An “always on” Connected Planning vision: How can machine learning help?

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What is business planning, really? It’s the plotting of how to get from point A to point B with the best available knowledge. It’s how we work to steer operations toward our objectives. The plan is the roadmap. But what happens when obstacles pop up? Or when the desired destination suddenly shifts or adjustments need to be made in the assumptions? That’s where conventional two-dimensional and pre-determined set of planning steps often fail and targets are missed. When roadblocks arise, or shifts happen outside of our control, planning needs to be augmented by real-time business signals.

Augmenting conventional planning with real-time signals means incorporating external market information with internal organizational data and performing all the data-crunching, forecasting, cost-benefit analysis, and risk assessments at every step—instantly. It means better-informed decisions, acting and reacting in real-time, and pivoting fluidly. This is somewhat unchartered terrain. Conventional and static planners are limited to a quarterly exercise on spreadsheets, or a disjointed program with each department going through the motions in their independent silos. The result is usually that plans are shelved due to obsolescence as quickly as they are created.

The future of planning is connected, ever-increasingly better-informed, continuous-execution with a feedback loop built in. The goal is to make incremental progress in a world where planning for improvement never stops. For short, we’ll just call it an “always on” planning. To give planning this turbo boost, you need the combined power of a Connected Planning platform with machine learning (ML) capability integrated.

  • Connected Planning
    Put simply, Connected Planning takes the forward-looking functions of every area in a company—operations, sales, workforce, finance, marketing, supply chain, and IT—and connects the dots. It’s a silo-busting, collaboration-enabling, performance-driver.
  • Couple that with machine learning
    Machine learning (ML) is when a computer or device acquires and interprets large amounts of data from many sources, and uses that data to “learn” to improve its processes either on its own, or with the supervision of a person. (For more AI vocabulary demystified, see our recent article, “When talking artificial intelligence, let’s clarify.”
  • The result: Machine learning as part and parcel of Connected Planning with continuous, automated insights and recommendations, extreme detail and accuracy in forecasting. I’m talking about predicting sales by individual product SKU at a store level for a specific day or week. In short, ML and Connected Planning mean real-time decision-making with more confidence, based on a continuous flow of timely, accurate, and actionable data.

Planning: Conventional and static vs. connected and always-on

In all business planning activities:

  • Goals are where you want to go.
  • Planning draws the map.
  • Execution is how well you can follow your map and adapt to changes.

Conventional on-road planning is done periodically, every month or every quarter—or when someone realizes that performance is off track, and corrections are needed. (Ack! Sales were lower than expected on the latest promotion. Do we need to change course for the upcoming one?) Even the more technologically sophisticated companies with statistical modeling need to pre-set the formulas and determine the logic used: that is, they manually get the analysis going.

Conventional planning models are static, only changing as people realize they need changing, and then only with considerable human input, sometimes missing the critical window crucial for decision making. On the other hand, Connected Planning is fluid and efficient, allowing companies to bypass obstacles without human intervention. It’s data driven in real time, to draw quick connection between business drivers and results.

Connected Planning, when augmented with ML is always on. Relevant data is always being gathered and analyzed, and it all happens automatically. This can include every kind of data, from sales reports to employee performance numbers, and from inventory stock to customer surveys. Even data that might not seem, on the surface, to be relevant (such as external information from the weather to stock market stats, or from a daily currency exchange rate to the president’s approval rating), if there are correlations, ML finds them and helps you connect the insight to your advantage. Connected Planning keeps all of these moving parts aligned.

Machine learning is the engine that keeps your planning running continually, with as little or as much human supervision as you say is needed. This lets computers do what they do best: evaluate enormous amounts of data at lightning speed. ML crunches the numbers and serves up actionable data, freeing up people to do what they do best (and what’s best for them to do): put their creative capabilities to the task and decide on pros and cons of various decision paths. Planners’ roles are enhanced if their time is spent exploring “what-if” scenarios with confidence, following their intuition to find other types of data that could influence specific business objectives. En route to achieving these goals, fewer obstacles become surprises. With the focus of bringing the right data to augment human knowledge and experience, ML also frees people up to bring the human factor: empathy, trust-building, caring—the values, needs, and meaning behind all we do—back into clear focus.

Static Conventional planning Always-on Connected planning with ML
Done periodically: monthly, quarterly, and as people note that a change in course is needed. Planning never stops, running behind the scenes all of the time.
ML is always crunching the data and serving up actionable info. It makes increasingly accurate and specific forecasts and predictions, not quarterly but continuously.
Done manually. Done automatically. Autonomously or not. You decide.
Models are static and change as people realize they need changing. Models are fluid, continuously updating as new information comes in.
What models can’t explain is labeled as an anomaly or randomness. Machine learning finds connections that humans might never think to link.
Inefficient, time-consuming, and obsolete in some cases. Provides a competitive edge by harnessing the power of information.
Has people doing what machines do best. Frees people up to do the uniquely human tasks, including creative problem-solving and bringing the human-factor (empathy, values, needs, meaning), into focus.

You always try to make the most direct route from point A to point B. And, as we learned from Pythagoras in elementary school, the shortest distance between two points is a straight line. Right? Wrong. On a flat, two-dimensional surface it is, but not in 3D. (The Scientific American explains it lightly in “football science,” here). The real world of business planning is certainly multi-dimensional where contours and carves seem to be redefined at an unprecedented rate.

Connected Planning with machine learning means when moving from point A to point B, you don’t get delayed by roadblocks.

See a glimpse into what the future of Connected Planning and machine learning could look like.

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