Our back-of-the-envelope insight was the pot at the end of our story-bow.
In 2001, working for a consulting company called BiosGroup, in Santa Fe, NM, I was put in charge of a project for a liquid air company. The objective was to develop software to optimize production and delivery of liquefied air products such as liquid nitrogen and liquid oxygen. We were hired by the company’s Production Department.
I listened to staff members from both the Production and Delivery Departments as each told their liquid air story. I fashioned diagrams and took notes to capture their collective narrative, integrating their individual stories into a single shared story. At the time, I didn’t fully appreciate that we were constructing both a silicon based computer optimizer and a carbon based mental simulation. Each time I reflected our accumulated story back to the staff, for their verification and adjustments, a community was growing around our shared story simulation. Though the staff of both departments had been living their separate parts of this story for years, this collective story community had never been formed.
On the silicon side, we created a rich computer optimizer employing original ant foraging algorithms. Our approach was innovative and was later featured in an issue of National Geographic. However, the optimizer was never fully deployed by the client. You might recall that we were hired by the Production Department. The optimizer focused on delivery which was housed in the Delivery Department. You can imagine the complicated human problems this stimulated. Deployment of our computer automated optimizer would replace a large number of Delivery Department manual routing jobs. The cost for the development of this specialized code was substantial. I felt uncomfortable when I recognized that it may not be deployed.
Then it happened. Our other optimizer, our co-created human simulation, delivered its insights. The savings from these insights more than paid for our computer optimizer development. Our shared story revealed a key inefficiency that the computer optimizer would have never directly detected.
Our insight was a subtle conflict between two rules. The Production Department had a rule that required delivery trucks to be filled completely whenever they hooked up to the storage tanks. Positioning tanker trucks, connecting them to a fill hose, and then detaching them, all took valuable time. So it was good policy to fill the trucks completely to reduce the number of fill ups.
The Delivery Department had a rule of their own. They paid a bonus to truck drivers for delivering all of their load and coming back empty. This policy was based on the perception that delivering more liquid air per day was better, and that leaving the truck sitting overnight with undelivered product was a waste.
So what was the insight? The delivery truck drivers would fill up at the storage tank, they would make their primary delivery, often requiring less than a full truck load, and then they would drive to one or more customers that had large tanks, that really did not need more product, to offload the rest of the load so they could return empty. This was the key: at the customer site, connection and disconnection of a truck tank took, on average, about an hour. The unnecessary deliveries were a repeated waste of time and driving. Reducing the number of wasteful deliveries amounted to millions of saved dollars per year.
When I showed this result to both departments, since we now held our shared story simulation in common, they quickly saw the difficulty. Our insight was the pot at the end of our story-bow. The benefits of this insight could easily be calculated on the back of an envelope. However the mental simulation that made it possible was complex and extensive. Our shared story was our simulation, and our simple result was its output.
Since this experience, I’ve been fortunate to engage some clients in purely shared-story based problem resolution. I believe that problem resolution is more effective, and more flexible, when the collective narrative is at the center. A shared story resolution costs much less to build. If a story resolution is engaged first, and if that story evinces value in developing a computer simulation, then the computer simulation will be more meaningful and effective. I’ve been involved in multiple computer simulation projects where I believed that: the real value was the requirements gathering conversations that the computer simulation inspired. This may sound odd coming from someone who dedicated great effort to develop his computer simulation skills. Problems are products of human consciousness, and they are experienced in how people feel. A human story based resolution can bring life enriching energy through community. What better resolution can we ask for?