How To Minimize The Impact Of Raw Material Prices On Your Margin (Part 2)

Anand Sundar

This is the second part of a two-part blog on the levers used by leading chemical companies to manage the risk and impact of raw material price. Part 1 discussed advanced supplier management, and part 2 continues with the importance of increasing visibility with digital technology and risk management strategies.

Using digital to increase supply chain visibility

Visibility across the supply chain is an essential component for managing raw material price increases. Product shortages, unplanned downtime, and external factors could disrupt suppliers, contract manufacturers, and even customers’ production schedules, despite supply contracts. This typically drives procurement to make spot buys where the plant material planners find the quickest source of supply to avoid production outages. Better supplier collaboration and visibility can reduce the costs associated with supply shortages.

  • Supplier collaboration: The use of digital technology like IoT sensors, business networks, and blockchain can enable and automate collaboration thereby eliminating cost. Advanced planning systems can automatically aggregate the raw material forecast by region and share with the suppliers through business networks. The ability of suppliers to meet this forecast can also be determined instantly with collaboration. The ability to automatically choose alternate suppliers based on price and quality is also available through business networks. The exposure created through spot buys can be greatly limited through process governance and increased visibility. Many companies are also showing an increased interest in blockchain for procurement of raw materials to increase visibility, collaboration, and price governance. Machine learning algorithms are being used in the network to automatically do price comparisons for utilities and other common commodity purchases.
  • Advanced analytics and simulation: We find that chemical companies are investing more in advanced planning and analytics tools that can run simulations and what-if analyses to determine the impacts of price changes and develop mitigation plans. Chemical companies are increasingly focused on sales and operations planning where the impact of raw material pricing is discussed. Advanced optimization algorithms are used to simulate various make-vs.-buy business scenarios and to understand trade-offs in those decisions. There is also increased interest in additive manufacturing as a means of lowering long-tail inventory, especially for maintenance, repair, and operations (MRO) items. Manufacturing networks enable sharing components’ designs with a local additive (3D) manufacturer that can deliver on demand. Advanced algorithms for predictive maintenance are integrated with scheduling, so MRO items can be ordered and contingent labor scheduled automatically based on downtimes. Some chemical companies are also beginning to leverage cloud platforms to gather large amounts of data and use digital technologies like artificial intelligence and machine learning to monitor and predict market conditions and price surges. Incorporating market insights like shipment delays and supplier production outages is key in predicting demand surges that could contribute to price increases.

Risk management

Raw material price is one of the key risk factors that could drastically increase supply chain cost. Companies focusing on exemplary margin performance must have the competency to mitigate risks and sustain price increases. There are several ways chemical leaders address this.

  • Embrace analytics: Leading chemical companies monitor oil price indicators and embrace exceptional analytical capabilities to be alert to impending fluctuations in oil as soon as possible. They must also know how various oil price scenarios will impact their portfolio. They periodically review the microeconomics of the cost structures and price mechanisms for their products on a chain-by-chain and region-by-region basis, establishing both the magnitude and timing of the impact from oil price movements and other key raw materials.
  • Hedge: Producers should take decisive action to understand what is the “right” amount of risk for their business, then eliminate undesirable risks by shifting them through contracting, financial hedging, and internal operational options (for example, sourcing alternative feedstocks). Integration and visibility will reduce the lag of recovering price over raws when practicing the most common means of price risk management through buying and pricing off published prices. In terms of direct hedging, although there are a limited number of direct hedging instruments on exchanges like NYMEX and ICE, correlations with more liquid instruments (crude, naptha, etc.) make proxy hedges an effective means to minimize some market risk.
  • Don’t forget foreign exchange: Recognizing FX exposure is extremely important, especially for global players. We find that leading companies leverage their distribution plan across the company to aggregate the total volumes of raw material purchases in every country to effectively hedge the currency.

Effectively utilizing the above levers can help chemical companies manage the volatility in raw material prices using advanced supplier management, collaboration, increased visibility, and risk mitigation. These advanced strategies can also help build a lean, responsive, and agile supply chain.

Learn more about using these levers to minimize the impact of raw material prices, supplier collaboration, compliance, visibility, and risk mitigation.

Anand Sundar

About Anand Sundar

Anand Sundar is an Executive Advisor of the SAP Chief Customer Office at SAP. He is passionate about using digital technology and business process excellence to extract value for customers. Anand previously worked for several years in various industries in supply chain operations and as a consultant and trusted advisor. He is a certified SCOR professional (SCOR-P) and part of the advisory board that developed the SCOR operations reference model and framework for standardization of supply chain processes.