Retailers have raced to keep up with the sudden drastic shift to online sales as physical stores closed down in an attempt to slow the spread of COVID-19. Artificial intelligence can help companies analyze how to make that move profitably.
2020 has been quite a year for all of humanity. We’ll likely be recounting stories about the pandemic in the same way that we talk about World War II, September 11, and other life-altering world events.
Arguably, the retail industry has faced some of the most significant pandemic-driven impacts in the shortest amount of time. Before the pandemic, e-commerce accounted for 13% of the retail sales in the United States. Given store closures and social-distancing measures, we can safely say that percentage has now more than doubled. For example, Stripe, an e-commerce payments platform, went from handling $1 billion in payments last year to more than $10 billion in transactions in the first six months of 2020. A potential 20x growth!
In many cases, consumer shopping habits may have changed for good. Take for an example how my own family buys groceries. We have gone from shopping solely in-store (Trader Joe’s, farmers market, and Whole Foods) to getting groceries delivered to us each week from Whole Foods (owned by Amazon) and Costco (delivered by Instacart). I’ve noticed that (after a rocky start) the quality of service provided has improved significantly over the course of this year: from having to wait up to a week for a delivery slot for Whole Foods to having food delivered to my doorstep in less than two hours. The question my family—and many others—is now asking is this: Will we ever go back to the grocery store? Is it worth it to lose two hours of precious weekend time to shop in a physical store?
Another consideration for retailers is the Amazon factor. Retailers and brands have to figure out how to coexist with and thrive in an ecosystem dominated by Amazon. It is possible! Let’s take the example of one of my favorite coffee brands: Equator. The company—which began in 1995 as a small operation out of Mill Valley, California—is one of the most popular gourmet coffee brands on Amazon. Recently, I switched from buying the product on Amazon to buying directly from their webstore. When I got my first shipment, I was pleasantly surprised to discover that the coffee had been roasted only the day before. Ah, the joy of a fresh roast!
Do you see how Equator coffee is being very shrewd about its e-commerce strategy? They’re fully present on Amazon, with a complete store front, but they also give customers who buy directly from them something extra. The consumer can choose between the convenience of Amazon or something special on the product/service side by buying directly from Equator’s website. I call this having a bimodal channel strategy.
This rapidly developing e-commerce environment poses many challenges that are likely causing supply chain leaders’ heads to spin and are keeping them up at night. In many cases, artificial intelligence (AI) solutions can help them navigate those new challenges. We like to think of AI as a catchall term to capture the idea of solving problems with algorithms and data. The algorithms could be from deep learning, machine learning, operations research, or another approach relevant to the problem. Let’s consider some of those challenges and potential solutions below.
Time to redesign your supply chain
Your current supply chain is probably optimized for a pre-COVID world—both in terms of the kind of products and services you offer and how you deliver them (for example, through a physical store).
You now need to rethink the smartest way to restructure your supply chain to fit the new reality. For example, with current e-commerce volumes, what delivery terms will you offer: same day, next day, or two-plus days? If it’s same day or next day, you’ll likely need to set up a network of dark stores or local warehouses—but how many and where?
There may also be other structural questions: What level of service will you offer for which product assortment? Can you segment customers based on ideas like customer lifetime value? Will you fulfill from stores? If so, how will you change your store merchandising and replenishment strategy?
These are all classic supply chain design questions that need to be addressed when structuring your supply chain to support this manifold increase in e-commerce volumes. Commercially available supply chain design software can help with the answers, particularly when used in combination with models to analyze customer lifetime value, product affinity (which products are likely to be ordered together), and other factors.
In response to an increase in online orders, one retail client recently accelerated the implementation of its e-commerce strategy, doubling the number of e-commerce fulfillment centers (FCs) from three to six. To determine the optimal locations for these three nodes, the team relied upon supply chain design software. In addition, they used tailored AI models to predict product affinities and an optimization model to determine stocking strategies. As a result, the team was able to configure the company’s stocking strategy to maximize the percentage of shipments sent from the closest node to the customer while also minimizing the total number of split shipments.
A new approach to capacity planning
If you have retail fulfillment centers (FCs), I am guessing you are constantly running into capacity issues. This could be due to seasonal spikes in demand, marketing promotions, a surge in inbound volumes during certain times of the week/season, temporary labor capacity constraints, or some other unforeseen reason.
AI can really help here. Prediction models built using deep learning techniques can help you understand the right volumes (units and orders) to process per day. (These models will need access to data around such things as your website activity, customer loyalty, historic transactions, and promotional activity.) Meanwhile optimization models can help match demand with the supply of capacity available in the system in an efficient manner.
FC managers often find these combinations of prediction and optimization models to be an upgrade over their current planning capability, which is typically a spreadsheet-based system or a workflow-based planning software provided as part of their enterprise resource planning (ERP) stack. The new generation AI-based planning solutions are very effective in alleviating order backlogs and helping set the right service-promise expectations with consumers. Additionally, these models can also help the business shape demand through controlled promotions, digital marketing, and more.
The secret to getting good results from these AI-based solutions is twofold. First you need to have a rich trove of data. The more data you can provide to make the system more intelligent, the smarter the model predictions are. Second, you need to use modern AI algorithms that can identify hidden patterns in the data and leverage them in the predictions.
A large Fortune 500 retail company found its e-commerce business was growing at a pace of 40+% year-over-year. The company was reluctant, however, to take the capital-intensive step of adding fulfillment capacity to its supply chain. Instead, the CEO wanted to explore whether a AI-enabled software solution could alleviate the problem. A tailored AI solution was built to help the company predict if it was going to run into capacity issues, whether due to a seasonal surge in demand, a promotion, or a shortage of labor. An optimization model then followed up these predictions with a suggested action plan, such as hiring additional labor, shaping demand, or deactivating certain promotions.
Once built and implemented, the business found the solution to be so dependable that it built its entire integrated business planning process for e-commerce around it. In addition, the operational efficiencies gained through the solution have allowed the business to postpone building a new fulfillment center by at least two years.
Root cause analysis of failures
No matter how well you run your e-commerce business, the sheer volume of daily orders processed inevitably means you will face some number of failed orders each day.1 This could be true for one of several reasons. Maybe you received a disproportionate number of orders close to the cut-off time, or maybe too many high-value orders got stuck in the fraud check process, or perhaps a disproportionate number of them required split-shipments. There’s a whole host of triggers.
When orders fail, you want to avoid a “blame game” between the various operational teams. The hard thing is that when orders fail, there is a waterfall effect that makes it very difficult to understand what really caused the failure based on simple data analysis.
This is where a prediction model that is trained to detect the root cause of these order failures can be very helpful. These powerful models can elegantly and efficiently inform you why an order failed, providing more granularity than any manual approach could on its own. Using this method can help put your business on a path to continuous operational delivery improvement. Additionally, the next level of evolution for these models is to have them tell us which orders are likely to be delayed before the failure takes place. This information can be used to expedite orders, inform the customer about the delay in advance, or determine possible workarounds.
One of the largest apparel and athleisure companies in the world has seen significant value in using AI solutions to help with detecting delivery failures. The company has seen benefits both in terms of improving consumer satisfaction scores and creating operational efficiencies. By leveraging this solution, supply chain managers can consistently uncover the true root causes of e-commerce failures and even predict them before they become a problem.
Smart inventory cleansing
E-commerce has this uncanny ability to proliferate your product portfolio—mostly because the business is no longer constrained by physical store shelf space. However, while this proliferation may be tempting, it is not healthy. You will end up holding a lot of inventory in your fulfillment centers, tying up both your working capital and precious capacity. It is therefore important to cleanse your system of “nonproductive inventory.”
With AI, instead of just using past data to make these inventory-cleansing decisions, you can build predictive models. Once these models are trained2 and back-tested,3, they can help you confidently decide which products you can continue to store (and where) and which you need to liquidate through your regular liquidation channels.
A fashion retail company recently faced an unexpected abundance of inventory due to COVID-19–related store closings and new product shipments not having anywhere to go. Instead of relying on human intelligence, which could be both biased and hard to scale, the chief supply chain officer asked the team to build a machine-learning model to make these decisions.
His decision proved to be the right one. The machine-learning model was relatively easy to build, as almost all the data needed was readily available. The model also was scalable, and (once the stakeholders understood that it was good at predicting which products were most likely to be unproductive) there was wide-scale adoption of the solution. The business is now committed to enhancing the solution with additional features (such as bringing in prediction around downstream liquidation revenue) and is expanding the scope of adoption to other divisions.
Opportunity knocks
The pandemic has hastened the adoption of e-commerce across the world in an unprecedented manner. The challenge, of course, is getting the business comfortable with the rapid change of pace that we are all experiencing. At the same time, this change also presents us with a huge opportunity to make our businesses more AI savvy. E-commerce is inherently a more digital process, which creates data: the fuel for AI systems. E-commerce also demands that the business be highly scalable, which is not feasible without a mindset to automate every process.
What we discussed in this article is just scratching the surface on using AI in your business. Observing the market and being open to new and powerful AI solutions can enable you to (a) be ready for longer-term e-commerce dominance, and (b) start using the technology to run smart e-commerce operations. My advice: Strap yourself in for an exciting ride!
Notes:
1. A failed order is not just the order that you cannot fulfill due to lack of inventory, but also the order that the customer does not receive at the level of service you promised.
2. “Training” is a term used in machine learning to describe building a model specific to a certain problem and/or dataset(s).
3. “Back-testing,” or “testing,” is a term used in machine learning to describe the process of testing a trained model against past data to understand how good the model predictions are likely to be.
The venture-backed fleet telematics technology provider Platform Science will acquire a suite of “global transportation telematics business units” from supply chain technology provider Trimble Inc., the firms said Sunday.
Trimble's other core transportation business units — Enterprise, Maps, Vusion and Transporeon — are not included in the proposed transaction and will remain part of Trimble's Transportation & Logistics segment, with a continued focus on priority growth areas following completion of the proposed transaction.
Terms of the deal were not disclosed but as part of this agreement, Colorado-based Trimble will become a shareholder in Platform Science's expanded business. Specifically, Trimble will have a 32.5% stake in the newly expanded global Platform Science business and will receive a Platform Science board seat. The company joins C.R. England, Cummins, Daimler Truck, PACCAR, Prologis, RyderVentures, and Schneider as a key strategic investor in Platform Science along with financial investors 8VC, Activant Capital, BDT & MSD Partners, Softbank, and NewRoad Capital Partners.
According to San Diego-based Platform Science, the proposed transaction aims to enhance driver experience, fleet safety, efficiency, and compliance by combining two cutting-edge in-cab commercial vehicle ecosystems, which will give customers access to more applications and offerings.
From Trimble customers’ point of view, they will continue to enjoy the benefits of their Trimble solutions, with the added flexibility of the Virtual Vehicle platform from Platform Science. That means Virtual Vehicle-enabled fleets will receive access to the Virtual Vehicle Marketplace, offering hundreds of new and expanded applications, software, and solution providers focused on innovating and improving drivers' quality of life and fleet performance.
Meanwhile, Platform Science customers will enjoy the added choice of Trimble's remaining portfolio of transportation solutions which will be available on the Virtual Vehicle platform, the partners said.
"We believe combining our global transportation telematics portfolio with Platform Science's will further advance fleet mobility and provide our customers with a broader portfolio of solutions to solve industry problems," Rob Painter, president and CEO of Trimble, said in a release. "Increased collaboration between the new Platform Science business and Trimble's remaining transportation businesses will enhance our ability to provide positive outcomes for our global customers of commercial mapping, transportation management, freight procurement, and visibility solutions. This deal will result in significant synergies along with tremendous opportunities for employees to continue to grow in a more-competitive business."
The acquisition comes just five months after Platform Science raised $125 million in growth capital from some of the biggest names in freight trucking, saying the money would help accelerate innovation in the commercial transportation sector.
Nearly one-third of American consumers have increased their secondhand purchases in the past year, revealing a jump in “recommerce” according to a buyer survey from ShipStation, a provider of web-based shipping and order fulfillment solutions.
The number comes from a survey of 500 U.S. consumers showing that nearly one in four (23%) Americans lack confidence in making purchases over $200 in the next six months. Due to economic uncertainty, savvy shoppers are looking for ways to save money without sacrificing quality or style, the research found.
Younger shoppers are leading the charge in that trend, with 59% of Gen Z and 48% of Millennials buying pre-owned items weekly or monthly. That rate makes Gen Z nearly twice as likely to buy second hand compared to older generations.
The primary reason that shoppers say they have increased their recommerce habits is lower prices (74%), followed by the thrill of finding unique or rare items (38%) and getting higher quality for a lower price (28%). Only 14% of Americans cite environmental concerns as a primary reason they shop second-hand.
Despite the challenge of adjusting to the new pattern, recommerce represents a strategic opportunity for businesses to capture today’s budget-minded shoppers and foster long-term loyalty, Austin, Texas-based ShipStation said.
For example, retailers don’t have to sell used goods to capitalize on the secondhand boom. Instead, they can offer trade-in programs swapping discounts or store credit for shoppers’ old items. And they can improve product discoverability to help customers—particularly older generations—find what they’re looking for.
Other ways for retailers to connect with recommerce shoppers are to improve shipping practices. According to ShipStation:
70% of shoppers won’t return to a brand if shipping is too expensive.
51% of consumers are turned off by late deliveries
40% of shoppers won’t return to a retailer again if the packaging is bad.
The “CMA CGM Startup Awards”—created in collaboration with BFM Business and La Tribune—will identify the best innovations to accelerate its transformation, the French company said.
Specifically, the company will select the best startup among the applicants, with clear industry transformation objectives focused on environmental performance, competitiveness, and quality of life at work in each of the three areas:
Shipping: Enabling safer, more efficient, and sustainable navigation through innovative technological solutions.
Logistics: Reinventing the global supply chain with smart and sustainable logistics solutions.
Media: Transform content creation, and customer engagement with innovative media technologies and strategies.
Three winners will be selected during a final event organized on November 15 at the Orange Vélodrome Stadium in Marseille, during the 2nd Artificial Intelligence Marseille (AIM) forum organized by La Tribune and BFM Business. The selection will be made by a jury chaired by Rodolphe Saadé, Chairman and CEO of the Group, and including members of the executive committee representing the various sectors of CMA CGM.
Economic activity in the logistics industry expanded in August, though growth slowed slightly from July, according to the most recent Logistics Manager’s Index report (LMI), released this week.
The August LMI registered 56.4, down from July’s reading of 56.6 but consistent with readings over the past four months. The August reading represents nine straight months of growth across the logistics industry.
The LMI is a monthly gauge of economic activity across warehousing, transportation, and logistics markets. An LMI above 50 indicates expansion, and a reading below 50 indicates contraction.
Inventory levels saw a marked change in August, increasing more than six points compared to July and breaking a three-month streak of contraction. The LMI researchers said this suggests that after running inventories down, companies are now building them back up in anticipation of fourth-quarter demand. It also represents a return to more typical growth patterns following the accelerated demand for logistics services during the Covid-19 pandemic and the lows of the recent freight recession.
“This suggests a return to traditional patterns of seasonality that we have not seen since pre-COVID,” the researchers wrote in the monthly LMI report, published Tuesday, adding that the buildup is somewhat tempered by increases in warehousing capacity and transportation capacity.
The LMI report is based on a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
That hiring surge marks a significant jump in relation to the company’s nearly 17,000 current employees across North America, adding 21% more workers.
That increase is necessary because U.S. holiday sales in 2023 increased 3.9% year-over-year as consumer spending grew even amidst uncertain economic times and trends like inflation and consumer price sensitivity. Looking at the coming peak, a similar pattern is projected for this year, with shoppers forecasted to drive a 4.8% increase in holiday retail sales for 2024, Geodis said, citing data from Emarketer.
To attract the extra workforce, Geodis says it will offer competitive wages, peak premium pay incentives, peak and referral bonuses, an expedited payment option, and flexible schedules. And it’s using an AI-powered chatbot named Sophie to serve as a virtual recruiting assistant.
“We acknowledge the immense responsibility we have to our customers to deliver exceptional service every day, and this is especially true during peak season,” Anthony Jordan, GEODIS in Americas Executive Vice President and Chief Operating Officer, said in a release. “Because peak season is the most business-critical sales period of the year for many of our retail clients, expanding our workforce is vital to ensure we have a flexible, dynamic team that can handle anticipated surges in demand.”