Between March, 2020 and May, 2021, the price of steel had risen more than 200 percent. Copper, another critical commodity for high-tech products, doubled over the same period. Over the last two decades, the same picture of rising volatility has been repeated across many categories of basic inputs, from fuels to foodstuffs. The underlying drivers of commodity-price instability are a complex cocktail of demand fluctuations, supply disruptions, and financial moves by market players. And for manufacturers, they create a headache. In many organizations, these embedded costs account for up to 30 percent of external spend in highly integrated supply chains, such as automotive.
Even mature procurement and product-development organizations struggle to gain a clear, in-depth understanding of the material and factor costs embedded in their products. With increased outsourcing, the bulk of those costs may be hidden in the upstream tiers of their supply chains. Just in the automotive industry, up to 80 percent of the value chain is outside the original-equipment manufacturer (OEM), and an estimated 60 percent of that comes from suppliers in the second tier or beyond. The picture is similar in consumer packaged goods, where about 70 percent of product value is added by suppliers.
Traditional approaches fall short
Traditional product-cost optimization approaches were born in an era of much greater price stability. Conventionally, companies look at product cost through two lenses: “product sold” and “category purchased,” each overseen by different staff. Product-cost specialists search for ways to optimize the bill-of-materials (BoM) of each product, then attempt to negotiate the best prices for components and materials from their suppliers. Category-management teams work across the product portfolio, finding ways to standardize specifications or consolidate spend with suppliers to benefit from economies of scale.
Mature organizations collaborate across both lenses, but the focus has always been on immediate supplier cost optimization and transparency. And traditionally, organizations could agree on a constant price for a part’s life, since underlying input costs did not change significantly and there was no “value loss” once the sourcing agreement was signed.
When input costs are volatile, however, traditional cost optimization has serious weaknesses. Companies may be exposed to unfavorable shifts in foreign exchange rates, transport costs or packaging prices, for example. And because their agreements with suppliers don’t explicitly recognize the contribution of basic material costs, they don’t share the benefits when prices fall. At the same time—and this has become painfully obvious for many in the recent COVID induced supply crisis—suppliers will increase prices or shift volumes within the bounds of standing agreements when inputs are rising in cost.
In recent years, some organizations have tried to account for embedded costs in a more sophisticated way. They may adopt a flexible approach to product specification or formulation, for example, switching between aluminum and copper conductors in electrical systems depending on the relative price of the two metals, or between sugar cane, beet, and corn syrup in packaged foods. Or they may use cost models in their negotiations with suppliers, and establish agreements that index part of their spend to the underlying commodity market.
But many companies still struggle to fully optimize embedded costs, for a variety of reasons:
- Raw-material and other embedded costs are an afterthought, or considered too much effort. It isn’t clear that identifying and tracking embedded costs across hundreds or thousands of parts is worth it, so material choice, utilization and supply chain setup are not prioritized.
- Costs are embedded deep in the product. Many components consist of materials that are hard to physically separate, measure, and model for cost-analysis purposes. Examples of such hidden materials include embedded foams in a wide range of products, nylon fibers molded into tires, and the copper, aluminum, insulation, and epoxy coatings found in electronic components.
- Costs are embedded deep in the supply chain. Embedded content may enter the value chain at tier-n suppliers, often located overseas, where costs are exposed to currency-exchange fluctuations.
- Supply chain partners don’t pass on benefits. Buyers could be two, three or four value-add steps away from the underlying raw material, commodity, or embedded cost element. That opacity benefits suppliers, at the cost of the buyer.
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Unlocking the treasure
Treasure stays buried when treasure hunters lack the skills to find it, or the tools to dig it up. Today, leading manufacturers are developing new ways to understand where embedded costs are hidden across their supply chains, and how to handle those costs in way that maximizes the potential benefits of input cost volatility while minimizing the risks (Exhibit 1).
While these approaches work as extension of existing advanced cost optimization approaches, such as the application of should-cost models to build a granular view of input costs, they depart from established best practices in three important ways (Exhibit 2):
- They start earlier, considering embedded costs from the early phases of product development.
- They go deeper, understanding three or four incremental levels of detail—such as the chemistry embedded in particular materials, or the currency effects in particular suppliers’ production locations.
- They move faster, using digital tools that track the composite impact of multiple embedded costs—different raw materials, exchange rates, factor costs, and so forth.
Embedded-cost optimization works best when it is deployed from the product concept stage, as that’s when companies have the most freedom to improve their designs. Product-development and procurement teams need to work together to select materials, set cost targets, and plan sourcing arrangements. These decisions inevitably involve trade-offs among technical requirements, in-house and supplier capabilities, and costs. With a comprehensive fact base and clear, upfront alignment, companies have a much better chance of getting key decisions right.
From a supply-chain perspective, critical questions need to be answered about whether companies should purchase materials themselves or leave it to their suppliers. The right answer can vary for different materials and products, depending on the context. Last, commercial terms and pricing mechanisms need to be considered, as these determine the allocation of risks within the supply chain. A leading automotive manufacturer, for example, uses data from its own historical product portfolio and competitor teardown benchmarking to identify multiple combinations of material choice, utilization, and cost. This allows it to identify best-in-class targets for future product components.
Looking deeper into embedded costs requires purchasers to find out things that only their suppliers currently know. That calls for some clever detective work, going far beyond the approaches used in conventional cost-modelling techniques.
To understand the materials, production processes, and supply networks involved in the components they buy, companies often employ specialized resources and outside expertise. Some players are applying technologies, such as infrared spectroscopy, thermogravimetric analysis, or energy-dispersion spectroscopy to understand the precise chemical composition of the materials used in supplied parts. Others are in an easier position. Where robust, long-term supplier relationships exist, parties may be more willing to share data to assist collaborative cost-optimization efforts.
Once they understand their embedded costs, companies can use a variety of techniques to generate value and minimize risks. They can compare typical versus actual scrap rates in the supply chain to determine should-costs for materials used. Or they can set variable pricing mechanisms with suppliers, so both parties can benefit from movements in underlying material prices and exchange rates. Deploying this approach rigorously, one major industrial player with tens of billions in annual spend and more than 10,000 unique part numbers was able to identify 1 to 3 percent annual savings opportunity—in addition to a one-time potential cost reduction of up to 10 percent on addressed commodities.
Application of advanced analytics, big data, and process automation can significantly improve an organization’s ability to manage embedded-cost volatility. Using digital cost-monitoring tools, sourcing-category managers can track commodity-price fluctuations, exchange rates, labor rates, and other embedded costs for their respective category—potentially identifying gaps and cost-saving opportunities in a matter of seconds. Live data feeds from multiple commodity-data sources help to create transparency, flag price outliers, identify alternative sourcing options, and quantify premiums for import parity. These tools provide near-term opportunities for value capture based on facts that suppliers typically cannot refute. The insights gained can also be used in negotiations with new suppliers or to deepen partnership arrangements with existing ones.
Various industrial players have implemented artificial intelligence–based tools that systematically scan commercial and technical spend-cube data to flag opportunities and facilitate management of the process at scale. One leading player with more than $2 billion in spend baseline established a central control tower to coordinate this effort, launching rapid sprint-style diagnostics to quantify opportunities and installing a negotiation factory to prepare for conversations with suppliers. Rigorous implementation in enabled annualized savings of between one-half and one percent—within just four weeks.
Capturing value via segmentation
Embedded-cost optimization means considering both the immediate manufacturer-supplier relationship and the broader market context. We find that most companies are stronger at the first of those elements and weaker at the second. The most mature procurement organizations have years of experience negotiating with suppliers and managing ongoing relationships. In contrast, adjusting to the market context requires a dedicated game plan that considers both price dynamics (inflating or deflating) and current risk exposure by commodity (such as spot buy or contract on index versus nonindexed contracts) (Exhibit 3).
To win, organizations must be clear about their ability and willingness to carry market risks, and agile in switching between playing offense (capturing incremental impact in deflating markets) and defense (minimizing the impact of an inflating market).
Some organizations may be tempted to adopt a conservative strategy that aims to hedge risk exposure, such as by basing prices for a portfolio of products on a standardized recipe of materials. This can leave value on the table, however, since it fails to capture savings achieved when suppliers find ways to improve material utilization or increase the fraction of lower-cost materials in their products.
Companies that leave it to their suppliers to worry about embedded costs risk being on the wrong side of every change, as suppliers use their information advantage to press for price increases during inflationary periods, then stay quiet when commodity prices fall.
Supply chain-cost volatility seems here to stay. Whether in an inflationary or deflationary situation, product-development and procurement leaders can go beyond traditional approaches to optimize embedded costs. If they start earlier, go deeper, move faster, and maintain the agility to respond to changes in the market context, successful teams will find plenty of hidden treasure in their upstream value chains.