What Is The Most Popular Demand Planning Software?

3. Change Management

Say a company wants a tool and has the budget. It should be smooth sailing, right?

Consumer goods brands vary greatly in several key factors, including which function owns demand forecasting and planning, where the necessary data lives, and how it’s siloed, what processes are followed (which within a company are constantly morphing or changing), what terminology and KPIs are used, and how they’re defined, and of course, how demand changes. All these differences create a lot of frustration for the team trying to implement any new tool. While software may be based on best-in-class workflows, it likely still doesn’t match exactly what the company is currently doing. Without proper change management, users will resist changes to existing processes, and adoption of the tool will be limited.

4. Ongoing Support and TCO

Finally, even if the initial implementation goes well, other issues that arise as a company grows or changes can lead it to abandon a tool.

• As the business evolves, teams may ask for changes to the tool to continue to meet their needs. These requests can result in costly change orders, which cause many budget owners to take pause. They may decide the added investment isn’t worth it and don’t go through with the changes. Eventually, users stop using the tool since it no longer serves their needs well.
• Another reason teams abandon tools after implementation is the need for a super user trained on the tool’s nuances. That resource is required to train and onboard new team members and serve as a troubleshooter for the general team because the tool is too technical for a typical user to learn and resolve issues on their own. Having a headcount focused heavily on support is often more than companies bargained for, and it’s not always easy to find someone with this skill set outside of IT.

Most demand planners we’ve spoken to want a more accurate forecast and would love a tool to help them. But only  if it offers good value, allows them to do ad-hoc analysis and scenario analysis, the methodologies used are transparent, and it is simple enough that they don’t need several days of training and a super user to leverage it effectively.

We also believe it’s important for a tool to provide a reasonable ROI, taking into account the Total Cost of Ownership. Those touting unrealistic levels of forecast improvement do themselves and the planners a disservice. Decision-makers need real examples of how the tool pays for itself in terms of value drivers like inventory savings, additional sales opportunities, and reduced loads on existing headcount, not just bold claims of how “black box” artificial intelligence will lead to major leaps in forecast accuracy.