How to Track, Forecast, and Create Sourcing Strategies for Opaque Metals

Some of our favorite metal sourcing challenges involve off-the-beaten-path requests for price intelligence regarding “opaque metals.” This term refers to metals that no known exchange-traded price firm or price reporting agency (e.g. MetalMiner and our competitors) publishes. We like to think MetalMiner continues to add price data across a broad range of “obscure” metals. This is accomplished by using the power of correlation analysis. These obscure metals include, but are not limited to tinplate, grain oriented electrical steel, non-oriented electrical steel, and tool steel, or ECCS, among many others.

The problem with not having accurate prices for obscure metals is that sourcing managers have few resources and tools to manage the spend, negotiate with suppliers, and – in the current environment – achieve cost reductions. Moreover, the solution does not solely lie in finding a price reporter with that specific data set. Sourcing managers can also use data analytics and correlation analysis to better understand the relationships among, quite frankly, anything.

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The Power of Correlation Analysis and Why You Should Care

Most of us are aware of at least some correlations. We know gas prices correlate with oil prices. A patch of freezing temperatures in Florida will impact the availability and price of oranges. Scrap prices correlate with hot rolled coil prices (though not as strongly as some suggest). We also know that the dollar trades inversely to commodities as a whole.

Now, thanks to great analytics tools and large data sets, it appears rather simple to correlate any two variables together. From there, one can determine if a strong positive or a strong negative (e.g. inverse) relationship exists. Understanding such relationships allows sourcing managers to “borrow” tried and true methods for markets with known prices, price movements, and forecasts. By relating them, they can then devise strategies for the more obscure metals.

In essence, this allows the procurement manager to move more direct material spend “under management.” 

Moreover, artificial intelligence (AI) is currently or will soon be taking on many of the laborious sourcing tasks. This includes the tasks which once ate into the sourcing manager’s time. Therefore, new tools and solutions around data analytics, specifically correlation analysis, can provide new opportunities for value-added work. Of course, deeper insights into the relationships across and among commodities can help drive:

 

• • New sourcing strategies for categories that previously had little to no managerial oversight.

• • New insights into “early warning signals.” These could stem from sales data that can be used and fed into procurement even beyond the data that comes from demand planning solutions.

• • The development and integration of these insights into risk management and what-if scenarios.

Value-Added Work for Procurement

The benefits of performing correlation analysis are quite clear. It not only offers opportunities for cost savings and the development of new sourcing strategies, but it also increases job satisfaction. By using new tools like AI to offload or reduce the amount of repetitive work, procurement managers can better focus on devising new sourcing strategies for obscure or hard-to-predict metal categories.

This is exactly what MetalMiner is covering in our May interactive chat: “Out of Low Hanging Fruit? How to Use Unconventional Cost Reduction Tools”, May, 2023 11:00 – 11:30 CT

Join MetalMiner CEO Lisa Reisman and VP of Business Solutions Don Hauser and data analyst Daniel Julius in a lively 30-minute fireside chat discussing use cases and specific correlations unearthed using data analytics tools. The duo will debunk common notions regarding correlations as they relate to the metals industry while sharing some little-known relationships. They will also discuss how these tools negate the need for multiple metal price subscriptions and provide tips for using these commodity relationships to forecast and ultimately generate cost savings.

Click here.

In summary, world-class sourcing organizations have become mini think tanks in that they acquire and use data to create insights that change buying behavior. Artificial intelligence, data analytics, and, specifically, correlation analysis can offer the next rung of corporate cost-savings. Moreover, data analytics offers more opportunities for procurement professionals to think, create, and deploy winning strategies.

When choosing a market intelligence partner, buying organizations ought to demand new capabilities, especially data analytics. This will allow them to continue driving more spend under management while simultaneously providing for more job pay off.