What is artificial intelligence? In truth, we’re still trying to hone in on a single definition.
There are many different types of automated systems in place, and they all serve very specific needs that don’t necessarily look like the popular conception of AI. For example, algorithms used by companies like Netflix can be classified as artificial intelligence.
They use machine reasoning to analyze data (in the form of movie ratings submitted by viewers) and suggest other options the viewers might like based on their taste. But the functionality is so limited that most everyday users don’t think twice about it.
There has been a lot of debate recently about whether or not AI will have a positive impact on society and the job market, with notable tech leaders like Elon Musk and Mark Zuckerberg remaining at odds on the issue.
But societal implications aside, the fact remains: businesses are using AI, and that’s a trend that won’t stop anytime soon. According to a Gartner survey (accessible to Gartner members) published earlier this year, 15 percent of supply chain leaders were in the process of integrating or planning to integrate AI applications into their operations.
If we look at one of the biggest names in the business, for example, we’ll see that Amazon has spent years developing artificial intelligence tools to handle as many daily operations as possible. These include recommendation algorithms served up by their online store, automated route picking in their shipping mechanisms and drone delivery technology.
To remain competitive, small and midsize organizations need to understand the potential uses and impacts of AI technology, how it’s being used by larger companies today and how they can incorporate it in their own operations.
Here’s What We’ll Cover
What Can AI Do?
At the moment, the biggest challenge for AI integration in the supply chain seems to be understanding its capabilities and applications, especially for small and medium-sized businesses. Before moving into a deeper discussion about the practical effects of AI in supply chain operations, though, we have to know what the technology is and what it is capable of. To that end, we’re going to take a look at some of the most common types of AI:
Machine learning. Any programming with the intention of identifying patterns or solving problems through the use of collected data. Deep learning and neural networks are versions of machine learning that go further by enabling a program to discover even more complex data. This category of technology has made many of the other disciplines possible.
Machine reasoning. Similar to machine learning, these programs utilize algorithms to gather information and execute tasks according to that information. The difference is that algorithms limit the kind of data that can be collected according to a more specific and repeatable set of parameters. Google’s PageRank and Netflix’s Recommendations are examples of algorithmic reasoning.
Natural-language processing. Also includes speech recognition, translation, and text-to-speech or speech-to-text capabilities. These technologies have been around for a while, but progress in machine learning has made them significantly more accurate in recent years. Apple’s Siri and Amazon’s Alexa are examples of virtual assistants that use language processing.
Computer vision technologies (CVTs). Anything to do with digital images, from capture and processing to determining meaning and context. This is the groundwork for things like facial recognition software, edge or motion detection, optical character recognition, etc. Apple’s new Face ID feature of the iPhone X is an example of CVT.
The system is capable of processing massive amounts of data, such as transaction records from over 11,000 Walmart stores, in order to show what people are buying in what areas. That information can then be used to adjust things like inventory levels and deliveries on the ground.
Robots. Designing machines (and systems to control them) that replace humans in dangerous settings or manufacturing processes to reduce death and injury or speed up production. One example that’s on the supply chain horizon is self-driving vehicles.
What Are the Advantages of AI in the Supply Chain?
Artificial intelligence increases efficiency and saves time and money. AI can eliminate human errors and inconsistencies that come from things like fatigue or time constraints by taking over many of the jobs that previously required human intelligence to accomplish. Examples of these changes include:
- Shipping and delivery can be automized with drones or self-driving trucks.
- Demand planning, forecasting and analysis can be done entirely by programs with machine learning and reasoning capabilities.
- Manufacturing lines can be fully robotized.
Additionally, while the introduction of robots and other automated technologies will replace some human roles in the workforce, it will also create jobs. According to Gartner, it’s estimated that 10 percent of large supply chain-dependent industries will create an opening for and hire a chief robotics officer by 2020.
Recent studies have shown that companies will also have to invest more money in hiring humans to manage, maintain or otherwise manipulate these automated systems. That means workers with backgrounds in IT, mechanics, engineering, or other technical fields will see more job opportunities with the increased use of AI.
Other valuable ROIs for AI in supply chain operations include:
- Smarter machinery and safer workplace environments for employees.
- More productive and positive interactions with customers with features like automated customer service.
What Are the Disadvantages of AI in the Supply Chain?
Just because the positive outweighs the bad, that doesn’t mean companies should ignore the potential downsides to artificial intelligence. By being aware of them, it will be a lot easier to prevent or mitigate them once AI works its way into common supply chain operations.
One thing to look out for right now is too much hype. AI is the shiny new thing right now, and that means business process outsourcing (BPO) providers have the advantage when it comes to marketing their products. Don’t be fooled into thinking you need more than you actually do from your AI system.
Before investing in artificial intelligence, take the time to consider what your business needs really are and organize qualified teams to research and implement new technology.
Speaking of getting organized, adjusting business practices will be another major hurdle to adopting AI. Despite the jobs that will be created in this process, the impact on your workforce is still something you’ll want to prepare for.
How Do I Prepare for AI in My Supply Chain?
As a smaller company, the most valuable thing you can do right now is prepare a solid foundation for success once you start to implement Ai.
That means making exploratory research a priority. Whether you just have one IT-focused employee take point or you have the capacity to assemble a team, you want people who are familiar with your business’s operations so they can identify exactly what you need. The team should consider things like:
- What current operations or practices could be replaced with machines
- Any potential features or functions your company might want to offer in the future, and how they could be automated
- Budgetary constraints
Since smaller organizations probably won’t have the resources to create in-house AI, the next step will be researching BPOs and supply chain management/enterprise resource management software vendors that offer AI integrations. Here are a few more things you and your exploratory partner(s) should keep in mind during this process:
Data, data, data. Self-learning and pattern recognition are going to be essential tools for any AI system you use in supply chain, so you’ll want your exploratory teams to keep data analysis capabilities top of mind when looking at BPOs or SCM vendors with AI integrations.
New talent. It’s going to be imperative that you bring on the right people with the right skills early, so work with your HR team as well as your top decision makers to thoughtfully identify the AI-oriented job skills and experience you need your new employee(s) to have.
Know what you’re already working with. You need to find out what integration options your current software vendors offer for AI technologies so you can include those upgrade costs into your AI budget.
Finally, recognize that AI won’t be a magic bullet. Its emergence in the supply chain market is not going to fix every problem, and it certainly won’t put an immediate end to talent shortages. Instead, think of AI as a strategy change that will help your business run smoother and be more competitive in your segment—because that’s exactly what it is.