Today, five critical factors directly affect a chemical company’s profitability. Products that had been specialties have become commoditized, resulting in increased competition and lower prices. Margins, which generally are based on a percentage of the selling price, tighten when prices are the highest because raw material and energy costs also tend to be highest then. Long lead times in building new plant capacity create supply swings. Dynamic raw material and energy costs make it difficult to consistently meet profit goals. Overseas competitors with lower costs are penetrating the domestic chemical market.
However, companies can minimize the deleterious effects and even improve profitability if they manage these factors properly. The key is making sound pricing decisions through price management. The necessary data to use this approach already exist in ERP [enterprise resource planning] systems.
Before discussing these factors, let’s examine what is meant by price management. It consists of: pricing analytics, deal management, price execution, forecasting and optimization (Figure 1). Companies are best served by focusing on pricing analytics, deal management and price execution first, preferably in tandem, and forecasting and optimization later. Price management builds upon sound business and pricing fundamentals already in place. Without them, price management will just allow poor processes to work faster and you will reach the same undesired outcome, just more efficiently.
This series of methods allows better insight to be gained from data. Pricing analytics can identify areas for action. One example is developing a better understanding of customer “willingness to pay” (WTP). This is a relative statistical quantity that is based on the price elasticity of each individual customer. Some customers are willing to pay more for a product than others. This can be due to the value your product creates in their processes, their relative profit margins or other valued services you provide. Regardless, by understanding a customer’s WTP (and tracking how this changes with time), you can set a price closer to that customer’s reservation price or limit and tailor your product offering to the things they value. This can lead to higher profits and more-satisfied customers than possible with uniform pricing.
Pricing analytics also can help by allowing for price differentiation among customers based on market segmentation, additional research and development costs, supplemental laboratory work, shipping costs, customer service required and other less-tangible product attributes. Figure 2 illustrates how market segmentation can be used to increase profitability.
Deal management involves structuring workflow to ensure profitable actions are taken. One example is managing of quotes. A key aspect is funneling all quotes before being sent to customers through the same channel for approval to provide a check on their profitability. Deal management also includes checks on customer contract compliance. For example, a given customer may have been quoted prices assuming certain minimum volume commitments. If that volume is not being ordered, then charging higher prices is warranted. Figure 4 depicts a common scenario. A manufacturer is willing to offer a greater discount from list price the more a customer purchases, as indicated by the line. However, because of the difficulty in managing data, firms end up discounting in the random manner shown by the discrete data points. Deal management, by addressing such inconsistencies, can help improve profitability when market conditions pressure margins downward.
Price execution relates to how pricing strategy is applied. It should include development of customer-specific price lists that can be readily distributed to salespeople and customers and frequent updating of these lists to reflect current conditions. This enables firms to act promptly based on changes in raw material costs, the calculated WTP and dynamic market conditions. It improves profitability by ensuring that price lists are current and tailored to the specific reservation price of each customer.
Now, with an understanding of price management, let’s examine ways it can be used to address those five key factors that affect profitability.
The lifecycle of a chemical product usually moves through a series of stages. First typically is a period of research and development or, at a minimum, process development associated with commercializing the product. This generally results in patents or trade secrets. Margins and price usually are high because limited competition allows the company to get full value for the product. With time, production becomes more efficient, reducing costs (although not necessarily raw material costs). However, also with time, improved products win market share. And, at the end of the patent’s life, other companies may be able to offer the product at a lower price. Because of this, it is important that a firm be able to recoup its research and development costs prior to the end of the patent’s life.
Afterwards, the maker faces eroded margins because of tougher competition and the possibility that market changes will reduce or even eliminate the need for the product. These factors transform a once unique, differentiated product into a commodity. This is called the “Wal-Mart Effect” because manufacturers now compete primarily on price. Other differentiators such as sales relationships and plant qualification trials required for new suppliers may help retain customers. However, these differences dwindle with time and, even if the original firm maintains its customers, prices and margins will suffer.
Commoditization is a reality that cannot be changed. However, a company can take steps throughout the product lifecycle to maximize overall profitability by applying price management.
Higher input costs
Higher crude-oil and other feedstock and energy prices directly increase costs but can have an indirect impact, too. For instance, rising crude-oil prices can depress demand by both industry and consumers. Nevertheless, there is still an opportunity for improved profitability. When raw material prices rise, customers understand and anticipate having to pay more for products. This makes it relatively easy to pass along price hikes. The question then becomes: How fast and to what level should prices be raised? If prices are raised too slowly, revenue is lost. However, if prices are raised too quickly, customers may significantly reduce demand, use a substitute product or switch to another supplier. As shown in Figure 5, there is an optimal price profile curve (i.e., price versus time) that will maximize profitability. The other variable is the ultimate price that should be achieved. This, too, will affect profitability — possibly for a long time after the new price floor is established.
The perils of long lead times
Putting new capacity in place is expensive and often takes years. Such a move represents dual risk. Failure to build capacity can result in a loss of market share and, subsequently, pricing power. However, the extra capacity can lead to excess supply. Complicating the situation, forecasts can be wrong, market conditions can change after a major commitment has been made, and competitors can make the same decision to build additional capacity.
If excess supply exists after bringing new capacity on-line, companies often drop prices so they can keep plants running at reasonable rates, although hurting margins. This problem has been around for decades in the chemical industry and is not going to disappear. Working to better understand competitors’ behavior as well as improve forecasts of future product demand can help deal with it.
Solution. Many companies still do not develop rigorous scientific forecasts. Instead they have business unit managers, who generally have chemistry or engineering backgrounds, prognosticate. It is preferable to supplement such predictions with a system developed by mathematicians and statisticians expert in forecasting. Such systems have been used with great success for both commodity and specialty products to predict both supply of raw materials and product demand. These forecasts can lead to better control over inventory levels, improved management of product volumes, more reliable and lower cost acquisition of raw materials, and even insights into potential behavior by competitors. They certainly offer an opportunity for improved profitability.
One common approach to forecasting is to ask potential customers about their future product needs. However, the quality of the information and the uncertainty in the predictions often isn’t clear. So, it is valuable to also perform statistically based forecasts. A variety of options exist, ranging from basic linear regression or curve-fitting of historic data to more sophisticated Bayesian Hierarchical forecasting. That technique uses a probability distribution based on historical data to forecast a future outcome. As time progresses, the probability distribution is updated as more data become available. In effect, the forecast learns and adjusts for market changes.
Volatility and profit goals
The challenge in setting and meeting profit goals is that their timescale exceeds that of the market dynamics. For example, over the past year, resin manufacturers have been raising prices on a monthly basis due to a combination of higher raw material costs and increased demand from China. Under such conditions, profit forecasts become complicated, resulting in increased error. Profit goals are based on those forecasts and are directly impacted by the higher error.
This can make good management look bad by not consistently meeting profit goals despite solid decision-making. The problem is that the decisions are not based on the most current information in the most useful form. Minimizing volatility and error in profit forecasts allows more realistic views of the performance of business unit management. If profit goals are better understood, then corporate-wide strategies can be better developed to focus on selling and producing the most profitable products.
Solution. This can be addressed using pricing analytics for aggregation of data across business units and functional silos to arrive at historical summaries. Additionally, pricing analytics ensures that profitability is calculated consistently across all business units. Having consistent data that are summarized in automatic systems minimizes human errors. Because this historical information is more reliable, goals can be established using a more accurate baseline.
Once historical information is assembled into an accurate format, forecasting techniques can bracket reasonable expectations of future profits. Sensitivity studies then can be performed to examine the effect of market conditions. This allows for forecasts based on solid data and sound statistical approaches, not gut feel.
A critical challenge affecting the industry is growing competition from overseas firms. These companies frequently have fundamental advantages such as lower cost of labor, less stringent environmental regulation, advantaged proximity to feedstock and governmental financial support. Because foreign competition is so significant, the bulk chemical industry may follow the path of the pulp and paper industry. Within the past five years, a large number of domestic pulp and paper companies have closed plants or gone out of business because of strong foreign competition. Like chemicals, pulp and paper production is very capital intensive. Only those with the highest margins survive.
Solution. To remain competitive in the global market, U.S. chemical companies must do at least one of three things. First, serve a domestic market out of reach to a foreign competitor, e.g., compressed gases. (It is extremely uneconomical to transport liquid nitrogen or cylinders of carbon dioxide from China.) Second, improve business processes so that margins are higher than competitors. Third, exploit non-economic product differentiators such as quality, customer service, inventory management, etc.
The first point may be difficult to change depending on the core competency of the company. However, pricing analytics can help identify low margin products where a company is not as competitive. Strategic decisions can be made based upon these data about whether to drop these products.
The second point can be addressed using optimization. This technique relies on forecasts and thus comes last. Optimization utilizes mathematical techniques such as linear programming, nonlinear programming, multiple integer programming and dynamic programming. It can improve profitability by: minimizing turnarounds associated with product switches; effectively allocating product manufacturing across multiple locations; giving production priority to higher-margin products; and optimizing product yields based on raw material, production and product prices. This approach does not address any fundamental advantages of foreign competitors but does ensure that domestic facilities operate as profitably — and thus as competitively — as possible.
The third point, non-economic product differentiation, can be tackled using pricing analytics to analyze customer-specific costs on a product-specific basis.
A way forward
Price management allows companies not only to improve their profitability in spite of challenging market conditions but also to create a strategic competitive advantage. When chemical firms become more efficient and better direct their resources, everyone wins, especially the shareholders.
Dr. Jeff Kabin is a senior business consultant with PROS Pricing Solutions, Houston, Texas. E-mail him at [email protected].