Dating as far back as the silk road trade, international trade has created a global supply chain with an expansive set of stakeholders. As we began to look beyond local resources for daily necessities and luxuries alike, a product’s journey - whether it be a raw ingredient or finished product - became more complex and environmentally taxing.
A quick trip to the grocery store (assuming you’re not having your groceries delivered these days) will exemplify this. Looking on the back of the product, you’ll see where it was manufactured, though this doesn’t mean that's where the entirety of that product originated. It may contain raw ingredients from many different parts of the world that made their own (undisclosed) journey, also known as the supply value chain. But you also don’t have to go that far. If you’re reading this article from your mobile device or even your desktop, there are plenty of components in the devices that were manufactured and assembled in different parts of the world. Each manufactured part, irrespective of an industry, creates carbon emissions, toxic waste, pollutants, and deforestation along the way. The many impacts of an item’s production on the environment may be destructive, and shockingly, mostly unknown to the consumer.
"There was no predictive model in place that would foresee the demand for toilet paper surge or that the demand for sourdough bread kits would go up by 600%... "
How Supply Meets Demand
As a result of predictive analytics models between supply and demand, the global supply chain typically runs with such acute precision. Consider food; consumers are likely to want to make pumpkin pie for Thanksgiving, so pumpkin growers prepare for November. Roses are sold on Valentine’s Day, so floriculture businesses, delivery companies, storage trucks, and even greeting card makers get prepared for one of their busiest seasons of the year. Of course there are outliers, such as the fashion industry, where the predictive models are less easy to gauge, causing overstock and end of season sales (we see you Black Friday) which result in unnecessary production and environmental damage. Though overall, there have been throughout history generally safe and tested prediction models based on consumer behavior for supply and demand that inform the entire supply chain.
Then came COVID-19.
Even before COVID-19, supply chain and logistics models were being affected by AI. On the consumer level, the likes of Amazon’s Alexa can predict when you’re going to run out of the toilet paper you ordered and automatically reorder it just when you need it most. Although predictive models do take into consideration shock disruptions such as trade tensions or environmental disasters - no one (and no AI) could predict just how deeply the pandemic would disrupt on a global level and the drastic way consumer behavior would change so suddenly. There was no predictive model in place that would foresee the demand for toilet paper surge or that the demand for sourdough bread kits would go up by 600% (as it did on Postmates,) or that PPE and medical equipment would become so scarce.
The global reach of COVID-19 meant that factories and all non-essential work were closed simultaneously all around the world. Even essential work like construction was halted or minimized, causing cement prices to fall globally in March after rising for two straight months.
If in the past predictive models could have prepared for a shortage in supply in a certain geographical area due to an environmental disaster, another factory on the other side of the world would be able to ‘cover’ for this demand. The pandemic disruption now affected not only the output of factories, but also the supply chain that enabled it to run. Take for example the crude oil that is used to fuel the machinery in a factory - suddenly all the factories were no longer running machinery, disrupting a predictive supply and demand model in the oil industry as well.
The impacts of COVID-19 on the supply chain were unprecedented precisely and ironically because of the complex globality of it. This disruption caused financial setbacks and was a rude awakening that highlighted the vulnerabilities in supply chains; namely that they must be made shorter, more transparent, and substantially more sustainable. As COVID-19 has moved us towards local production and a movement to become less reliant on large webs of global supply chains, the answer to sustainability (financial and environmental) is in Industry 4.0 technologies.
Saas platforms allow companies to leverage their own data in order to prepare for the unexpected. Below are a few solutions that help companies make strides towards a more sustainable and efficient supply chain.
C3.ai is enterprise AI that unifies data across organizations - from business objects (customer, order, contract), physical systems and subsystems (engine, boiler, chiller, compressor), to computing resources and services (database, stream processing). Following the trend of enterprise data companies and their successful IPOs such as Palantir and Snowflake, C3 is on it’s way to “solving the previously unsolvable”, as per the company’s website.
Noodle.ai is a machine learning solution for manufacturing with zero waste. Understanding how much excess inventory there is or how many trucks are driving empty is just a few ways Noodle uses AI to bring about sustainable change in the supply chain.
Crisp is a company using data to communicate more efficiently across businesses and parts of the supply chain in real-time.
Robotics and Automation in Storage and Delivery
A crucial part of a supply chain is the storage and delivery of any product. The transportation of goods - whether that be from a factory to a warehouse or to a final consumer comes at a hefty environmental and financial cost. However, advances in robotics and automation are creating more efficient ways to deliver and store products, creating less down time with less fossil fuels being burned along the way.
Attabotics provides 3D robotic goods-to-person storage, retrieval and real-time order fulfillment in order to reinvent supplies chains.
BionicHive has developed sqUID, an autonomous robotic fleet with 3 dimensional movement capabilities, that allow for fully flexible warehouse operation - human free.
Focusing on Last-Mile Delivery
Re-thinking last-mile delivery is especially important to a concerned consumer. Fabric has established micro fulfillment centers using robots and AI. By having many micro fulfillment centers in city centers, an item’s journey to its final destination is much shorter than it traditionally would be.
Bringg is another solution enabling businesses to offer last-mile delivery and adapting to click & collect. Bringg offers several supply chain SaaS solutions and is working with some of the world’s leading brands to improve pickup and delivery speed, capacity, and customer experience at scale.
Lowering the dependency on global trade decreases supply chain vulnerabilities. This means creating your own raw materials. One company facilitating this is XJET by providing industry-leading high definition 3D printing solutions for revolutionary products in ceramics and metals.
In order to stay competitive, players with the latest predictive models will be the ones leading the race when it comes to appeasing the end consumer and the environment. The COVID-19 disruption may have just been an opportunity to clean up supply chains and bring about the realization that building resilience in the supply chain, to persevere another pandemic or unprecedented force majeure, will come from leaner, sustainability practices supported by innovative tools.