Early in my career, I recall a comparison that a senior leader shared between a three-legged stool and the capabilities required for successful transformation projects. Just as a functioning stool needs sturdy, proportionate and capable legs, a business transformation effort requires thoughtful consideration and planning across people, processes, and technologies to be successful and unlock the enterprise value proposition. Proper consideration of the people dimension included the change management associated with the new ways of working, workforce training and qualification, modified or new role definitions, new or modified organization models, and new or modified reward structures. Process dimensions often focus on the work process changes required to support the new activity. Technology usually included a new solution(s) or platform(s) that would need to be configured so that the workforce could use it to achieve the objective.
Though project methodologies have advanced significantly since I first ran across the three-legged stool framework, I have often come back to it as a reference point throughout the project lifecycle. During project initiation, it was a helpful memory jogger for me to ensure that sometimes overlooked dimensions required for transformation were appropriately planned, such as change management. In project execution, reflection on this framework helped me identify issues in how these capability areas relate to each other, for instance, in matching a new process and training to solution configuration. As projects were completed, after-action reviews and project close-out activities captured significant learnings, and this simple framework was a good way to articulate these learnings to key stakeholders and future project teams. Though data was often discussed, it was often in relation to master data readiness (vendor or customer master) or the accuracy or timeliness of a leadership report or dashboard.
Through the acceleration of advanced technologies and capabilities such as artificial intelligence, machine learning, the industrial internet of things, advanced automation, and cloud/edge computing, the importance of data cannot be overstated and requires a fundamental change in how we articulate the requirements for the success of business transformation efforts, and how we sustain the results after the project is completed.
Data governance is critical to this enterprise’s capability and requires diligent work to align and document KPI definitions and owners, data sources, data fields within those sources, data validation requirements, and the like
At Mondelez, we are leading the future of snacking, and our supply chain has made significant progress in several key areas in our shelf-to-field digital journey. Three examples from our transformation efforts highlight how we are thinking differently about data.
Early in our journey, our focus was on identifying and visualizing the most critical key performance indicators (KPIs) so that we could assess our progress as a team from a common set of metrics. Our resulting intelligence platform contains over 40 interactive dashboards providing user-based visibility to global service, cost-effectiveness, health, safety and environmental results, quality, cash and capital, customer service logistics, manufacturing, and procurement. Data governance is critical to this enterprise’scapability and requires diligent work to align and document KPI definitions and owners, data sources, data fields within those sources, data validation requirements, and the like.
In the past few years, we have also accelerated our digital supply chain planning progress, with a majority of the company’s demand forecasts created by machine-based algorithms. Though this required heavy lifting on the people, process and technology components of the three-legged stool, the success of this effort also relied heavily on our ability to identify, cleanse, transfer and ingest data from multiple internal and external sources over time. In addition, as models need to be adjusted to account for new forecasting factors, new data sources that promise even more refined outcomes continue to be explored, requiring a much broader understanding of data governance.
We have also augmented our safety training with Virtual Reality capabilities, enabling associates to enter a virtual environment that mirrors their factory and practice safety procedures that may be required during normal operations. We plan to expand our Virtual Reality capabilities over time to reinforce training in safety and process standards. In this effort, data ownership takes on a different meaning. For instance, qualifying users in VR before they are allowed to interact with machines on the operation floor will require close governance to ensure the qualification is completed for each operator. Separately, the digital layout of the operation should reflect the training and qualification requirements, and a close partnership with safety and operations leaders is needed to keep the digital landscape relevant. Similarly, the process steps that are being trained could be defined by operation, technology platform, location, or machine, requiring another level of ownership and governance.
So, though I do not have anything in particular against stools, I have started using the four legs of a chair as a model, namely people, process, technology, and data. It is a simple framework that can augment our standard program and project methodologies and can be a great way to engage stakeholders across the enterprise.
