Part 1 of this blog series analyzed typical issues that retailers face in their cutthroat competitive environment and concluded that traditionally available packaged retail enterprise applications are sub-optimal, provide only stovepipe views, and demand constant manual intervention by a highly sophisticated user. This is especially true in the case of handling ever-more difficult products and assortments (e.g., big ticket slow-moving items, sized merchandise, etc.).
The article then introduced Quantum Retail Technology, an up-and-coming company with a budding install base, who has an intriguing mission and value proposition for retailers that have to deal with a slew of tricky retail items. What follows now is my discussion with Chris Allan, Quantum’s chief strategy officer.
Quantum’s Secret Sauce
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
PJ: How do you enable the link between assortments, pricing optimization, and inventory optimization (IO)? Do you cover any other retail issues beside these (e.g., store workforce planning & scheduling, planograms, task execution, etc.)?
CA: All of the modules of Q are deployed on the platform and share several components and engines that ensure that they see not only the pieces of the problem that they are directly solving, but the holistic impact of those changes on other business processes. As said in Part 1, the Q: Assortment and Range Planning module dynamically updates and adjusts performance projections on the fly, based on decisions made by the user.
The underlying forecasts impact downstream ordering and subsequent replenishment and allocation processes automatically. In addition, all ranging information, constraints around pack and distribution multiples, and the merchandising strategy are also shared and initiate downstream workflows around planning orders, prioritization, and distribution.
Although Q does not execute price optimization decisions itself, there is a standard application programming interface (API) to receive price changes and promotional information to ensure that any and all demand-shaping effects that are anticipated are taken into account in the supply side of the problem. Namely, we all know that the goal of the promotion is to change consumer demand, i.e., the cause the so-called “demand lift.” The demand forecast must be adjusted to reflect both the scale and timing of that change in anticipated demand
Alongside the demand lift, there is often a required shift in how that promoted product is supplied to stores. Often inventory will be placed in stores ahead of the event, and there is also the question of how stores should be stocked coming out of the event. Thus, Q is also solving problems such as how the inventory should be built upstream the supply chain to support the promotion and the level of inventory that should be in stores at the end of the promotional period. Q can also feed a sales forecast that takes into account inventory effects into the price and promotional optimization processes to improve performance projections and analysis.
Every inventory decision that is made by Q is optimized to achieve the merchandise and financial goals specified, and lives within the constraints of the assortment and ranging. Range planning, from setting the concept, purpose and direction through to selecting the products and finalizing the price, distribution, and sales forecast, is an iterative process. That process typically involves buying and merchandising folks working hand in hand, often with somewhat opposing objectives. Q automates store clustering for effective management of ranging process and automatically determines the optimal depth for the plan. Users can review and manage through a variety of attributes and dimensions unique to each area of business.
In order to align assortments with customers, Q utilizes objective-based forecasts generated by Q:Forecasting and Order Planning to project impacts of assortment changes. In order to rationalize assortments, Q looks at measures such as Profitability (GMROI) and Sales Disparity to see how well a merchandise area in a cluster is performing. Users are alerted with recommendations to increase to decrease the range to support the way that consumers are buying that product.
Q does not handle store planning, workforce scheduling, planograms, and task execution per se. However, these systems can benefit by utilizing Q’s results and recommendations as an input.
Ingrained Poor Habits are Q’s True Competition?
PJ: How do you view your competitive landscape, i.e., why do you win or loose to certain competitors?
CA: There is no strong pattern in the competitive landscape - we often see the big enterprise resource planning (ERP) vendors in accounts, but we have not lost deals to them. The strongest competitor is actually a ‘do nothing’ or ‘try and build this in house’ attitude. These decisions are often made with short-term costs in mind and perceived risk, but the risk of doing nothing, or focusing effort on one piece of the problem that cannot scale, is actually far greater.
PJ: Would you like to share your views towards going multi-tenant on-demand (subscription-based) or not?
CA: Quantum has offered a hosted solution for the last four years and every customer takes advantage of that during the implementation phase, at least. Hosting the solution allows us to better serve our customers through a deep understanding of the operational environment and the ability to manage patches and upgrades and to be proactive in addressing any customer data specific issues.
Multitenancy is a natural extension of scaling this hosting operation to further serve our customers. There will always be a few retailers, however, that need their systems to be hosted inside the firewall.
PJ: Although the trends have been positive lately for Quantum (and the retail sector in general), what issues/challenges are still keeping you asleep at night (e.g., retail ERP and/or supply chain management [SCM] guys eating your lunch)?
CA: Definitely not the ERP/SCM guys eating our lunch! One thing keeping us awake at night is how many of the longstanding myths of retail are still held as facts by many retail IT organizations. There is a lot of unlearning to be done - and much of the misinformation comes from old technology, old constraints, and old thinking.
Somewhat linked to this is the role that some IT organizations are playing within the retailer. It is time to move on from supporting infrastructure and transactional systems, to being a strategic partner to the business and fully understand their needs and priorities and align investment accordingly. These two issues (i.e., relics) are really holding some retailers back - and they will struggle to compete.
PJ: Do you have any parting comments to add?
CA: In addition to our system’s adaptive nature (i.e., to constantly evaluate and react to ensure progress towards stated objectives), there has been the users’ ability to use it quickly and with immediate results. This is due to our system’s simple and intuitive navigation, ease-of-setup (configuration), and exception-driven (i.e., via workflows and alerts) automation of complex operations.
This leads to quick and simple implementations in fewer than 20 weeks, while the system’s light footprint means smaller investments, lower total cost of ownership (TCO), and quick return on investment (ROI). Hosted options are available, as mentioned earlier. Our six current customers that were mentioned in Part 1 have reported fewer than 12 months to reaching a 100 percent ROI, measured through test and control groups.
To that end, Quantum has developed the Agile Customer Experience (ACE) implementation methodology, with an approach that focuses on speed to value for clients. This is done by implementing in short “solution sprints” rather than in traditional big-bang waterfalls or consultant-heavy methodologies. ACE features the following generic solution sprints with typical expected duration times:
- ROI: Performance Visibility, Value Identification (6 weeks)
- Forecasting, Seasonality, Lifecycles (4 weeks)
- Strategies and Inventory Targets (4 weeks)
- Order Planning and Events (4 weeks)
- Allocations and Distributions (4 weeks)
- Final Acceptance Testing (4 weeks)
- Roll Out
Dear readers, your comments and opinions with regards to typical retailers’ issues and solutions are more than welcome. I would certainly be interested in your experiences with various retail software tools in general and with Quantum in particular.
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