The past three years have been quite unstable for the global economy and have drastically increased the volatility of commodity prices. Right from the COVID-19 pandemic, the war in Ukraine, the impending global recession, the inflation, up to the central bank rate hikes – all of them have contributed to the turbulence in the economy.
Let’s illustrate this with a real-time example: Before Russia invaded Ukraine in February, crude oil was trading at $90 per barrel and it rose to $120 per barrel in March. But since its peak in June, oil prices have fallen below the Ukraine pre-war levels, partially rebounded in October and have been volatile since then.
Managing such fluctuations could be a business threatening issue for many companies. Therefore, commodity price risk management is a critical responsibility of procurement teams. In order to control the budget and ensure profitability, they are required to procure commodities at the right price, at the right time and in the right quantities by constantly monitoring market movements. This is easier said than done as precise price prediction of commodities is impossible.
However, the uncertainty can be reduced to a large degree by using a few proven methods.
Contents
1. Methods to manage commodity price fluctuation
a. Using advanced predictive analytics
b. Fixed price contracts for relatively low volatility products
i. Fixed Price Contract
ii. Futures Contract
c. Purchasing by linking commodity portion to an index
d. Understanding the macroeconomics of suppliers
e. Sourcing alternate products or producing in-house
2. Key to successful implementation of the strategies
Methods to manage commodity price fluctuation
1. Using advanced predictive analytics
One of the ways procurement teams can tackle the dynamic nature of commodities is by buying and managing inventory based on data-driven planning. Advanced predictive analytics tools of the day can forecast whether a commodity price is likely to rise or fall and the magnitude by which it moves. They generally use algorithms and complex price forecasting models by studying the components influencing a specific commodity price.
Even though 100% accurate predictions are seemingly impossible, these forecasts are reliable and help reduce the uncertainty to a great extent, thus unlocking huge savings. It also helps in estimating the right budgets, making quick buying decisions and planning for potential future risks.
Many companies utilize these forecasts to align their suppliers’ production capacity with demand or required volumes to mitigate any delivery failures for critical commodities.
2. Fixed price contracts for relatively low volatility products
a. Fixed Price Contract:
One of the most common strategies used by a majority of the firms is signing a Fixed Price Contract with suppliers. These contracts are designed for a certain period of time during which the supplier has to provide the commodity at a predetermined fixed price and is not allowed to change the price in accordance with market fluctuations.
This type of agreement typically works well for products that have low volatility, steady volumes throughout the year, high inventory carrying costs, and predictable forecasts. Fixed Price Contracts are great for aligning the purchase cost to the budgets planned and eliminating the chances of incurring losses whenever there are spikes in commodity prices.
However, sudden surges in the price index that last for a long period due to unforeseen circumstances and an unstable political environment could lead to huge losses for suppliers. In rare cases, it could drive them out of business. In order to protect supplier interests and build strong relationships, procurement teams could include clauses for price negotiations in the contract to deal with such scenarios. This will also help the buying team generate savings from a sudden drop in prices.
b. Futures Contract:
This is another instrument used to hedge against price-associated risks. In this contract, the price is fixed for a certain commodity but the purchase of the same would be done at a specific period in the future. Companies leverage such agreements when there are clear signs and reliable forecasts of commodity prices increasing.
3. Purchasing by linking the commodity portion to an index
Commodities whose prices are highly volatile and lack reliable forecasts could be procured by index-linked purchases. In this, a cost model is worked upon considering the material cost, processing cost, overheads, profit, and other associated costs. The material cost is linked to the index of the commodity and other cost components are kept constant.
With the help of this model, the buyer can arrive at the ‘Should be’ cost of the commodity and negotiate with the supplier for a fair price. If the confidence of the commodity forecast model is high, then overbuying when the prices are low and underbuying when prices are high can be planned and executed.
Depending on the nature of the commodity, some businesses use a mixed approach. A fixed rate is negotiated for a certain portion of the quantity and the rest will continue to fluctuate based on the market index. The primary advantage of this strategy is that it gives the buyers maximum control over their procurement allowing them to benefit from favorable market prices.
4. Stronger negotiations by understanding the macroeconomics of suppliers
Apart from minimizing price risks, procurement teams need to ensure a consistent supply of commodities. In today’s situation, running a mere Supplier Due Diligence or Risk Assessment during the supplier onboarding process is not sufficient. It is important to regularly monitor the financial stability, production capacity, inventory holding, portion of supplier’s business the buyer contributes to, and recent delivery performance to mitigate supplier risk.
An integrated system providing real-time monitoring of the market indices and giving supplier intelligence during procurement is critical.
Buyers can also leverage the intelligence for negotiations and generate additional savings when needed. For example, if the buyer contributes to a major portion of the supplier’s business at any point in time, he/she can negotiate a better deal even when the market prices are high. Or if the supplier is operating only at 40% of his plant capacity, there is an opportunity to negotiate for cost reduction irrespective of the market index.
5. Sourcing alternate products or producing in-house
In certain cases, when the price of a commodity is highly volatile in the short term and has been in an upward movement over the long term with no indication of when the price is going down, the above discussed strategies or methods may not be viable.
External disruptions like war, trade sanctions, high trade tariffs, political turmoil, and similar situations disrupt supply chains and make forecasting extremely difficult and halt businesses completely as seen during the COVID-19 pandemic lockdowns.
Enterprises can also plan for alternatives to risky commodities or create in-house sourcing options to protect themselves against these risks. Although the one-time setup cost and the associated R&D costs would be high, the ultimate ROI could greatly outweigh the investment.
Key for successful implementation of these strategies
In order to implement these methods successfully, firms need to consider them as a part of a comprehensive risk management strategy to protect the bottom-line. A thorough understanding of the risk involved and consensus from different departments across the supply chain is needed.
Another critical component required to implement these strategies effectively is accurate forecasting. In recent times, the advancements in artificial intelligence have improved the accuracy by a great margin. A combination of Deep Learning & Natural Language Processing (NLP) models are used to analyze both structured and unstructured data to generate invaluable insights.
Primitive forecasting models were limited in terms of processing the information from news, social media posts, emails, and messages that have a high impact on commodity prices. Now, NLP models have helped interpret the language in these media and gather data points with techniques like sentiment analysis thereby improving the accuracy of commodity price forecasting drastically.
It is imperative for sourcing teams to find effective solutions for the risks posed by the fluctuating prices of commodities. With the advancements in AI/ML technology, it is possible for sourcing teams to strategize their sourcing contracts and create effective plans for managing their commodity purchases.
