In this 6-part series, guest blog writer Michael Lamoureux, a freelance procurement expert, explores and takes a deep dive into spend forecasting. With years of deep domain craft in sourcing and supply chain, he shares the secrets of what it takes to keep up in today's fast moving economy. Read Part 1, Part 2, Part 4, Part 5, Part 6.
As per our first post, analytics is the new hotness (yet again). And this time, it's not the old and busted hotness of the first generation Business Intelligence tools that, while revolutionary at the time, leave much to be desired.
The reason analytics is the new hotness is that modern analytics systems have gone beyond simply presenting a descriptive overview of historical spend that, by the time it was compiled was often of limited use, to providing a predictive capability that allowed an organization to predict future demand and spend based upon current demand patterns and integrated market intelligence.
The basis for this predictive capability is trend analysis. While trend analysis is nothing new in finance, stock markets, and inventory management, the reality is that it is rather new in the domain of spend analysis. Why? There are a couple of reasons for this. First of all, historically, spend "analysis" focused on compiling descriptive overviews of past spend. Secondly, traditional trend algorithms were seen as the domain of inventory planning or price forecasting.
But if the whole point of spend analysis is to understand what you're spending to identify the right opportunities, then trend analysis needs to be part of the domain of spend analysis as opportunities are not just sourced today, they are sourced for the next month, the next quarter, the next year, and sometimes the next three to five years. In order to select the right opportunity to source today, one has to understand the impact of each potential opportunity over time.
This requires the integration of at least three types of trend analysis and projection:
Demand Forecasting Across Products and Components
Just because there is a big gap between the price an organization is currently paying for a product and the current market price does not mean there is an opportunity. If demand trailed off between the compilation of the historical data and the current time, either due to a product entering end of life or an operation closing down, then the opportunity will be quite limited.
Success requires identifying those categories where demand is significant, static or increasing, and the price differential (which can be expected or locked in) is enough per unit to make a strategic sourcing event worthwhile. For starters.
It's also analyzing component and raw material requirements across product lines to identify those components and raw materials that are used across products and that are increasing in both demand and price. By identifying those products that are likely to increase in price and stay static, or increase, in volume, the organization can proactively identify good sourcing opportunities that would otherwise be missed. Even if the savings is low today, preventing future increases is very profitable cost avoidance.
And, if the company contracts for a lot of custom manufactured goods from smaller suppliers with limited buying power, it can also identify raw materials that an organization could buy on behalf of its suppliers at a lower cost. For example, if it bought tons of steel, it could buy at a lower price and then have the material delivered direct to its suppliers at its cost, taking cost out of its supply chain one level down.
Cost Forecasting using Market Price, Commodity, Energy, & Labour Forecasts
Market Intelligence only identifies what you could be paying today -- it doesn't tell you what you will be paying tomorrow if you don't source today. If costs are decreasing, and there is no urgency to source, then the best thing to do is wait until sourcing has to be done, and if supply exceeds demand, put the demand to auction at the last minute. But if costs are increasing, sharply, sourcing today will avoid cost increases tomorrow.
But sometimes you can't forecast based on (market) price alone, especially for custom manufactured products. That's where deep market intelligence on raw material, energy, and labour costs is required. With this information, an organization can build not only should cost models for today, but with deep cost forecasting at a category level, it can build should cost models for tomorrow and identify those categories and products it should be sourcing versus what it should have been sourcing last year.
Outside of the financial markets, an often overlooked area of forecasting that is critical to identifying both the right categories to source and the right time to source is currency forecasting. An opportunity only remains if the relative value of the currencies in play remain the same. If a US organization is buying from China, but the renminbi rises sharply against the US dollar, then the savings tomorrow will not be the same as the savings today even if the price is locked in for a year.
The right buy is not only the right product from the right supplier built at the right location and shipped using the right method, but the buy that is also negotiated, and paid, in the right currency. In this instance, if the organization also sells in China and has a fair amount of renminbi at its disposal, then the organization should be cutting the contract in renminbi (and not US dollars) and paying within China.
This is, of course, assuming that China would still be the best buy. If the other option was Taiwan, with a slightly higher cost today, but the projection was that in two months the New Taiwan dollar was going to fall and remain weak for a year, then the best contract, taking future expected cost into account, might be with the Taiwanese supplier that would be higher for two months, but lower for ten. The reality is that, in global supply chains, currencies can make, or break, sourcing opportunities.
Once an organization acquires a modern spend analysis platform with the ability to project these trends over time, it can start to more accurately forecast demand and predict future costs, which allow it to zero in on the true opportunities of today and tomorrow, not the opportunities of last quarter or last year. This completely changes the game. No longer will any time be wasted strategically sourcing categories in decline, or strategically sourcing categories where the current situation is almost as good as it gets, and the best thing to do (with the price increases coming down the pipe) is just extend the current contracts as long as the suppliers will do so.
Plus, once an organization has trend analytics capability, it can re-run the projections and analysis on a quarterly or monthly basis, identify when the forecasted trends change, and when it might need to take a harder look at a category, supplier, or geography before a significant price change or disruption happens. This puts it light years ahead of where it was before when it only acquired annual insight into a category or supplier, by which time it might be too late to do anything about an emerged situation.
And this, our friends, is why analytics is the new hotness yet again.
About the Guest Writer
Michael Lamoureux, aka the doctor, is the Editor-in-Chief of Sourcing Innovation (.com), a resource for sourcing, procurement, and supply chain professionals who are interested in improving themselves and the overall performance of their organizations. A regular contributor to Spend Matters, he is a Computer Science PhD who has been heavily involved in the Sourcing and Supply Chain Space since 2000 and the e-Commerce space since 1997. As a freelance procurement consultant with extensive expertise in sourcing, procurement, and supply chain processes, he aims to continually push innovation in and beyond the supply chain space. With particular expertise in analytics, modeling, and optimization, he is able to dive much deeper into technology and core issues, striving to help businesses with their internal knowledge transfer, positioning, and planning problems.