Shaking up demand planning with statistical models: Balancing human ingenuity and statistical effectiveness

In the ever-evolving world of forecasting and demand planning, we see the human effort involved in forecasting intensifying despite the analytics and technological tools available. The art and science of predicting demand for products and services have become more complex than ever before. To add to the challenge, there is a looming shortage of skilled professionals in the field, making it imperative to attract the new generation to the job. The big question here is: how can we remove the routine tasks from the demand planner job, while ensuring we deliver top-notch results? The answer might lie in a blend of human business knowledge and the power of data-driven statistical models in demand planning.


demand planning improvement with help of statistical models

Empowering demand planners in the digital age 

Let’s face it: the job of a demand planner isn’t always the most glamorous or appealing to the younger workforce. For some the typical perception of a demand planner conjures images of someone endlessly crunching spreadsheet numbers in a dimly lit office, removed from the excitement of the market and customer interactions. In this age of rapid technological advancements, how can we free up vital time for the demand planner to interact with various stakeholders? 

One promising approach is to harness the wonders of modern technology and analytics to reduce the tedious aspects of the job. Instead of demand planners getting bogged down in manual number crunching, what if they could spend more time engaging in meaningful conversations with account managers, marketing, and customers? This is where the importance of best-in-class statistical forecasting models comes into play. By leveraging cutting-edge statistical approaches, demand planners can free themselves from the shackles of routine data manipulation, and instead focus their efforts on strategic thinking and dynamic decision-making.

 

Read our related blog: Declining forecastability: Can your portfolio still be forecasted?


Elevating demand planning
through statistical models
 

By automating the routine, repetitive tasks of demand planning, we create room for demand planners to add their valuable insights and creativity to the mix. They can build stronger relationships with account managers, gain a deeper understanding of customer needs, and refine the forecast when necessary. The demand planner of the future should be a strategic thinker, a collaborator, and a communicator, rather than just a number cruncher. 

Statistical forecasting isn’t about replacing human judgement; it’s about enhancing it. With the right tools and technology, demand planners can make informed decisions based on robust data-driven insights. Statistical models, equipped with the power of big data, adding machine learning techniques where needed, can provide a solid foundation for forecasts that demand planners can then fine-tune according to their unique domain knowledge, market insights, and experience into the process.

 

Reshaping the role of the demand planner

The art of demand planning is at a crossroads. It can remain a monotonous job focused on manual data crunching or it can transform into a dynamic, exciting role at the intersection of technology and human ingenuity. The future of demand planning is a blend of human effort and statistical effectiveness, and it’s an exciting path to be on. 

Are you ready to discover how statistics can build a solid foundation for your demand planning as well as bring more value to the role of your demand planners? The Fast Forecast Scan provides you with rapid insights into the main demand characteristics and forecastability of your business. It reveals the highest possible statistical forecast accuracy that can be reached and identifies the main opportunities for improvement. All in just a few days. Watch the on-demand demo of the Fast Scan here.

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