Underlying Technology in Digital Planning Twins
From a technology perspective, a Digital Planning Twin is a constraint-based digital representation of an enterprise’s full value chain – including all commercial, operational, and financial components. Its cloud-based architecture ensures security and scalability, while no-code implementation and pre-built templates get businesses up and running fast without IT or data science resource drains.
All of the above combines to create a safe and forward-looking environment where leaders can understand the complexities of interactions, disruptions, and changes across the enterprise. And given the pace of change and new disruptions, speed is of the essence. With a Digital Planning Twin, users can
concurrently run unlimited scenarios and add new ones quickly to balance complex trade-offs. AI and built-in prescriptive analytics quantitatively answer the question of “what should we do?” to optimize outcomes.
Schedule a call with River Logic to talk about the potential of Digital Planning Twin technology for your organization.
Defining Objectives and Ensuring Alignment
Successful transformation efforts start with a clear definition of, and alignment on, the problem(s) the work needs to solve and the decisions it needs to inform. Examples include gaining a better understanding of how demand impacts operations and cost structures or determining where and how many distribution centers to build to best balance minimizing resource requirements while ensuring SLAs are met. In many cases, early insights surfaced via the Digital Planning Twin will trigger more questions or constraints, all of which can be quickly incorporated into an updated model.
Alignment as described above also implies getting buy-in from all key stakeholder groups (e.g., sales, retail, ops, manufacturing, etc.). As we shared in a previous post on Business Continuity Planning, having a rock-solid plan won’t matter if those that will be needed to carry it out won’t get behind it. One especially important piece of the adoption and alignment equation is Finance, typically at the senior-most levels. For example, if a CFO is not part of the process and doesn’t consider the model to be a truly representative comparison between baseline and the possible future state, buy-in and momentum are lost.
Data Requirements for a Digital Planning Twin
Effectively articulating the fundamental problem or question that needs to be solved or answered is also critical because it will inform the data required to solve or answer it. There is a tendency to think that more data is always better, but that isn’t always the case or the only factor. What’s needed to fuel an effective Digital Planning Twin is a sufficient volume of the right data – data that represents all key drivers and constraints.
The technology also makes it simple to integrate across system environments with multiple data sources, schedule updates, and automate data management tasks. Still, iterative dialogue and collaboration are needed to ensure that a solid aggregated planning unit is in place to run the different scenarios and that data drill-down is enabled to make the outcomes actionable. This is a very instructive and insightful part of the process as it forces a deep conversation about “how we need to think about the business.”
From Possible to Pilot to Policy
As improvement insights and P&L promises come into focus via the Digital Planning Twin, the question then becomes “how do we make them happen?” Answers may center on adjustments to sourcing strategies, vendors, supply partners, transportation lanes, or a combination of those and other elements. When it comes to applying those adjustments, pushing all of one’s chips into the middle of the table straight away isn’t prudent. That’s where piloting enters the transformation equation as a means of testing the recommended actions in a controlled and right-sized real-world environment.
Piloting policy changes in selected geographies generates a true result. Leaders can then compare that to the model to determine the presence of any gaps and whether refinements are warranted. This could include enhancements that make policies dynamic, that is, dependent on and automatically adjusted following data on in-the-moment conditions such as demand levels, resource availability, or timing. Proving out the policy changes and related adjustments provides the confidence and assurance needed for wider roll-out across the enterprise.
Unlocking Real Business Transformation
A Digital Planning Twin enables organizations to translate a fundamental question of “What is the best and most profitable way to service demand under different sets of constraints?” into prescriptive actionable answers. While forecasting tools may be insightful in constructing a long-term multi-year forecast, they cannot instruct an organization in terms of what to do about the information they present. A Digital Planning Twin provides a powerful complement to those investments that bring them to life.
Transformation trepidation is real and understandable, but organizations owe it to their customers, shareholders, and employees to continually try to make things better. A Digital Planning Twin will help them do just that in a safe and secure way that alleviates concerns, quantifies risks, and maximizes the impact of their decisions.
