Previously in this series, Portfolio Review Part II, we introduced the importance of technology in making the Portfolio Review and S&OP processes successful. In this post, we’ll get into the three key business benefits of cloud-based forecasting and planning:
- Baseline forecast accuracy
- Decision making
Baseline forecast accuracy
Among all possible IT investments, increased forecast accuracy is the single biggest (and surest) driver of direct savings, revenue, and total return on investment. That’s because improved demand forecasts set the stage for improved planning and execution across all parts of the organization.
Forecasts are the starting points for policies and decisions around pipeline planning, new-product introductions, market adoption, product lifecycle, resource allocation, and more. With statistically driven forecasting, these policies and decisions are no longer guesses, gut feelings, or simple consensus averages. Instead, they’re based on real trends and patterns identified in individual SKUs, whole product lines, customers, sales reps, campaigns, or even larger rolled-up data sets.
Collaboration in cloud-based forecasting
If step one is achieving the most accurate baseline forecasts possible, step two is the ability to improve on them using the knowledge and insight of your workforce. Such insights might include a new competitor, an upcoming product campaign, a planned change in pricing, or anything else consequential that is not in the historical data.
Organizations use collaborative systems to mine collective insights so that cloud-based forecasting takes in account more than just history. Again, you start with a statistical baseline, but then you refine it with additional information, such as marketing promotions, ad campaigns, and new sales. All of this intel can come together in the monthly Demand Review, which then informs other review processes that determine financial planning and budgeting decisions.
Decision making (with advanced, prescriptive analytics)
This is where we get into the power of advanced analytics and automation. These capability sets are the keys to optimizing policies, plans, and decisions around the product portfolio – with the least effort.
Statistically derived assumptions and forecasts are an absolute must. If the starting points are incorrect, everything that follows is flawed, including predictions about new product sales, mature-stage demand, component lifespans, or where current investments in R&D will lead – all things critical to planning and strategy. That’s not to say that an attractive UI or an easy integration with Microsoft Office are not valuable features; they are. However, they don’t reduce safety stock, increase service levels, or provide any of the hard financial returns possible through increased forecast accuracy.
Let’s be clear; without an advanced statistical forecast engine, you’re paying for attractive features, not investing in a sustainable net return. As stated earlier, a smartly designed workflow with adjustment-and-override tools that help business users improve baseline forecasts is crucial. The input of the workforce, and the means to easily track and record that input, adds tremendous value in terms of transparency, accountability, and accuracy in cloud-based forecasting and planning. It works especially well with a built-in approval process and automated, system-wide updating.
Advanced analytics in cloud-based forecasting
This is the most powerful competitive differentiator: advanced-analytic modeling and simulation matched with advanced automation. This combination is a game changer.
Advanced-analytic software not only helps you see the future, it helps you understand the realm of possibilities and probabilities. You can run exhaustive simulations, predict multiple outcomes, and execute best-possible courses of action. At Vanguard Software, we’ve seen tremendous value in advanced analytics within our own client base.
Many rely on Vanguard Software’s cloud-based forecasting optimization models to set best-possible policies, plans, and targets for sales, inventory levels, and financial planning. Others use them to tackle tough resource-allocation problems, such as choosing the right product mix to maximize profit, given limited raw materials and resources.
Among the heavyweights in the Vanguard modeling and simulation arsenal is the Monte Carlo simulation, which we’ll delve into in Part IV of our Portfolio Review series. Stay tuned!