8 min read
It’s no secret that artificial Intelligence is changing the uniform and work apparel markets for the better in terms of reliability and efficiency. In fact, it’s playing such a crucial role in various key divisions like manufacturing, distributing, and in other areas of the logistic supply chain that we likely will never see a return to a time before it. Basically, it’s here to stay and it’ll only keep evolving from here
Let’s talk about how AI is changing in each major member of the supply chain.
As for uniform apparel manufacturing, which is a labor-intensive industry, mundane tasks like sorting and sewing can now be performed with better accuracy at faster speeds, which reduce costs and strain on the workers. AI-enabled machines and robots can easily combine the fabrics together with accurate results.
For uniform apparel distributing, AI is automating the logistics and supply chain processes for faster delivery and it’s finding alternative routes for vehicles that could’ve otherwise been derailed or delayed by unplanned circumstances such as poor weather or road blockades. All of which is cutting the logistic supply and shipping costs across the board.
In uniform apparel retailing, AI is seeing it’s biggest evolution. Machine-learning tech is providing an automated solution to monitor customer activities while shopping online to know what kind of products they prefer to buy and which they ignore, so the AI can recommend similar items to them based on their history and saved preferences. Things like Groups are also working to improve the online shopping experience by providing personalized experiences for customers of a specific type (industry, job type, etc).
Overall, websites that employ the use of AI to support chat bots, consumer search and product recommendations and the like already sell significantly more than comparable websites without AI. In fact, Gartner mentions that, come 2022, companies with AI capabilities will see a 30% increase in e-commerce revenue. But in order to maximize that potential online profitability, members of the supply chain must enhance their e-commerce efforts with machine-learning technology and here’s a few ways you can do so:
Have you ever struggled to pinpoint your exact size online? Do you prefer to only view products that have your size available? These concerns and more have been answered with a new piece of AI-based technology that provides size recommendation solutions based on your psychographics and preferences and applies them to your future product searches and purchases.
At this point in e-commerce, almost every touchpoint in the customer journey can be personalized and likely should be in order to secure repeat business and turn strangers into advocates of your brand. Most companies are employing the use of API’s, software, and AI to make every impression, every interaction, and experience with individual customers to be unique and exclusive to them. The truth of this point cannot be more sufficiently proven by the invention of AI-based size recommendation solutions. When visiting a uniform e-tailer and looking at their products, this AI solution presents itself as another automated system that can accurately recommend your correct size based on your psychographic information and personal preferences.
Size selection is one thing, but what about being able to maintain and manage your inventory with AI and predict future market demand? It’s already a delicate system to begin with. You have to keep enough stock in the warehouse to ensure the business keeps moving, but not having enough drains limited cash reserves. Elon Musk once said that one out-of-stock item can bring a business to its knees. That’s why you need AI for inventory management. Not because you want to replace the current system and employees you have and trust, but to team them up together in order to create an efficient, reliable automated system that’s overseen by humans: the perfect pairing.
In order to make effective use of all real-time data now routinely generated on the internet and to stay competitive, the supply-chain processes need a redesign. Amazon, for example, implemented artificial intelligence throughout their inventory operations and employed AI methodologies such as time series prediction and reinforcement learning. User demand, supplier backorders, warehouse optimization, and stock levels are all being guided by AI systems. And they’re already seeing huge improvements to their processes. One key implementation of AI for inventory is demand prediction. As the name suggests, the general idea is to build a time series prediction model that can estimate what demand will be like for the coming days across all items in your inventory.
Have you ever noticed apps like Netflix have personalized recommendations for new shows based on your preferences and previously watched content? This personalized product recommendation AI tech exists everywhere including the uniform and workplace apparel markets, too.
Because customers are becoming accustomed to product recommendations and personalized experiences, retailers are faced with the challenge of offering similar experiences for shoppers across channels. It’s about pushing more and more products in front of customers’ faces.
Integrating an AI recommendation engine requires huge amounts of enterprise data from the client company. The data is used to train the machine-learning algorithm to recognize info within product listings and customer information in order to correlate them and form recommendations. You can pretty much train AI to learn anything.
