What part of your current forecast error baseline is (easily) avoidable? Do you have a clear line of sight into the lowest achievable error for each SKU/Location combination? Learn how to identify and (largely) eliminate the avoidable error with help from vyan.ai.

As AI becomes ever-smarter, it begs the question: who is better when it comes to demand forecasting: AI or humans? Read this article to learn best practices in optimally blending human and machine intelligence to produce best in class performance.

Future is increasingly nothing like the past. However, most companies continue to forecast with exponential smoothing models to blindly extrapolate history into the future. Some are applying ML to continue to blindly extrapolate in a more 'sophisticated' manner: this is a dog that won't hunt if you are dealing with a product that is price sensitive and often promoted.

We recommend a better approach: Demand Modeling & Optimization. Apply Supply Optimization concepts, but now to the Demand side.

How is your demand forecasting performance perceived by your Manager? Do you look like an angel delivering miracles consistently or a devil intent on getting it wrong no matter what. How do you set improvement targets without a proper benchmark? Learn how Forecastability Bands can help (Lowest Achievable Error | Highest Tolerable Error) and how you can generate them autonomously from your forecasting software.

Now set targets with a clear sense of feasibility and (significantly) reduce that cost of forecast error. All good news for your customers, your shareholders, and your demand forecasting process participants.

Do you suffer from high forecast error at granular (Product / Location) levels in the short-term? Are you either drowning in detail or disconnected from the ground realities? Need a better way to manage?

Learn how AI-powered Forecast Level Optimization can evaluate forecasts across all levels, without any incremental planner effort as well while this significantly reduces the forecast error. Now you can delight your Customers, Finance team, and Demand Planners at the same time.

Most businesses engaging in Demand Forecasting calculate and report Forecast Error. However, not many calculate and report the Cost of Forecast Error.

Learn how to calculate and significantly reduce the Cost Of Forecast Error across Over-Forecasting (Inventory Excess & Obsolescence Costs, Inventory Carrying Costs) and Under-Forecasting (Lost Sales / Margin Erosion due to Expediting).

Demand Planners have one of the toughest and thankless jobs. They are expected to deliver precise and accurate estimates of future demand every month in face of highly volatile and seemingly unknowable demand across thousands of Product/Location level planning combinations. What makes their job even more complex is inflexible and primitive planning solutions from the pre-AI era, which are only marginally better than Microsoft Excel. Demand Planners are unfairly judged either way: either because they went conservative resulting in stockouts and lost sales or because they went aggressive resulting in excess inventory. They just can’t thread the needle every month across thousands of combinations, but that’s the expectation from them.

Demand Planners need a Digital Planning Twin: an AI engine that can evaluate demand patterns at a granular Product/Location level relevant for Supply Chain decision-making (what inventory where when), learn from both historical data as well as forward-looking demand drivers to produce an Optimal Forecast which both senses and optimally shapes Demand.

Traditional approaches to Demand Forecasting are trend-extrapolation based, similar to driving a car by looking into the review mirror. This is intuitively dangerous, and now increasingly fatal given polluted history due to pandemic and other disruptions driven by wars and recessionary pressures, etc. What Demand Planners need is a forward-looking engine that can both enable demand forecasting based on relevant driver trends as well as enable an interactive demand shaping or an automated demand optimization powered by AI. Such Demand Shaping & Optimization is critical in the bi-directional orchestration of Demand and Supply in a mature IBP process. Typical IBP solutions follow an archaic sequential S&OP process of Demand Review followed by Supply Review. This approach locks a Demand statement, then tries to identify the least cost based supply response. Flaw with this cost optimization approach is that it misses out on margin optimization opportunities: through modeling changes in pricing, promotions, new product launch schedules, etc. to leverage current supply & capacity to optimize margin.

Learn how to alert managers on forecast error BEFORE it occurs, not after the fact. Vyan.ai tracks all human overrides (across sales, marketing, demand planners) to the touchless forecast it generated, builds SKU, SKU/location, human stakeholder specific profiles to learn where they override, by how much, and when it helps vs. when it hurts. Vyan.ai uses these insights to both improve the Touchless Forecast proposal when overrides have helped in the past and also alert Managers when overrides are likely to hurt.

Demand Planning processes often include human overrides to account for forward-looking market intelligence missing in the statistical forecast engine. However, this also opens the door for wishful thinking or outright gaming. Learn how to separate the good overrides from the bad overrides real-time with a high degree of confidence.