Omnichannel commerce is a disruptive force that is changing how companies interact with customers as well as how goods are purchased and orders are fulfilled. Once associated with Internet-only vendors, it has become a condition of survival for retailers, manufacturers, and distributors all along the end-to-end value chain.
Most of companies' attention so far has been devoted to the "front end" of the value chain, such as putting in place digital engagement strategies to grab consumers' attention. While these efforts have been effective in assuring mobile and Internet sales, for most companies executing on those sales—fulfilling, delivering, and sometimes taking back orders—has been unprofitable. For that reason, they must now turn their attention to their supply chains to ensure they are able to not just capture additional sales, but to do so profitably. This requires looking at omnichannel commerce as an integrated, end-to-end supply chain strategy, from suppliers through customers and from planning through execution, rather than simply as a front-end initiative for interacting with customers.
To help supply chain professionals understand the benefits of that approach and how to achieve them, this article will explain the concepts involved in omnichannel and will outline 10 key focus areas in the supply chain for enabling an integrated end-to-end strategy that yields profitable growth.
Any product, shipped from anywhere
Many companies have been involved in multichannel commerce, which means they are supporting multiple channels for interaction with and delivery of goods to customers. Omnichannel is much more than that. Central to the omnichannel concept are sales transactions executed through the commingling of brick-and-mortar stores and e-commerce—unlike multichannel, where companies may have separate ways of interacting with customers and separate supply chains with their own physical assets and supporting processes for each channel. In omnichannel, the digital e-commerce and physical-store worlds meld together, creating a seamless, personalized customer experience across the various channels a company uses to interact with its customers. As shown in Figure 1, channels can come in the form of information paths for communication (customer interaction channels) and physical paths for delivering goods (customer fulfillment channels).
The challenge for companies engaging in omnichannel commerce is to provide a seamless customer experience across the information paths while simultaneously carrying out a rapid, flexible, and profitable response for the delivery and return of goods across the physical paths. Consumers would have the same experiences and capabilities available to them whether they placed their orders via a smartphone, via a Web browser on a tablet or computer, or in a store.
That is part of what makes omnichannel both exciting and challenging: the number of fulfillment permutations, each of which may have a different economic outcome in delivering the desired product, at the desired price, location, and time window. For example, some retailers are transforming their stores to function not just as shopping locations, but also as distribution points. Consumers can buy products while they are physically in the store, order online and have the product shipped from the store to their home or other desired location, or order via the Internet and pick up the merchandise in the store ("click and collect").
Consumers also expect to get a product from wherever it is stocked, at the same price they would pay for that item in their local store. This is often referred to as "the endless aisle," which means that a customer can view and buy the complete range of products that a company sells, irrespective of where the products are physically stocked.
The extent to which companies enable endless-aisle capability depends on a number of business factors, such as growth and profitability. For example, a retailer has to decide whether it will allow a customer in one city to view the inventory that is located in another city thousands of miles away, and whether it will support shipping from one place to another. Complicating matters is that the customer may want the order shipped to a local store, to his or her home, or to some other location, such as an office. (Or perhaps even to an automobile: Volvo recently introduced an electronic service that allows a car to be turned into a delivery drop point.)
With more customers expecting to have access to any product, shipped from anywhere, at the same price as if it had been sourced locally, the costs incurred as a result of incorrect inventory and assortment planning are soaring. Moreover, the rapid growth in the use of always-on smart mobile devices for browsing, comparison-shopping, and buying has led to price transparency, increasing the need for companies to compete on range, availability, service, innovation, and other factors. Unfortunately, many companies continue to make decisions about business policies and supporting strategies that focus on capturing revenue, without giving consideration to the profitability of such decisions.
The big question: How to deliver profitability
E-commerce sales are growing significantly faster than are sales through traditional brick-and-mortar stores. In 2014 in the United States, e-commerce sales grew by 15.4 percent, while traditional brick-and-mortar sales grew by roughly 3 percent.1
Few companies break out e-commerce results from their overall numbers to provide visibility to segment revenue and profitability, but it seems clear that e-commerce has been unprofitable for most companies and dilutive to margin for almost all of them. A December 1, 2014, article in The Wall Street Journal titled "How the Web Drags on Some Retailers" stated that very few companies make money on e-commerce sales; profit margins of those that do are significantly less than the profit margins for traditional store sales.2 In short, the major beneficiary of e-commerce has been the consumer. The challenge now is for companies to figure out how to turn e-commerce growth into profit margins that are consistent with their traditional business models.
Figure 2 illustrates the profit challenge facing traditional brick-and-mortar retailers as they grow their e-commerce sales. (These are general estimates and do not reflect any specific retailer.) Additional shipping and handling costs that are not paid by customers represent margin erosion; those paid by consumers are margin-dilutive, since they only cover costs. In this example, the retailer operates with a 30 percent gross margin in its traditional brick-and-mortar business model. Different fulfillment permutations necessary to support e-commerce sales growth have different margin-erosion profiles.
As a point of reference, Amazon spends 9.8 percent of its overall revenue on shipping. This is partially offset by shipping revenue (shipping costs paid by customers), which amounts to 5.1 percent of overall revenue. Thus, net shipping costs to Amazon are 4.7 percent of revenue.3 Therefore, traditional brick-and-mortar retailers that provide an e-commerce capability similar to that of Amazon's can expect approximately 5 percentage points of gross margin degradation, as this is not part of their traditional business model. Since most retailers operate with net margins of 3 to 4 percent, shipping costs alone can result in unprofitable sales.
The goal of an end-to-end omnichannel supply chain strategy is to capture e-commerce sales while staying above the gross margin break-even point, and ultimately to leverage the supply chain to deliver gross margins comparable to those of a company's traditional brick-and-mortar operation. The implication is that supply chains will have to be much more flexible. Furthermore, systems and processes that manage supply chains will have to provide much more decision support in order to provide individualized but profitable responses to each customer.
The basic operational framework to support omnichannel involves four major process areas: strategy and structure; planning; fulfillment; and learning and automation (Figure 3). Strategy and structure establish the flexibility and cost-to-serve boundaries within which the supply chain will operate. Planning and fulfillment processes execute within these boundaries. Learning provides a feedback loop between fulfillment and planning and strategy, while automation adds tools for linking business strategies directly to supply chain operational processes.
Let's take a closer look at planning and fulfillment. Planning is designed to maximize a company's offer value proposition, including the specific range of products, the price, and the bundling and interaction between products tailored to a specific individual; this is coupled with a time-phased plan of deployments of these products to various physical locations across the supply chain for a complete offer value proposition. Fulfillment is designed to maximize a company's service value proposition, including delivering and returning goods in the way that is most convenient to the customer. Fulfillment must also provide short-term decision making around offers, price, and lead times based on the availability of products, workforce, and other assets, which ultimately was determined by upstream planning processes.
Capabilities based on orchestration and decision rules improve efficiency but require greater levels of sophistication in processes and technologies. Here's an example: A consumer purchases a product online and specifies that it be delivered to her home. Fulfillment processes and technologies must instantaneously determine the best place from which to source that product. The product is available in a distribution center (DC), in Store A, and in Store B. However, the DC inventory has already been allocated, and planned replenishments will not be available for another two days. Meanwhile, Store A has available inventory, and it is closer to the customer's home, so transportation costs would be lower. But demand for the product at Store A is forecast to be higher than at Store B, and Store A will be short on labor capacity for the next several days. Store B, by contrast, not only has the necessary inventory, but also has planned excess labor for the next several days. However, Store B is further away from the customer's home than Store A, so transportation costs would be higher.
In this situation, each order fulfillment option has different costs. Fulfillment processes and systems must consider all of the options and make the best profitable decision while satisfying the service needs of the customer. An additional level of sophistication might involve fulfillment processes that can provide trade-off offers to the consumer. For example, if it is more economical for the company to ship from the DC, but the product will not be available for another two days, a "micro offer" can be made to the customer: wait an additional two days in exchange for a slightly lower price. This offer would be made only if its overall profitability to the company is superior to that of sourcing from Store A or Store B. This is an example of demand shaping, in which visibility to excesses and shortages in the supply chain are used to influence buying patterns. There are many such demand-shaping opportunities in an omnichannel world.
The 10 key focus areas
As noted earlier, an integrated, end-to-end omnichannel supply chain strategy is critical to profitably capturing e-commerce sales. There are 10 key focus areas in the supply chain that enable such a strategy. Figure 4 summarizes these focus areas within the four major business process areas: strategy and structure; planning; fulfillment; and learning and automation. The sections that follow discuss each focus area.
1. Implement flexible, many-to-many relationships in the physical supply chain based on flexibility versus cost-to-serve trade-offs. This is a network activity that defines the level of flexibility a company wants to employ for the receipt and shipment of orders. The vision of omnichannel is to provide the flexibility to order from anywhere, source from anywhere, deliver to anywhere, and return to anywhere. But flexibility comes at a cost. For example, enabling the concept of an endless aisle, in which the customer can order from anywhere and have the inventory shipped from anywhere, requires the flexibility to locate inventory, and pick, pack, and ship from any stocking location to any location the customer desires. If the seller offers free shipping, the shipping cost represents margin erosion relative to the margin that would have been obtained if the product were bought in a retail store. If the product is shipped from a store, the cost of picking and packing also represents margin erosion if additional labor is required for such activities.
The flexibility of the network design and its associated cost-to-serve (CTS) will establish the "profitability envelope" within which the supply chain operates to support omnichannel strategies. Cost-to-serve is important in understanding the margin implications of various fulfillment options, such as those shown in Figure 2.
To understand the trade-offs between fulfillment flexibility and the cost of providing that flexibility, it's necessary to conduct a network design and cost-to-serve analysis. This analysis identifies "from-to" relationships and helps to determine which nodes in the supply chain should be used as omnichannel sources of supply, including warehouses and DCs, stores, and pickup locations. Some retailers may choose to use some, all, or none of their stores as sources of e-commerce supply. For example, a CTS analysis may suggest that endless-aisle capability be limited to a certain geographic area. Conditions change quickly in e-commerce, so this type of analysis should be conducted on a regular basis to determine whether fulfillment options should be expanded or cut back.
2. Enable stocking locations—whether they are warehouses, retail locations, or pickup points—to receive, pick, pack, and ship single-item orders, and to likewise handle returns. Supply chains have largely been designed for efficiencies, driven by scale and economic order quantities. The predominant pattern of the past has been to "supersize" everything—plants, ships, warehouses, and stores. One challenge in an omnichannel world is to deliver single-order quantities with the economics offered by supersized assets.
In those cases where network design and CTS analyses determine that it makes economic sense, the store may become a shopping location, warehouse location, pickup location, return location, or a combination of these. Decisions regarding which "from-to" permutations to support should be driven by the flexibility and growth versus cost-to-serve analysis that was done as part of the network design activity. Among the factors the analysis should consider, for example, are the costs associated with establishing store locations and warehouses as sources of supply for e-commerce sales. This requires some physical reconfiguration, employee retraining, and technology. Moreover, store locations with in-store picking operations require warehouse management system (WMS) capabilities. Product location information from store-floor and space-planning systems can be integrated into in-store picking systems to provide store associates with accurate location information.
3. Plan and execute localized assortments, and personalize offers based on data-driven "personas." An assortment represents the variety, configuration, and range of products that will be made available to a specific selling location to maximize sales. In the past, this was based on maximizing sales per square foot of physical retail space in an attempt to maximize the return on assets, which include physical assets, machinery, and inventory.
In order to provide assortments tailored to local tastes, companies should augment their existing processes with data-driven techniques. This means leveraging "big data." Big data is a general term that refers to the rapid growth in volume, velocity, and variety of the world's digital data. In this particular context, it refers to "customer sentiment" data that comes from various sources, such as Internet reviews of products and services, Twitter, and Facebook. Traditional rules-based computing techniques are not good at processing and adapting to such data. The emerging technique for processing this type of data is machine learning, which is based on algorithms that can recognize data patterns, learn from the patterns, offer insights, and become "smarter" over time, just as humans do. These techniques currently are being employed to process location-specific shopper data to develop shopper segments, or "personas," and then to recommend product assortments that maximize revenue from target consumer segments.
A product assortment that closely matches the desired products and associated attributes for targeted shoppers at a given location drives a high level of efficiency in the fulfillment process. Assortments that missed the mark in the past would result in stockouts; in today's world, they also result in sourcing from an alternative, more expensive point in the supply chain.
4. Employ a common demand planning and management process across all channels, and incorporate big data and machine learning into that process. The term "independent demand" refers to actual customer demand; it is distinguished from "dependent demand," which is upstream in the supply chain and is calculated based on inventory-replenishment rules. In an omnichannel world, forecasts for independent demand must comprehend traditional in-store sales, online sales, and returns. These forecasts will drive all other plans across the supply chain—for warehouses, transportation, store labor, and inventory. It's important to consider location-specific buying preferences in omnichannel demand planning. In order to understand location-specific demand, the information that a product cannot be sourced from a local store, either because it is out of stock or because it is not part of the local assortment, must be fed back to the assortment and demand planning processes.
Independent-demand forecasts (as well as plans) at individual stocking locations may be used to calculate available-to-promise (ATP) for use in fulfillment decisions. ATP provides a time-phased view of available inventory and resources; this information is used to make a promise to the customer that a specific product will be delivered by a specific date. In an omnichannel environment, making sourcing decisions to meet customer demand is based on looking at ATP across multiple locations. This is more sophisticated than past approaches, which typically looked at availability only at a particular location. Furthermore, it may be important to look not just at availability within a specific time bucket, but also at the dynamics of future demand for a given location. If demand at a specific location is expected to be high in the near future, it may be better to source from a different location.
Companies want to make source-of-supply fulfillment decisions based on a number of factors that create the most profitable decision at a given point in time as well as over the time horizon of their plan. For that reason, ATP, which has been known to manufacturers for decades, will become a big focus area in retail. In an omnichannel operation, locations such as warehouses that previously dealt only with dependent demand (calculated demand from downstream warehouses and retail locations) will now also have to deal with independent demand (end consumer demand from the e-commerce channel). These forecasts will also be used to drive labor planning at retail stores and other fulfillment locations.
Demand planning, therefore, will increasingly be driven by big data analysis. This includes shopper data from point-of-sale systems, and unstructured social media data gathered from various sources, including Twitter and Facebook. Demand planning processes must be increasingly dynamic and data-driven, using machine learning techniques. For example, in the past, demand forecasting systems required causal factors to be configured based on response models established through offline analysis. In the future, machine learning capabilities will dynamically analyze social media data and provide an early warning of changing market conditions and shopper behavior, while automatically updating the demand forecasting systems. Likewise, the analytics described earlier can be used to understand a shopper's propensity to return certain merchandise—information that can then be used to forecast returns.
5. Synchronize distribution planning, warehouse management, transportation management, and store operations. Fulfillment operations, including factory distribution, warehouses, transportation resources, stores, and associated labor, have traditionally been planned and executed in silos. This creates time latency between each area, a situation that historically has resulted in the "bullwhip effect," a phenomenon in which upstream operations become out of sync with downstream customer demand.
Successful omnichannel execution requires zero latency across core fulfillment processes. Previously siloed functions such as distribution, warehousing, and transportation should become aware of upstream and downstream constraints. For example, distribution and transportation plans should be aware of downstream warehouse dock, space, and labor constraints. This prevents things like shipments showing up at warehouses that don't have the dock space or the labor to process them, causing the warehouse to be "overrun." Synchronization of these operations provides much more flexibility in responding to customer demand for any product, anywhere, at any time.
6. Implement a distributed order and inventory management system to provide a single view of orders and inventory, and to execute orders across assets. Traditional brick-and-mortar operations assort and forecast for physical store locations, and then generate replenishment orders upstream into the supply chain. This results in orders against warehouses, transportation, and ultimately factories that produce the ordered goods. In addition, traditional order management systems source from single points in the supply chain. An order comes in and is immediately pegged to a predetermined inventory location; if the inventory is not there, it generates an ATP based on the next scheduled availability of the inventory at that location. These systems are not designed for sourcing from multiple locations or to handle orders with multiple line items that must be sourced from one location and delivered in different ways (all together, separate, or grouped).
Distributed order management (DOM) and distributed inventory management systems, by contrast, provide a single view of inventory across multiple inventory management systems. If a company has multiple divisions with different order management systems, the DOM system can provide a single ordering view to the customer, and can then gather information and make decisions using the various back-end systems. These solutions, which have been available for about 15 years, are very useful in omnichannel, where retailers need a consolidated view of orders across brick-and-mortar and e-commerce nodes as well as the ability to source orders from multiple inventory-stocking locations.
DOM systems must be configurable to handle a host of complex scenarios, including sourcing from multiple locations both within and outside of a supply chain (for example, a partner's products sold through a company's own website), as well as orders that are sourced from one location but returned to another. In order to operate, therefore, the DOM system must be integrated with different systems that are processing orders and managing inventory along the supply chain, including warehouse management, transportation management, and distribution planning. And finally, a DOM system must also be armed with the information and logic necessary to make profitable sourcing decisions.
7. Deploy next-generation profit-, constraint-, and allocation-based available-to-promise (PCA ATP). Available-to-promise, originally developed for master planning in manufacturing, is particularly suitable and adaptable for omnichannel fulfillment decisions. A new capability called multisource ATP is emerging in omnichannel. It allows retailers to rapidly view ATP across multiple sources of supply—warehouses, stores, factories, and in-transits—and make decisions based on the most profitable supply location at a given point in time (Figure 5). While this type of analysis is not entirely new, it can now be done instantaneously and on an enormous scale, across huge numbers of orders and multiple sources of supply. The new solutions allow for flexible decision rules that can rapidly traverse the supply sources and decide the source for each individual order. These capabilities are even more powerful in orders with multiple line items, where the seller may or may not be able to source all of the items from a single location.
There are added complexities when sourcing from locations such as retail stores. The primary demand source for physical stores comes from shoppers who walk in and buy items off the shelf. In this situation, they may be holding inventory (thereby making it unavailable) from the time they remove something from a shelf until the time they check out. A further complication is that shoppers often pick up items from one location and place them at some other location in the store. This creates the problem of "floating inventory," in which a certain amount of inventory is not in its intended location.
Thus, shoppers' behavior can lead to discrepancies between what a store inventory system says it has on hand and what it actually has available. Rules used for fulfilling online orders from such supply sources must take this uncertainty into account. An effective omnichannel fulfillment program should understand such complexities but not try to conquer them all at once. It is better to focus on order fulfillment reliability first, and then add more sophisticated capabilities that increase profitability.
ATP that has to deal with these types of situations, which cannot easily be calculated or quantified, may have to employ approximations, or "fuzzy logic." Saving a sale by sourcing the product from an undesirable location could be the best decision in some situations but not in others, and the ATP capability must be able to recognize and prioritize those trade-offs. For example, if a product is not available in the location that offers the best margin, then it may make sense to source it from farther away, depending on the demand dynamics for that product at that location. Inventory age at the more distant location may indicate that a markdown is impending, but the online customer is willing to pay full price right now, albeit with free shipping. If the cost of the markdown is greater than the cost of the shipping, then this is a win for both the company and the customer.
When customer profiles and history are available to the order management system, omnichannel provides a rich set of fulfillment options for engaging in this sort of demand shaping. Companies can start with decision-making processes and general rules, followed by more sophisticated rules that can be codified and configured into the software. These more sophisticated capabilities allow for more complex sourcing decisions. For example, the least-cost source for an online order may be the local store, but the forecast for that item in that store shows a planned promotion that will cause a spike in demand. Sourcing from that store, then, may cannibalize planned sales, and from a holistic standpoint, it would be better to source it from somewhere else.
This logic can be served up to all sales interfaces, including e-commerce and store associates, to not just provide endless-aisle visibility, but to also ensure that sales are made with the best profit profile for the business.
8. Synchronize product returns with assortment, buying, and demand planning. The return rate for e-commerce sales is significantly higher than for traditional brick-and-mortar sales. E-commerce returns can range from approximately 5 percent for basic items to as high as 60 percent for high-priced fashion items.
This makes returns management a major concern in omnichannel retail. As previously noted, it is important to forecast and process returns as part of the planning and execution processes. At the execution level, decisions must be made about the best disposition of returned inventory, and returned merchandise has to be routed to the appropriate location, whether for full-price sales, markdown, repair, or disposal. Both forecasts and visibility to actual returns, including their final disposition, should be incorporated into material buying plans so that buyers are netting orders against returned items.
Information about an individual customer's history of returning items should also be captured. This data can be used to determine whether there is a correlation between certain customer segments and their propensity to return merchandise. "Propensity to return" can then be used as a factor in shopper- and segment-specific personalization. For example, promotions and offers can be tailored to shoppers with a historically high rate of returns to improve their overall profitability.
9. Create a "learning loop" among fulfillment, planning, and strategy. Capturing fulfillment execution data provides useful information about the profitability of sales transactions. For instance, items that could not be sourced locally and were shipped from other locations represent margin erosion; this information should be fed back to assortment and demand planning so those functions can adapt assortments and stocking levels to ensure higher profitability in the future.
Fulfillment statistics can be quickly analyzed and compared against target and optimal numbers. Gaps can be identified and planning and policy administration advised accordingly. This information can also be fed to a cost-to-serve "data warehouse," where it can be analyzed to help companies understand how to adapt business strategy and structure. This CTS data is also critical for making ATP and associated supply-source decisions.
10. Use orchestration dashboards to automate policy management for strategy, planning, and execution. Omnichannel strategy and associated decision making must be driven by overall business strategy. This can be done through a combination of process and technology. An "orchestration dashboard" provides the means to input business strategies (such as growth, profitability, and operational measures like customer service and inventory) and then translate them into operational policies (for example, always fulfill from the fastest source or from the most economical source, or vary it based on the historical customer profile). These decision rules are then distributed to business processes and supporting systems across the end-to-end value chain.
Omnichannel has an impact on most core areas of supply chain management, including planning and fulfillment. The policies associated with these processes, therefore, need to be synchronized with the business goals of the enterprise. For example, companies may want to operate at various points along the spectrum between growth and profitability, particularly for e-commerce sales during the period when brand development is important to long-term success. A growth-at-all-costs strategy would result in significantly different fulfillment policies than a profitability-only strategy. Furthermore, the dashboards should be set up to provide statistics from the learning loop to understand gaps between the business strategy and actual fulfillment.
Technology: The indispensable enabler
Omnichannel has become a tidal wave in retail in the past five years, as consumers in mature markets have increasingly used the Web and mobile devices to interact and transact business. It is an exciting, disruptive development that is pushing supply chains and the practice of supply chain management toward "supply chains of one." In other words, the end customer or consumer is gaining increasing power, requiring supply chains to provide a perceived personalized experience to each customer, based on a specific value proposition.
Omnichannel intersects directly with segmentation, another important topic in supply chain management. Both involve tailoring supply chain responses to specific customers while preserving or increasing profit margins. It is simply too expensive, however, for retailers and manufacturers to reconfigure their physical assets for the purpose of tailoring service to individual customers. Instead, companies must tackle the omnichannel challenge with the very weapon that created the challenge: information technology.
Technology is a key enabler of the 10 steps to profitable omnichannel commerce identified in this article. With the right tools, policies, and processes, companies can enable virtualization of supply chain assets, including inventory, providing the ability to view these assets as an integrated whole and orchestrate them to profitably fulfill demand.
1. "Quarterly Retail E-Commerce Sales 4th Quarter 2014," U.S. Department of Commerce, Bureau of the Census.
2. Suzanne Kapner, "How the Web Drags on Some Retailers," The Wall Street Journal, December 1, 2014.
3. Amazon.com 2014 Annual Report.