Workshop 198 Report: Strategy Dynamics; Introduction and New Developments, Presented by Kim Warren

The key takeaway from Kim’s workshop was that combining core System Dynamics (SD) theory and value curve analysis* facilitates strategic decision-making that impacts performance over time.

Kim’s “crash course” (probably not a good use of the cliché given that Kim’s examples involved airlines) had four tenets. First; performance is a function of resources, second; changes in resources are themselves a function of existing resources, third; use value curve analysis to manage key resource flows and attributes (making use of co-flows), fourth; managing resource attributes and resource flows with an appreciation for resource inter-dependencies determines performance over time.

To illustrate the approach Kim used an airline example. “You have to start from where they are thinking, so for analyzing an airline you need to ask ‘how do airlines make money?’ “. Kim instructed; start by laying out the income statement where airline revenues are generated from ticket sales and food service while costs are related to marketing, airport fees, planes, maintenance and customer service costs ‘Revenues minus costs’ lead to two measures of performance; free cash flow over time and the present value of free cash flow over time. Each revenue and cost item must be linked to a resource. Ticket sales and food service revenues come from customers, a key resource. Marketing, maintenance and customer service costs relate to the firm’s staff resources. Planes are resources. Kim drew this example on an overhead to graphically represent the connection between resources and performance.

After assigning each group to select a firm or business to model on their own, he presented a more elaborate example highlighting resource inter-dependence and value curve analysis using RyanAir as the example. For this example Kim provided an overhead view of a MyStrategy stock-flow diagram with integrated graphs for each stock and flow variable. The RyanAir model resources were customers, routes, aircraft and staff. The model had four ‘management decision variables’; the inflow rates to three resources (staff, routes & aircraft) and an auxiliary variable ‘average fare per journey booked’. The ‘average fare per journey booked’ management decision variable was used to influence a key customer resource attribute, ‘average journeys per customer per year’.

Once the stock-flow diagram was explained, Kim went on to show how value curve analysis can be used to co-ordinate adjusting all the management decision variables. The essential question value curve analysis helps answer is; ‘Why do customers want to fly on RyanAir? ‘. The value curve analysis involved five factors customers use to assess the value of purchasing RyanAir tickets; ticket price, number of connections to other flights, choice of leisure destinations, add-on services and reliability The value curve factors were used to co-ordinate adjustment of decision variables in order to manage the customer resource inflows and outflows. Kim stressed the need to manage resource inflows and outflows separately. It was mentioned that value curves can also be used to manage staff resources by helping answer; ‘why do people want to work for us? ‘. Bottom line; value curve analysis surfaces the causal factors that drive resource inflows and outflows.

Three groups that presented their models at the workshop provided insights. One group that modeled a gold mine highlighted the point that not all firms have ‘customers’, in the case of the gold mine, they had no direct customers ; all gold mined was in fact sold before it was mined, into a market for gold. Another group that modeled a regional insurance company made a point that the flow from potential customers to customers does not depend on value curve analysis directly because potential customers don’t know enough about the insurance firm to make such assessments; instead that flow depended indirectly on value curve analysis via the firm’s reputation that resulted from the firm’s actions to meet its customer’s value curve preferences. A third group that modeled Boeing’s defense business brought to light the fact that the U.S. Government actually provides its value curve criteria (NASA TRL criteria) on a web site. This transparency of the customer’s value curve criteria could be useful to practitioners and academics alike. The Boeing model also addressed the issue of whether or not to include a supplier as a resource. Kim made the point that if the supplier’s products were highly sought after and competed over, then it would make sense to include them as a resource within the model. Finally it came to light in the Boeing model group’s work that revenues from both aircraft orders and R&D services were both referred to as ‘contracts’; highlighting how the model can be much more communicative to users when their own terminology is used in the model itself. That is the model included a supply chain of ‘contracts’ as resources that generate revenues instead of aircraft orders and R&D services orders.

C. Michael Reilly

*‘Value curve analysis’ is based on conjoint analysis from statistics and is used widely in marketing.