supply chain predictive analytics

Supply Chain Predictive Analytics and Customer Service

Service. It’s what makes the difference between an average company and one that stands out from the rest. With product margins shrinking every day, manufacturing companies are looking to service to make up the lost revenue. Enter supply chain predictive analytics.

We are all familiar with predictive analytics these days as it’s being leveraged in virtually every industry. It is estimated that the predictive analytics market will grow from $4.56 billion in 2017 to $12.41 billion in 2022. When it comes to supply chain predictive analytics, it may best be defined as the ability to use data to predict future supply chain activities. According to MarketsandMarkets, “The major reason for the rising adoption of predictive analytics can be attributed to the need for minimizing the risks and frauds that occur every day.”

The supply chain is fraught with risks, involving many moving parts and roles, each with a critical role in enabling a company to get its product or service to the end customer. Suppliers, manufacturers and retailers will all come into contact with the product, including companies responsible for delivering essential parts to the manufacturer. With so many touches, a single hiccup can instantly send shock waves up and down the supply chain. Service suffers, then customer satisfaction and finally, the bottom line.

Related: Supply Chain Risk Management

Supply Chain Predictive Analytics and Risk Management

An article from the Harvard Business Review recognized the relationship between risk management and customer service. While efficient risk management can reduce waste, supply chain duplication and costs, increasingly more companies recognize its power to impact customer satisfaction. A hiccup on the service side of the business has a similar effect on the company as a production disruption from any of the supply chain stakeholders.

To minimize risks, shippers and brokers need to know their risks before they ship. Companies, therefore, often invest time and dollars into supply chain risk management (SCRM) strategies. One of the first steps is to understand the potential risks and predict their plausibility. Some of these risks are easily understood, perhaps because they’ve happened in the past and lessons have been learned. Of course, many things are difficult to predict, such as political instability, natural disasters and terrorist attacks. While these may be more extreme than common supply chain risks like infrastructure outages, the financial condition of suppliers and labor risks, they all work together to threaten on-time delivery.

It’s that on-time delivery that drives customer satisfaction. It’s the service customers perceive they are receiving. Customers don’t care why their part or their product is delayed. They only blame the company with which they placed their order.

Related: Transportation Risk Management: A Guide

According to, “Disaster can strike at any stage or level of the supply chain, so responsibility extends to logistics and procurement professionals. It takes a village to ensure supply chain resilience.” With every link of the chain facing both similar and unique risk profiles, it’s easy to see how challenging it can be for every stakeholder to hold up their end of the deal in order for a product to make it out the door on time. It’s a rather remarkable feat of industry to manufacture and deliver anything if you think about it.

Supply Chain Predictive Analytics and Logistics

Risk assessments are, of course, a fundamental exercise in any manufacturing organization. Part of that practice is to predict what may happen at every stage. One of the most critical links in any supply chain is logistics. No product can be manufactured or delivered without parts. Those parts must travel from supplier to manufacturer, who must then ship their products somewhere else, sometimes to another manufacturer. The chain continues until the final end user receives their expected product.

With logistics front and center, companies are using supply chain predictive analytics to ensure those parts and products have the best chance of making it to their desired locations on time and in their expected condition. This critical link in the supply chain must utilize as much relevant data as possible. Simply checking a few weather reports and choosing the standard mode of transportation is no longer sufficient.

Today’s supply chain predictive analytics uses sophisticated technology that can make logistics predictions more accurate further out so smart decisions can be made. Unlike manual reporting, supply chain predictive analytics combines big data with “little” data to paint a comprehensive picture of what may happen up to 10 days before tender. The software detects risks such as:

Inclement Weather

It doesn’t always take a massive storm to slow shipments. Sometimes, it’s much simpler, such as black ice, high winds or even dust storms. These and many other weather events can happen anywhere along the intended lane. It would take a person hours, if not days, to collate all of the weather forecasts across the entire shipment route. The software, however, can amass this data on an hourly basis for 7-10 days out and then provide an analysis of its timing. It can even map the risks on a map by lane, by market,  or a single plant.

Related: How Shippers Can Reduce Freight Spend Using Weather as a Strategic Advantage

Extreme Temperatures

Extreme temperatures may not impact the lane, but they can affect the quality of the products being shipped. Many products need to be kept at a constant temperature and any temperature fluctuations can render the products useless or even harmful. When companies know the temperature ranges for every leg of the shipment journey, they can not only protect the freight, but they can make informed decisions about which mode of transportation is best, if a refrigerated truck is necessary, or if another lane would be less risky.

Social Hazards

While rare, social hazards do exist. These can come in the form of criminal activity, protests, political rallies, even parades. It’s important for shippers and brokers to plan the logistics around these events to avoid delays or damage to the cargo. Predictive analytics sources information from multiple authorities, giving shippers and brokers valuable data on how best to avoid such situations.

Natural Disaster Impact Zones

Hurricanes, tornadoes, floods, landslides and even wildfires can wreak havoc on shipping lanes. They may announce their arrival weeks ahead or they may surprise impact zones and interrupt the normal flow of business in those impact zones for weeks. Using supply chain predictive analytics, however, companies can work these disasters into their risk analysis and logistics planning many days ahead.

Related: The Florence Effect on Logistics

Infrastructure Outages

Infrastructure outages are one of the more common risk factors in logistics. A bridge may be under construction or a highway may be down to one lane for miles, for instance. Sending a shipment along affected lanes can mean unplanned delays and damage to the cargo, both impacting customer satisfaction. Using predictive analytics, however, companies can instantly and clearly see the location of each risk and an indication of how likely that risk will impact the shipment on each day, even by the hour. Decision-makers can determine if it’s worth adjusting the timing of the shipment, mapping out a new lane or choosing an alternate mode of transportation.

Supply Chain Predictive Analytics and Decision-Making

The data that the new supply chain predictive analytics can detect and analyze is a powerful tool for decision-makers. It can paint a visual picture of all of the potential risks and the probability of each risk at any point of the route so stakeholders can come to an agreement on how to minimize interruptions, costs and delays.

Instead of guesses and assumptions, decision-makers have real, accurate, reliable data that can be viewed in real-time or as much as 10 days out. The supply chain predictive analytics helps answer the why, when and how. No more surprises. No more dissatisfied customers. Even when the risks cannot be avoided, companies are better able to keep their customers informed with actual data to set expectations and deliver good customer service.  

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