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How data science can enable better demand forecasting and planning

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How data science can enable better demand forecasting and planning

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In a previous post, we talked about how tough it can be to get the various parts of a business — operations, sales, finance, etc. — to “sing in harmony” to achieve better demand forecasting and planning. It’s like getting a group of singers to perform together with each one singing the right part, at the right time, in the right key. Using a mix of advanced analytics tools, backed by machine learning, is crucial for achieving that.

But let’s extend the metaphor: if those singers are joined onstage by an orchestra, who supplies the instruments and keeps them in tune? Answer: the data science team.

Data science duties for better demand forecasting and planning

Whether they work within a line of business or as a standalone, centralized function, data science teams are responsible for making sure that internal customers can optimize demand planning and forecasting. That applies no matter which data sources are used — even when the data is siloed, and regardless of whether it lives on-prem, in the cloud, or both. Oh, and it all must be done while explicitly demonstrating adherence to any requirements for security or regulatory compliance.

To deliver on this, it’s crucial that data science leaders hone their ability to influence finance leaders and the heads of business units as they choose and validate tools. The core task is then to find the most relevant models to produce quick insights, and to do it in a collaborative way that enables line-of-business staff — the musicians at the concert, in our analogy.

Ultimately, it means running predictive models to accurately predict demand, as well as sophisticated mathematical models that can factor in business constraints and that allow operational decision makers to evaluate different scenarios and trade-offs to find the best course of action for the business. Ideally, it also means that the tools at hand help you move from data prep to prediction easily and quickly. In other words, when the musicians mount the stage, they need to have the right instruments tuned up and waiting for them.

Challenges faced by data science teams

Yet without the right tools in place, tuning those instruments can take a long time. Many data science leaders will tell you that their teams spend most of their time in early modeling and experimentation phases, which can make everything bog down for weeks or even months. That’s hardly the way to create, curate, and deploy models in time for next year’s annual plan!

If you want to maintain a happy, highly productive data science team that delivers value for the business quickly, how do you empower them so they can spend most of their time in the deployment and operationalization of models? Read on to find out how IBM gives data science teams the right tools to address three key challenges.

Challenge #1

Enabling faster deployment and operationalization of models to empower — and demonstrate value to — the business


  • IBM SPSS Modeler reduces the time required to go live using an extensive library of out-of-the-box machine learning algorithms.
  • IBM ILOG CPLEX Optimization Studio enables rapid model building, the ability to test multiple scenarios through what-if analysis, and multiple deployment options that allow business users to access models from IBM Planning Analytics.

Outcomes: Your team will deploy machine learning algorithms and optimization models in days, not weeks or months, using their choice of programming language, while meeting security and governance mandates and reducing the risk of rework.

Challenge #2

Using sophisticated mathematical models that factor in business constraints and trade-offs to produce multiple scenarios for evaluation, then collaborating with line-of-business leaders to iterate and refine those models.


  • IBM ILOG CPLEX Optimization Studio incorporates powerful optimization engines to create mathematical programming models and constraint programming models; what-if analysis capabilities allow easy evaluation of the impacts of multiple alternative scenarios.

Outcomes: You’ll solve complex optimization problems at the speed required for today’s business environment while avoiding costly rework later.

Challenge #3

It’s difficult to acquire and retain qualified data scientists and data engineers, which can lead to gaps in your team, the inability to quickly fulfill requests for new models, and the need for people to learn on the job.


  • IBM SPSS Modeler empowers visual data scientists and analysts to develop and deploy models without knowing programming languages, at the same time that it allows them to collaborate with programmatic data scientists.
  • IBM ILOG CPLEX Optimization Studio provides the flexibility to develop models using either Optimization Programming Language or general programming languages by leveraging C, C++, C#, Java, or Python APIs.

Outcomes: Your team will be more productive and have an easier time collaborating among themselves and with business users, plus they’ll have the flexibility to use existing programming skills without being restricted to any proprietary language as they develop models.

Getting your data science team the tools it needs

Only IBM gives data science teams the complete set of tools to create and deploy the right models for better demand forecasting and planning. They’ll have the same advantages already enjoyed by thousands of enterprises that use these tools to create mission-critical models for everything from manufacturing supply chains to global weather patterns.

Your business deserves to operate in harmony when it comes to forecasting and planning, and your line-of-business leaders are doing all they can to get everyone to the stage on time to perform from the same sheet of music. Will you have the instruments tuned and ready when they get there?

To learn more about thriving in the face of these challenges, check out our SmartPaper on Accurate forecasting and optimized planning.”

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