Inventory forecasting and replenishment in an omni-channel world
Omni-channel retailing has changed the entire nature of inventory demand. The ‘demand’ fulfilled from a location adjusts based on what stock is available in the network. New delivery options and supply methods are constantly being added which makes the previous inventory positioning strategy less than ideal. Customers select from the available delivery and inventory options, but, their final selection may not reflect their first choice, ie. their true ‘demand’. These have all contributed to making inventory management a real challenge, one that over 50% of retailer’s report was their top supply chain business challenge in RSR’s Supply Chain Management Report. So, how can retailers adapt their inventory management processes for this new world?
Aligning on the basics
Like most new things, definitions and terms are used inconsistently between and even within organizations. So, to be sure we’re talking about the same things, here are our definitions -
Selling Location = the location where the customer purchased the item
Fulfillment Location = the location that supplied the inventory to support the sale
Delivery Location = the location where the customer took possession of the item
Demand Date = the date the customer decided to purchase the item
Sale Date = the date when the fulfillment of the item occurred, and the sales financial transaction was recorded
What’s wrong today
Today, many retailers forecast at sku, location, week/day based upon the fulfillment location and the sales date. This approach ends up repeating the same mistakes of the past. Say a customer purchased an item online and the fulfillment center closest to them was out of stock, so the item was fulfilled from an alternate location. When looking to determine where to place stock in the future, you’d want to place that stock at the closer fulfillment center vs. the less cost effective location, but forecasting based upon the actual fulfillment location for an order will result in it being in the wrong spot. And, the approach becomes even less appropriate as a retailer’s logistic networks evolve, for example, changes like stocking/owning the item instead of drop shipping it from a vendor, or the addition of temporary capacity to support peak periods. Additionally, customers have options to pick up orders and may do that a week after they made the reservation or may even back-order a product that isn’t available. If the forecasting is based upon the sale date, inventory may not be available at the right time to make that commitment next time or better serve customers.
Additionally, returns are frequently not accounted for the inventory plan, and, if they are, they are often forecasted as a % of the demand at that location. With online returns rates often over 30% and the typical time between the customer returning the product and it being available to sell being over 2 weeks, not accounting for the missing inventory in your plan can cause availability issues and excess inventory at the end of life. Additionally, with over 50% of customers preferring to return products in store (1), even if purchased online, forecasting returns only based upon stores sales misses a huge portion of returns and completely misses returns that show up at stores that didn’t stock the product.
Finally, the demand for product at a location is no longer just dependent on what happens at that location. If the customer checks online and sees the product isn’t in stock at their typical store, they may go to another nearby store. This new behavior presents new opportunities to change where stock is located so that you can serve all your potential demand, and it also presents great challenges in determining what customers really want.
Steps on the journey
Here are some proven steps you can take to adjust your inventory management processes for the new world of omni-channel fulfillment and that will improve your customer service, lower your supply chain costs, and increase your inventory productivity.
Step 1 - Capture a complete set of sales and available delivery information
It’s important to put in place the right foundation to understand customers’ desires. A key part of that is being sure you retain transactional data in a spot that is accessible and can be quickly enriched and summarized. The transaction log should capture where the customer purchased the item (i.e. the selling location), when the order was placed, the location that fulfilled the order, the date the order was fulfilled, and the delivery location and requested delivery service. This will then unlock the ability to analyze the data in light of your current and future supply network, better understand customer behavior, and so much more.
Additionally, it’s important to track what delivery options were available and intended so you can develop a true understanding of demand (i.e. could the product be purchased in store, delivered the same day, delivered in two days). This information will enable determining how much demand was lost when a store didn’t have enough stock to satisfy an in store pickup. Or it can be used to identify non-representative sales, such as a store selling a product that was returned there but wasn’t normally stocked there.
Step 2 - Regularly restate your sales history to be at the preferred fulfillment location
Re-stating your sales history based upon your current fulfillment network will enable you to make the most of your existing forecasting and replenishment systems. First, you should double check that the sales date for forecasting is based upon the customer demand date, not the financial sales date. This will ensure you have stock at the right time going forward. Then, a process should be developed to look at all your transactions and map them to the ideal fulfillment location based upon the current network design. Stores sales are the easiest; you just map the demand to that same location. For online sales the delivery zip and service (i.e. 2 day delivery, ground) should be used to find the ideal fulfillment location that stocks the desired product. Since the ideal fulfillment location can change regularly due to changes in network design, fulfillment centers’ stocking assortment , etc., it’s important that the process is able to regularly run and update history.
Additionally, your ‘out-of-stock’ detection logic for your online fulfillment locations may need to be tweaked. The typical approach of looking at historical inventory levels at that location to determine if sales were representative won’t work since sales will be made if inventory is available anywhere in the network. This can easily be adjusted to check if stock was available at any location in the fulfillment network.
With these changes in place, you’ll be able to prevent repeating the same mistakes and be able to place stock in the right spot in your ever changing supply network.
Step 3 - Forecast returns based upon the delivery location and account for them in your inventory plans
The first step in creating a returns forecasting process is moving your ‘demand’ forecast from ‘net’ sales to ‘gross’ sales. Then, it’s time to create the actual returns forecast by each location. Each store’s forecast should utilize the sales that occurred there and the online sales in the area around the store. A simple starter approach (2) is to calculate returns as a function of the % of gross sales from previous weeks. Then, the online returns can be allocated to stores and fulfillment centers by calculating the historical % of online returns received at that location. Finally, these can be combined to calculate the forecasted returns at each location in your supply network.
Then, the returns forecast should then be incorporated into the allocation & replenishment systems based upon how the product will be handled. If the product returned to the store is sold there, the forecasted returns can be treated almost like a receipt at that location. If products returned to a non-stocking store are shipped to a central fulfillment center, that flow should also be represented.
Step 4 - Forecast by delivery location and then determine the fulfillment location in your replenishment solution
When the most preferred fulfillment location for an order runs out of stock, the distributed order management (DOM) solution will start allocating stock from less preferred location to capture all possible sales. That’s great, but it creates ‘extra demand’ at the non ideal fulfillment locations, spillover demand. If that spillover demand is not accounted for, too little inventory will be ordered and your network will be perpetually short of inventory. This can even trigger vicious cycles of out-of-stocks among fulfillment locations; an effect that a recent paper from MIT labeled whiplash.
Example of Whiplash
However, it is possible to anticipate spillover demand and account for it in the inventory planning process. The first step is to start forecasting online sales by service (i.e. ground, 2 day) and delivery location. That delivery location can be the carrier’s shipment zone or the zip-3 for the delivery location; this gives enough granularity to understand shipment costs while not being too sparse to forecast. Then the replenishment logic should be adjusted to take that forecast and determine where it will be served based upon the projected inventory availability. This will enable better allocation of stock to the most cost-effective fulfillment locations and ensure that sales aren’t lost.
Step 5 - Adjust your inventory positioning so you can take advantage of the flexibility in your fulfillment capabilities and account for the uncertainty of demand
Moving a unit to store no longer means it’s stuck there since you can now use it to serve online orders as well as demand that walks in the store. This flexibility opens up all new inventory stocking strategies. For example, it may make sense to push more product to store since it increases the chance of selling to a customer who walks and is unlikely to purchase if it’s not there while not impacting the ability to use it to support online sales. Your allocation and replenishment capabilities can be expanded to take advantage of these new possibilities. The solutions should evaluate how the inventory decisions perform across all the ways that demand may occur and then choose the result that delivers the best financial outcomes. This will lead to increased sales and efficiency in your supply chain by letting you truly take advantage of the flexibility that omni-channel fulfillment provides.
Step 6 - Develop a true picture of demand by accounting for trade-offs customers make
With omni-channel retailing, customers are presented with the product and delivery options a retailer can provide at that moment. However, if other options were available, the customer’s selection might be different. To account for this, a true demand model with demand transference impacts (i.e. incremental sales and cannibalization) similar to what is utilized in assortment optimization to understand trade-offs between different products is needed. Additionally, the allocation and replenishment models need to be tied closely to the demand model, so they can understand the impact that replenishment decisions have on demand. If you can tie all this together, you’ll have a more predictable supply chain and a great model to understand other merchandising and supply chain decisions.
What do you think?
While we’ve laid out our view on the journey, we know there are multiple ways of getting to the same place. It would be great to hear your feedback and recommendations. . Here are a few questions that come to mind -
What retailers are the most mature in this omni-channel inventory management, and, what are they doing?
How frequently are delivery flow paths (i.e. stops on the path to the customer) being adjusted? How frequently should they be adjusted?
What steps are need to adjust inventory management processes to account for new vendor or supplier capabilities?
How likely is it that retailers will start to flex their supply networks at peak capacity (i.e. utilize more 3PLs during Christmas)?
What other steps can be taken to improve existing systems that work at sku, store, week?
What novel approaches have you seen to determine the best place to position inventory? Any probabilistic methods such as Markov chains? Any machine learning methods? Any reinforcement learning based methods?
How big is the impact of customers’ switching their delivery preference based upon the available options? Should this be a concern or is it too small to worry about?
What do you see as the next step on your journey?
Cognira is a boutique consulting and software analytics company that focuses exclusively on retail merchandising and supply chain. Cognira’s goal is to make science easy for retailers, and to help them get the most value from their advanced science solutions. We bring decades of experience leveraging science, analytics and scalable technology to improve retail decisions.
Per a study by Navar
That starter forecast can be enhanced be enhanced a number of ways, but two important enhancements are to account for impacts of holidays, which increase the return rate and lengthen the return time, and to better identify products with higher than typical return rates.