In this video, Miral Gandhi, Ikigai Analytics Strategist, shows a demo of aiCast, Ikigai's innovation which enables you to forecast with limited historical data. Increase forecasting accuracy and reduce over/under forecasting by incorporating Ikigai’s aiCast to account for short-term demand fluctuation. Learn more https://www.ikigailabs.io/time-series-forecasting
Watch VideoWelcome to the demonstration of our ai planning tool at a key guy the key guy is a no-code end-to-end AI-powered data app platform meaning it covers all the steps of data work from ingestion to preparation all the way down to taking actions and collaboration and we're based on the cloud and easy to use as spreadsheets. We are Tech based out of MIT as well.
So next, I'm going to go to the actual aiCasting tool here. What we've heard from customers that we work with is that forecasting accuracy is extremely important, especially for the planning phase that comes next. What we've done is we've been able to leverage great techniques that allow us to forecast accurately with as little as two to three weeks worth of data versus two to three years. What this means is that we're able to apply our forecasting metrics to things like new product launches, highly seasonal items and when consumer demand is highly variable, so in other words, all the cases where historical averages of two three years won't work that's where Ikigai abilities shine. In addition to that, we're able to incorporate things like product substitutability as well as common product bundling.
As you can see here we have several different lines and being is a holistic data platform means we're able to do is provide all the available forecasting models alongside aiCast from Ikigai. So as you can see here we have deep AR from Amazon, we have Prophet from Meta, and we have lstm by Google, so you can actually click on and off of these and you can see the difference versus the aiCast version as well. Also, you can see right here from our aiCast forecasting ability our value add is over 12 million dollars in this case and all of this is going to really depend on how you as an organization want to demonstrate your forecast whether that's being in an Overstock or under stock position this is something that you can sort of adjust accordingly. Finally, you can drill down by various levels so whether that's the SKU, whether that's the store level or even the color and if you have any additional metrics you can add those as well. We can adjust accordingly here. in addition to that you're also able to adjust the velocity as well as days out so you can see here if you're if you come to this knob and increase the velocity to 23, you can see it adjusts the forecast accordingly. in addition to that if you adjust the days out, you'll be able to see the forecast change here as well. Finally, once you get done with that aspect you can operationalize this and that's when we'll go to our next step which is the ai planning module. Thank you for watching and we'll see in the next video.
In this video, Miral Gandhi, Ikigai Analytics Strategist, shows a demo of aiCast, Ikigai's innovation which enables you to forecast with limited historical data. Increase forecasting accuracy and reduce over/under forecasting by incorporating Ikigai’s aiCast to account for short-term demand fluctuation. Learn more https://www.ikigailabs.io/time-series-forecasting
Welcome to the demonstration of our ai planning tool at a key guy the key guy is a no-code end-to-end AI-powered data app platform meaning it covers all the steps of data work from ingestion to preparation all the way down to taking actions and collaboration and we're based on the cloud and easy to use as spreadsheets. We are Tech based out of MIT as well.
So next, I'm going to go to the actual aiCasting tool here. What we've heard from customers that we work with is that forecasting accuracy is extremely important, especially for the planning phase that comes next. What we've done is we've been able to leverage great techniques that allow us to forecast accurately with as little as two to three weeks worth of data versus two to three years. What this means is that we're able to apply our forecasting metrics to things like new product launches, highly seasonal items and when consumer demand is highly variable, so in other words, all the cases where historical averages of two three years won't work that's where Ikigai abilities shine. In addition to that, we're able to incorporate things like product substitutability as well as common product bundling.
As you can see here we have several different lines and being is a holistic data platform means we're able to do is provide all the available forecasting models alongside aiCast from Ikigai. So as you can see here we have deep AR from Amazon, we have Prophet from Meta, and we have lstm by Google, so you can actually click on and off of these and you can see the difference versus the aiCast version as well. Also, you can see right here from our aiCast forecasting ability our value add is over 12 million dollars in this case and all of this is going to really depend on how you as an organization want to demonstrate your forecast whether that's being in an Overstock or under stock position this is something that you can sort of adjust accordingly. Finally, you can drill down by various levels so whether that's the SKU, whether that's the store level or even the color and if you have any additional metrics you can add those as well. We can adjust accordingly here. in addition to that you're also able to adjust the velocity as well as days out so you can see here if you're if you come to this knob and increase the velocity to 23, you can see it adjusts the forecast accordingly. in addition to that if you adjust the days out, you'll be able to see the forecast change here as well. Finally, once you get done with that aspect you can operationalize this and that's when we'll go to our next step which is the ai planning module. Thank you for watching and we'll see in the next video.
