BorgWarner gave a terrific presentation at this year’s ISM annual conference regarding Commodity Price Risk Management that I thought I’d share. Borg Warner is a global Tier 1 automotive firm (60 locations in 19 countries) that buys 80,000 metric tons of commodities globally, including Steel, Copper, Resin, Aluminum, Powdered Metals, etc.
The presentation could have just as easily been called “profit risk mitigation”. The homegrown system (called ‘commodity engine’) they developed out of their Morse TEC division allowed them to help mitigate commodity price risk by linking commodity purchase price forecasts and resultant contracts to the final assemblies sold to customers in order to appropriately pass on the cost increases/decreases – and thus reduce margin volatility. Such margin smoothing is the dominant objective for most firms facing commodity exposure. They try to ‘smooth the market’ rather than ‘ride the market’ or ‘beat the market’. The last strategy can be fruitful if you are very good at advanced hedging, but most firms try hedge through simpler means, such as this “volatility pass through” approach.
An industry like Chemicals that is sandwiched between commodity markets is very familiar with this issue. But, in discrete manufacturing, it is much trickier because you have to traverse a multi-level Bill of Materials and TCO model (including material libraries, cost estimation rules, cost adders, etc.) – and then hook it up to the forecast on the demand side and to commodity forecasts on the supply side. THEN, you can layer in the existing contracts to help plan your risk mitigation strategy. This process takes different forms in different industries:
- In CPG, this can be a make or break value proposition and getting forward visibility of when your product will turn unprofitable (if you can’t pass on costs to the customer). I highlighted a blinded case study of a CPG firm in the following Supply Chain Management Review article that is similar to the BorgWarner story. The CPG firm (sells high volume household items into Grocery Retail) was able to predict when certain products would likely turn unprofitable so they could change mix/assortment, design, etc. to stave off the impact of the volatility.
- In High-Tech, this end-to-end view is also well known. Look no further than HP in their Price Risk Management capability (which they now call Procurement Risk Management) that you can read about here, here, and here. If you dive into the details, you can see that building the data warehouse is just the start. The real fun begins in using analytics to build the scenarios and optimize the contracting/hedging strategies. It’s too bad that HP wasn’t able to commercialize some analytic offerings here, and I had those discussions with them a decade ago on this. The conundrum was whether to make a penny on the analytics revenue and lose a dollar on lost competitive advantage by bleeding the IP out to competitors.
BorgWarner, however, found plenty of advantage just through the visibility and transparency of the data. It set itself up as a pass-through entity (basically like a contract manufacturer) and allowed a tight linkage between their suppliers and customers, thereby not getting caught in the middle of a price/cost volatility squeeze. This thereby reduced the ‘noise’ in the system with customers and suppliers alike and had other benefits that we’ll write about in the next part of this already lengthy post.
The benefit of such data transparency across the value chain is the same as in good old fashioned spend analysis. And, in fact, it is merely an advanced form of spend analysis. Spend = cost * volume. And it occurs along multiple points in the value chain. But, rather than just a siloed internal single-tier historic look at spend magnitude, it’s about predicting future spend by analyzing the “outside-in” business scenarios and the drivers of spend across the extended value chain: demand price/volume forecasts, input cost forecasts, capacity, contract positions, risk/reward tolerance of the trading parties, price elasticities, etc.
Of course, even with this complexity, we’re still scratching the surface. We’ve not even brought into the issues regarding multi-tier processes such as a “buy-sell” which can be combined with using trading companies and tax advantaged countries. The technology implications of these scenarios are not just ever-increasing complex analytics, but equally complex execution in contract management; supply planning collaboration, multi-tier order management, etc.
We will do many more pieces in the future on such advanced supply analytics, but also what providers are doing on the ground with these early adopters. Many vendors have portions of what I described above, and I won’t name the names here and now (but if you are a provider and think you do this type of analysis, please contact me privately so I can kick the tires, talk to a client reference, and get you featured), but the provider types are broad:
- Broad Business Intelligence / analytics
- Commodity risk management pure plays
- BPO / KPO providers
- Sourcing optimization tools
- Supply chain planning / optimization
- PLM tools
- Cost estimation tools
- Hybrid manufacturing / service provider firms
- And, of course, the most dominant vendor: Microsoft (in the form of Excel and Access used in homegrown applications)
In the next blog post, we’ll finish the discussion on BorgWarner and the implications of this type of analytic solution on Procurement with respect to elevating its value proposition to the business.