Contextual AI Asset Commodity Price Forecasting Tool
A multinational automobile giant based in Europe, regarded as one of the biggest producers of premium cars was looking at employing a data-driven approach to price forecasting commodity prices. The client’s procurement and supplier quality team forecasted the price of commodity and used it for budget planning and vendor negotiations for the subsequent years. The team used qualitative or judgmental techniques to forecast the price of commodity. They made educated guesses based on intuition, knowledge and experience. High volatility in the price of commodities due to different factors like the exchange market, political and geographical events lead to a lot of planning inefficiency and budget gaps. The client wanted to seek a data-driven approach to forecast the commodity prices.
The automobile industry, much like many other industries, has felt the pinch from rising commodity prices. The intermingling and co-dependencies of multiple global factors puts commodity markets in an extremely delicate position worldwide, where a landmark decision, a calamity or an unpredicted event is all it takes for ripple effects to translate to fluctuations in commodity markets. The resulting price fluctuations are especially problematic for businesses – as volatile market dynamics deliver a hard blow to procurement budgets. With raw materials, like steel, glass, aluminum, plastic rubber etc. accounting for nearly half of automobile manufacturing costs in the past, it’s grown essential to foresee price forecasts, even in the event of sudden commodity market movements. Inaccurate forecasts can translate to steep costs for businesses, which cannot be entirely transferred or passed on to consumers. It becomes essential for businesses and specifically procurement leaders to have an accurate forecasting tool that can predict movements in the commodity market, ahead of time.
There are many factors that contribute to the volatility of commodity prices, ranging from market inflation to demand rise, supply chain hiccups, macroeconomic and geopolitical changes, and more. With investors, traders, buyers and many other players influencing commodity market movements, and endless market information available online, it’s understandable why commodity prices oscillate as often, influenced by herd mentality.
While the concept of monitoring and predicting commodity prices itself is not alien to procurement leaders in the automobile industry, the means to predict price fluctuations evades a formulaic response despite its longstanding prevalence, and what could otherwise prove to be a time-saving and cost-saving tool for procurement teams. Indeed, price predictions in many industries continue to take a conservative approach even today - from closely monitoring market movements to trusting hunches based on information in industry networks and the interwebs, intermittent insights from expert consultants, and many others. However, a major differentiator that has caused procurement managers to revisit the conservative approach is that commodity volatility is no longer confined to local or closed markets, as they extend to a global scale, making it far more difficult and unfeasible to tap into immediate supplier networks to gain accurate commodity pricing intelligence.
It’s only natural that automobile category leads have chosen to lean on a more reliable and time-saving approach to predict commodity prices over time, given the large volumes of commodities that they grapple with on a daily basis, and the small cost differences that snowball to major losses as a result. Consumer Price Risk Analytics (CPRA) has grown more and more relevant over the years. The surplus of data available today, further facilitates building pinpoint prediction tools with near real-time accuracy on price forecasts, helping procurement leaders have more effective negotiation discussions on commodity prices with suppliers, thereby saving automobile businesses anywhere between hundreds of thousands to millions in commodity purchase expenses.
A Commodity Price Forecasting Tool can be treated as a contextual AI asset, where it is contextualized to the specifics of the automobile business – such as data, processes etc., and the accompanying industry dynamics, with the capability of generating accurate commodity price intelligence to procurement teams. With the help of our proprietary platform Co.dx, our commodity market analytics experts are able to speedily codify price forecasting algorithms using learnings from previous problem statement blueprints, to build a contextual AI asset that answers pertinent questions associated with price forecasting, specifically for each business and industry.Broadly speaking, here are essential capabilities of a typical price forecasting tool that can equip category managers to make smarter procurement decisions and mitigate commodity risks
A machine learning tool can infinitely mine online data in real-time to build price prediction models, based on developments in global news and events. This would essentially be a tool that continually learns and evolves. Therefore, the longer it runs, the better it gets at forecasting commodity prices.
ML tools can collate data from reliable sources and display it an interactive real-time dashboard so the user can monitor events impacting the price of commodity on an ongoing basis. The tool can be customized to notify category managers on specific market trigger events, pivotal to their business. Early price warning systems can help users make informed decisions associated with the commodity, and the ripple effect it may have on market prices in the immediate future.
Recommendations can be treated as compasses that guide procurement teams on the most suitable action to take in the existing market situation. Here again, market data and newsfeed associated with the commodity is closely monitored by the tool. These engines mine through mounds and mounds of market reports to gauge their veracity before factoring in only valid data. Contextualizing the engine to business data sources and relevant supply-demand scenarios allows to further manage commodity price risks more effectively, by gaining a comprehensive view of cost drivers. Here again, the engine would learn iteratively to prescribe a better set of actions, over time.
In addition to price forecasting, tools can also offer overview of budget and spend profile analyses, cost deviations, cost savings and more, helping category managers to budget more efficiently by digging deep into their commodity purchase data. Factoring in commodity purchase impact on cash inflow, EBITDA etc., can further assist in commodity purchase planning and weighing associated monetary risks.
Accurate forecasts demand a distinctive solution approach, one that employs both judgmental forecasts (forecasts based on qualitative analysis, market expertise of a variety of factors and analysis of supply and demand fundamentals) and statistical quantitative model forecasts based on historical price information. A keen view and understanding of market dynamics that can be feature-engineered to build a contextual AI asset, can help in proactively predicting commodity prices.Let’s take a look at steps that go into building a well-rounded forecasting solution:
Let’s now do a deep-dive into a sample solution that was built by TheMathCompany to solve the commodity price forecasting problem for the premier automobile manufacturer to understand how the contextual AI asset is built and mobilized, with a real-life example.
The multinational automobile manufacturer, which was one of the biggest producers of premium cars, was looking at employing a data-driven approach to price forecasting commodity prices, to combat high-volatility in the market. A major challenge was with the battery-grade commodities like Lithium and Cobalt as their prices have been extremely volatile. The other challenge was the unavailability of import/export data, global inventory levels, and other commodity-related prices.
The figure above indicates the different challenges in the project
Commodity price forecasting requires an in-depth domain knowledge of commodities and a thorough understanding of both qualitative and quantitative approach of forecasting.Here’s the approach we followed to get a holistic view of commodity price levers:
The figure indicates the step-by-step approach in solving the problem