Commodity Price Forecasting Tool
The client is a multinational automobile manufacturer, based in Europe. They are one of the biggest producers of premium cars.
The client’s procurement and supplier quality team forecast the price of commodity and use these forecasts for budget planning and vendor negotiations for the subsequent years. The team used qualitative or judgmental techniques for commodity price forecast. They made educated guesses based on intuition, knowledge and experience.
High commodity price volatilities due to different factors like the exchange market, political and geographical events which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to drive commodity price forecast accuracy.
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.
Commodity price forecast is challenging as it requires an in-depth domain knowledge of commodities and a thorough understanding of both qualitative and quantitative approach of forecasting.
Historical data study helped in identifying price trends, benchmarks, and impact of various global events on the commodity price trends.
Commodity prices are affected by external events, which can be captured by identifying peaks or change in trend/pattern in price data.
Key drivers of commodity price can be identified by understanding the evolution of commodity into the market.
Impact of events and drivers on commodity price can be understood by using statistical techniques likes correlation, hypothesis testing and generalized linear model (GLM) and the impact, in turn, can be used as a reference when such events occur in future.
Quantitative forecasting is broadly classified into univariate, multivariate and advanced techniques. Due to the limited amount of data and monthly level granularity, it is ideal to use univariate or multivariate forecasting techniques.
Although there are many methods used for accuracy calculation, MAPE (Mean Absolute Percentage Error) is used in time series forecasting because it is independent of the scale of the output variable. MAPE for Naïve method and other methods was calculated across different time stamps and finally, the model which showed better performance across time was selected.
Below is the list of univariate techniques used:
Below is the list of multivariate techniques used:
A Tableau dashboard was created so that the procurement and supplier quality team can easily consume the output. The dashboard had multiple commodity price forecasts, comparison of the forecasts with the expert judgemental forecasts and insights on the past price trends.
While the forecasts were relatively better than the expert forecasts, procurement managers still had to rely on observing the market behaviour to look out for any geopolitical events, economic events or any other news that would directly impact the price of the commodity.
To account for this, a new feature was developed in the dashboard that actively lists the news from various websites and displays the headline, which on clicking, would redirect to the news article. This feature would help the procurement manager to understand whether the price of the commodity will increase or decrease and based on the judgement he can modify the forecast.
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