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.
When crude oil prices are low, many chemical manufacturers benefit because their raw material prices are correspondingly low. Additionally, low crude-oil prices generally stimulate the economy because they help increase corporate profits and consumer spending. However, customers recognize that costs are lower and, in turn, demand lower product prices. Because margins are high, manufacturers drop prices to avoid losing market share. Here, too, an optimal price profile curve will maximize profitability. A company needs to lower prices at a rate to avoid losing customers but not so quickly that it leads the price drops. Profits decrease because margins are based on a fixed percentage of the selling price.
Solution. Pricing analytics effectively uses real-time data to provide insights on prices, costs and margins as commodity indices change. Using all of this information in concert can allow firms to become more profitable during dynamic market conditions. Rather than passively having profits shaped by market forces, companies can be more responsive and even proactive in increasing profits.
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.