This is the fifth blog in a series entitled “Accountants are from Mars, Operations are from Venus”. The objective is to highlight both the problems and solutions to key issues impacting agile planning and forecasting. In short, the key issue is many “budgeting” systems were created for cost control and were built around the chart of accounts. When operating departments “plan” (could be weekly, monthly, etc) they think in terms of operational metrics which are not directly related to the chart of accounts. More specifically operations understand how to plan the operations and finance knows how to budget costs and both need to better understand the other group’s perspective for a successful planning process to be created.
Some suggestions from previous blogs include:
1. Creating a metric oriented culture
2. Use those metrics to drive the plan
3. Create templates to bridge the process and allow the budget preparer the flexibility to create calculations and do some remedial modeling using excel formulas
The third step mentioned above is only an interim step to quantify some of the models operations use. The objective is to quantifying the operational planning calculations and to help the finance group understand the business operations in mathematical terms to gain a perspective on what operations is thinking. The fourth step in this process is to convert these “ad hoc” excel formulas (models) to a defined modeling approach. I consider a defined modeling approach:
- A rules based approach that has a defined process and workflow
- Supports flexible rules based calculations
- Provides the ability to launch user configurable reports after a model is run to analyze results
What I’m describing is a modeling workbench where the budget preparer has different tools to model at both a macro and micro level. This workbench would be used to leverage the formulas created in the “template bridge” (step 3 above) to create a robust planning environment
Unfortunately there are very few software solutions that offer a robust and defined modeling approach. In my next blog I will provide a better description of features and examples of a robust modeling environment.