How do you securely plan who, what, where, and when at the lowest level? Where planning usually falls down is when you try to marry the planning outcomes to operational decision making. In a plan you make decisions at high levels that are generally thought to be accurate based on historical trends. It’s a best guess using all of the information available at hand, and short of having a bat-phone to Cleo the Psychic, it’s your best option. My problem isn’t with the concept, or whether you use a 2 year CAGR versus a 3-month rolling average, it’s with how the planning assumptions are applied and how execution flows as a result.Anyone in the business of creating a forecast for the future that has tried to turn the assumptions they’ve made into actionable results knows how difficult that can be. One of the primary reasons is that the finance and operational clocks are different. Finance thinks in terms of quarters (or maybe even months if you have a bold team with some decent tools), while operations thinks in terms of weeks or even days. This means creating an actionable plan that ties to the finance view in terms of expectations starts out with an enormous reconcilement challenge.The next biggest challenge is going from the aggregate in terms of geographies, segments, channels, people, products, etc… to the finite. Big plans are created across big subsets. For example, when creating a quarterly volume plan you might look at regions such as Americas/Europe/Asia, then break down products by high-level product categories such as Hardware/Software/Services. Maybe you even go down to a channel level and compare direct versus indirect or partner sales. Here the problem with relating the forecast to actionable plans is similar in that operations needs to know exactly where, who, and what, to go with the when. Without execution detail to reference on the plan the teams end up building their own plans that may or may not tie out to the high-level executive strategy. Even if you have a robust process around marrying these two together you end up with inconsistencies driven by my favorite Excel issues: emailing different versions of the plan back and forth resulting in confusion on the latest iteration, troubles with concurrent users accessing the same files over a network, and summarizing spreadsheets collected from numerous sites and teams into one cohesive plan.Finally, the last but possibly even greatest challenge can be in making sure the numbers tie out. Are the high-level figures by location, time, and product matching the low-level build-up from the teams in the field? When these numbers don’t match you can end up with differences in product and timing mix that results in profit misses or shifts in seasonality that weren’t communicated or expected.The answer lies in using a system that not only ties the top-down process to the bottoms-up directly, allowing for reconcilement and analysis of gaps, but in creating a top-down plan that goes ALL THE WAY DOWN. Take your assumptions at a regional level for growth and see the actual impact at a SKU/Loc level. When you grow each SKU by its own CAGR rather than the group CAGR you’ll generally get a much more accurate result, with a rolled up number that could be quite different than would be built by staying at a high level. With a top-down plan at the lowest level you can actually compare apples-to-apples with the field teams’ assumptions and understand opportunities with a granularity never possible before. Put this system in the cloud and you’ll have a system that the entire team worldwide can access, review results in real-time, and everyone will always be on the same version of the truth.Tuesdays with our Solutions Consultants is updated the last Tuesday of every month on the Anaplan Blog. Interested in connecting with Tom to discuss this post further? Get in touch with him: @tommyj314
or us: @anaplan
and let’s talk!About the Author: Tom Jones has been with Anaplan as a Solutions Consultant since April 2012 on the Pre-Sales team. Tom aids the team in demonstrating the value of the Anaplan platform to customers by drawing on experience across a wide set of industry verticals including Healthcare, Banking, Transportation Logistics, and Retail. Prior to Anaplan, Tom helped to define and built best-practice financial modeling, planning, and analysis tools at Best Buy in the Entertainment group as a Senior Financial Analyst. He holds a BS degree in Computer Science from the University of Minnesota, as well as an MBA in Finance from the Carlson School of Management at the University of Minnesota.