IBM’s Global C-suite study recently validated that data-driven organizations are 178% more likely to outperform their peers in terms of revenue and profitability. It’s no surprise that more and more companies are moving beyond basic Financial Planning and Analysis (FP&A) and toward adopting a mindset of Gartner’s newly-dubbed “Extended Planning & Analysis” (xP&A)—or what we at IBM have been calling it for years: “continuous integrated planning”—to cut through data silos, extract key metrics and business intelligence insights, and ensure that strategic plans and decisions are driven by a holistic approach.

But with the amount of data generated by businesses growing exponentially each year, scaling has become an especially complex issue for companies. And in times of dynamic and rapid change, relying purely on historical patterns for scenario planning is insufficient. It’s no longer just about getting finance teams the data they need or relying on the CFO to improve your bottom line. Today’s decision makers across the organization should be leveraging deep data, deep simulation, and predictive—as well as prescriptive—analytics to optimize financial and operational planning. Sales and operational (S&OP) planning turnaround time tends to be even shorter with tight decision-making windows that provide little room for error. Plans can change overnight and therefore need to be flexible, fast and adjusted in real-time across the enterprise. IBM Planning Analytics with Watson can help.

Our xP&A solution helps you streamline integrated business planning across every part of the organization, automate key processes and augment human intelligence by utilizing predictive capabilities to create more accurate, consistent, and timely forecasts. We’re the only partner that can provide a truly modern, AI-powered planning solution complete with the strongest scenario planning capabilities in the industry, empowering you to plan continuously. Here’s how.

The importance of scenario planning and what to look for in an xP&A solution

 Scenario planning must evolve across your entire organization to include all relevant stakeholders and meet the dynamic nature of the market. As we continue to emerge from the volatility impacts of COVID-19, it is increasingly clear that relying on historical data alone for forecasting, budgeting and strategic decision-making simply isn’t enough. Companies today are rethinking traditional frameworks around planning processes to include automation, machine learning and significantly more robust predictive and prescriptive capabilities that incorporates a wider array of data, including external data such as weather, market indices etc, to drive more accurate predictions and decisions. To thrive during the next expected or unexpected disruption and secure true business value, companies need to embrace continuous planning as a core tenet of their risk, innovation and resilience strategy.

Simulations help to solve problems in real-time by analyzing and understanding the opportunity, risk and alternative solutions.  While scenario planning has traditionally been focused on “what-if” capabilities to produce a range of driver values, IBM’s point of view is that the scenario planning of today requires several critical capabilities for more confident decision making and improved performance, including:

  • What-if: Driver-based planning and modeling efforts that provide users with sandboxes to test in while also incorporating AI/ML to better understand and learn how different variables might impact eventual outcomes
  • Predictive: AI-powered predictive forecasting that operates across multiple variables and can also detect patterns and outliers for more accurate, consistent, and timely forecasts— so you can best predict what’s going to happen (e.g. plan for demand) and where your business is headed
  • Prescriptive: Prescriptive analytics to prescribe the best course of action when using constraints, opportunities and weighted scores to recommend decisions—so you can not only predict what’s going to happen, but also decide how you’ll handle it (e.g. resource management and how to meet demand)
  • Scale: The ability to enable massive data combinations and high participation in scenario planning and ensure data synchronization across multiple applications and models associated with the platform in order to enhance the what-if, predictive and prescriptive capabilities.
  • Advanced “Big Data” management: The integration and transformation of large amounts of financial and operational data that extend fp&a principles to reach the benefits of xp. With Big Data management, massive amounts of data can be ingested from multiple sources into a centralized, governed, real-time planning and analytics platform that offers a “single source of truth”

Why IBM Planning Analytics with Watson

 the predictive analytics edge

With new built-in predictive forecasting capabilities, IBM Planning Analytics with Watson puts the power of algorithmic forecasting in the hands of users — even those without data science skills — for more accurate, consistent, and timely forecasts. Our user-friendly Excel front-end makes adoption easy across line of business users, so the people who need to make micro-decisions throughout the company can do so. Our high-participation platform also allows you to scale for massive increases in volume of data or users synchronized across multiple applications and models. After all, if you can’t scale quickly and efficiently, you can’t really call your strategy “xP&A.”

But a sound strategy also revolves around trust — trust in logic, empathy and transparency, especially when it comes to AI and ML. IBM Watson recently announced new capabilities designed to help businesses build trust in their data and AI planning models. Building trust in models also means providing enhanced explainability, understanding and communication for models and their resulting predictions. That’s why we’re planning to bring a new statistical details page to IBM Planning Analytics  with Watson in Q2 2021 to provide more transparent and easy-to-understand facts about how a forecasting prediction was generated. Too often, planning and analysis can feel like a black box of information privy only to the on-the-ground analysts who generated the plan. But with true extended planning and analysis, it’s critical that all areas of the business have input and insight into the process in order to better understand how a plan or forecast was created. And as IBM battles against unjust bias in AI, this kind of transparency is more important than ever to not only getting it right, but doing what’s right.

The built-in predictive capabilities in Planning Analytics with Watson helps departmental or line of business users leverage predictive capabilities; however, we understand that businesses are complex and that is why we’ve introduced enhanced integration with IBM Watson Studio for Predictive Analytics and Decision Optimization.  Watson Studio’s modeler now includes more advanced predictive capabilities that allow the user to customize forecasts by leveraging newer, multifaceted algorithms, methods and variables. Users can now work hand and hand with their data scientist to build more complex models that reflect their business. Additionally, this allows organizations to factor in competing goals and priorities, find the optimal answers within a given set of constraints, and explore more scenarios to arrive at the best possible outcome. In short, the combination provides more confidence in your continuous planning process.


See how some of our clients are benefiting from IBM Planning Analytics with Watson

How bakery company Vaasan used AI to upgrade their planning

To provide high quality products and services to their customers, large bakeries like Finland baker Vaasan rely on very short planning cycles that are informed by various data sources from across the organization. When the COVID-19 pandemic first hit, Vaasan’s demand doubled overnight, putting their supply chain under significant pressure. But with the help of predictive analytics, they are able to operate with less excess capacity, as well as predict energy consumption and costs, and build long-term product plans. As a result of Planning Analytics with Watson, Vassan is seeing higher profits and customer satisfaction. Vaasan is also currently building a model being tested today to analyze cost center trends in order to quickly save planners hours of sifting through data manually at the end of the month.

See how IBM Planning Analytics with Watson helped–>

Vapo Oy integrates AI to transform Finnish energy business

February is usually Finland’s coldest month, with temperatures averaging from -22 to -3°C (-7.6 to 26.6°F). For the communities that Vapo serves, having reliable heat is critical – especially in Lapland, where winter temperatures can drop to a staggering -50°C. Because of the high stakes environment, strategic planning and ERP best practices have long been central to Vapo’s mission.  But when the Finnish government enacted stronger environmental laws to combat the climate crisis in 2019, Vapo needed to adapt.

See how IBM Planning Analytics helped Vapo shrink their carbon footprint–>

Ancestry: Makes planning on the IBM Cloud part of their DNA

As interest in genetic background information has grown exponentially in recent years, Ancestry needed a planning tool that could grow with them—and maintain stability throughout. With over 10 million DNA customers, identifying new methodologies to help Ancestry scale efficiently and ensure 24/7 performance management was critical to success.

See how IBM Planning Analytics helped–>

Today’s top business leaders are embracing a holistic view of planning and analytics. Business planning is no longer seen as a task relegated solely to the finance department, but as a company-wide mindset and strategy. In order to benefit from this xp&a evolution, don’t forget to take an especially close look at the depth of your offerings’ scenario planning features, as outlined above. To learn more about IBM Planning Analytics, reach out to us today.

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