Businesses need to effectively predict and forecast to ensure they have enough inventory, among other things. If inventory is limited when demand is high, your business will have lost out on some serious cash.
With supply chains becoming more and more complex, it's become harder for planners to predict trends and develop complementary plans. Supply chains are increasingly collecting more data, and customers are demanding faster service. To effectively meet all these needs, automated advanced analytics are required.
Will Machine Forecasting Replace Human Planners in Supply Chains?
Artificial intelligence uses algorithms to cast predictions and forecast trends. Algorithmic forecasting has limits that machine-based learning can't overcome, which means that our judgement will not be taken over by machines for quite a while. However, machine learning is faster are more accurate when combing through large amounts of data. Over 200 studies have compared the predictive nature of artificial intelligence to human experts, with algorithms almost always outperforming unaided human judgement. In the few cases where artificial intelligence didn't outperform human experts, the results usually concluded in a tie. New methods have been discovered for improving human judgement, though, making it unbiased in the process. Algorithms don't have the power to completely replace human intelligence, but analyzing the data they provide can improve accuracy.
So what can this do for Supply Chains?
Data is everywhere in supply chains. Businesses know how long it takes to make a certain product, how long a consumer waits, how much inventory can be produced in a day, etc. According to IBM, we now produce over 2.5 quintillion bytes of data daily. 80% of that data is unstructured and invisible to current technology. How can a planner effectively analyze all that data? It sounds next to impossible. Having artificial intelligence to sift through this information will allow businesses to learn faster, be proactive, and sell more.
Artificial intelligence and its branch offs (machine learning, algorithms) can now be packaged up nicely and sold to businesses through supply chain planning software. These packages include a form of pattern recognition software that identifies trends, providing your business with information to prepare and act accordingly. If there are peak times of the year where your inventory sells out fast, this technology can identify it. When the time comes, you can ensure there will be enough for consumers who want, distributing your product effectively. Artificial intelligence can also help your transportation issues. If large quantities of trucks need to go out with products during a certain time, companies can schedule more. Rather than having customers wait longer for their purchases, they'll be delighted when the product arrives ahead of schedule.
A great example of using artificial intelligence predictability can be seen at Danone. Danone wanted a more reliable way to forecast the impact on their baseline demand created by their marketing efforts. Doing so was difficult as many variables were involved. They decided to add analytics with machine learning capabilities to assess their demand planning. What Danone found enabled them to ensure timely manufacturing during peak demand and helped the company balance their inventory. When it comes to hard numbers, their forecast accuracy jumped to 92%; they saw a 30% reduction in lost sales; a 6% increase in net ROI during the first year; and demand planner workload cut in half.
Artificial intelligence and machine learning can more quickly analyze the data that supply chains generate. By using this technology, supply chains can determine trends that affect their inventory levels and arrival times. Therefore, they can be better prepare for their business cycles. Using artificial intelligence in your supply chain will help you make sense and act on all those numbers you're receiving.
Watch our recorded webinar on reducing supply chain costs here: