The biggest shift for me in dealing with the e-Commerce channel was a mindset. I had approached planning for this channel with the same tools and perspective that had worked for me in planning for retail and distribution customers. These were not effective for this channel.

So I had to change my approach and look at using different tools.

I found that it also requires training salespeople to think differently about planning their online business, as it requires a more agile, hands-on approach to planning. Here I outline some of the key differences and also present some ideas for managing demand planning for this channel.

Who’s My Customer?: Unlike the more familiar retail and distribution channels, where demand is linked to a specific customer or location, in e-Commerce the customer is often not clearly defined. Online ordering masks the customer’s identity and location, making it difficult to see how your products are perceived in the market or where the sales are occurring.

 Lumpy & Volatile Demand: In addition, demand is often quite lumpy, and changes in how items are managed and promoted can cause demand to fluctuate wildly. And then there is the issue of hoarding, where customers buy large quantities of a product to restrict availability and control pricing. And the explosive growth of sales for some e-tailers is an additional challenge.

 Data? What Data?: Another challenge is that in many cases there is limited historical data available to assist with planning. And even if there is data available, it is often of limited usefulness. For example, historical POS data for online sales can be significantly impacted by any or all of the following:

• Price-matching
• Hoarding
• New listing / De-listing
• Flash sales
• Availability
• Customer comments (good and bad)
• Availability of competing products

All of these distort the historical data and require significant time to properly analyze how to adjust for these activities.

What forecast?: Another challenge I face in dealing with customers in this channel is getting good forecasts. Some e-tailers do provide forecasts to their suppliers, but the assumptions and algorithms that go into compiling these forecasts are often not clear, and often don’t clearly reflect the impact of past historical events such as promotions or new listings and de-listings. In addition, it’s often difficult to find out what products might be competing with your own products without spending significant amounts of time online comparing the products that are suggested alongside your products. And tracking potential lost sales also requires significant time to analyze correctly.

Supply challenges: There are also challenges for the supply side of this business, as lumpy demand makes planning production and supply quite difficult. Calculating minimum stocking quantities and safety stocks is often difficult and can lead to significant inventory costs if done incorrectly or not managed and maintained. Long lead times will add to the difficulty, as quickly re-stocking high-velocity items can be challenging and potentially quite expensive.

Structure: We found that the structure we had for our brick-and mortar business was not effective in dealing with the e-Commerce channel. We couldn’t simply add managing the online business to the workload of a planner who was also handling brick and-mortar customers. Since the online channels required a different approach and different tools, we set up a separate sales and demand planning team for the e-Commerce customers. This allowed the e-Commerce team to focus on this unique channel and develop the tools and processes that were most appropriate to this channel.

So Why Bother?

With what I have written so far, you may be wondering why any company would try to plan demand for their online business. And I would not blame you for feeling this way. But let’s finish by looking at some of the practices that can make your demand planning for this channel more effective.

Mind Your Own Business First

When I first started managing demand for our online customers, I focused on how these customers ran their business. I analyzed their shipments and sales and tried to anticipate the demand. Since the demand varied wildly, I was often quite wrong in my estimates. I found a better approach was to manage my side of business first, and then adapt it to what I knew of my customer’s needs. Here are my examples of how I approached some of the issues listed earlier in this article.

Managing Lumpy & Volatile Demand

My solution here was first to stratify my items so that I focused first on the items that were most important to the business. In one case there were 12 items that generated almost 80% of the total annual volume for one customer. By improving the forecasts for these 12 items, I would be supporting the majority of the expected demand and any potential sales growth. And I also noticed that there were many items that generated only small volumes over an entire year. While these also needed attention, improving the forecasts on

these items would add little value to the business. Next I assigned all e-Commerce skus to a forecasting model that was reasonably accurate over the available history of the items. I use a 4-month weighted average of shipments as my default model, and I compare this to the sales for the same period last year.

I know it won’t be accurate for all the items, but it gives me a point of reference for comparison. Each month I compare the model forecast to the actual demand and adjust where necessary. And knowing that demand in this channel can vary wildly, I’m willing to tolerate a high bias in any single period. But when the bias remains high (> 30%) over 3 consecutive periods, I know it’s time to re-evaluate the model for the item.

Managing Data By Building Your Own

Since many e-Commerce customers didn’t have reliable POS or inventory data available, I built my own database. I started with the shipment data available from our own system and plotted the annual demand for all the items together to get a high-level view of the overall business. It also showed me where there was seasonality.

Then I broke this data down by individual item volume, and then grouped these into subcategories, such as top items, highly promoted items, highly seasonal items and onetime promotional items. This allowed me to tailor my forecast model selection to each item. It’s not perfect, but better than having no data at all.

Managing The Supply Side

I used the database that I built to help manage the supply side of this business. Knowing how demand varies

by month throughout the year allowed us to set minimum stock and safety stock levels by item, which significantly decreased our inventory levels. We also adjusted these based on the lead time to replenish the supply, allowing us to maintain or adjust these levels if the lead times shifted.

Adapting Our Structure

I learned early on that the online channel required a different approach, and a key aspect of this approach was having demand planners and salespeople dedicated to this channel. The challenges of limited historical data,

volatile demand and uncertain forecast modeling require that the planner and salespeople invest significantly more time in managing this channel than would be required in the more predictable distribution and brick-and mortar channels.

With A Realistic Approach You Can Effectively Plan Your eCommerce Business

Effectively planning demand for online customers is challenging – but it can be done. Above all it requires thinking differently about your business and how you can support these customers. My experience shows that despite the volatile demand and often limited historical data, we can develop the tools and processes that will allow us to map and plan demand and adjust our supply to support this growing channel.

And this capability will be all the more important as this channel continues to be a focus for many retailers and continues to grow rapidly. Effective demand planning in this channel requires a unique commitment of resources that will greatly reward the companies willing to make the necessary investment.

Omnichannel: The sales and business strategy of meeting the customer in their preferred place of purchase. Simple, straightforward, and an absolute challenge for all aspects of a company. This is no different in forecasting and planning. There are now more voices in the meetings. There are now more differing needs. Now, we have more chaos in a process that already lacked precision.

Here are the lessons my company and I have learned while implementing an omnichannel sales strategy, and the impacts on forecasting and planning.

All channels needs a voice

With the primary goal of S&OP being to get everyone to the same one number for the company to work towards, a key goal is buy-in from all parties. With an omnichannel, the number of voices and signals has increased. That means the number of people to buy into the number has increased. It is just as important for a channel manager to believe and buy into their number and plan as it is the head of sales to buy into the overall number. Treating each channel manager and their signal with that level of importance helps improve the signal coming in and helps improve that key relationship. With all channels heard and respected, the aggregated number is one the key channel managers are all able to support and get behind. Any shortfalls can be discussed openly, and any big gains can be supported openly.

As Finance will be the first to tell you, all channels are not equal

However, all channels are not equal

It is important that all channels buy into their channel’s plan and the overall number. But, as Finance will be the first to tell you, all channels are not equal. We started with seven channels we received demand signals from to forecast and plan. They were wholesale (including key accounts), direct to customer (DTC) e-commerce, external e-commerce (ex. Amazon), industry focused discount channel, corporate partner channel, discounted closeout channel, and international channel. All seven channels were on different margin plans, had varied products available, and ultimately had different methods of achieving the same goal: sell product. This creates the challenge of needing a hierarchy and priority further down the supply chain. But it also drives a need to focus efforts on cleaning up a demand signal for supply planning and proposed purchasing. Otherwise, there will be product planned and supplied that was never meant for a healthy margin. This can create tension with Finance and hamper future supply planning and purchasing conversations.

We end up with three channels as our primary breadwinners and four channels supporting the business

It comes down to balance and communications

As the process continued to evolve, the big lesson learned was the importance of balancing the channels’ inputs and signals correctly. Wholesale business is the biggest volume and revenue in the company but does not represent the revenue and profits the DTC channel does. While we focus on units in planning, Finance will be sure to let you know they want the 50% to 60% margin coming from DTC over the 25% to 35% margin coming from other channels. But Finance also wants the velocity of wholesale, as it creates the bulk of the revenue. Marketing knows it influences DTC the most, but it also drives business to the external e-commerce channel. We end up with three channels as our primary breadwinners and four channels supporting the business. The business in the four support channels must augment the primary three channels, or it needs to solve inventory and distressed issues without damaging the primary three channels.

Forecasting and Planning learned that all aggregations must be dis-aggregated and reviewed

In the end, disaggregate and review

With these lessons learned and the challenges faced, forecasting and planning learned that all aggregations must be dis-aggregated and reviewed. Products have been purchased that were only signaled in the closeout channels. Products were under purchased because of support channels under signaling and then taking inventory from the primary channels. Products have been over-purchased because of too strong a signal in the support channels. The lesson is to review the signals at the channel level and flag any anomalies between the primary and support channels. If the industry influencer channel is signaling high on a product and wholesale isn’t, there needs to be a manual review and meetings had with channel managers regarding the gap. If the closeout channel is going on a run of product to close out but it’s still selling strong on the external e-commerce sites, then prioritization needs to happen to achieve higher revenue and margin to keep the product flowing in the primary channel. This level of review smooths out the forecasting, improves the deliverable to supply planning and purchasing, addresses potential channel conflicts during planning instead of during selling, and adjusts the plan for fewer gaps and pitfalls.

In conclusion, omnichannel business strategies are a key focus for all retail goods-based businesses going forward. Customers are smarter, savvier, and have more options than ever before. Businesses have to go to the customers now, not sit back and wait for them. So be prepared for new challenges and new ways to manage, forecast, and plan the business.

Join us at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from May 6-8, 2019 and learn how to successfully navigate the omnichannel era with machine learning, data science, predictive analytics, and up-to-date S&OP from industry leaders from Fortune 500 giants and innovative disruptors. Held at Harrah’s hotel, it is 2/3 days of world-leading insight and networking.

The fashion industry is undergoing seismic shifts due to omnichannel and social media disruption that make predicting and managing demand more difficult than ever. Two MIT graduates and supply chain professionals share their research findings on how machine learning can improve forecast accuracy by up to 50%, and lay the foundation for truly agile supply chains.

We Must Have An Answer To Social Media & Omnichannel Disruption

Over the past several years, the fashion industry has been undergoing several challenges. Some of these challenges are supply chain-oriented including long lead times from design to delivery. Other challenges were more related to the microeconomic shifts in the global economy like the emergence of omnichannel and social media. These have led to shorter product lifecycles, obsolescence of the retail calendar, and consequently more volatile and uncertain demand.

The Emerging Need For An Agile Supply Chain In The Fashion Industry

To cope with this changing retail landscape, companies must develop agile supply chains that focus on speed, responsiveness and flexibility. An agile supply chain should enable fashion retailers to respond rapidly to changes in demand, in terms of both volume and variety. Since demand forecasting plays a critical role in supporting upstream supply chain planning decisions, the previously mentioned shifts have made forecasting for fashion products much more challenging.

How Can We Improve Forecast Accuracy To Meet Today’s Strategic Needs?

To answer this question, myself and my research colleague Vicky Chan carried out research at the Massachusetts Institute of Technology as part of our master program in supply chain management. The sponsoring company who participated in this research was a leading US-based footwear retailer. The type of products we investigated were seasonal (first launch) footwear which, in this retailer’s case, represent more than 50% of the SKU count.

The challenge with forecasting demand for these products lies in the lack of historical data

The challenge with forecasting demand for these products lies in the lack of historical data, which traditional time series techniques rely heavily on. For this retailer, the typical lifecycle of a seasonal product is around two to four months. With an order lead time of up to five months from the manufacturer, there is no opportunity to read and react to actual sales data.

The Potential Of Machine Learning And Artificial Intelligence

Besides point-of-sale data (POS), what other types of data can be leveraged to predict demand? We explored the use of product attributes, calendar, lifecycle, price and promotion, as well as store count to build a forecasting model for the seasonal products.

To that end, we see machine learning-based forecasting techniques as potential candidates for demand forecasting for seasonal products. Unlike traditional forecasting methods, machine learning techniques are able to process a large number of predictor variables not confined to sales history, determining the ones that are significant. In building the forecasting model, we explored the use of different machine learning techniques, including regression trees, random forests, k-nearest neighbors (k-NN), linear regression and neural networks. In addition, clustering and classification techniques offer the opportunity to identify similar existing products such that their sales history can be leveraged.

Cluster, Classify and Predict

We analyzed the data to identify significant predictor variables influencing demand for footwear products. Results show that store count, calendar month and lifecycle month (which month in the lifecycle the sale occurs in) are the top three numerical variables impacting demand. Color, material and gender were the top three categorical variables. Two models, a general model and a three-step model, were then built utilizing product, calendar, lifecycle, price and store count attributes to predict demand. The general model directly takes in the variables for prediction, while the three-step model involves clustering and classification to identify similar products, before moving on to prediction.

The machine learning model improved the retailer’s forecast accuracy by more than 50%

Model Performance

The results show that the two forecasting models we developed achieve better forecast accuracy compared to the sponsoring company’s current performance. They both improve the retailer’s forecast accuracy by more than 50%.

While the general model serves as a starting point for easy implementation of the machine learning forecasting framework, the three-step model further offers visibility into the importance of the different underlying factors that impact demand. The project results also demonstrate the value of forecast customization based on product characteristics which offer additional improvement to forecast accuracy. In conclusion, we believe this research not only benefits our sponsoring company, but also other fashion retailers forecasting for seasonal products.

This article is based on research carried by Majd Kharfan and Vicky Chan, two forecasting and supply chain professionals participating in the the Supply Chain Management Master’s course at Massachusetts Institute of Technology (MIT). 

The role of machine learning in forecast accuracy will be discussed at IBF’s upcoming Business Planning, Forecasting & S&OP: Best Practices Conference from October 16-19, 2018 in Orlando, F.L.  Speakers from NIKE and PUMA will reveal the implications of machine learning for the fashion industry, and how it cuts lead times and meet the challenges of the omnichannel environment. REGISTER NOW FOR EARLY BIRD PRICING. 

This holiday season, I find myself reflecting on omnichannel forecasting, and my days as a buyer for Bonwit Teller in the late 1980s. As buyers, we developed our own merchandise plans (our version of a forecast), gross margin plans, and turnover plans. What we called “forecasting” was essentially a top-down planning approach whereby the organizational plan was allocated across different departments based on senior management’s opinion about trends.

Then, that departmental plan was further divided among the class and category plans, again based on the intuition of buyers and divisional merchandise managers. We did not use any statistical modelling, and we didn’t even have the benefit of Excel spreadsheets to assist with plans, not to mention any forecasting software.

As buyers, we managed our inventory levels and open-to-buy. Purchase orders were committed 8 to 12 months in advance of delivery for each of the seasons. And for fast-moving products, if Bloomingdales and Saks Fifth Avenue hadn’t beat me to the reorder, I would be fortunate to receive a replenishment shipment. Markdowns were synchronized throughout the store—regardless of sell through patterns—and were aligned with a pre-designated sale event. It is abundantly evident that today’s retail environment differs dramatically from my years in the industry as a buyer and a merchandise control manager in the 1980s and 1990s. The rapid and widespread adoption of mobile computing has revolutionized the shopping experience, and omnichannel retailing has emerged to meet the evolving consumer needs.

What Is Omnichannel Retail?

Omnichannel retailing provides consumers with a seamless shopping experience during which they can move freely across physical, online, telephone, and mobile technology environments to fulfill all their shopping needs. This includes information gathering, transferring ownership and taking possession of products, accessing post-purchase support, and returning unwanted items. Indeed, omnichannel has generated new retailing terms and acronyms such as BOPIS (buy online, pick up in-store), BORIS (buy online, return in-store), mPOS (mobile point-of-service), showrooming (gather information in-store, buy online), and web rooming (gather information online, buy in-store).

Identifying Variables That Affect Sales in Omnichannel Forecasting

A key factor to omnichannel success is unrestricted access to inventory across all channels, including in-store, website, mobile app, etc. Among the challenges for retailers in creating this fluid shopping experience is identifying the newly emerging channel-related variables that impact sales, and determining the optimal location for staging inventory to deliver the best customer service at the lowest cost. With these different shopping methods, there are inevitably challenges and trade-offs that must be made.

A typical omnichannel customer might begin her shopping experience by browsing through a catalog while drinking her morning coffee, then logging onto the retailer’s website to see if the sweater she found in the catalog is in stock in a nearby store. She locates a store that has two units left in stock, and clicks the “Reserve in Store” button, as well as clicking the Facebook button found on the retailer’s website to share a photo of the sweater with her friends. After receiving 30 “likes,” she drives to the store where the sweater is being held aside for her by a sales associate. She tries on the sweater and decides she needs a different size that the store doesn’t have, so she uses the retailer’s app on her mobile phone to order for home delivery. Shortly after leaving the store, she phones the retailer’s call center to request that the order be scheduled for overnight delivery. In this example, the customer utilized catalog, website, social media, in-store, mobile app, and telephone seamlessly to complete her transaction. This seamless transaction is only possible because the retailer integrated its channels with technology that enables unrestricted access to inventory.

Omnichannel retail demands more sophisticated modelling and software than those currently used by most companies.

Evolution In Omnichannel Retailing

An example of the emergence of the omnichannel phenomenon would be the progression from single channel retailers with a brick-and mortar presence, into multi-channel retailers with the addition of a mailorder catalog operation. Similarly, many single-channel direct marketers, such as Cabela’s and Coldwater Creek, adopted a multi-channel approach by opening brick-and-mortar locations to extend their catalog operations. The dualchannels allowed retailers to enhance their product offering, and to reach customers that did not live within driving distance to their stores. Eventually, the addition of a website extended the multi-channel strategy further. Until very recently, the common practice among multi-channel retailers had been a siloed approach to managing the multiple entities.

Each channel had its own merchandising teams comprised of separate sets of buyers and merchandise control analysts (i.e. forecasting and planning), with a staff of allocation analysts dedicated to the store-line merchants to allocate SKU quantities to stores using inventory management systems (IMS). The different merchandising groups were often housed in separate locations, as was the inventory they purchased. For many years, retailers and their customers suffered through the inefficiencies of this bifurcated approach.

A Single View Of Shared Inventory Is a Must

Consider the example of a national retailer with dozens of brick-and-mortar locations in the Northeast, where they experienced a severe winter. They quickly sold through their in-store winter coat inventory early in the season, while the dot.com warehouse still had plenty in stock. By the time the store-line buyers had placed and received their back-up order for which they paid expedited delivery fees, the dot.com buyer was offering a second markdown on the slow-moving inventory, some of which were the same styles being concurrently delivered to the stores for sale at full price. If the company had adopted a single view of shared inventory, product could have been allocated from the distribution center for store delivery, thereby achieving higher margins, fewer missed sales, and faster turnover. Other inefficiencies stemming from siloed channel management include the reduced ability for buyers to leverage volume during vendor negotiations, different pricing and promotional activity on the same products across channels, and the inability for customers to return products purchased online to a brick-and-mortar location.

Omnichannel Puts Customers In Control

Today, with mobile computing and shopping apps, consumers have much greater visibility into the stores’ inventory, pricing, and promotional activity, and they have begun demanding a consistent, seamless shopping experience. If the website is running a 10% discount on purchases over $100, the customer challenges the store associates to apply the same discount in the store. If a customer is shopping in a store and sees that the $200 item they are holding in their hand is $180 on the same company’s website, they insist on paying the lower of the two prices in the store. Or when the customer sees the $200 price tag, then finds a lower price on another company’s website—the practice referred to as showrooming – she actually completes the purchase online from the competitor because the retailer won’t match the competitor’s lower price. Thus, it was the consumer armed with technology that compelled retailers to adopt the omnichannel shopping experience.

Retailers that don’t leverage this opportunity to improve omnichannel forecasting will find themselves at a competitive disadvantage

Omnichannel Forecasting Is More Art Than Science

Omnichannel in most retail environments is really more art than science. Most retailers have moved beyond the model where the buyer, forecaster, planner, and allocation analyst are one and the same. Although retailers have established a separate staff for merchandise planning/forecasting, the planners typically are not formally trained to use, and therefore don’t employ, sophisticated statistical models. We’re talking exponential smoothing, regression, or decomposition that are used by many IBF members. However, given the use of Customer Relationship Management (CRM) systems to accumulate “big data” of customers’ purchases as well as their keystroke interactions with a retailer’s online app, website, social media postings, and other points of contact, more sophisticated data analysis is available and is necessary to determine customer demand patterns.

The goal is to allow the customer access to the company’s inventory regardless of the physical location of the customer or the inventory.

For example, retailers can measure the conversion rate resulting from “push notifications” via social media or text messaging that alert customers to a time-sensitive promotion, combined ith geolocation data indicating the customer’s proximity to the nearest store when they clicked on the notification.

Omnichannel and Integrated Inventory Are Here To Stay

A September 2016 report published by Boston Consulting Group, “Digital or Die: The Choice for Luxury Brands,” offers an “omnichannel index” measuring the level of importance of the ability of consumers to reach a brand through multiple channels. Millennial, Generation X, and Baby Boomer segments scored 86%, 84%, and 75%, respectively on the omnichannel index, indicating multiple channel access is “somewhat important” to “very important.” The implication is that all age groups are moving freely across the Internet and physical store locations to make purchase decisions, leaving behind a trail of keystrokes and transactions that can be analyzed for predictive patterns.

Leveraging this rich data to predict demand will require new skills and more sophisticated technology than what is currently being adopted by most retailers. Retailers can hire forecasting experts familiar with statistical modeling, or they can provide training on techniques and technologies for their employees. Retailers that don’t leverage this opportunity to improve omnichannel forecasting will find themselves at a competitive disadvantage compared to those who embrace the data.

Implications Of Integrated Inventory

Among the most common trends in omnichannel is the recognition of the need for an “unrestricted” or “shared” view of inventory across all channels. The goal is to allow the customer access to the company’s inventory regardless of the physical location of the customer or the inventory. The first challenge is how and where to deploy the inventory, and several models exist. Each of the models has trade-offs between cost and customer responsiveness. Each of the models requires use of a sophisticated Distributed Order Management (DOM) system to assist in optimization of the fulfilment location decision, requiring inventory field of vision across all stocking locations. The fulfilment location can be selected based on multiple criteria, including proximity to customer location, unit cost to ship, speed of delivery, and least likely store to sell the product. Below, I will discuss the trade-offs associated with three different models of direct-to-consumer (DTC) fulfilment in an omnichannel environment: dedicated fulfilment centers, store-level fulfilment, and vendor-managed fulfilments.

Dedicated Fulfilment Centers

The most cost-efficient model of a direct to- consumer (DTC) shipping-cost-per unit basis is to process all DTC inventory through a dedicated fulfilment center (FC) that is designed for the sole purpose of DTC shipment of “onesies.” In this model, all DTC orders are shipped from the FC. Whereas a distribution center (DC) is designed for bulk shipments to stores in which product is stored in palletized cartons, an FC is configured for ease of picking single SKUs for DTC shipments. All items are unpacked upon receipt and stored as individual units, similar to what you would find in an Amazon FC. In an omnichannel environment, the advantage of configuring an FC for maximum responsiveness to consumer orders must be considered alongside the disadvantages associated with carrying separate inventories stored in the FC, the DC, and the stores. Aggregating inventories in fewer locations (known as inventory risk pooling) lowers overall inventory levels and results in fewer stockouts. Some retailers house their FC and DC inventories under the same roof, but it still requires dedicated inventories and square footage for the separate operations. The advantage to configuring an FC and DC under the same roof is the ability to transfer inventory within the four walls if one of the two areas is faced with an out-of stock while the other still has product in stock.

Store-Level Fulfilment

An alternative solution is to establish DTC fulfillment capabilities in the store location. An advantage is the proximity to the consumer. Some retailers believe their best defence against Amazon’s sophisticated home delivery fulfillment strategy is to deploy more inventory to the stores and establish a pick-pack-and-ship operation in each store. This approach leverages many store-shipping locations—potentially hundreds of shipping locations across the country—to compete with Amazon’s network of 50 fulfilment centers and 20 sortation centers. Another advantage is the increased revenue for the store to offset the reduced foot traffic experienced by so many brick and mortar locations. The disadvantage to this option is the necessary investment in resources, technology, and training to establish mini FCs in each retail store that is designated as a fulfillment location. Decisions must be made regarding which store associates will be responsible for fulfillment activities, and how much of their time should be dedicated to fulfillment versus serving customers on the floor. Among the retailers that are investing in store-level fulfillment capabilities are Target, The Home Depot, Desigual and Macy’s.

Vendor-Managed Fulfilment

A third alternative is fulfilment direct from the vendor. This option allows for “extended aisle opportunities” for retailers to sell goods on their website for which they don’t have room within the space limitations of the retail stores and distribution centers. An obvious advantage is reduced inventory carrying costs. A risk is the loss of control over the level of customer service. An additional risk is the lack of dedicated inventory. You may forecast sales of 100,000 units of an item, but when the consumer orders roll in, the vendor may have already sold their inventory. Ultimately, the goal of a fulfilment strategy is to achieve acceptable customer service levels at the lowest possible cost by aligning supply and demand. The process to accomplish this supply-demand integration is Sales & Operations Planning (S&OP).

Fulfilment Strategy And The S&OP Process

In a series of JBF articles in the 2004- 2005 Fall, Winter, and Spring editions, Larry Lapide explains that S&OP is a mid-range tactical process designed to align supply and demand. Among the first steps to a successful S&OP process is to create “a baseline forecast and rough cut demand and supply plans,” which are “aggregated, synthesized, and translated” to identify supply-demand imbalances to ensure sufficient supply to fulfill demand. In an omnichannel environment, aligning supply and demand becomes daunting when considering the multiple channels that must be forecasted, and the potentially hundreds of inventory locations from which those sales might be fulfilled.

The cost of shipping product in bulk from the DC to stores is approximately 0.05%of the retail value

The supply side issues are equally as daunting, and will require complex decision making and data analytics to optimize outcomes. In the Journal of Business Forecasting article “How to Make the S&OP Process More Robust,” by Debashis Sinha (Spring 2015), he explains the importance of senior management involvement in the S&OP process. He says, “Equally important is that the strategic vision of a company is well aligned with its financial objectives. A good S&OP process can easily achieve it.” The shift in balance of sales from store to e-commerce channels, and the DTC fulfillment location decision both have substantial impact on contribution margins. For example, in one of my conversations with a manager of a retail DC, he estimated that the cost of shipping product in bulk from the DC to stores is approximately 0.05%of the retail value. In this cost-efficient model, customers assume the responsibility of transportation and delivery to their home.

Alternatively, the cost of shipping DTC from the DC is 10 times costlier at 0.50% of the retail value. The cost of DTC fulfillment from the store was estimated to be 1.0% to 5.0% of the retail value or higher, depending on the product. As consumer purchases increasingly shift from in-store to online, the margin implications associated with shipping must be considered. For example, the National Retail Federation predicts 2016 holiday sales to increase 3.6% to $655.8 billion  while e-commerce sales are expected to increase up to 10% and could reach $117 billion, or 17.8% of total retail sales. The margin impact of fulfilment decisions on financial objectives could be substantial, and that conversation would emerge during S&OP discussions.

The Bottom Line

A robust S&OP process enhanced with integrated supply and demand planning technology can make the forecasting and planning process much more manageable. While S&OP process adoption has increased among manufacturers, the adoption rate among retailers has been much slower. With the hockey-stick growth patterns of omnichannel shopping, retailers would be well served to consider implementing formal S&OP processes and technologies to remain competitive with the likes of Amazon.

This article first appeared in the Winter 2016/2017 issue of the Journal of Business Forecasting. Access the database for industry-leading articles or receive every issue (and a host of other benefits) with IBF membership.

Understanding Just How Revolutionary Omnichannel Really Is

It has changed the way manufacturers sell their products and the way consumers buy them. Manufacturers now sell their products not only to wholesalers, distributors, and retailers but also directly to consumers. Logistics companies now deliver many more packages, mostly small and less bulky, both domestically and internationally.

What’s more, they do it faster than ever before. With these innovations come both challenges and opportunities. It is up to us as demand planners to use the information available in this new paradigm to make our demand planning more effective.

By selling directly to consumers, manufacturers have also become retailers. With that, they bypass the middleman, which raises their profit margins. Just as valuable is access to true consumption data, which greatly improves forecasts and provides a market for products that have already matured in traditional markets.

Omnichannel Means Planners Can Shape Demand Like Never Before

This change also makes it easier for manufacturers to shape the demand, and provides an opportunity to test new products before releasing to brick-and-mortar stores. It even allows for the launch of niche businesses, with Dollar Shave Club and Just for Men being key examples. In short, the data available to us in this new omnichannel world is a potential goldmine. But it’s not all smooth sailing.

There are many challenges that come with omnichannel, too. Among others, it increases the cost of serving consumers. Manufacturers are now in direct competition with their retailer customers. To alleviate that, they distinguish their products by changing packages and configurations. The result? Increased costs.

Transparency Means Lower Costs And A Race To The Bottom

With smartphones and shopping apps, consumers have more visibility of prices on the web and on the shelf. If they find lower prices on a retailer’s or competitor’s website, they want the retailer to match them. Increased transparency makes consumers smarter, and smarter consumers mean tougher business – they want lower prices, they want better quality and they want it faster. Are retailers and manufacturers racing to the bottom, chasing ever smaller profit margins, and if so who will emerge victorious? For now, with the rise in store closings, the odds are stacked against retailers.

Cannibalization is another problem. When a manufacturer starts selling directly to consumers, it cannibalizes its own business base. In seeking higher profit margins by selling direct, they are cutting off their retail clients, unaware of the limitations of this path. As if this wasn’t complicated enough for demand planners and forecasters, omnichannel retail adds to the points of sale, which can reduce the quality of forecasts and increase inventory.

Changes are not going to stop, they will continue apace. Consumer patience is getting thinner and thinner; they want goods now, not hours or days later. Technology is going to improve further, making it easier and quicker to place orders. The ways products are packed and shipped will change, so that consumers can get whatever they want, whenever and wherever they want it.

Analytics Is The Natural Companion To OmniChannel

Big data and predictive analytics will change the way businesses market their products. There will be more and more target marketing, targeting consumers with specific attributes based on demographics, age, and buying habits. This will increase ROI for those who can manipulate their consumer data. This information means we have the power to not just meet demand for existing products, but also test the demand for new products. This is a real gamechanger.

Omnichannel Is Here And It Provides Multidimensional Consumer Journeys

This vision of omnichannel retail is not a fantasy, it is already happening. To succeed, businesses must embrace changes, not resist them. Forward-looking companies are already doing it. Walmart and Kroger are expanding their pick-up services so that consumers don’t have to waste time in finding what they need, nor do they need to wait in line for check-out. Companies like Warby Parker (eye glasses) and Bonobos (men’s clothing)—two of the larger e-commerce-driven retail companies in the United States— are considering building store footprints where consumers can see and touch products before placing an online order. Not to buy instore, but to check size and style before the product is shipped to their home. Combining the digital with the physical creates a new shopping experience—try before you buy and still benefit from the low online prices. Customers get the product at the price they want and companies enhance their brand by providing a multidimensional consumer journey.

Similarly, Amazon Go is going to change the way consumers buy goods. Consumers will walk into a store, use an app to log into their Amazon account, pick up whatever they need, and then walk out without ever going to a check-out counter. Just as the Internet took brands off the street, omnichannel is putting them back, except now it is more affordable for consumers. There are without doubt both challenges and opportunities in the omnichannel sphere – it is time to help position your organization to overcome the former and benefit from the latter.

Journal of Business Forecasting Volume 35 | Issue 4 | Winter 2016-2017

The Journal of Business Forecasting has been providing demand planning, forecasting, supply chain, and S&OP practitioners with jargon-free articles on how to improve the value of their roles and company performance from improved forecasting and planning for over 30 years.

Download a preview of the latest Journal of Business Forecasting.

Click here to join IBF and receive a JBF Complimentary Subscription.