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Dynamic demand forecasting using Machine Learning, coupled with advanced optimization techniques to revolutionize inventory and assortment decisions.
Self-learning algorithms that can handle scale of thousands of SKUs and large store & warehouse networks.
Almost all traditional demand forecasting techniques perform sub-optimally in the lifestyle domain. There needs to be context aware (selection, availability, promotions, seasonality, events, etc.) approach to forecasting which normalizes for the past context and forecasts for a future context. Further, you need algorithms that can work well for sparse data sets as quite often you have limited data and you need an ability to filter out noise from signal.
Additionally, inventory movement decisions are connected - store allocations influence replenishment, inter store (or inter warehouse) movements and online fulfilment from stores. There are trade-offs to be done across these decisions (over product life cycle) and they need to be optimized together. Often you don’t know where the improvement opportunity lies and it’s very hard to solve these problems simultaneously. Retailers end up applying heuristics and rule-based approaches to solve these problems which are either sub-optimal or time consuming.
The limitations of out-dated forecasting techniques and rule-based decision making for optimization will not suffice for the future we are entering.
Artificial Intelligence and Machine Learning, combined with advanced optimization techniques like reinforcement learning and dynamic programming are poised to change the way we will make inventory movement decisions.
At SprintAI, we leverage ensemble of machine learning algorithms to sift through multiple data sets to better understand your future demand streams across your network. Unlike traditional techniques, these algorithms don’t depend on linear relationship between variables and can un-cover non-linear impact of different factors in tandem.
We then run millions of possible scenarios over the entire product life cycle with different stocking levels and inventory & fulfillment policies to arrive at the most optimal product decisions to be done at a point of time.
Our Holistic Approach to Inventory and Fulfilment Optimization
We have a two-pronged approach to making inventory movement and fulfilment decisions:
1. Granular (Store X Product level) and accurate forecasting of future demand across both online and offline channels based on future context
2. Real-time optimized inventory movement or fulfilment decisions at the current moment, considering the entire product life cycle across the network