Dynamic Store Merchandising (DSM)
A Solutions Overview DSM is delivered as a “DSM Appliance” from IBM, and executes centrally supporting each retail store within the chain as a unique business with its own demographics and performance outcomes. Optimize the operational and financial performance of each store and the overall group performance is automatically delivered. The following image is an illustration of the workflow architecture and where each component is applied to the delivery of process optimization and customer communications. DSM is comprised of four separate components. MIQ provides the baseline data management, and functions as a “data pump” exposing all the data elements required for the effective execution of DSM. While MIQ may be deployed to deliver this capability in sectors beyond retail, in retail it exploits functionality specifically aimed at optimizing retail performance. This document summarizes the capabilities of DSM and its components, OIM (inventory management), P+ (real‐time optimized pricing) and SIQ (the most advanced customer messaging toolset). 1. MerchandiseIQ (MIQ) – is the data management “Appliance” coordinating all data federation requirements for DSM’s execution. MIQ is a J2EE compliant product with a range of data integration and connectivity elements that are applied to expose and receive those data elements essential to the execution of the in‐store processes applied to optimizing performance. In addition to the automated data management capabilities, MIQ is a RAD tool that has a Lisp‐like Java artificial intelligence engine at its core. With MIQ it is possible to configure logic parameters and create optimized code execution without the need to write code. The logic engine is exposed through a natural language interface within MIQ that enables parameter configuration via drop‐
down selections described in retail nomenclature. MIQ in isolation is a powerful real‐time ETL engine. MIQ enables the automated management of all process dependent data elements, regardless of data source, application, or schema. It is an ideal tool for managing content consistently across any multi‐channel merchandising strategy. In the delivery of retail, and in this case, DSM, is only used by the implementation team. When applied as a separate data management appliance, the user resources will be trained. 2. Optimized Inventory Management (OIM) – OIM is the process execution engine managing the detection and correction of all incidences of Inventory Risk (IR). OIM executes in support of all stores, and DSM is designed to scale support to chains with thousands of stores. OIM continuously tracks sell‐through rates for every SKU assigned for optimization by the retailer. Sell‐
through rates are recalculated at every item scan for the SKU and this is then applied to a Page 1
recalculation of the demand curve through to the day/time planned for the next store replenishment of items for that SKU. Over time, OIM learns the demand curve pattern for that store by time‐of‐day within day, week, month and year. This is an ongoing learning capability that delivers the “expected” supply curve required to meet the demand. As you would expect, every store will vary from every other store on this metric, and optimizing store performance is dependent on enabling these variances to be addressed according to the dynamics of a given store, not a “one‐size‐fits‐all” response to store inventory and supply. OIM also maintains real‐time inventory at the SKU level within each store, and at the point of each item scan OIM: a. Measures the sell‐through rate for a defined time period, (as configured according to category and/or SKU) b. Applies this “current” sell‐through rate to recalculate the actual demand curve for the SKU through to the time to the next replenishment c. Compares the recalculated demand curve against the available supply for the SKU to expose any imbalance, while also calculating the actual supply “cover” provided for that SKU through the next replenishment d. Exposes any variance in the balance between demand and available supply that will translate into an “inventory risk”. This is a condition that will result in financial performance losses unless addressed e. Where an inventory risk variance is detected, OIM generates a range of action initiatives to ensure the imbalance between demand and supply is corrected, while also managing the various data points of inventory cover, replenishment quantity and time adjustments, along with margin management, to ensure GMROII targets are met f. Where a SKU is overstocked then OIM will recommend a “minimum” markdown required to accelerate sell‐through rates for the SKU and rebalance demand with supply. OIM uses P+ to analyze and provide the markdown intelligence that assures the sell‐through rate to b accelerated with minimal loss of retail margin. This is the self‐learning functionality within P+ that enables DSM to apply a common, single, code base, which executes to uniquely optimize each store’s product performance in response to the operational dynamics of that store. P+ is designed to automate price adjustments and coordinate the inventory/replenishment data points of OIM to enable MIQ to dynamically create the customer messaging content for in‐store communications, regardless of the communications device g. OIM manages inventory at several levels for a SKU: i. Single store inventory where all store items are on shelf and DC replenishment is direct to shelf ii. Multiple store inventory where there is a back‐of‐store warehouse replenishing the shelf between store replenishment cycles from DC iii. Within both the above, the ability to maintain inventory by: Page 2
− Shelf count − Back‐of‐store count − Balance‐on‐hand − Reserved (for click & collect or click & deliver) − Project minimum, (where minimum shelf quantity >one) − With “sell‐by date” identifier (in case of packaged perishables) h. To this level of inventory precision is OIM’s tracking of all products’ price/margin performance in conjunction with sell‐through demand. The extended array of performance data points provides OIM with a unique capability for predictive, (quantified,) analyses exposed in real‐time to store managers via retailer selected tablet devices, such as IPads or equivalents. The objective is to always ensure supply matches demand, including on‐line orders fulfilled in store, and that gross margin return on inventory investment (GMROII) is maximized across all products. i. With OIM managing inventory counts at shelf, back‐of‐store, reserved, project (minimum), and by sell‐by‐date in the case of packaged perishables, it is then applied to not only detecting the inventory risk events, but also correcting the problem before the store suffers the predicted negative financial impact, e.g., if under‐stocked detected then OIM will generate a predictive alert of the impending out‐of‐stock (OOS) condition for the store should that precede the replenishment timing for the product. Additionally, and of greater importance, OIM is able to prevent shelf OOS events by applying the sell‐through rate to determine time‐to‐OOS‐on‐shelf. This enables OIM to generate predictive alerts on shelf OOS events before they occur and ensure the store associate replenishes the shelf to planogram facing count before the shelf count decrements to zero, or project minimum, according to configuration. It also monitors the process to ensure the replenishment task assignments are executed j. DSM may be deployed to apply OIM to the production of predictive analyses only, where there is no automated corrective actions applied involving price adjustments and/or replenishments to store. In this case there is no execution of SIQ and no connection to the various in‐store customer communications devices. The DSM analyses are “Predictive Risk Analytics”, where DSM is exposing all GMROII performance trends creating inventory “risk” events. These events are occurrences that will lead to losses if not addressed. This is the USP of DSM analytics, in that they are truly predicting risks before the risk translates into a problem. Additionally, DSM analyses provide guidance on all required corrective actions to be applied to demand chain, pricing and assortment performance. 3. Price+ (P+) – is the DSM component responsible for analyzing and recommending adjusted price points in all cases where the standard retail price is not delivering the demand sell‐through rats commensurate with the available store supply. It is important to note that P+ is not a substitute for the generally applied price optimization solutions “setting” forward pricing, whether for every‐
day retail price points, or those to be applied to promotions. P+ is triggered in response to unpredicted overstock conditions requiring a rapid, but minimal margin erosion, price markdown Page 3
to accelerate sell‐through before the overstock necessitates deeper markdowns, or, in the case of perishables, shrink that loses 100% of retail price plus disposal costs. P+ is continuously measuring price performance in conjunction with the OIM inventory management in order to progressively “learn” the minimal markdown requirement to accelerate sell‐through by day and time for every product in the store. P+ applies advanced algorithms to its price optimization, and enables real‐time corrections to price points based on inventory risk events. 4. ShopIQ (SIQ) – this is the component applied to the creation and management of all outputs to the customer communications devices. SIQ is both a RAD environment for the creation of templates; complex, multi‐layered, templates may be developed visually and delivered in days, not weeks. It has its own embedded rules capability and is fully integrated with MIQ as an automated source of informational content to be applied to the communications images, whether to print or to electronic devices. SIQ manages currencies across all international formats and maintains templates that are generated according to inbound content. In this way a multi‐layered single template can accommodate many different output formats. A key advantage is the coordination of all content from MIQ, whether informational or transactional. The combination of MIQ, OIM and P+ enables DSM to provide the most advanced GMROII performance analyses of any offered in the retail sector. This analytics function is a fundamental part of a full DSM execution, but may be deployed as a stand‐alone analytics engine. Its differentiation resides in its ability to expose “risks” before they translate into losses. DSM Predictive Risk Analytics (PRA) – this is the analytics engine of DSM that exposes the specific performance of a store, including all inventory risk detections, margin losses arising from inventory risk, most impacted SKU/categories, slow or non‐
moving SKUs and the overall financial losses these occurrences impose on the GMROII within each specific store. PRA is the first phase in any DSM deployment; verifying the business case for a full DSM rollout while exposing those SKUs delivering the problems. There will always be the 80/20 rule in these situations, where a concerted effort to optimize GMROII performance on the offending 20% will deliver 80% of the loss recovery to profit. The following is a summary of all the capabilities provided by DSM once fully deployed with devices in support of those in‐store processes and customer communications able to influence buying behavior at the point‐of‐purchase. 1. Eliminating Inventory Risk – DSM’s OIM actively eliminates inventory risk arising from imbalances between demand and supply. DSM executes in support of a very simple objective, which is the balancing of product supply to “actual” product demand. Imbalances between demand and available supply are the source of almost all store losses, and in the case of perishables, DSM exposes the potential to gain back as much as 50% of store profit from already sunk product costs, (in the benefits propositions we only assert a recovery of 25%, but a higher rate of recovery is expected). DSM monitors the sale of every single product, and upon detecting the sale, DSM calculates the sell‐through rate for a time period assigned to the determination of “dynamic” Page 4
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demand. Determining this “demand” cover of product inventory occurs in real‐time and is recalculated at the point of every sale of the measured product. In a perfect store environment, the demand inventory cover calculated by DSM would match the available supply (actual) store inventory for the product. DSM maintains its own accurate “supply” inventory count to ensure this measurement is always correct. Through its real‐time detection, DSM will automatically apply the minimum discount required to restore the balance between demand and supply, and avoid last minute excessive discounting to beat the sell‐by date. Eliminating Shelf OOS losses – managing the imbalances between demand and supply also mitigates losses arising from out‐of‐stock conditions, as the prevention of any OOS occurrence, and/or the automatic recommendation of an alternative product when the original is truly OOS can add as much as 1.5% of total sales to the store’s operating profit. DSM will send an alert to your store associates warning them that they will have to replenish a shelf in the appropriate amount of time that reflects the stores actual trading that day. Minimizing Post‐Promotions Mark‐Downs – addresses that inventory risk caused by excessive overstocking arising from promotions that fail to sell‐through in line with projected promotions targets. DSM monitors promotions sell‐through rates in real‐time to ensure promotions are running to plan. The process of “demand signaling” is applied to deliver immediate alerts to store management when sell‐through rates drop, while enabling parallel communications to SCM. Reactive Dynamic Pricing – enables intelligent markdowns to be applied in response to inventory overstocks and competitive threats. Maximized GMROII across all products – ensuring the best possible return on inventory investment while minimizing inventory‐carry. Additionally, DSM enables the retailer to realize “projected” sales and profit targets by week of year. Vendor Managed Inventory (VMI) – DSM adds value through its ability to track sell‐through rates and inventory levels for all products. DSM will automatically send replenishment alerts to both the supplier and to the retailer management in accordance with the control requirements of the retailer. So long as permitted by the retailer, DSM will detect any unpredictable inventory movements and trigger real‐time alerts to the supplier. In this way the supplier is made aware of the true inventory and sell‐through rates for the products it manages directly; thereby taking costs out of the process, while also eliminating, or minimizing, both over‐stock and out‐of‐stock occurrences within their supported product range. Store Associate Task Assignments – this is a key area of impact. It is evident in the most automated discount generation based on inventory risk, where alerts are delivered to store management, and data outputs to the retail management systems and SCM, (where required). It then extends into several other areas, such as: a. Predictive shelf out‐of‐stock alerts to store associates notifying the need to restock an item within a specified time frame to avoid an “actual” OOS condition arising b. Ability to alert store manager when POS throughput reaches certain nominated thresholds that may require lane support to be applied c. Control of baking cycles aligned to sell‐through rates in bakery items d. Control of cooking cycles for all cooked meats. Also coordinated with cooked meat sell‐
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e. Monitoring of store associate verification of task completion f. Reporting on task execution performance 8. Production Planning – one area of extreme losses in perishable items is the over‐supply of items subject to production planning and scheduling, whether within the store, or where outside suppliers undertake the production in accordance with scheduled “demand” replenishment. The challenge is always the search for the optimum balance that ensures no lost sales through OOS conditions arising from production shortfalls, and the losses arising from discounting, or worse, waste, arising from over production. The key areas targeted by DSM’s production planning are: a. Real‐time predictive scheduling of baking cycles aligned to sell‐through rates in bakery items, including pastries b. Scheduling of cooking cycles for all cooked meats. Also coordinated with cooked meat sell‐
through rates c. Preparation requirements for fresh sliced meats derived from whole meat inventory d. Accurate scheduling of all pre‐prepared items, such as sandwiches, salads, sushi, soups e. Reporting on performance of production based perishables 9. Enterprise Demand Forecasting – the inventory risk detection requires TA and OIM to recalculate sell‐through rates and demand for every store SKU at the point of every scan. This capability is primarily applied to the elimination of inventory risk, but it is also a mechanism for absolute demand forecasting precision at the SKU level within each store. This can be applied to the retailer’s F&R solution set in accordance with the retailer’s specifications at the point of the decision to proceed with a full store rollout. 10. Support for print and electronic communications – DSM applies MIQ to support content provisioning of all customer communications, whether via printed shelf tags and signs, ESLs, Displays, kiosks, customer smart phones, or retailer supplied scanners for self checkout. This management of live informational and transactional content from any data source of any data model within any application is a key advantage of DSM’s MIQ platform in any environment focused on influencing customer behavior with point‐of‐purchase persuasion. This is a key consideration when product and customer information is dispersed across many differing business unit applications and data sources, including web sites, and external advertising, (in all its forms). When using graphical ESLs, there will typically be many different data sources for all the attributes required to create the ESL content. DSM will automatically ‘detect’ and update the print or electronic displayed content when the source data changes. In‐Store Warehousing and Dynamic Pick Lists DSM includes advanced capabilities within its OIM component. These are configurable, and may be switched on (or off) at any time. Applying these are optional add‐ons is logical, as not all stores will maintain a back‐of‐store warehouse functioning as the intermediary between the DC/Supplier and the shop floor (front of store). These functional extensions support the primary objectives of DSM, being: 
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Maintain supply in store in balance with actual demand Ensure no product goes OOS on shelf so long as it exists in BOS Maximize GMROII for all products at all times With the warehousing enabled, DSM extends the prior OIM capabilities with the following: Page 6
1. Ability to map shop floor (store) into physical “zones” (categories) able to be maintained, (changed), by the retailer through a DSM provided interface. 2. Ability to map back‐of‐store (BoS) warehouse by “locations” specific to the store and configurable by the retailer at point of implementation 3. Maintain product allocations within both front‐of‐store zones (in this case by zone planogram space plan) and warehouse locations (no planogram), as well as the BOS warehouse location product association with the store zone 4. Enable optimal warehouse location space allocations based on product sell‐through performance in shop. High volatility products should be prioritized to high availability warehouse locations. DSM will automatically guide these alignments specific to the stores’ sales dynamics 5. Eliminate visual gap scanning using OIM’s shelf inventory management. Each FOS zone is now dynamically creating “batch” pick lists and scheduling these to ensure zone fulfillment before any zone product goes OOS on shelf. These zone pick lists are dynamically scheduled by OIM to optimize personnel resource allocations to BOS warehouse picking in response to zone pick lists. OIM will provide the store manager with a timeline resource plan optimized for that store by each opening hour where BOS picking is to be provided. OIM will create a resource plan that meets pick requirements with minimum personnel 6. Accommodate urgent picks where a customer may require a quantity unable to be fulfilled from available shelf inventory where the available inventory has not hit the threshold for an OIM pick trigger. These will be scanner activated by a store associate and execution controlled by OIM 7. Ability to prioritize “picking” whether scheduled or event triggered, (i.e., gap scan or customer intervention), by store location and by individual products within that store location, e.g., milk cannot be allowed to go OOS on shelf 8. Adjusting minimum inventory count levels on shelf to modify the frequency of pick list generation thereby enabling OIM to further optimize pick schedules so they are accommodated with the available personnel resources 9. Automatic alerts of time‐to‐shelf‐minimum from the real‐time demand measurement alongside the insertion of that product into the pick schedule for the store location. The objective is to progressively eliminate all visual gap checking in support of shelf replenishment, outside the urgent interventions driven by unusual customer requirements 10. For food/grocery, automatically creates accurate demand replenishment feeds for in‐store production, (bakery, meat cuts off primal, cooked meats, such as roast chicken, etc.). In this way DSM ensures production matches actual demand 11. Automatic management of store and warehouse availability to meet demand. This includes both store (shop floor) and .com (click&collect) fulfillment, as DSM manages inputs from the .com sales for collection, and maintains these as “reserved” inventory for the specific store. Whether .com is fulfilled from store (shelf) or warehouse location, DSM maintains the product item count within category, and within location, (FOS and BOS warehouse). OIM ensures visibility of availability of product, i.e., if OOS in warehouse, but available in store, this store fulfillment will be exposed to warehouse personnel. 12. Tracks the store’s sales and fulfillment performance for both .com and personal shoppers. Note: this is often referred to as “managing availability to promise” in the case of on‐line sales, but it must take cognizance of the store’s need to support the availability for both .com and “shoppers with legs” in‐store 13. Exploits the retailer scan activity to ensure improved accuracy across each of the inventory count components of: a. Shelf (store) Page 7
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b. Back‐of‐Store (warehouse) c. Reserved d. Sell‐by‐date for packaged perishables (not immediately beneficial, but worth noting in functionality) The scan functionality also enables analyses of shelf facing performance in cases where the same product may be subject to promotions requiring its availability in, say, aisle, end‐cap and gondola locations in‐store. DSM monitors the scanned replenishments to each of these facings; using these replenishment counts to analyze the sales performance of the product by each facing. Progressively, DSM will “learn” the replenishment counts so it can “suggest” an acceptably accurate replenishment requirement to the warehouse when scheduling location/facing replenishment from warehouse to store Managed and controlled adjustments to planogram facing(s) in cases where a product is on back‐
order and the store manager needs a “controlled” ability to temporarily change the shelf space plan Tracks both scheduled and event triggered store replenishment from warehouse to ensure completion and apply verification of item counts in store and warehouse arising from replenishment. To a large extent, this scan capability enables DSM to minimize the need for any cycle counting Accepts DC/supplier deliveries into BOS warehouse, allocating delivery products to warehouse locations and tracking both cages and pallets for optimum usage while held within BOS warehouse. Once free of products, the cages and/or pallets are assigned for return to DC/supplier Ability to auto‐generate replenishments to VMI suppliers and track delivery verification via the scan systems’ processing of these receipts in response to DSM generated replenishment requests Eliminates “over‐estimating” of pick lists, as DSM is measuring sell‐through to determine shelf cover and ensure shelf is only replenished to planogram facing count maximum. With DSM, store replenishments, whether scheduled or event triggered, will always represent the “real” store replenishment requirements Ability to manage warehouse stock on date code (using sell‐by date” function) to ensure warehouse replenishments to store are always first in/first out. This is specific to food/grocery sector Maintains visibility of all stores and DCs; providing the functionality to analyze what inventory might be shipped from one store to another in concert with each store’s demand requirements Ability to provide product inventory counts of ESLs where these are applied in place of printed shelf tags. With ESLs OIM will show its count of remaining shelf inventory along with the available inventory in BOS and the location where the product will be found. This can be automatically exposed or triggered by a store associate using a hand‐held device for controlling such data exposure. With print, the store associate would use a scanner on the shelf barcode and obtain the same data The following are some slides illustrating the way the shop front‐of‐store zones and back‐of‐store warehouse locations interact with the overall supply (now demand) chain continuum within retail. The concepts of DSM recognize the reality of all stores being different, and that difference extends beyond differences arising from customer demographics and blurs into physical layouts, available space, ease of access and all impact the logistics of shelf fulfillment in support of store demand. DSM addresses these differences and makes them irrelevant in the context of store performance optimization. Page 8
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