Use Cases

True to our roots as sourcing professionals (once our day job, before we ran MetalMiner), we help companies gain a better understanding of the raw material inputs and other costs associated with the purchasing organization’s metal component, sub-assembly or assembly. We do this by breaking apart the constituent elements of whatever the client ended up buying.

Recently, a colleague dropped us a line wondering how the steel mills could raise prices when raw material costs appear on a flat-to-downward trend. Back in the days (pre-2003) when commodity prices fluctuated within a narrow band, the relevance of price data for industrial metal-buying organizations appeared less than necessary.

But volatility resulting from emerging market demand and the rise of commodities as major investment opportunities provided the impetus for the development of price data tools. Here we examine methods buying organizations can deploy to get the most out of metal price data as our own service, MetalMiner IndX℠, has undergone a long-needed overhaul.

Primary Uses of Pricing Data

The methods range from the obvious, two of which we will cover here, to the less-obvious, which are found in the drop-down sections below.

The primary use of price data services, in our opinion, serves as a means for a company to track their purchasing performance against the market. In other words, daily price data helps sourcing managers and procurement professionals identify and track market trends and allow them to time purchases whenever possible in the dips, as well as track company performance against the market.

Perhaps more importantly, daily pricing data allows the buying organization to explain to management why it took the buying decisions it did. Ten years ago, buying organizations barely tracked these kinds of movements, but today this represents the likely first step of any metal procurement organization.

A second rather obvious use of daily pricing data involves price escalator and de-escalator clauses used for contracting purposes. In rapidly rising markets, the use of these types of clauses within contracts grows, but when markets decline, so too does the use of these tools. Whereas price escalator and de-escalator clauses certainly offer buying organizations some assurance in terms of how their own contracts will follow underlying market price trends, distributors and producers often complain how they get deployed in practice, particularly when market conditions can make their use problematic.

Furthermore, indexes work well for certain products — particularly semi-finished aluminum, copper and stainless products — when surcharges and volatility tie directly to base metal markets, but price correlation becomes weaker for items with higher value-add where the metal price falls below one-third of the total cost of the item (pipe and tube products often fall in this category).

Moreover, if an organization consistently buys under market year-in, year-out, the use of these types of clauses would not help the buying organization’s cost position (though we’d argue most organizations don’t beat the market over time).

Pricing Data Not Accepted Here

Perhaps a trickier issue for buying organizations to manage involves the buyer and supplier agreeing on the price index to use. Whereas the Midwest aluminum ingot price has become the de facto standard for pricing aluminum contracts, a steel contract has not become industry standard (we have seen buying organizations use CRU, the Steel Index, SteelBenchmarker and others) so gaining two-party agreement can become a challenge.

Nevertheless, as time goes on, we believe pricing standards will emerge.

As the use of price data becomes more ubiquitous, what companies do with the data has become far more strategic. Savvy sourcing organizations have gotten into the business of transforming price data points to actual market intelligence, serving as the basis for specific sourcing strategies.

Using Price Data for Negotiations

Though the use of price data for negotiations may appear obvious, how companies actually deploy these models suggests a wide range of practices. In addition, pricing data can serve both on the front end of a negotiation process as well as on the back end. Let’s start with the front end.

In this case, buying organizations may opt to use daily pricing data when they strategically source a metal category in which the metal portion of the total cost “floats” against market, but the sourcing organization competitively bids and holds fixed the value-add premium. We have seen this use case employed dozens of times in our strategic sourcing engagements.

Often, buying organizations deploy what we call the “3-bid monthly buy,” essentially bidding out the company’s total metal requirement on a monthly basis. Under that scenario, the buying organization doesn’t realize that it often places both elements of its purchase up for a spot market bid (both the metal and the value-add).

More strategic buying organizations bid the category and competitively bid and hold fixed the value-add portion and merely bid the metal premium. Sometimes a buying organization can win using the 3-bid method, but over time, we’d contend that the strategic sourcing process of splitting out the two results in a lower total cost of ownership.

So how do metal buying organizations use daily price data on an ongoing basis? Invariably, they use daily metal price data when producers announce price increases. Our anecdotal evidence suggests this case presents itself more commonly for steel purchases than other metals.

The scenario plays itself out this way: let’s say US Steel announced a $60/ton increase on carbon flat-rolled steel to the spot market price. Buying organizations begin scratching their heads. We’d receive emails such as this: “I’m trying to figure out how much US Steel’s raw material costs have gone up. Their $60/ton increase to the spot market price, I don’t see it from a raw material aspect.” In short, buying organizations use daily price data to track raw material costs.

Pricing Data Used for Global Sourcing

We see a couple of uses of price data in the field of global sourcing. The first involves the use of the data to monitor global markets and identify when it may make sense to globally source semi-finished products.

Whereas the service centers and distributors take greater advantage of global sourcing opportunities for semi-finished metal products than OEMs (though some of the very largest OEMs remain exceptions) by tracking global price points, companies can take advantage of arbitrage opportunities.

We’d argue the bulk of Western manufacturers more likely buy more value-add metal products from global suppliers (vs. semi-finished products) such as fasteners, sub-assemblies, etc., and for them, the value proposition of using a daily global price data service allows them to keep their overseas suppliers honest in terms of the impact of metal prices increases on the total cost of the particular item sourced globally. We see this in our own MetalMiner IndX℠.

For advanced use-case scenarios for daily metal price data, click below.

We’ve already covered some of the more obvious use cases including tracking a given company’s purchases against the market and using the intelligence to conduct more effective contract negotiations with suppliers (check out the sections above). Here we aim to cover some of the more advanced use cases, though we’d still contend that some of these more advanced applications remain in the realm of “would like to have” as opposed to “already using,” so to speak.

Forecasting Remains the Holy Grail

Without a doubt, when asked why a buying organization would like access to a pricing service, invariably the organization will say it wants specific metal forecasts. Some even express interest in running statistical regressions to build out predictive modeling and forecasting tools on their own.

Metal market volatility, which exploded beginning in 2003, has transformed industrial metal buying organizations. Savvy organizations have moved up a curve from simple purchasing and requisition while paying minimal attention to price trends, to intelligence and analytics to better understand cost factors impacting metal prices, seasonality trends and market dips; then to outright forecasting to better time purchases and deploy strategic sourcing strategies.

To buy forward or purchase on the spot market, to lock in on a long-term basis, or to commit specific monthly tonnages have profoundly altered the metal-buying process. In an environment with little volatility, organizations can ignore these considerations, but when markets move in double-digit percentages in a period of eight weeks or less, buying organizations need to become far more strategic.

Correlations Provide Useful Intelligence As Well

For those organizations less interested in trying to predict metal prices with any degree of accuracy yet still try to understand that correlations also provide insight into underlying demand, the ability to access a daily price service allows buying organizations to model and visually identify those correlations that help dissect specific market trends.

For example, just as the steel producer industry closely tracks housing starts, the ABI (Architectural Billing Index) and monthly automotive sales, buyers of stainless steel products can plot key raw materials, including ferro-alloys and nickel, to help monitor underlying stainless dynamics and potential impacts on surcharges.

In addition, new metal pricing tools will allow buying organizations to cross-reference multiple raw materials so buying organizations can understand underlying base metal trends, for example, as compared with iron ore prices or precious metals. As web-based tools, price services can provide a far more comprehensive intelligence platform for the industrial buyer.

Taking it One Step Further

But to really make the most of metal price data services, industrial buying organizations will want to embed the data with existing business intelligence, ERP and sourcing/procurement systems to conduct more sophisticated and robust spend analytics. The combination of metal pricing data overlaid with a company’s actual purchases provides the type of intelligence and insight to allow companies to identify hedging opportunities, better time purchases, quickly identify when a company should buy on the spot market (e.g. falling markets) and put contracts in place with escalators/de-escalators.

Below, we identify how producers might use daily price data services, as well as examine how industrial buying organizations can use the same data for slightly different purposes.

In our final installment on uses of metal price data, we could characterize the use case as “battlefield intelligence.” What we mean is taking the same price data available to anyone, but using it for a more targeted purpose.

Anti-Dumping and WTO Violations

Rules-based free trade as set by the World Trade Organization forms the basis of how many countries engage in global trade. Yet each country has its own governmental agencies set up to enforce such agreements, as well as serve as the go-to resource for any alleged trade violations.

Though the International Trade Commission (ITC) for the US Department of Commerce evaluates a broad range of manufacturing sectors and products, metals have long comprised a great majority of the anti-dumping cases brought by US industry against a range of countries. US producers often identify anti-dumping concerns when they see certain patterns of behavior, often without any hard data.

For example, the steel industry closely monitors steel product import levels, licenses applied for and percentage growth/declines of various products by country. When triple-digit increases occur, for example, as they did for pipe and tube products in 2007 and the early part of 2008, steel producers filed an anti-dumping case. Producers also glean intelligence by looking at existing customers, order history and sheer market share in an attempt to identify whether imports have suddenly surged, leading to the question of a dumping violation. However, though the sheer volume of imports for a particular product may prove a necessary condition, it may not act as a sufficient condition to confirm dumping has occurred.

But imagine a world where domestic producers could compare their prices against foreign producer prices in the foreign companies’ local markets. Such a service would provide US producers with an instant means of identifying potential violations, particularly in context of a direct price comparison between foreign vs. domestic market prices. Domestic producers could also use the service strategically as a means of optimizing margins and pricing products inside what we call “the arbitrage opportunity.”

In other words, domestic producers could use the data to price their own products less than the cost a foreign producer would charge along with freight, financing and other shipping charges. In essence, the US domestic price need not undercut the foreign country’s FOB or ex-works charges, but it needs to undercut the total landed cost.

What’s Good for the Goose

On the flip side, industrial metal buying organizations who have purchases large enough to justify global sourcing (e.g. container-load shipments of semi-finished materials) can also identify when it makes sense to source globally vs. domestically. As producers may wish to close arbitrage opportunities, buying organizations can more effectively identify and take advantage of them.

A telling note: we have seen a spectacular growth in MetalMiner readership among the producer and mining communities. Nearly all of this growth has occurred in the supply chain management function (e.g. procurement or supply chain groups) within many of these large producers. Undoubtedly the price data, forecasts and analysis covered on the site — while geared specifically toward manufacturing organizations further downstream from the semi-finished product producers — it should come as no surprise to see that this producer community also seeks raw material sourcing intelligence, metal price forecasts and economic and policy analysis.

Downstream manufacturers may wish to pay heed. What producers do with price data contains lessons for the downstream buying community.