic component market gains the most from incorporating data-driven decision-making over manual data collection processes. You can get started by strengthening your supply chain with Datalynq.