February 1, 2024

The Benefits of Predictive Procurement for Buying Electronic Components

Datalynq Team
An image of someone typing on a laptop.

Procurement in the electronic components industry is a major process that heavily relies on manual input. A procurement team spends most of its time communicating with sales representatives or searching online for part offers. This data is usually stored on long Excel sheets with dozens of columns to earmark information on every detail, from inventory availability to manufacturer part number (MPN) to price changes.  

This necessary but excessively tedious process requires focus and attention. Even with brilliant staff, procurement documentation may contain outdated or inaccurate information. Human error occurs for various reasons but often results in production delays, inventory challenges, time and capital loss.

Electronic component use is growing. Some BOMs have over 4,000 electronic components, making the procurement process extensive. Digitalization using artificial intelligence (AI), machine learning (ML), and predictive analytics can help procurement teams streamline processes and optimize workflows while reducing time and increasing cost savings.

Four Ways Predictive Analytics Boosts Procurement

As the electronic components industry grows, using digital tools to provide data-driven insights will be pertinent to retaining a competitive edge. Data analytics gathered through disruptive technology like AI and predictive analytics can give organizations insights on how to outperform rivals who may or may not use advanced technology.

These insights can also inform leadership on how to perform workflows better, showing opportunities for automation and thereby freeing up time for employees to focus on more innovative and strategic operations.  

Though procurement heavily relies on manual processes, it benefits the most from digitalization through advanced technology. Market intelligence tools paired with application programmable interfaces (APIs), which connect e-commerce sites to a company’s enterprise resource planning (ERP) system, are one such example. Design engineers can assess design risk within a market intelligence tool, selecting the most disruption-resilient electronic components before creating a BOM to send to their purchasing team. Once received, buyers can find offers for components on their BOM through the API integrated into their ERP system and purchase components without ever leaving their system.  

This optimizes procurement processes by ensuring the components are easy to source and offers can be found and compared quickly with assuredly accurate information. Companies that utilize this system can make better decisions faster and get their product to market quickly.

Another way users can digitalize their procurement process with new technology this year is by using predictive analytics. Through statistical models and AI, users can proactively boost their procurement strategy with predictive analytics.  

1. Utilize More Data for Greater Accuracy

Mountains of data are generated daily within the electronic components supply chain. According to McKinsey, one of the challenges with procurement today is that less than 20% of the available high-value analytics is used. This is mainly due to the large extent of data generated and the limited time and focus procurement teams have to derive valuable insights from insignificant information.

Predictive analytics can automatically generate insights for greater sourcing decisions based on market intelligence, historical information, and real-time shifts. Unlike manually generated data insights, the data is not susceptible to human error, leading to outdated or inaccurate information. Likewise, decisions can be made faster as it takes a program far less time to deliver data analytics.  

Furthermore, through predictive analytics and its accuracy, users can gain transparency and visibility into workflows, finding out where budget costs are allocated and if they can be reassigned based on need.  

2. Cost Savings

Due to increased transparency and visibility into the procurement process, digital tools can help optimize cost savings a business can gain. In a 2023 report by CNBC, decreased human error and operational efficiency saw 75% optimization in maintenance costs.  

Predictive analytics can alert users to future risks and disruptions, allowing organizations to create proactive strategies to avoid losing opportunities and revenue by falling into such traps. Combined with automation and AI, this can represent thousands to millions or more in savings over time.  

3. Predictive Procurement Can Benefit the Entire Organization

Advanced technology in procurement can help an entire business benefit over time. With predictive analytics alerting buyers to future risks and opportunities with high accuracy, teams can focus on innovating new workflows or discovering new solutions previously unseen due to inaccurate information or time devoted to procurement.  

The MIT Technology Review states that procurement teams stand to gain the most from technological advances like predictive analytics. ‚ÄúTheir access to rich data sources ranging from contracts to invoices enables AI/ML solutions that can illuminate the insights when combined with this data,‚ÄĚ the report explained. ‚ÄúActing on these insights can unlock new capabilities that can enhance decision-making and improve spending patterns across the organization.‚ÄĚ ¬†

The report continues. ‚Äú[AI and analytics can] empower organizations to respond to emerging business priorities, such as adopting more socially responsible purchasing practices.‚ÄĚ ¬†

4. Better Proactivity from Forecasting Models

Predictive analytics can help identify future constraints, alerting users of which components are at risk for possible disruptions or bottlenecks compared to others. Procurement teams can work alongside design engineers to weed out risky components, including sole source, obsolete, or end-of-life (EOL) parts. Suppose these electronic components cannot be avoided. In that case, procurement teams can leverage predictive alerts and an e-commerce API system to schedule orders in advance, far before a disruption or component obsolescence occurs.

Predictive analytics is valuable because it gives manufacturers greater flexibility and deeper insights to make faster and more accurate decisions. Original equipment manufacturers (OEMs), contract manufacturers (CMs), and electronic manufacturing service (EMS) providers benefit from proactive rather than reactive procurement strategies as, often, the latter is usually too late.  

This can be seen no better than with the automotive chip shortage, a subset of the more significant semiconductor shortage during the Covid-19 pandemic. Automotive manufacturers canceled orders initially, believing the initial drop in market demand would last through the pandemic. When OCMs lowered production capacity the following year and consumer demand began to ramp up, automakers were left at the end of a long line of businesses desperate for components.  

Had predictive analytics been utilized, automotive manufacturers could have been alerted to the changing market ahead of time and notices for which components would see shortages because of disruption, like pandemic lockdowns.  

Datalynq is the Premier Predictive Procurement Tool

To prepare for the challenges and opportunities of the electronic components market, an organization should utilize advanced technology that offers predictive analytics to boost procurement efforts. Sourceability, a global distributor that buys and sells thousands of electronic components, has a collected data history that dates back nearly a decade, allowing Sourceability’s various tools to perform deep analyses on market trends, including Datalynq.

Datalynq leverages Sourceability’s vast data history and real-time market information on inventory and pricing trends from a global e-commerce marketplace for electronic components, Sourcengine. Datalynq uses predictive analytics and alerts to forewarn users of future disruptions and the likelihood of their impact on specific electronic components, helping procurement teams be more proactive in their component sourcing.  

With Sourcengine’s API, organizations can utilize Datalynq’s predictive analytics to optimize their procurement process and schedule deliveries straight from their ERP system. After last year, many organizations lack the capital to fund reactive and aggressive buying practices. Predictive procurement strategies can help optimize purchasing without the risk of human error, supply chain disruptions, or market uncertainty impacting the viability of production lines.

Sourceability and Cadence are working together to bring Datalynq's premier market intelligence into OrCAD X Live BOM to help engineers design smarter.

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