Bargaining with Bayesian game theory
A Nordic leader in consumer goods was suffering from serious profitability challenges in a key category and needed an immediate commercial turnaround to bring the category back to health. A substantial profit improvement was achieved by harnessing Bayesian game theory to dynamically optimize contract pricing.
Plummeting prices had destroyed profitability in a key category
The company was suffering from serious profitability challenges in a key category and needed an immediate commercial turnaround to bring the category back to health. Aggressive price competition, collapsing quality premiums, and the expanding bargaining power of dominant buyers had destroyed profit margins. Consumer prices and market volumes, however, had not budged. To break out of the challenging situation, management wanted to implement a more strategic, structured, and analytical pricing approach and create a predefined pricing plan for the upcoming sequence of contract negotiations.
A dynamic pricing plan would make contract negotiations a breeze
We dug deep into our economics and analytics toolkit to develop a unique pricing approach that would 1) account for the intricacies of the production set-up and the competitive situation, 2) dynamically optimize pricing under substantial uncertainty about competitor behavior, and 3) yield a pragmatic game plan that provides clear guidance throughout the entire contracting sequence. Working closely with the sales and controlling organizations of our client, we created a thorough understanding of the strategic game being played. Our solution used Bayesian game theory to deliver an optimized pricing plan for all upcoming negotiations.
The approach produced an immediate bottom line payoff and a lasting capability uplift
The initiative had an incredibly short payback period as the new approach delivered a substantial profit improvement right away from the initial contracting period. In addition to the immediate bottom line impact, the initiative helped improve the client’s analytical and pricing capabilities, which should drive profitability in the future.
GAMifying wholesale pricing
A wholesaler was having trouble managing and developing pricing across a huge number of customers and SKUs. August was able to deliver newfound clarity and actionable insights from an advanced profitability analysis.
Huge price and profitability dispersion were difficult to explain away
As a wholesaler, our client had been forced to contend with huge numbers of customers and SKUs across a range of product categories and locations. It was having difficulties managing and developing pricing with such a huge amount of variety. Pricing and profitability varied greatly across customers and SKUs, but top management didn’t understand, what was creating the differences in profitability. Numerous explanations and excuses were given, but the underlying reason behind the variance had been evading top management. To advance the pricing development agenda, a more structured and holistic approach to profitability and pricing analytics was badly needed.
The vast complexity was tackled with a combination of advanced statistics and business insight
The August team brought its statistics A-game to the project, applying generalized additive models or GAMs to solve the analytics challenge. Flexible multivariable modeling allowed the team to disentangle unexplained deviations in price and profit margin from typical pricing differences explained by the data. This allowed the team to detect underperforming products and customers that needed special attention from sales. Moreover, the modeling approach allowed us to decompose the predictable variation in customer profitability into differences explained by the products purchased on one hand and by different transaction and customer characteristics on the other. The analysis revealed, for example, that discounting was heavily driven by product-specific volumes rather than total purchases, which incentivized cherry-picking rather than the concentration of purchases. This was poorly aligned with the wholesaler’s value proposition.
The approach allowed the company to accurately pinpoint pricing problems
The application of advanced analytics to shed light on product and customer profitability gave our client a better grasp of their pricing performance and produced actionable fact-based insights. This laid a solid foundation for fixing the pricing of poorly performing products and customers and for developing the overall pricing logic.
Keeping shelves stacked and customers happy
A specialty retailer had suffered from empty shelfs in the previous high season. The reasons were unclear, but the company could not afford another lost peak season.
Empty shelves were leading to unsatisfied suppliers and customers
In the previous high season, the specialty retailer had seen many shelf places empty out without warning. The demand forecasting and replenishment cycle was not filling the shelves according to customer demand. Suppliers were claiming that they were losing sales because of the shortage and customers were unhappy to find that stores did not have the product they wanted. The Head of Supply Chain suspected that the challenges might be related to system functionality and how the process was set-up, but a definitive answer seemed elusive.
Simple changes to parameters went a long way to fixing the issue
August built a detailed understanding of how the ordering system created demand forecasts and gave suggestions for replenishment orders. This required a surprisingly detailed understanding of the algorithms used by the system. Also, the human process around the system was broken down into pieces. A detailed understanding of the dynamics revealed several options to improve shelf availability. Improving demand forecast accuracy turned out to be only a small part of the problem. Changing the parameters in the replenishment model was much more effective at improving shelf availability in the peak season.
Several longer term improvements to the processes and practices were suggested as well – these would have a more indirect impact on the original target, but would go a long way to averting similar problems in the future.
Improved availability contented customers and satisfied suppliers
Shelf availability improved drastically for the next peak season. An internal discussion about the need to develop critical systems was put on hold and focus was placed on developing the human practices around the ordering process.
Optimizing a complex supply chain
A large process industry company needed to improve its service to customers but keep its complex supply chain efficient. Together with the client, August designed a supply chain concept that improved delivery promises to customers. August used Advanced Analytics to vastly improve inventory content and delivery routing.
Delivery performance was insufficient and competitors were creating pressure to improve availability
The company, an industrial manufacturer with a Europe-wide processing network, needed to improve the performance of its supply chain. Sales felt the pressure as competitors were offering good availability, but the supply chain team thought that inventories in the network were not healthy. Sales and the supply chain lacked a common understanding of what an efficient and competitive set-up would look like.
New service levels and product routings were just the start for improving the supply chain
The joint Client-August team benchmarked the competitions’ current delivery promises and planned an improved service promise to up the ante. A series of Advanced Analytics approaches were needed to get to the bottom of the issue. Savings were identified in product routing through a careful analysis of the optimal supply network. Inventory content needed to be revised as well, when genetic algorithms revealed poorly functioning inventory levels. Implications to order-to-delivery process were planned as well.
New services underlined a completely revitalized supply chain
Our client was able to move forward on a commercial launch of its new delivery time promise. Changes in the inventory content were implemented and part of the deliveries were rerouted, which dramatically improved overall cost efficiency. The analysis created a basis for developing automated stock replenishment practices and for renegotiating deals with subcontractors.