{"id":8774,"date":"2020-11-09T10:13:55","date_gmt":"2020-11-09T15:13:55","guid":{"rendered":"https:\/\/demand-planning.com\/?p=8774"},"modified":"2020-11-09T10:16:56","modified_gmt":"2020-11-09T15:16:56","slug":"do-data-scientists-make-good-demand-planners","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2020\/11\/09\/do-data-scientists-make-good-demand-planners\/","title":{"rendered":"Do Data Scientists Make Good Demand Planners?"},"content":{"rendered":"

\u201cI think this is the perfect career move,\u201d Nicolas Vandeput, founder of SupChains, says of incorporating data science skills as a Demand Planner.<\/b><\/p>\n

Like so many industries, the way we\u2019ve done things in the past in demand planning may not be the way we do things going forward. The line between data scientists and demand planners is blurring.<\/strong><\/p>\n

There will be a time in the future when answers become a commodity, and questions are the premium. Adopting a scientific curiosity and understanding the right questions to ask will become a valuable skill.<\/strong><\/p>\n

\n

The effect of data science on demand planning and supply chain planning can\u2019t be underestimated. Demand Planners can no longer rely on how we\u2019ve always done things.<\/span><\/p>\n

Does that mean, however, that data scientists can be effective demand planners?<\/span><\/p>\n

We spoke with author of <\/span>Data Science for Supply Chain Forecast<\/span><\/a><\/em>\u00a0and founder of SupChains<\/a>, <\/span>Nicolas Vandeput<\/span><\/a>, who laid out what data scientists bring to demand planning and how demand planners can leverage data science to evolve in this rapidly changing field.<\/span><\/p>\n

\u201cI think this is the perfect career move,\u201d Vandeput says of incorporating data science into your demand planning repertoire, on an episode of IBF\u2019s <\/span>On Demand podcast<\/span><\/a>.<\/span><\/p>\n

We know data science is not demand planning. There are significant differences in these fields. But there can be valuable synergy between them, as well.<\/span><\/p>\n

Vandeput unpacked exactly what Data Scientists and Demand Planners can learn from each other, how a data scientist can apply their expertise to this field, and how a Demand Planner can become the supply chain Data Scientist in your company.<\/span><\/p>\n

Adopting A Probability Mindset In Demand Planning<\/span><\/h2>\n

Demand Planners live in a world of ambiguity. We\u2019re never \u201cright\u201d; we just hope to be close in our forecasts. This lack of precision can be a challenge for Data Scientists, who specialize in answers.<\/span><\/p>\n

Is that ambiguity a problem that data scientists need to overcome?<\/span><\/p>\n

Vandeput doesn\u2019t think so. \u201cIn data science, you try to be as accurate as you can, but you totally accept being 99% [accurate],\u201d he says. So, data scientists accept some ambiguity, too \u2014 but they typically have a clearer understanding of just how much there is.<\/span><\/p>\n

He says this is because of the \u201cscience\u201d part of data science. A scientific mindset is all about experimentation, observation, and curiosity. Data Scientists, then, must be able to test new ideas, accept failures, and move on to the next idea.<\/span><\/p>\n

The real difference that a data scientist brings to the table is a probability mindset.<\/b> While Demand Planners are comfortable with ambiguity, data scientists can accept that ambiguity, but also consider the probability of accurate forecasts with a given model and adjust their models to achieve higher probability of accuracy.<\/span><\/p>\n

Adopting a probability mindset, rather than simply accepting traditional levels of ambiguity, could help demand planners achieve more accurate forecasts.<\/span><\/p>\n

The Project vs. Process Workflow<\/span><\/h2>\n

We might see Data Scientists and Demand Planners as complementary but distinct roles that require different skill sets. In that case, a breakdown of roles would look something like this:<\/span><\/p>\n