This post is by James Brenza co-author of the Innovative Leaders Guide to Implementing Analytics Programs.
As we hurriedly boarded the plane for our 6:00 AM departure, we sleepily threw our carry-on items in the overhead compartments and hoped for a calm, restful flight. The challenges started with the pilot’s innocuous announcement that we may have a short wait for de-icing. That short delay became a 30 minute line followed by a 90 minute wait for de-icing equipment repairs. Despite the pilot’s polite updates every 15 minutes, sleep was a lost opportunity due to the stress of missed connections and passenger calls to customer service to rebook connecting flights. Have you ever had a travel misfortune like this? Doesn’t it seem like the slightest disruption anywhere in the continent can disrupt the flow for the entire day (or longer)?
As seasoned travelers embrace winter’s grip, we brace ourselves for a plethora of difficulties. We all seem to encounter flight cancellations, late arrivals, insufficient or ill-equipped rental cars and overbooked hotels. I always find it interesting that other travelers complain that it wasn’t always this bad. Have you ever considered what changed? Is it possible that with more data and stronger optimization models that we’ve over-optimized based on profitability?
When creating optimization models, one of the very first steps is to pick an outcome. If the selected outcome includes only profitability, full asset utilization, minimal inventory or razor thin times to repurpose assets (e.g., airplane turnaround time, restaurant table turns per shift), we can certainly drive to that outcome. But if you don’t consider the ecosystem and especially the customer, you can actually sub-optimize the business ecosystem with excessive optimization. A few simple examples include:
- Profit optimization is frequently accomplished at the expense of workforce capability and resiliency.
- Operational efficiency optimization can compromise the customer satisfaction.
Don’t turn your back on optimization in haste! There’s no reason to throw in the towel yet. One of the best remedies has been available for decades (yet easily forgotten). The Balanced Score Card was developed by Robert Kaplan and David Norton in 1992. The approach is still operationally sound and perfectly complements optimization and executive analytics.
By considering the ecosystem impact of excessive optimization, you can ensure you establish the countering measures during your development process. Since these measures need to incorporate an extremely broad view of the enterprise, they are best aligned with the executive view of the system.
As your team considers the optimization outcome, the leader should challenge them to consider the risks created by excessive optimization. They can brainstorm outlandish extremes to help make the point clear. After they consider all possible sinister outcomes, the next step is to consider the counter measures or optimization models that will prevent such extremely negative outcomes. It’s possible some of these outcomes should be factors in the primary optimization model (e.g., incorporating the cost of accommodating and compensating displaced airline passengers while optimizing fleet utilization).
It’s critical to associate and integrate the competing models while conducting what-if analysis or simulations. As various scenarios are modeled, the impact on the counter measure should be examined. Even if the models appear to be balanced for the “standard” outcomes (e.g., first standard deviation), the models should be stress tested for the edge cases and counter measures examined.
Since the results are vital to the ecosystem, core operating measures or KPI’s placed on the executive dashboard should have the counter measures visually aligned and updated at the same frequency. Unless all levels of the organization understand and monitor the impact of myopic optimization, the enterprise will increase risk to unacceptable levels.
Just in case you’re wondering about our flight disruption and connecting flights, many of us were fortunate that the connecting aircraft was delayed due to a mechanical fault. We were equally fortunate that it was promptly repaired considering the next available substitute aircraft wasn’t available for 5 hours. That’s a shocking delay considering this airport was a hub for that airline. By failing to model adequate substitute hardware during a predictably difficult travel season, they’ve embraced and accepted a significant level of customer dissatisfaction.
Are you doing the same thing to your customers? Are you prepared to ask the difficult questions and respond to the challenging answers? How are you leading your teams to ensure your optimization strategy is balanced?
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Photo credit: www.flickr.com frakus