The year is 2050. An industrial hemp farmer is making his budget for the year and needs to know exactly how much he’ll produce, the chance of crop failure, and how long each crop will need to reap the most profit.
An app on his paper-thin tablet lets him dress his fields in the most efficient formations. He can play around with them, seeing the benefits and drawbacks of each strategy.
Sounds like a tech-heavy futuristic dream, but we are closer to this than most realize.
CBD manufacturing has the benefit of maturing as an industry during one of the biggest technology revolutions in history.
Artificial Intelligence is already being used by traditional farmers. What they learn will be used to eliminate some of the biggest threats to CBD oil manufacturing like pests, diseases, and growth time.
AI and Pest Detection
In India, over a third (35%) of all agricultural production is lost to pest infestations. It’s no surprise that many communities are overexposed to high levels of pesticides since farmers use it with less care than a preteen hosing themselves down with cologne before the big dance.
But it isn’t the farmers’ fault. Access to agriculture experts is hard to come by and most simply can’t afford to foot the bill for a professional on top of their already slim margins.
The only viable cost-effective solution is to use AI coupled with cloud technology.
How AI Democratizes Farming
Large agricultural operations used to be the only ones with enough money in the bank that could afford to hire scientists for assisting in production. But a new experiment in building a scalable solution to the plant diseases in India has shown a 95% success rate.
How are Indian farmers using AI to prevent crop loss?
Researchers trained their AI program just like you’d train a new employee on the production line. Taking pictures of all the possible healthy and sick plants took over 7 months.
This data was spoon fed to the AI program along with advice and knowledge from experts.
A team of agricultural experts, over half a year’s worth of data, and real-time advice would normally cost a farming corporation millions in research and development.
But with all of this condensed into an app, farmers can snap a picture of their plants and get immediate real-time advice on what to do next.
AI is turning crop failure around with technology that makes diagnosing plant disease as easy as using Snapchat.
Big Data Trailblazes the Way for More Companies to Be Successful
AI and big data sound like they’re going to add some awesome features to the manufacturing business in the future, but what about now? How can big data give you an edge now when you’re starting a new CBD brand?
A group of researchers began listening to Twitter users.
By analyzing key phrases throughout hundreds of thousands of Twitter posts they were able to pin down how much of a return CBD brands can expect to see from social media use. They left this research open to the public so newcomers could see directly into one of the fastest changing industries right now.
By tracking Twitter users following CBD brands, businesses can segment their advertising to better appeal to each customer.
Millennial moms looking for anxiety relief are going to want different content than a construction worker using CBD balm for his back problems.
Social media bleeds data like a harpooned walrus, giving us a window into what customers really want.
manufacturing can pivot towards the demands of the customer instead of the other way around. Companies of all sizes and experience levels can only dream of the opportunities big data will deliver them over the next few years.
The Future of Risk Assessment and CBD Manufacturing
Picture this: you’re running a profitable B2B CBD brand wholesaling to stores around the country.
You get an email from a new store just opening in LA. They have no track record in the industry but want to stock your highly rated CBD softgels.
But what are the risks to you and your business? What if you ship their order and they go bankrupt before they can even pay off the invoice you sent them?
Now you’re out thousands. You were just trying to give a fresh entrepreneur the same support other business owners gave you when starting out.
Wouldn’t it be awesome to know every possible outcome and risk associated with taking on a new client?
By using AI we can actually separate these type of orders from our core business. They’re treated like a brand new project.
Monte Carlo Simulation
How is this possible? Using a trusted risk management equation, the Monte Carlo Simulation, AI can calculate possible supply chain risks.
How does Monte Carlo Simulation work? Say we’re in a casino. You’ve heard the house always wins, but how do we know for sure? Using a computer coding language called Python the potential of winning on both the gambler and the casino side can be used as variables.
With our new computer program, we can know to calculate what the chances of losing money as a gambler are. The more times a gambler plays, the bigger the chance of losing money.
But what does a casino have to do with manufacturing? Just like a casino, manufacturers take a risk with each order they fill.
A brand just starting out may just want a sample run of 500 bottles of CBD gummies. But the margins on these small orders are much smaller than regular orders.
Without proper planning, the CBD manufacturer could end up losing money from not planning ahead for possible mistakes.
Monte Carlo Simulations have been a standard risk assessment formula for businesses since first introduced by a UC Berkeley professor in the 60s.
But there are limitations to Monte Carlo. While it can accurately predict probabilities, it loses a lot of its credibility when it comes to infrequent situations. Custom orders are commonplace in CBD oil manufacturing and the smallest mistake can result in big financial losses.
Monte Carlo is great for predicting outcomes when there aren’t many possible outcomes. For example, many businesses use these calculations to predict sales volume of a new product.
So a Monte Carlo simulation would be used by a company, like Apple, to estimate sales volume of the latest iPhone.
But what happens when there’s more than just yes or no outcomes?
If we want to know more than whether a new iPhone will sell or not, like whether Samsung’s new device will affect Apple’s sales.
This is where fuzzy logic comes in…
How AI Takes Monte Carlo Simulations up a Notch
One of the best parts of AI programs is their ability to reason. Computer programs that don’t use AI often have conditions programmed that are either activated or not.
Going back to Apple, a sale could be represented by a 1 and no sale by 0. But what about potential customers like those that might be considering an upgrade or signed up to Apple’s email list.
These customers fall outside of the simple yes or no range when it comes to buying a new phone.
And when it comes to CBD manufacturing, a new account comes with no historical data to base possible outcomes on.
Fuzzy logic leaves more room for one-time events like a supplier late on their delivery. Although accuracy is debatable, fuzzy logic doesn’t require any historical data to use, so it works just as well on a new account as an old one.
Artificial Neural Networks (ANN)
Now it’s time to talk about real AI. Monte Carlo Simulations and fuzzy logic are precursors to real machine learning.
You’ve heard that term before, machine learning. But what does it really mean?
Many often mistake well-designed programs for AI, when they aren’t. Amazon’s Alexa, for example, might start suggesting music you like based on past purchases. But this isn’t learning. It’s just a regular computer program with thousands of different responses.
Anything outside of what it’s been programmed to do won’t work. Alexa isn’t learning from you she’s just adding to her memory.
For a machine to learn, AI researchers use something called artificial neural networks (ANN). Just like the neurons in our brains, AI creates an artificial version of this.
So instead of just adding to its memory, AI programs can actually take in new knowledge without being programmed to.
For example, facial recognition is a hot topic AI issue. Rather than programming the computer with millions of different photos, you can give it a few examples to base its decisions on. So if you wanted your program to recognize whether a human is in a photo, you’d train it by giving it several photos labeled as “no person” or “person”.
When confronted with a photo outside of its examples, it will use its past experiences to determine whether this photo has a person in it or not.
You’ve heard about cloud computing, but what about cloud manufacturing (CMfg)?
Instead of using the cloud as a convenient place to store customer data, we can actually allow access to manufacturing resources through the cloud.
CMfg was born out of enterprise resource planning (ERP) software. Backend tasks, like payroll, were some of the first tasks companies starting using ERP for. But ERP programs still require lots of upkeep.
When cloud providers started offering ERP management as a service, businesses were quick to relieve themselves of the extra cost of keeping up on IT.
Leave It to the Experts
Every business needs IT services, but hiring an IT team comes with lots of problems.
Incompetent employees, constant updates, and simply not having the funds to support an in house IT infrastructure are common reasons for switching to the cloud.
With new manufacturing tech and applications created daily, the cloud is the only option for getting your hands on new technology as soon as it’s released.
The traditional route of developing a software application and finally getting it to market months or years later isn’t just inefficient, no modern ERP software company could stay in business doing this.
When you’re hungry you don’t plant a sapling hoping it will grow and produce fruit by dinner time.
You go to the grocery store and buy the finished product.
At some point, that orange was nothing more than a sapling years away from being able to grow fruit.
But that doesn’t matter. The price of the orange covers all those processes happening in the background we don’t even think of.
AI Isn’t the Only Way to Simplify CBD Manufacturing…
Imagine another scenario. You wake up to an inbox full of messages. You have a pile of orders that would make even an Amazon fulfillment center nervous about getting everything done on time. But you don’t have to lift a finger.
You’ve partnered with the best CBD manufacturer in the industry.
Not only did they work with you day after day to create a custom formula no one else has the rights to sell, but they can also package and ship out your orders too.
You get to stick to what you do best — sales and marketing.
Spring Creek Labs has total control over their CBD farming methods, but customers still need your brand to market new products to them. When it comes down to it, running a CBD brand is no different than running any other kind of company.
You already know how to take a brand from zero to sixty. Trying to build your own supply chain would be a ridiculous waste of your time. As an experienced entrepreneur, you know that. And there’s no need to.
Spring Creek Labs is 100% vertically integrated. They aren’t desperately taking plants from any source they can find like many CBD manufacturing facilities.
From soil to the lab, Spring Creek Labs never uses unidentified ingredients.
You can’t blame the other guys for doing it, they don’t even know where their plants are really coming from. Don’t take risks where you don’t have to. Email us now for your free custom quote today!