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Securing better outcomes for plastics sourcing

ChAI
Published by ChAI
Aug 2 2023
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A large plastics converter saved over 10% on feedstock costs for a 6 month period using ChAI’s forecasts to identify the best time to engage with suppliers.

Even in conventional years volatility in commodity prices can reach highs of 30%* (in 2020 the peaks for some key commodities were as much as 207%). Given that commodity spend generally represents up to 70% of revenues for major industries**, it is obvious that identifying the best time and price at which to secure key commodities is pivotally important for many businesses.

In this short case study we discuss one example where a large materials manufacturer was able to secure more margin by identifying the best time-tobuy with ChAI’s explainable predictions on benchmark prices that were highly correlated to the organisation’s raw materials’ costs.

*Oliver Wyman
**McKinsey

Ch AI Case Study Plastics

Challenge

ChAI was engaged by a large Asian plastics converter in June 2020 who recognised the importance of incorporating artificial intelligence (AI) in their procurement and sourcing processes. The business is a key link in the plastics supply chain for several producers and a number of large conglomerates in FMCG and pharmaceuticals. The Managing Director (MD) of the converter was keen to explore how they could secure more margin by identifying the best time to buy LLDPE feedstock from their suppliers, before converting it into packaging products.

Problem

In 2019 the converter had experienced strong results, however, these had been somewhat degraded by the first half of 2020 and at the point of introduction were something of a distant-memory. Although the downturn in performance was partly due to Covid 19, it was also due to the fact they had made a series of errors emanating from their sourcing strategies. These errors had compounded to maleffect. Moreover, due to constraints inherent in their operating model, they were limited on the time horizons between which they could place orders for delivery. They needed to confirm orders three months in advance of delivery. While this was impacted by the orders they received from their customers, they also had some room in which to maneuver. They were able to hold feedstock that had been procured at a favourable price for up to three months before selling the finished packaging product at a pre-agreed date and price. Although the business was not on the brink of bankruptcy, they also recognised the necessity of an active and agile approach to sourcing based on consistent and explainable intelligence.

Solution

The MD directly began incorporating ChAI’s daily predictions into her sourcing workflow for LLDPE. As is invariably the case the quotes that the business received from their suppliers were strongly correlated with the prices on exchange which then gave them a fair, third-party market price as a reference. This signal enabled the MD to benefit from crucial insight, prompting her to place an order when the plastics price was at a relativelow. Through ChAI’s predictions the MD was able to identify the best time and price at which to place feedstock orders. Whatsmore, ChAI’s emphasis on explaining its predictions through AI-generated commentary and Influence Insights empowered the MD and her team to negotiate from a position of strength, giving them the ammunition to counter supplier conditioning and justify their own counter-proposals.

The Outcome

After monitoring ChAI’s predictions through an initial trial period and reviewing ChAI’s extensive backtest performance data, the plastics converter was able to employ ChAI’s forecasts to great effect. Using ChAI’s strongly correlated AI-powered price predictions, the MD and her team identified the best period to purchase within (between 5th October and 26th October 2020).

Consequently they increased their order for this period so that they secured 70% of their coverage for Q1 2021 between these dates.

Had this decision been delayed by two weeks, the price would have been suboptimal at 11% higher than the relative low identified according to ChAI’s predictions. Furthermore, if the MD and her team had waited until early December to act the price would have been over 20% higher. Given the thin margins and significant volumes built into their operating model, such a price swing would have had a very negative impact on the businesses fortunes. Thankfully, ChAI enabled the MD to avert this potential outcome. This is just one example of the significant value ChAI helps its customers to capture. On average, ChAI’s predictions assist sourcing specialists to secure up to 25% more margin and RoIs of more than x70.

Ch AI Case Study Plastics2

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