Flexible, automated, goal-based allocation and replenishment
Getting the right product to the right place at the right time requires massive time, money and effort. Business and financial goals, merchandise planning, store clustering and decisions on what to buy and where to sell it must all be considered months ahead. While there may be a set of tools to drive and semi-automate replenishment, these systems do not adapt to changes in the retail environment. Instead they put retailers further away from the action, leading to lost sales that could have been avoided.
Q enables retailers to see exactly what is going on at the product and SKU level. Life-cycle, seasonality, promotions and preferences are all considered in order to achieve merchandise and financial goals.
Continuously monitor local demand to maximize full price sales //
The goal of getting products to the right place in order to maximize full-price sales sounds straightforward enough. Yet because of the unpredictable nature of retail, there will always be guesswork. While pushing all product out at once may be a calculated move, this immediately restricts a retailer’s options, leaving no room to make the most of full price sales. Q starts from a different point: by understanding how the product will sell through its entire life on a location by location basis.
Having made that initial assumption, it then continuously re-evaluates how it expects the product to sell- in real time. This enables retailers to understand which stores will offer the greatest potential for full price sales – over the remainder of the product’s life.
Q never loses sight of the goal //
Unlike other systems, Q is goal-directed. That means that no matter what happens, Q never veers away from the desired outcome. Eg: ‘We want to maximize sales subject to a minimum service level and presentation’. The focus then is to drive the product towards that goal on a location-by-location basis and uses available inventory to continuously maximize the ability to achieve the merchandise and financial goals. Regardless of external changes, Q finds the best solution dynamically without user interaction. That’s because the goal hasn’t changed: only the best way of achieving it. Unlike other systems, this significantly reduces the amount of user touches required to make the replenishment system effective.
