How AI-powered predictive analytics can transform today’s complex supply chain – use cases

A dozen factors decide the faith of successful logistics operations. Unless it maintains accuracy, timeliness, and resiliency, it fails to accomplish distribution and customer demands. But, logistics is not only about loading goods and delivering them at customer points. It has more to do with the interest of mankind.  

Reports suggest that the logistics industry is expected to grow to $1.2 trillion by 2020, meaning it needs a robust infrastructure to operate efficiently globally and meet customer demands faster. However, in the past thirty years, the circumstances were different from that of today. It had growing challenges, which big logistics operations have been able to overcome, while others still struggle today.

Challenges of the Logistics and Supply Chain

The optimization of supply chain and logistics is a big challenge for the business. It requires the brute force comprising larger warehouses, larger ports, bigger ships, and a large fleet of delivery vehicles to meet the growing demands. And it requires scaling supply as fast as possible. There lies the biggest problem for logistics and supply.  It just adds to the complexity with a large number of stocks remaining idle at warehouse or ports for days and even months, leading to woes for recipients. It costs logistics and companies their repute, while idle stocks are expensive to bear.

However, this deep-rooted inefficiency can be bypassed using AI-powered predictive analytics. Predictive analytics is one smart way to increase precision in tracking freight and packages using available data and improve efficiency.

Well, there are more ways to use predictive analytics in supply chain and logistics to transform its environment. More interestingly, this phenomenon is a smart technique to reduce waste while increasing productivity.

But, What Is Predictive Analytics?

Predictive Analytics is an Artificial Intelligence-based technique that harnesses data from various sources, especially from data that the company already owned -historical data. The practice parses these data sets or algorithms to assign scores to various user segments. These scores actually point at some probabilities, rather than making absolute predictions about anything. Simply put, it applies the theory of probability using a massive volume of data, and derives data-driven results that hint at what is likely to happen in the future depending on the historical data.  Today, supply chains and logistics can use Predictive Analytics to discover specific trends, patterns, and metrics by tracking historical data and establish predictions.

Business today can leverage Predictive analytics for many reasons, such as inventory management, planning, and demand forecasting. Predictive Analytics can help build patterns to create a fast and agile response for future developments.

Various Use Cases of Predictive Analytics in Supply Chain and Logistics

Leveraging predictive analytics gives scopes to logistics to create solutions in time and improve productivity, and cost-savings. Predictive analytics has been a key phenomenon to harness real-time after-sales-service value for every industry. With AI implementing costs decreasing possibilities over time, it may foster a better possibility for you to leverage it. So, let’s find out essential use cases of machine-learning or AI-based Predictive Analytics to transform your logistics and supply chain.

Predictive Pricing

Many manufacturers still follow the old practice of pricing models based on Excel spreadsheets and cost-plus models. However, the problem with this infrastructure is that it creates confusion over different pricing structures of different parts and products at varied locations. This can affect customer experience and prevent profit-making at an optimal level. An attribute known as the k-nearest neighbors algorithm is used in predictive analytics to alleviate confusion.  Then a set of factors, including weather conditions, location, seasonality, and demand can be mapped to harness real-time gestures and automatically set prices to adjust to the market.

Predictive Demand Forecast

No business can sustain an effective demand forecast model. Demand Forecasting is a process that predicts the trends of future sales based on historical data. It facilitates informed decision making about everything relating to warehousing needs, inventory planning and even marketing efforts. It’s a key attribute to making data-driven decisions about expected future sales. Not only does it help build robust predictions about the product demand, but it also helps gauge patterns of after-sales service success. This is an efficient method that helps enhance visibility across the supply chain to forecast accurate demands. Leveraging this technique, manufacturers can enhance service after sales without affecting costs.

Machine learning and Artificial Intelligence-based predictive analytics use gradient boosted machines, support vector machines, and Neural Networks to help optimize stocks, thus eliminating overstocking.

Predictive demand forecasting comprises four categories, including macro-level, micro-level, short-term, and long-term to be used to parse data depending on different lengths of time of the year, factors and conditions.

This helps the supply chain increases uptime of the operation, reduces risks, and improves customer service.

Predictive Maintenance

50% of services used by the supply chain are deemed ineffective as they are unlikely to be available when needed. This means indefinite product downtime, a decrease in revenues and poor customer experience.

When it comes to predictive maintenance, it can detect an event before it is about to occur. IoT embedded devices and sensors, they make predictive analytics a top-notch service to improve situational awareness and prevent any emergency event. IoT and smart sensors can detect a failure of parts much before the real occurrences, proactively routing information to appropriate executives or repair centers to alert them and take appropriate action to prevent it. Predictive Maintenance helps the supply chain prevent excessive inventory and costs, avoid expenses related to operation disruption and downtime, and improve the process of part refill rates. And it improves customer relationship.

To leverage the predictive analytics in the most efficient manner, SynergyLabs considers integrating all of the sales channel data with the data system and create a cohesive picture of the entire work process. Thus, we enable you to develop a platform to use predictive analytics across various work processes and help you make a data-driven informed decision. SynergyLabs, your trusted AI consultancy to accomplish stock optimization and deliver the right experience your customers want.