Impact

The forecasting framework helped improve accuracy by 10 “ 30% basis points across commodities, with ability to explain fluctuations with external events.

The procurement teams are now able to plan their budgets more accurately and negotiate better prices with vendors, leading to significant cost savings.

Problem Statement & Challenge

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 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 which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to forecast the commodity prices.

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 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 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 which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to forecast the commodity prices.

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.

Approach

Commodity price forecasting is challenging as it requires an in-depth domain knowledge of commodities and a thorough understanding of both qualitative and quantitative approach of forecast

Historical data study helped in identifying price trends, benchmarks and impact of various global events on the price of the commodity.

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.

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.

Volatility in the price of commodities 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 forecast the commodity prices.

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 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 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 which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to forecast the commodity prices.

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 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 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 which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to forecast the commodity prices.

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 Order-to-Cash (O2C) division of a leading CPG company wanted to improve cash inflow by transforming the invoice-to-cash collections process. Traditionally, as invoice-to-cash collections is a reactive process, the collection agents follow-up with customers only when an invoice is delayed beyond payment terms, or a dispute is raised. This approach is slow, manual and expensive.The O2C division noticed that the delinquent invoice payments were directly affecting the EBITDA and cash inflow, and in turn leading to inconsistencies in financial stability.

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 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 which lead to a lot of planning inefficiency and budget gaps. Therefore, the client wanted to seek a data-driven approach to forecast the commodity prices.

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.

Solution

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.

A Tableau dashboard was created so that the procurement and supplier quality team can easily consume the output. The dashboard had multiple 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.

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