Amazon is growing on-demand services after reporting a 20% sales increase in 2015.
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The JustEnough system is designed to automatically pull information from multiple applications, including order management, transportation management, procurement, warehouse management and, for store and multi-channel retailers, store point-of-sale. It then produces graphical reports including recommendations for matching purchase orders to forecasted demand.
Cooking.com’s application focuses on daily updates of orders and inventory levels, and it tracks the time from when purchase orders are created to when goods are received in the retailer’s warehouse. This enables the retailer to compare the performance of different vendors and match promotional schedules with the expected time of delivery while also deciding which and how many products to order, Handlen explains.
Cooking.com has deployed the licensed, on-site version of the JustEnough system integrated with a .Net platform. JustEnough also offers an on-demand system delivered over the web.
Demand forecasting can be most difficult for short-lifecycle items such as apparel, which require additional aspects of forecasting. At high-end apparel merchant Brooks Brothers, the use of multiple forecasting tools from JDA Software Group Inc. has boosted accuracy in forecasting demand over the past year from 35% to 60% in its retail stores and from 27% to close to 100% in its outlet stores, reports Heidi Loncki, system administrator. “Our goal is to have 90% to 100% accuracy for every SKU in every store,” she says.
Brooks Brothers uses the web-enabled JDA Demand application within JDA’s Supply & Demand Optimization suite, incorporating technology from Manugistics, which JDA acquired last year. JDA Demand is designed with five statistical forecasting methods that consider separate data characteristics of individual products to forecast demand based on past sales, a process that can be most effective in forecasting consumer goods like paper towels that remain in the market for a long time, says Paula Natoli, product director of JDA’s demand and collaboration applications.
But retailers of products such as consumer electronics and fashion apparel, which frequently change in features and styles, usually need additional means of forecasting demand. “Sometimes there isn’t enough history for a mathematical solution to generate a forecast. It’s like trying to find a pattern, but you can’t with only a few pieces of information,” Natoli says. “Then companies may want to use lifecycle or seasonal forecasting tools.”
Brooks Brothers uses JDA’s Seasonal Profiling application, which generates seasonal profiles for individual products to augment sales forecast data in JDA Demand. These profiles figure seasonal fluctuations in demand based on such factors as weather patterns, holiday periods or school schedules, and also apply these factors against local as well as regional or national markets. Combined with general sales forecasts, the seasonal profiles led to the sharp increases in forecast accuracy at Brooks Brothers, Loncki says. “We substantially increased forecasting accuracy with the new profiles,” she adds.
To prepare the Seasonal Profiling application to produce effective profiles of products, Brooks Brothers worked with JDA analysts to factor in the proper set of data points, such as the expected availability of SKUs, new store locations where particular SKUs are expected to be in demand, and how weather patterns could affect seasonal sales.
End-of-season sales then are compared against forecasts. If forecasts come in outside of expected accuracy parameters, Brooks Brothers will go back and analyze data inputs to see if either individual profiles or the overall forecast were poorly prepared. “If it’s the profile, maybe the SKU doesn’t need a profile,” Loncki says, adding that sometimes it’s more effective to combine data on more than one SKU to develop a new profile.
In addition to drilling down to demand forecasts for short-lifecycle products with particular seasons, more retailers are also trying to focus more on individual stores and local markets as opposed to entire chains and regional or national markets, experts say. While some older demand forecasting systems, particularly those designed for the largest retailers, may focus more on demand across an entire chain, “more retailers today are recognizing a need for more store-level forecast data to address top-down forecasting requirements,” Aberdeen’s Viswanathan says.
Rather than planning just from the bottom up, or starting with companywide forecasts and then fitting them to individual stores, a top-down application starts with forecasts tied more closely to each store’s product mix, factoring in things such as regional consumer interest, weather-related demand, and local promotional activity by stores and local competitors.
Web-based integration supports this further by providing more seamless data sharing among assortment planning and forecasting tools, which help retailers plan different product line-ups for different stores, JDA’s Natoli says.
Other store-focused demand planning tools include the direct-store-delivery tool HighJump Forecast Advantage, from HighJump Software, a unit of 3M. Launched earlier this year, the Forecast Advantage application is designed to let direct-store-delivery vendors, which manage their own inventory, such as baked goods in convenience stores, view on web-based handheld computers demand forecasts in a corporate database based on prior sales, current inventory levels and additional data such as expected sales from holiday weekends.
By viewing such forecasts while tending to each store, a direct-store-delivery driver can see how many items to allocate to each location, says Chad Collins, vice president of global strategy.
Regardless of what demand forecasting technology can do, retailers still must blend a good mixture of art and science in planning products and sales, many experts say. One of the causes of inaccurate demand forecasts, for example, is failing to factor in prior sales lost because of poorly conducted promotions.
“We can show retailers that at 32% of their stores last year they didn’t have the right price on a product because maybe the store or department manager forgot to put the promotional price up, so they sold 14 units a day instead of the expected 17,” TrueDemand’s Peters says. “So then we tell them in our forecast not to roll back their next plan to 14 units a day, just do the promotion correctly and plan for 17 units.”
TrueDemand, which caters mostly to large retailers, bases the licensing costs of its software-ranging from $200,000 to $2 million a year-on a retailer’s number of SKUs and stores, Peters explains.