Why Predictive Analytics Solutions Make Sense for the Supply Chain
With the predictive analytics market to continue growing, it is no surprise that companies all across the board are looking for ways to keep up with the constant change of consumer demand in this digital age.
In a world where retailers are adopting multi-channels and customers are expecting speed and immediacy, old forecasting methods are not good enough to keep things rolling.
As logistic companies depend heavily on timeliness and accuracy for success, they are realizing the many benefits of machine learning on every level of the supply chain.
The Major Benefits for Predictive Analytics for the Supply Chain
With expectations to grow from 4.56 billion USD to 12.4 billion by 2022, there is no doubt that the predictive analytics market is rife of opportunity. Countries all over the world are taking note of an undeniable truth: predictive analytics is absolutely necessary for their process. What was once left to the end or not included at all, has become a top priority for many manufacturers around the globe.
As MHI’s CEO, George Prest stated, “The speed at which supply chain innovation is being adopted—coupled with rising consumer expectations for any time, anywhere service—is stressing traditional supply chains to near-breaking points.”
Manufacturers that do not evolve their supply chain model to meet consumer demands will fall behind the trial. There are a plethora of ways the supply chain model can benefit from using predictive analytics technologies. Some of these include:
- Identifying real-time patterns – Advanced analytics has the ability to track and detect purchasing behavior real-time, providing manufacturers and logistics companies as a whole greater insight into the real-time patterns of their customers.
- Track and analyze data accurately – Although analytical models have been around for a long time in manufacturing, the quality of data has never been good enough to predict as accurately as hoped. However, with the advent of advanced machine learning algorithms, these large piles of data sets can be analyzed with greater accuracy and speed.
- Improving overall operations – Using advanced analytics to look ahead and forecast the demand for specific inventory will better manage resources. As the manufacturer can take faster and better actions, every area of operations can become more efficient.
- Better demand forecast – Often times, manufacturers need to make quick judgments of their product, examining the type, quantity, and time in which the item may be needed. Up till now, historical data or past experiences have helped them forecast the demand, yet things can get trickier during seasonal or promotional events.
- Price optimization – Manufacturers are able to harness the power of advanced analytics in adjusting their prices according to market demand. Machine learning algorithms dig through a myriad of data points, from location, seasonality, and product attributes to make more accurate price predictions.
- Prevent defects, reduce downtime – One great way in which predictive analytics technologies help manufacturers is to identify defects. By catching these issues ahead of time, companies can reduce downtime on machine loss and save on costs. The blend of predictive analytics and IoT helps manufacturers to take care of their machines by giving them information on when they need to be replaced or requires better equipment.
When Advanced Analytics Meets Supply Chain Operations
As seen from all the benefits listed above, there is no denying how advanced analytics is helping manufacturers see the importance of implementing it into their supply chain model.
These improvements in business are not a matter of ‘if’ anymore.
They are a matter of ‘when.’
According to a study done by Accenture, they found that companies that adopted a big data analytics strategy into their supply chain saw definite pros, “[it helped] them improve customer service and demand fulfillment, experience faster and more effective reaction time to supply chain issues, increase supply chain efficiency, and drive greater integration across the supply chain.”
Furthermore, the survey also discovered that the companies that adopted advanced analytics into their day-to-day supply chain operations, rather than on occasion reaped more benefits as a whole.
Not only were they able to shorten the order-to-delivery cycle time by over 51%, but they also improved the cost to serve by 35% and increased their customer and supplier relationships as well.
Scrap the Old, Embrace the New
In a world as chaotic and uncertain as ours, where consumer demand changes with the quick blink of an eye, logistic companies have no choice but to throw away the past methods of their supply chain model and adopt the new.
Without this fundamental shift in their operational model, companies will fall behind and disappear into the dust.
There are countless benefits that come from investing in predictive analytics solutions today (as seen above). Just as many industries have been learning to adopt and adapt, the logistics industry must do so too.
In the end, using AI and predictive analytics will drastically enhance supply chain operations, optimizing pricing strategies, inventory management, and improving overall operations. Reducing risk and costs that occur from downtime are another added benefit.
Considering how time-sensitive and accurate logistics companies need to be with their connecting networks, harnessing the power of artificial intelligence will be one sure-fire way to overcome these efficiency hurdles. As data grows by the millions, solutions like Chain of Demand will be a definite way for companies to transform themselves from being reactive to highly proactive and intelligent.