In our prior Supply Chain Matters posting we called attention to the evolving attraction for leveraging predictive analytics in supply chain decision-making practices which has added to the continued pent-up demand for data scientists. We highlighted a guest contribution indicating that big data and more predictive analytics capabilities can be non-effective if not preceded by a rigorous review in determining if current key performance indicators (KPI’s) and business metrics are actually capturing the true drivers of business outcomes.
During SAP’s recent 2015 Sapphire and ASUG conference, SAP co-founder and Supervisory Board Chairmen Hasso Plattner’s conference keynote touched upon this very aspect, which warrants repeating. He touched upon the notion of the boardroom of the future, not being occupied by reviewing historically based KPI’s but rather “fact-based management.” Hasso described this as a “massive change on how companies manage information” and further, “we cannot hide data anymore”.
That last statement may well resonate with our readers since too often, KPI’s are selected to measure can-do performance areas tied to individual organizational, team and personal bonuses that do not necessarily link to an overall business outcome required for products, processes, margins and/or risks. They are too- often, anchored in past performance coupled to a consensus of what can be comfortably accomplished vs. what should be expected given the industry and business environment. Concerning or bad news can be hidden until it is too late for the business to overcome the effects.
In his keynote, Hasso addressed such a change as “moving from dashboards to active boards.” That is an important and far different metaphor.
It implies continuous and changing analysis grounded in overall outcomes and assumes that business events will indeed be constantly changing and that performance metrics should set both a target and a constant moving analysis of potential outcomes based on various business and product scenarios. Such a moving analysis assumes that organizations and teams can be fluid and flexible, responding to market opportunities, threats or risks in a more proactive and collective manner and in the context of best desired outcomes. It further implies that management is very actively engaged in understanding how the end-to-end supply chain is contributing or detracting from desired and/or expected outcomes. Bonuses and performance are tied to best enterprise outcomes vs. individual outcomes.
Such a change does not occur overnight and will take time to evolve. As noted in a previous commentary, executives need to be granted the broadest end-to-end supply chain leadership and accountability with certain mandates to address existing value-chain challenges and to improve business outcomes. Supporting staff with data science skills, while critical, are not the primary skill need. Knowledge of the business, the end-to-end supply chain, and organizational change management needs to be coupled to data science skills.
In the meantime, we advise supply chain leaders to indeed recruit talent with data science skills, and then rotate these new superstars among various supply chain functional and geographic assignments. Challenge them with local problems and with introducing positive overall change. Insure active mentorship and sponsorship with the end goal being a select group of business analysts that can take on the most difficult challenges while garnering the respect of others.