By Moataz RashadRecently Nintendo has been facing a problem many consumer electronics manufacturers envy– consumers love their game consoles! But now the iconic company has a brand image problem, ie. why can’t it accurately forecast the demand, and ensure its new products are available at its retail partners. See this and this.In a nutshell, there are two problems in play here: a) Demand forecasting, and b) On Shelf Availability. In this case, Nintendo is struggling with both. Firstly, they under-forecasted initial demand for the new product at the launch date that they selected. Secondly, subsequent to the actual launch, they didn’t coordinate their supply chain with the retailers’s data (point of sale and distribution network) to ensure On Shelf Availability for the product so its eager customers can buy it.On Shelf Availability is a sub problem of demand forecasting that deals with optimizing the likelihood that a customer who walks into a store (physical or online) searching for a product X, will find that product both in-stock and, in case of physical stores, actually on the shelf to complete the purchase.It is clearly the one metric that ensures no sales are lost. If a customer intends to buy, they will find the product available at the time and location of their choice. This problem is a key supply chain problem that’s been around for decades. It requires close real-time collaboration between the manufacturers and the retail channels with daily updates of on-hand inventory and point-of-sale transactions. But most importantly, it requires deep-learning to solve it at scale. Unfortunately, traditional machine learning techniques deliver only 55–65% accuracy which costs millions in lost sales. They are unable to ingest the numerous external signals that impact demand, and they are unable to handle the massive volume of data the tier1 manufacturers and tier1 retailers deal with. So they’re left with building numerous disparate models instead of one large monolithic deep learning engine that can learn from all the patterns and transfer learnings from one store’s patterns to a sister store etc.A deep learning solution is able to handle all kinds of data feeds that impact the customers’ demand patterns, including economic and demographic data which vary by vertical and product category. It also ingests the PoS and inventory data along with daily feedback from the manufacturer and retailer in order to adapt and self-tune its predictions in real-time. That’s what Nintendo and comparable major enterprise manufacturers need in order to address these key supply chain challenges that could potentially make or break the company.
For the past couple of years, many well-respected folks have opined on the future of retail, claiming that all brick and mortar retail is dead, dying, or in a vegetative state, and that the future of shopping therefore lies entirely online! See “future of shopping” and “retail stores will die”I certainly wouldn’t mind that notion materializing at all, since for obvious reasons, the more commerce moves online–which is an irreversible trend–the better for us at Vufind, a startup building the AI layer for eCommerce.However, I think that the hypothesis is flawed. The future of retail is AI-commerce, i.e. smarter shopping experiences in every sense of the word, whether it happens online, offline, or hybrid. “Smart” as opposed to “not so smart” commerce, as in what you often see now i.e : a) having to wait in line to buy a staple product for which there is little choice, minimal price variation, and there’s no emotional investment in the purchase b) being chased after you’ve left a store (offline or online) and abandoned an item to be shown the same exact item over and over (aka retargeting) or c) the seller keeps telling you those who bought this also bought that, as if all shoppers are automatons (aka collaborative filtering)See this emarketer presentation for more detailed proof points on consumer data regarding how they view online vs. offline shopping.It is indisputable that online retailers have much more attractive cost structure, and are advantaged by the digital consumer and product data that can drive decisions in real-time at nano-second speeds. However, offline retail has some advantages as well, including immediate delivery, try before you buy, impulsive buys, and lower return rates.Some online-offline hybrid experiences are already happening. For an example of online companies expanding offline, just monitor the investment moves of Amazon, one of the most forward-thinking online retailers globally, and you’ll observe that they are serious about online-offline interplay. Similarly, Apple retail has a hybrid show-room/store model which is being copied by other consumer electronics companies. On the other hand, a forward-thinking brick and mortar retailer such as Nordstrom is experimenting with exciting online-offline hybrid experiences with the smart mirror for example. It will be intriguing to see the A/B test results of these efforts as they will pave the way for even more fascinating experiences. Courtesy: Nordstrom-ebay fitting room So what is smarter commerce, or AI commerce? AI commerce has one broad goal: matching the shopper with their desired product by factoring in their style, brand preference, price range, size, fit, and delivery time, in order to effect a frictionless speedy transaction.If a brick and mortar retailer builds a sophisticated site/app but they are primarily interested in driving traffic to their physical stores, then they aren’t fostering an AI commerce experience. By the same token, if an online-only retailer gets user feedback in favor of same-day delivery and ignores this input, then they aren’t developing an AI commerce experience. So what would an AI commerce experience entail?Let’s start with some definitions since AI is such a land-mine these days, what with everyone from famous loved entrepreneurs such as Elon Musk and Bill Gates to the occasional scientific journalist speaking on the topic.AI in this context refers to the field of Artificial Intelligence; software algorithms that use pattern recognition, computer vision, deep-learning neural networks, or whatever other techniques we practitioners come up with, to produce intelligent decisions that impact the shopper’s user experience. Such decisions include recommendations, merchandizing, dynamic pricing, promotions, placement of products on sites/apps/store-window, or whatever decision the marketing/product management executive needs the algorithm to assist with that day. In other words, we’re not talking about an artificially intelligent entity such as a robot, vehicle, drone, or anything else that can do any harm to people in the real world. And we’re certainly not talking about “Strong AI” which is where most of the fears of the critics revolve, where the algorithms would decided to learn from data outside their domain, and in fact will consume all accessible online knowledge, and for some reason they’ll take special interest in learning about destroying humanity!In AI-commerce, we’re only discussing deep-learning software running in the cloud with no tentacles in the physical world, and where it’s trained on where to look, what to look at, and what type of output we’d want it to produce, and it’s only domain is commerce. This begs the question– how is AI different form Business Intelligence (BI) which has been the traditional software layer of eCommerce? At the highest level, the fundamental difference is that BI is static or doesn’t learn from the data, while AI’s missions is to continuously improve and learn from the data to get “smarter”. In other words, Business Inteligence is a misnomer, it’s really business reporting, or business stats– dry static stale reports on what’s already happened a while ago.So what do these AI algorithms work off of? the online shopping experiences have the upper hand here, because every action is already digital, cloud stored, and hence trackable and learnable in real-time. Every action including product impression, image click, recommendation click, purchase, like, save, share, cart-abandonment,email open, email link click, etc, is fed to the algorithms with the goal of making the shopper’s experience smarter and more pleasing.We believe a true AI-commerce experience should accommodate most of the following:1. Smarter visual recommendations: A smarter experience means that you get to see a recommendation that visually matches your style preference so much that it persuades you to purchase the recommended item rather than the item you originally searched for. We get super stoked when we count those transactions.2. Smarter personalized recommendations: If someone is a loyal repeat user of an etailers site, then they do in fact expect a personalized experience that accounts for more than gender and age group, regardless of whether or not they use a social login. If they have purchased before, browsed, liked, and abandoned carts before, they expect the site to be smart enough to know their taste preferences and which products they would like to see. How is this different than (1) ? A user might be looking at a heather grey lambswool sweater, and you could show her ski goggles and cycling shoes, because you’ve learned that she’s into the sports associated with these items and haven’t purchased them in over 9 months. You might even show her girl’s soccer shoes because she bought a pair for her daughter last February. Or you might surprise her with an item that she’s never bought from your site before because it matches her style.3. Smarter merchandizing: the more AI-powered decisions the retailer employs, the smarter the product mix in their catalog. As such, their buyers would know exactly what to stock each season based on transaction trend analytics, thus eliminating waste across the supply chain therefore saving time, and resources.4. Psychic Personal Shopper Apps (PSA): A truly intelligent PSA wouldn’t just have a warm voice and uses Google and Amazon to run searches for you and send you a list, it would know what you’d want and would just get it delivered to your door step. And if you dislike the product, you tap a button and it’ll get it returned for you as well. It’ll know that it’s time to re-order socks, or that winter is coming 2 weeks earlier, so it’ll order those thermal under-shirts. And it’ll buy a different tasteful gift for your best friends’ birthdays each year, and it’ll ask you what the friend said about it so it gets better at gifting. Most importantly, it’ll do all that algorithmically without any *crowd-sourcing* or asking you to fill out forms.5. Smarter “show-rooms”: Recall when show-rooming was considered a threat, well it isn’t, it’s part of the shopping experience now, and every online and offline retailer designs for it. However we think in the very near future, the physical shops will evolve into AI powered show-rooms, amazingly chic physical spaces that allow shoppers to touch, feel, try on, learn about the origin of the product, or quickly browse similar products to explore. But not necessarily wait in line or carry it home, unless in fact they do need it that exact moment.6. Smarter payment: Some of the amazing international etailers we work with offer cash payment upon delivery. Some have added BitCoin. Some experimenting with pick up at partner physical stores. Retailers should certainly make it as frictionless as possible to pay for the item you’ve already invested the time and emotional energy to shop for and decided to purchase. So smarter payments means choice, speed, convenience, and quite frankly none of those hideous 2.9% fees, which I personally think should be eliminated.7. Social: Many interesting developments there, but that’s for another article.The history of commerce goes back thousands of years. It’s catering to a human need that’s existed since the oldest civilizations used the barter system. The future of commerce, however, is truly awesome; it’s intelligent commerce powered by AI, easier, faster, cheaper, more relevant and more delightful. It’s already happening.Are you excited about smarter commerce?–would love to see your thoughts below.
By Scott Lyon“Manufacturing Loses Most Jobs Since Election” reads the latest headline.According to the latest job report, the U.S. manufacturing sector once again reported job declines (4,000 jobs lost). This further extends an unfortunate erosion in our country’s manufacturing base and in fact Institute of Supply Management (ISM) surveys suggest job losses will continue.Although efforts by the current administration to reverse the trend toward “off-shoring” as well as aggressive incentives prepared by the states (e.g. check out Wisconsin’s just-announced Foxconn package) may prove helpful in the short-term, however, clearly the manufacturing sector needs to reinvent itself and develop new market opportunities.One increasingly popular theme for doing so involves Artificial Intelligence (AI). Namely, could deeper machine learning enable manufacturing companies to either reduce costs and/or drive incremental revenues without launching new products or securing additional customers? There are numerous conceptual opportunities suggesting the answer is yes, here are three recent compelling use cases:Two ex-Apple engineers have created hardware which takes photos at critical junctures of a manufacturing line and can pinpoint in real-time when devices are being assembled incorrectly (all without requiring on-site visits by managers or quality control inspectors);GE launched an internal IT initiative which is now providing advanced analytics to external customers for example advanced sensors for their oil / gas clients can better predict blade health and determine optimal replacement times;One of the leading semiconductor companies is leveraging DeepVu’s AI engine to determine the “optimal production allocation” for maximizing chip revenue and optimizing yield amongst multiple contract manufacturers;In each of these three examples, manufacturers are leveraging their existing data and service offerings to drive either reduced equipment failures or greater revenue-per-channel.
Every supply chain executive is concerned about optimizing margins in this competitive environment. Supply chain costs are typically 60-70% of operating costs and hence a real focus in executive discussions. Supply chain leaders are constantly asking themselves questions about how to do things faster, cheaper and better.Are you paying the lowest possible price to source parts for this product to optimize the BoM while maintaining the same quality? Is that a question you have the tools to answer? Due to the following common complexities, it’s very likely that manufacturers are overlooking inefficiencies in their sourcing and overpaying large sums over time:Multiple suppliersPrice fluctuationsReliability and quality factorsStock availabilityBusiness guidelinesEco and government compliance requirementsFor supply chain professionals that are close to this process, they understand that it’s not a simple spreadsheet exercise to look for the lowest possible price for any given component. The lowest price could have been an anomaly due to a supplier wanting to offload stock at that particular time. 8 months later that supplier could have increased the price, no longer hold the quantity needed, decreased in quality rating, or another supplier could now have a higher likelihood of a lower price. Multiply this problem by the number of suppliers and components that you have and it becomes a large, complex data problem. At the same time, the amount of savings and contribution to margin lift is also extremely significant when optimized across this scale.Here are some different approaches on how you can optimize your supplier pricing breakdown:Mathematical modelsMathematical models have been used to optimize supply chains for decades. They are an improvement from eyeballing spreadsheets but they are also limited in that they can’t account for all of the variables very well and are static. The models also need to be updated regularly and you may find the technique you were using is no longer relevant to your current business operations and you have to change models completely.Machine LearningWith increased access to compute power, many organizations can now turn to machine learning to process large amounts of data and produce insights. This approach is more adaptable and eliminates the need to manually select a specific type of model for your supplier sourcing. However, machine learning engineers are a rare and expensive resource. Traditional machine learning techniques require tedious manual feature engineering before developing the models. Also, traditional techniques work best on small datasets and cannot handle all datasets from different product lines or different geographies together. Each dataset has to be analyzed and modeled separately which doesn’t scale.Deep LearningThe most advanced form of artificial intelligence, deep-reinforcement learning also takes advantage of increased access to compute power but takes it to another level by solving problems as humans would by reacting in real-time to dynamic changes in the environment. The major benefits of this approach are:constantly learning and continuously improvingdoes not require manual feature engineering so it scales gracefullyoutperforms the most with massive datasetsproven to deliver the highest accuracyThis is the approach that DeepVu applies to solve for the supplier pricing breakdown problem in the most accurate way possible. Because we have built the product to train from historical supply chain data and provide the optimal output, our customers do not have to invest in a data science or engineering team to achieve the end goal. The reduction in costs from doing this optimally is significant for all manufacturers that have numerous suppliers and components. See our website for more information.