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.
Specifically, the new global average robot density has reached a record 162 units per 10,000 employees in 2023, which is more than double the mark of 74 units measured seven years ago.
Broken into geographical regions, the European Union has a robot density of 219 units per 10,000 employees, an increase of 5.2%, with Germany, Sweden, Denmark and Slovenia in the global top ten. Next, North America’s robot density is 197 units per 10,000 employees – up 4.2%. And Asia has a robot density of 182 units per 10,000 persons employed in manufacturing - an increase of 7.6%. The economies of Korea, Singapore, mainland China and Japan are among the top ten most automated countries.
Broken into individual countries, the U.S. ranked in 10th place in 2023, with a robot density of 295 units. Higher up on the list, the top five are:
The Republic of Korea, with 1,012 robot units, showing a 5% increase on average each year since 2018 thanks to its strong electronics and automotive industries.
Singapore had 770 robot units, in part because it is a small country with a very low number of employees in the manufacturing industry, so it can reach a high robot density with a relatively small operational stock.
China took third place in 2023, surpassing Germany and Japan with a mark of 470 robot units as the nation has managed to double its robot density within four years.
Germany ranks fourth with 429 robot units for a 5% CAGR since 2018.
Japan is in fifth place with 419 robot units, showing growth of 7% on average each year from 2018 to 2023.
Businesses are cautiously optimistic as peak holiday shipping season draws near, with many anticipating year-over-year sales increases as they continue to battle challenging supply chain conditions.
That’s according to the DHL 2024 Peak Season Shipping Survey, released today by express shipping service provider DHL Express U.S. The company surveyed small and medium-sized enterprises (SMEs) to gauge their holiday business outlook compared to last year and found that a mix of optimism and “strategic caution” prevail ahead of this year’s peak.
Nearly half (48%) of the SMEs surveyed said they expect higher holiday sales compared to 2023, while 44% said they expect sales to remain on par with last year, and just 8% said they foresee a decline. Respondents said the main challenges to hitting those goals are supply chain problems (35%), inflation and fluctuating consumer demand (34%), staffing (16%), and inventory challenges (14%).
But respondents said they have strategies in place to tackle those issues. Many said they began preparing for holiday season earlier this year—with 45% saying they started planning in Q2 or earlier, up from 39% last year. Other strategies include expanding into international markets (35%) and leveraging holiday discounts (32%).
Sixty percent of respondents said they will prioritize personalized customer service as a way to enhance customer interactions and loyalty this year. Still others said they will invest in enhanced web and mobile experiences (23%) and eco-friendly practices (13%) to draw customers this holiday season.
Census data showed that overall retail sales in October were up 0.4% seasonally adjusted month over month and up 2.8% unadjusted year over year. That compared with increases of 0.8% month over month and 2% year over year in September.
October’s core retail sales as defined by NRF — based on the Census data but excluding automobile dealers, gasoline stations and restaurants — were unchanged seasonally adjusted month over month but up 5.4% unadjusted year over year.
Core sales were up 3.5% year over year for the first 10 months of the year, in line with NRF’s forecast for 2024 retail sales to grow between 2.5% and 3.5% over 2023. NRF is forecasting that 2024 holiday sales during November and December will also increase between 2.5% and 3.5% over the same time last year.
“October’s pickup in retail sales shows a healthy pace of spending as many consumers got an early start on holiday shopping,” NRF Chief Economist Jack Kleinhenz said in a release. “October sales were a good early step forward into the holiday shopping season, which is now fully underway. Falling energy prices have likely provided extra dollars for household spending on retail merchandise.”
Despite that positive trend, market watchers cautioned that retailers still need to offer competitive value propositions and customer experience in order to succeed in the holiday season. “The American consumer has been more resilient than anyone could have expected. But that isn’t a free pass for retailers to under invest in their stores,” Nikki Baird, VP of strategy & product at Aptos, a solutions provider of unified retail technology based out of Alpharetta, Georgia, said in a statement. “They need to make investments in labor, customer experience tech, and digital transformation. It has been too easy to kick the can down the road until you suddenly realize there’s no road left.”
A similar message came from Chip West, a retail and consumer behavior expert at the marketing, packaging, print and supply chain solutions provider RRD. “October’s increase proved to be slightly better than projections and was likely boosted by lower fuel prices. As inflation slowed for a number of months, prices in several categories have stabilized, with some even showing declines, offering further relief to consumers,” West said. “The data also looks to be a positive sign as we kick off the holiday shopping season. Promotions and discounts will play a prominent role in holiday shopping behavior as they are key influencers in consumer’s purchasing decisions.”
Supply chains are poised for accelerated adoption of mobile robots and drones as those technologies mature and companies focus on implementing artificial intelligence (AI) and automation across their logistics operations.
That’s according to data from Gartner’s Hype Cycle for Mobile Robots and Drones, released this week. The report shows that several mobile robotics technologies will mature over the next two to five years, and also identifies breakthrough and rising technologies set to have an impact further out.
Gartner’s Hype Cycle is a graphical depiction of a common pattern that arises with each new technology or innovation through five phases of maturity and adoption. Chief supply chain officers can use the research to find robotic solutions that meet their needs, according to Gartner.
Gartner, Inc.
The mobile robotic technologies set to mature over the next two to five years are: collaborative in-aisle picking robots, light-cargo delivery robots, autonomous mobile robots (AMRs) for transport, mobile robotic goods-to-person systems, and robotic cube storage systems.
“As organizations look to further improve logistic operations, support automation and augment humans in various jobs, supply chain leaders have turned to mobile robots to support their strategy,” Dwight Klappich, VP analyst and Gartner fellow with the Gartner Supply Chain practice, said in a statement announcing the findings. “Mobile robots are continuing to evolve, becoming more powerful and practical, thus paving the way for continued technology innovation.”
Technologies that are on the rise include autonomous data collection and inspection technologies, which are expected to deliver benefits over the next five to 10 years. These include solutions like indoor-flying drones, which utilize AI-enabled vision or RFID to help with time-consuming inventory management, inspection, and surveillance tasks. The technology can also alleviate safety concerns that arise in warehouses, such as workers counting inventory in hard-to-reach places.
“Automating labor-intensive tasks can provide notable benefits,” Klappich said. “With AI capabilities increasingly embedded in mobile robots and drones, the potential to function unaided and adapt to environments will make it possible to support a growing number of use cases.”
Humanoid robots—which resemble the human body in shape—are among the technologies in the breakthrough stage, meaning that they are expected to have a transformational effect on supply chains, but their mainstream adoption could take 10 years or more.
“For supply chains with high-volume and predictable processes, humanoid robots have the potential to enhance or supplement the supply chain workforce,” Klappich also said. “However, while the pace of innovation is encouraging, the industry is years away from general-purpose humanoid robots being used in more complex retail and industrial environments.”
Third-party logistics (3PL) providers’ share of large real estate leases across the U.S. rose significantly through the third quarter of 2024 compared to the same time last year, as more retailers and wholesalers have been outsourcing their warehouse and distribution operations to 3PLs, according to a report from real estate firm CBRE.
Specifically, 3PLs’ share of bulk industrial leasing activity—covering leases of 100,000 square feet or more—rose to 34.1% through Q3 of this year from 30.6% through Q3 last year. By raw numbers, 3PLs have accounted for 498 bulk leases so far this year, up by 9% from the 457 at this time last year.
By category, 3PLs’ share of 34.1% ranked above other occupier types such as: general retail and wholesale (26.6), food and beverage (9.0), automobiles, tires, and parts (7.9), manufacturing (6.2), building materials and construction (5.6), e-commerce only (5.6), medical (2.7), and undisclosed (2.3).
On a quarterly basis, bulk leasing by 3PLs has steadily increased this year, reversing the steadily decreasing trend of 2023. CBRE pointed to three main reasons for that resurgence:
Import Flexibility. Labor disruptions, extreme weather patterns, and geopolitical uncertainty have led many companies to diversify their import locations. Using 3PLs allows for more inventory flexibility, a key component to retailer success in times of uncertainty.
Capital Allocation/Preservation. Warehousing and distribution of goods is expensive, draining capital resources for transportation costs, rent, or labor. But outsourcing to 3PLs provides companies with more flexibility to increase or decrease their inventories without any risk of signing their own lease commitments. And using a 3PL also allows companies to switch supply chain costs from capital to operational expenses.
Focus on Core Competency. Outsourcing their logistics operations to 3PLs allows companies to focus on core business competencies that drive revenue, such as product development, sales, and customer service.
Looking into the future, these same trends will continue to drive 3PL warehouse demand, CBRE said. Economic, geopolitical and supply chain uncertainty will remain prevalent in the coming quarters but will not diminish the need to effectively manage inventory levels.