Quantum Retail’s Secret Sauce

by Deepak Sharma on Thursday, March 10, 2011

Interview of Chris Allan, Quantum Retail Technology’s Chief Strategy Officer with P.J. Jakovljevic in Technology Evaluation Centers where he talks about Quantum’s Q Platform, competitive landscape and what wakes them up at night.

Quantum Retail: Challenging the “Enterprise Apps Establishment” and Retailers’ Mindset – Part 2

PJ: What is your killer value proposition that other retail software “usual suspects” (e.g., Oracle, SAP, SAS, JDA, etc.) fail to provide? In other words, what are the pain points that only you can cure for your customers (and with what typical benefits)?

CA: The Q Platform (explained in Part 1) actually solves the problems that these other vendors mainly talk about solving–and delivers on the business case every time, with proven, measurable results. Quantum has developed the concept of managing by Merchandising Strategy–determining the role of the product within the customer offering, such as being an image item, loss leader, traffic driver, etc. (see Part 1 for more details).

Users are not asked to select from an overwhelming number of forecasting algorithms and replenishment algorithms, and to set a slew of tricky parameters up around each of those algorithms for every stock-keeping unit (SKU) in every store. Q takes the chosen Merchandising Strategy and understands the objectives of the product from both a financial and a merchandising perspective and ensures that every inventory decision that is made is aligned with achieving those objectives.

The way that customers buy product changes over time and Q adjusts automatically to react to those changes, ensuring that alignment is maintained throughout the products’ lifecycle. This is very different from having to actively maintain the ordering, allocation, and replenishment configurations for every SKU in every store and manually ensure that the system is set up correctly (which is the value prop of our aforementioned competitors).

In the process of understanding items Q considers over 30 dimensions of product behavior including average sales, maximum sales, demand, days between sales, lost sales, days between stock-outs, current inventory, last stock-out, weeks of supply, percent in stock, etc. Beyond these typical sales and inventory metrics, Q also understands the following:

  • When the issues happened, e.g. an out-of-stock on Monday has different gravity than out-of-stock on Saturday
  • Variations in contributing factors such as lead times, lifecycle, and customer service level
  • Variability and uniqueness in sales such as volatility, lumpiness, lost sales, demand vs. sales
  • Finally, and perhaps most importantly, profitability metrics such as gross margin return on inventory investment (GMROI)

These capabilities have led to retailers being able to have a high degree of automation with Q using exception management to highlight only those areas where users should be spending time in the system. Typical results achieved and verified (by Quantum’s customers that were mentioned in Part 1) are as follows:

  • A 2.2 percent full-price sales increase (in fast fashion)
  • A 5.6 percent sales increase (in general merchandise)
  • A 4 percent increase in gross margin
  • An 11 percent inventory reduction
  • A 40 percent reduction in overstocks