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In the year 2000, supply chain optimization software companies were hot. Optimization was the new Messiah that would lead supply chain practitioners out of the desert. Wall Street agreed. They put a high valuation on i2 Technologies, the leader in supply chain planning and optimization.
In that year, i2’s leading competitor, Manugistics, acquired Talus, a price optimization solution. Price optimization based on price elasticity models is optimization on steroids. Price elasticity shows the responsiveness, or elasticity, of the quantity demanded of a good or service to a change in its price. Mathematically, it shows the percentage change in the quantity that is demanded in response to a one percent change in price.
At that time, advanced statistics, not optimization, were being used for demand forecasting. Price elasticity promised to help move industry beyond forecasting demand to profitably shaping demand. And the core supply chain process, Integrated Business Planning, seeks to profitably balance demand and supply. Price optimization promised to greatly improve that process.
In that time period, revenue management tools were being used by the Airline and Hotel industries. But its use had not spread beyond those two industries.
At the time, the Manugistics acquisition of Talus seemed like a great move. And for a few years, I pestered Manugistics to allow me to talk to customers that had implemented this solution in other industries. Manugistics could never provide a good reference. And over time, the discussion of price optimization in supply chain management dwindled. I always wondered why.
In a discussion with Andres Reiner and Craig Zawada, the CEO and Chief Visionary Officer respectively at PROS, I learned why. PROS describes themselves as a “revenue and profit realization solutions” provider. In mathematical terms, they describe what they provide as not being price elasticity software, but rather a “win rate elasticity” solution. And PROS has managed to successfully implement its software to more than 40 industries.
Mr. Zawada explained that when companies that sell products or services to other businesses – a business to business (B2B) environment – those companies don’t have enough variation in their pricing for a traditional price elasticity calculation. Further, business to consumer markets have much more price elasticity than B2B. For a core commodity, a company is going to buy the product; it is not a buy/not buy situation like in B2C. And while pre-buying for volume discounts is possible, that does not much affect the total volume bought over time.
PROS uses a variety of transactional, customer and product attributes to create micro-price segments. As one example of the kind of logic used, a customer buying a commodity that represents a very small portion of their total spend would not be as sensitive as if were a significant percentage. In the food industry, food prices are affected by commodity prices, so PROS seeks to forecast commodity prices, and use the relationship of the commodity price to the finished product price to help forecast how likely a customer is to buy a food product at a certain price. PROS also recommends which finished products will optimize revenue. For instance, for milk producers, it may be better to produce skim milk or yogurt instead of whole milk or cheese. Or for a beef producer, depending on the season, it may be smarter to cut roasts rather than steaks to maximize revenue.
In the year 2000, supply chain optimization software companies were hot. Optimization was the new Messiah that would lead supply chain practitioners out of the desert. Wall Street agreed. They put a high valuation on i2 Technologies, the leader in supply chain planning and optimization.
In that year, i2’s leading competitor, Manugistics, acquired Talus, a price optimization solution. Price optimization based on price elasticity models is optimization on steroids. Price elasticity shows the responsiveness, or elasticity, of the quantity demanded of a good or service to a change in its price. Mathematically, it shows the percentage change in the quantity that is demanded in response to a one percent change in price.
At that time, advanced statistics, not optimization, were being used for demand forecasting. Price elasticity promised to help move industry beyond forecasting demand to profitably shaping demand. And the core supply chain process, Integrated Business Planning, seeks to profitably balance demand and supply. Price optimization promised to greatly improve that process.
In that time period, revenue management tools were being used by the Airline and Hotel industries. But its use had not spread beyond those two industries.
At the time, the Manugistics acquisition of Talus seemed like a great move. And for a few years, I pestered Manugistics to allow me to talk to customers that had implemented this solution in other industries. Manugistics could never provide a good reference. And over time, the discussion of price optimization in supply chain management dwindled. I always wondered why.
In a discussion with Andres Reiner and Craig Zawada, the CEO and Chief Visionary Officer respectively at PROS, I learned why. PROS describes themselves as a “revenue and profit realization solutions” provider. In mathematical terms, they describe what they provide as not being price elasticity software, but rather a “win rate elasticity” solution. And PROS has managed to successfully implement its software to more than 40 industries.
Mr. Zawada explained that when companies that sell products or services to other businesses – a business to business (B2B) environment – those companies don’t have enough variation in their pricing for a traditional price elasticity calculation. Further, business to consumer markets have much more price elasticity than B2B. For a core commodity, a company is going to buy the product; it is not a buy/not buy situation like in B2C. And while pre-buying for volume discounts is possible, that does not much affect the total volume bought over time.
PROS uses a variety of transactional, customer and product attributes to create micro-price segments. As one example of the kind of logic used, a customer buying a commodity that represents a very small portion of their total spend would not be as sensitive as if were a significant percentage. In the food industry, food prices are affected by commodity prices, so PROS seeks to forecast commodity prices, and use the relationship of the commodity price to the finished product price to help forecast how likely a customer is to buy a food product at a certain price. PROS also recommends which finished products will optimize revenue. For instance, for milk producers, it may be better to produce skim milk or yogurt instead of whole milk or cheese. Or for a beef producer, depending on the season, it may be smarter to cut roasts rather than steaks to maximize revenue.