Reducing Inventory at the Store Level
A recovering economy is prompting retailers to expand consumer touchpoints 
and enterprise-wide assortments. By integrating analytical demand forecasting processes, chains can more accurately allocate merchandise, avoid markdowns and make a stronger impact on their bottom line.
The apparel industry is ramping up assortments and giving shoppers even more choices — with fast-fashion topping their shopping lists — and retailers need to be agile when exploiting this merchandise. This inventory dies fast, and when retailers are left with stale inventory, it erodes margins and takes up space.
These factors are intensified as retailers expand their multi-channel strategies, adding pressure to step up their game in delivering merchandise when and how shoppers want it. This is hardest for chains that still support manual processes and legacy systems and conduct forecasting and merchandising operations within disparate organizational divisions.
Ending Stockpiling of Inventory
Successful merchandise planning requires a predictive demand forecasting strategy. Bob’s Stores learned this lesson right before the recession. The 34-store chain used to stockpile inventory, which was costly and reduced turns. Even worse, it used a basic legacy system and Excel spreadsheets to manually calculate safety stock, making inventory orders inaccurate and producing higher levels of safety stock that tied up capital in non-moving inventory.
“The only way to compete was to lower our on-hand merchandise and better manage inventory to match consumer demand,” says Victor D’Amato, vice
president of planning and analysis for Bob’s Stores.
The chain added a cloud-based demand management system from JustEnough Software that generates inventory forecasts based on pre-established factors, including existing safety stock levels and shopper demand. This helped the chain reduce its holding stock by 16% and in-store stock levels by 9%.
Centralized Analytics
Analytics are critical when fast-fashion is a core merchandise component. Women’s fashion retailer Dots transitioned its store and inventory processes to a centralized analytical platform, helping it make more accurate store plans chainwide and execute them through store-level allocation.
“We used an aging allocation system, but when we shifted to a size-selling model, our previous allocation solution was too cumbersome,” says Tiina Kaljot, divisional vice president for planning and allocation for Dots.
The new platform from MID Retail automates store forecasting. “The solution allows more visibility into our merchandise and demand at store level,” says Kaljot. “We have more methodologies available to analyze our business and execute accurate store plans.”
Fine-Tuning Merchandising Mix
Carhartt is also fine-tuning its merchandising mix with the help of Predictix. The work wear retailer wanted a better handle on shopper demand, and with the Predictix platform, it will gain insight into demand throughout its client base both at the store and SKU level.
“By analyzing this information, we are able to analyze trends and do a better job of diving into style and SKU levels, and then, based on movement information, target assortments to specific locations,” says Jeff Gragg, CIO of Carhartt. “The end goal is to become a category management partner with the retailer, to help drive revenue and profitability.”
and enterprise-wide assortments. By integrating analytical demand forecasting processes, chains can more accurately allocate merchandise, avoid markdowns and make a stronger impact on their bottom line.The apparel industry is ramping up assortments and giving shoppers even more choices — with fast-fashion topping their shopping lists — and retailers need to be agile when exploiting this merchandise. This inventory dies fast, and when retailers are left with stale inventory, it erodes margins and takes up space.
These factors are intensified as retailers expand their multi-channel strategies, adding pressure to step up their game in delivering merchandise when and how shoppers want it. This is hardest for chains that still support manual processes and legacy systems and conduct forecasting and merchandising operations within disparate organizational divisions.
Ending Stockpiling of Inventory
Successful merchandise planning requires a predictive demand forecasting strategy. Bob’s Stores learned this lesson right before the recession. The 34-store chain used to stockpile inventory, which was costly and reduced turns. Even worse, it used a basic legacy system and Excel spreadsheets to manually calculate safety stock, making inventory orders inaccurate and producing higher levels of safety stock that tied up capital in non-moving inventory.
“The only way to compete was to lower our on-hand merchandise and better manage inventory to match consumer demand,” says Victor D’Amato, vice
president of planning and analysis for Bob’s Stores.
The chain added a cloud-based demand management system from JustEnough Software that generates inventory forecasts based on pre-established factors, including existing safety stock levels and shopper demand. This helped the chain reduce its holding stock by 16% and in-store stock levels by 9%.
Centralized Analytics
Analytics are critical when fast-fashion is a core merchandise component. Women’s fashion retailer Dots transitioned its store and inventory processes to a centralized analytical platform, helping it make more accurate store plans chainwide and execute them through store-level allocation.
“We used an aging allocation system, but when we shifted to a size-selling model, our previous allocation solution was too cumbersome,” says Tiina Kaljot, divisional vice president for planning and allocation for Dots.
The new platform from MID Retail automates store forecasting. “The solution allows more visibility into our merchandise and demand at store level,” says Kaljot. “We have more methodologies available to analyze our business and execute accurate store plans.”
Fine-Tuning Merchandising Mix
Carhartt is also fine-tuning its merchandising mix with the help of Predictix. The work wear retailer wanted a better handle on shopper demand, and with the Predictix platform, it will gain insight into demand throughout its client base both at the store and SKU level.
“By analyzing this information, we are able to analyze trends and do a better job of diving into style and SKU levels, and then, based on movement information, target assortments to specific locations,” says Jeff Gragg, CIO of Carhartt. “The end goal is to become a category management partner with the retailer, to help drive revenue and profitability.”