The goal of demand forecasting is typically to get a more accurate forecast and drive down inventory cost. A better forecast will lower costs for retailers by controlling inventory levels, lend insights to space planning, help back room inventory space, lower carrying costs, and limit excess inventory. When working with retailers, these are some of the areas we focus on.
Inventory Levels. A better forecast helps control inventory levels at stores as well as DCs and reduces the cost of carrying aged inventory, meaning less inventory dollars are aged out and can reduce shrink.
Space Planning. Forecasting allows for better space planning in stores with more accurate forecasts. For example, knowing that an item that sells 20 annually at any given location may not require 10 facings at every store, whereas a more popular item from a forecast perspective, may benefit from having more facings.
Storage. A better forecast aids in overstock/understock issues and saves “back room” inventory space due to just in time (JIT) replenishment. For example, as the old stock is about to run out, the new stock is being brought in and displayed resulting in less customer disappointment when items are out of stock.
Carrying Costs. Accurate forecasts allow for better allocation of purchasing dollars. For example, if an item is forecasting 1000 sales annually but is budgeted for 3000 items, the extra budget can be freed to put towards other items that may need more support.
Optimized Inventory. In retail, it is key to have the right amount of the right product at the right place at the right time which allows for money to be more intelligently spent on tactical decisions rather than excess inventory. A better forecast also allows for seasonality to be intelligently accounted for and included in the demand/inventory planning process.
These are a few of the areas we focus on when determining how a better forecast can benefit a retailer. Each area has a significant impact on cost. When we do a benefit analysis, we ask what the current forecast accuracy is and then calculate what impact a 3% or better improvement would mean across different categories. As more and better tools become available with machine learning, achieving a 3% improvement becomes much more feasible. We would be happy to do a high level assessment to help you justify a project.
The goal of demand forecasting is typically to get a more accurate forecast and drive down inventory cost. A better forecast will lower costs for retailers by controlling inventory levels, lend insights to space planning, help back room inventory space, lower carrying costs, and limit excess inventory. When working with retailers, these are some of the areas we focus on.
Inventory Levels. A better forecast helps control inventory levels at stores as well as DCs and reduces the cost of carrying aged inventory, meaning less inventory dollars are aged out and can reduce shrink.
Space Planning. Forecasting allows for better space planning in stores with more accurate forecasts. For example, knowing that an item that sells 20 annually at any given location may not require 10 facings at every store, whereas a more popular item from a forecast perspective, may benefit from having more facings.
Storage. A better forecast aids in overstock/understock issues and saves “back room” inventory space due to just in time (JIT) replenishment. For example, as the old stock is about to run out, the new stock is being brought in and displayed resulting in less customer disappointment when items are out of stock.
Carrying Costs. Accurate forecasts allow for better allocation of purchasing dollars. For example, if an item is forecasting 1000 sales annually but is budgeted for 3000 items, the extra budget can be freed to put towards other items that may need more support.
Optimized Inventory. In retail, it is key to have the right amount of the right product at the right place at the right time which allows for money to be more intelligently spent on tactical decisions rather than excess inventory. A better forecast also allows for seasonality to be intelligently accounted for and included in the demand/inventory planning process.
These are a few of the areas we focus on when determining how a better forecast can benefit a retailer. Each area has a significant impact on cost. When we do a benefit analysis, we ask what the current forecast accuracy is and then calculate what impact a 3% or better improvement would mean across different categories. As more and better tools become available with machine learning, achieving a 3% improvement becomes much more feasible. We would be happy to do a high level assessment to help you justify a project.