QueBIT’s typical client is a Vice-President of Finance, CFO, or an operational executive - say an Inventory manager or Vice-President of Supply Chain. Our typical conversation with them involves an aspect of digital transformation in their planning, analysis or reporting processes; and how technology may help them increase business agility, improve forecast accuracy, speed up planning cycles and make better data-driven business decisions.
At QueBIT, we LOVE the business of Business. We see technology as a way to make business better by letting machines take care of mundane details, while liberating humans to focus on high-value activities like gaining insights, solving problems and innovating.
Recently, a frequent topic of conversation has been xP&A or “eXtended Planning and Analysis”. xP&A represents technology-enabled integrated business planning across various functions of a business such as Finance, Operations, and Sales. In most companies these functions plan in siloes with the Financial Planning and Analysis (FP&A) folks taking care of the annual budget, and quarterly financial forecast while the Sales and Operations Planning (S&OP) people do rolling demand forecasts, and capacity planning (for example). FP&A and S&OP are complicated time-sensitive processes that require extensive coordination and are hard enough to complete separately. They are also on different schedules – FP&A is most often on a monthly cadence, while S&OP may be working by week or day.
Even so, a CEO who takes a top-down view usually doesn’t care about process complexity. The CEO expects the revenue forecast in the financials to be consistent with the sales forecast as well as the demand and capacity forecast. After all, if the sales team claims they can sell much more than capacity constraints would allow, there may be a problem!
Many companies solve this cross-silo consistency problem through extensive, and expensive, reconciliation meetings. Besides taking up precious time and energy, this approach can reduce forecast accuracy because each group has built their plan based on different assumptions, and the final compromise may have more to do with negotiation skills than factual data.
The alternative approach embraced by an increasing number of forward-thinking companies is to use technology to set common data and modeling foundations for ALL the plans. All planning processes start with historical data. If this historical data is sufficiently granular, you can roll it up in different ways to serve different constituencies. For example, if you start with daily data, you can easily get to weekly data, monthly data or quarterly data simply by adding it up in different ways. Similarly, if you start with detailed product codes (or SKUs), you can easily get to Product Categories, or Brands, and maybe even Business Units (depending on how your organization defines a “Business Unit”).
Once you have a shared base of historical data that has been validated and that everyone trusts, you can build models of your business to construct your plan. The models will likely combine Artificial Intelligence and Machine Learning (AI/ML) powered predictive analytics with constructive driver-based techniques. For example, you may use predictive analytics to generate a detailed demand forecast based on historical data, and then combine this with a pricing scenario to arrive at your revenue forecast. The operational team may then look at the demand plan by week to drive capacity planning, while the finance team may look at the revenue forecast by month, for the rolling financial forecast. The starting points are the same, but the models, the reports and the downstream analyses can serve a range of business needs. And if any assumptions change, or if you need to play what-if games with different assumptions, you can easily do it across all parts of the plan.
This is the power of building models, and it is the promise of xP&A.
While you certainly can’t get there with spreadsheets (as we explained here), there is in fact no single technology solution that can do it. What it takes is a combination of business process change, technology infrastructure to support the changes, and a practical incremental execution plan that builds on a sequence of small successes.
Even though business process change is a key ingredient and technology alone cannot solve the problem, the right choice of technology significantly decreases costs, time and effort – which contributes to the probability of success. IBM Cloud Pak for Data is an exciting technology platform for you to consider that checks all the boxes needed to support xP&A (and many other business) requirements in today’s world.
Let us begin by breaking down the tasks that are part of an xP&A deployment:
Think of IBM Cloud Pak for Data (CP4D) as a Lego base plate that comes with an unlimited supply of Lego pieces called “services”. Not only are there services to perform each of these individual tasks, they work seamlessly together in a highly scalable and performant way. Furthermore, just as you can carry a Lego base plate around, you can deploy the CP4D – with its services anywhere! You can put it in your own data center, or in AWS, Microsoft or IBM’s Cloud. You can even scale it outwards, which is like connecting many rectangular Lego base plates to form a larger rectangle!
The CP4D services come in two flavors: “Base Services” are free with the CP4D platform while “Extended Services” cost extra and are purchased as premium add-on “cartridges”. The services that are most useful for xP&A use cases include:
Click here to read IBM’s official answer to the question “What is CP4D for Data?”.
Whether CP4D is the right choice to support your xP&A initiative depends on several factors including:
Contact us at info@quebit.com to find out more about whether CP4D is the right choice for your xP&A data strategy.
Thank you to Mike Cowie, Walter Coffen, Dat Nguyen and the rest of the QueBIT CP4D Guild for their help and contributions to this blog post.