Virtually every industry is either already using predictive analytics tools to some degree or is exploring them as part of digital transformation efforts. CIO defines predictive analytics as “a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision.”
Predictive analytics tools are trending
Companies around the globe are using predictive analytics tools to automatically draw insights from past and current data in order to forecast trends in the near and future terms. As demand increases, the global predictive analytics market is expected to reach $11 billion by 2022, growing at a CAGR of 21%.
These predictive tools are giving businesses across industries deep insights into aspects of their business that they may have never considered before, such as the potential and severity of risks days, weeks and even months ahead of time. Time is of the essence because it gives business leaders a head start on mitigating those risks. Armed with data, leaders can make decisions based on factual evidence rather than historical trends alone.
When it comes to logistics, this forward-looking data is everything, giving companies the ability to operate leaner, avoid risks, and gain a competitive advantage. Predictive analytics tools optimize the supply chain and streamline logistics by improving forecasting accuracy so businesses can be less reactive and respond in the appropriate way to potential disruption.
Avoiding disruption and delays
Logistics is complicated, plagued with various destabilizing factors that can quickly disrupt the normal flow of goods. Companies are hard-pressed to keep their delivery commitments, many bound to OTIF standards that offer little forgiveness if a shipment is delayed by a certain amount of time or if the order fails to arrive as ordered. While the fines for failure seem steep, companies on the receiving end of shipments have their own bottom line to consider. Over or understocked warehouses, empty shelves, or damaged goods cost them money and potentially lost customers. In order for them to retain their profits, they must require their supply chain partners to do their part.
Predictive analytics tools use algorithms, machine learning and other intelligent technology that gathers massive amounts of disparate data, both current and historical, to make forecasts about the future. In a matter of seconds to minutes, the technology identifies patterns and trends, the likely impacts of a range of factors, and best- and worst-case scenarios. These insights are virtually impossible for humans to gather, at least in such a short timeframe – an important consideration given the critical nature of timely shipments.
When business leaders can improve their ability to accurately identify risk factors and anticipate the likelihood of a range of future events, they can have important conversations and engage in strategic planning to prioritize and mitigate the threats that pose the highest risks for causing supply chain disruptions.
How predictive analytics tools are helping with on-time deliveries
Weather is an inevitable risk to logistics. For example, earlier this month, Baton Rouge, Louisiana was inundated with rain from a low-grade hurricane that hit hundreds of miles away on the southern Texas coast. While flooding was expected along the Texas coast, there wasn’t much warning in Louisiana.
To put this into perspective, Interstate 10, one of the most traveled shipping lanes, has a long stretch across Baton Rouge and near the Texas coast. Without predictive analytics tools in place, shippers and carriers likely risked many shipments sending them through the Louisiana portion of the route. While they may have planned alternate routes in Texas, they may have felt the impact of the storm on their shipments in Louisiana if they didn’t have the data they needed in time to make alternate plans.
On the other hand, predictive analytics tools would have anticipated the likelihood and severity of rain bands impacting Louisiana, as well as the percent chance of flooding along the Interstate-10 corridor in south Louisiana. The software is able to score such risks so decision-makers don’t waste precious time analyzing the data to determine if the risk is real. Instead, the software automatically assesses the risk and presents each risk with a score that creates a common language for risk. Leaders can understand the risks in the same way, leading to more productive conversations and data-backed decisions.
In this use case, it’s easy to see how predictive analytics tools change the game, giving leaders two things they need most to reduce their risks: accurate data and time. The software is not only diagnostic and predictive, but it is also prescriptive, recommending the best options to save a shipment from delays. Decision-makers can model the different scenarios to get a better sense of the impact of their decisions on the entire supply chain and their delivery commitments. Again, this speeds decisions so leaders can alter plans and set proper expectations with the customer well before the shipment is expected.
Weather and climate issues (storms, wind, snow and ice, extreme temperatures), infrastructure outages (utility outages, construction, traffic), natural disasters (hurricanes, tornadoes, earthquakes, mudslides) and wildfires, social issues (unrest, crowds), terrorism and other factors that can interrupt shipments are also of concern. The earlier companies can predict these events, the sooner they can strategize on ways to avoid them. If they can’t be avoided altogether, predictive analytics tools provide the necessary data to mitigate them.
Predictive analytics tools for facilities risk management
Logistics is just one of the supply chain areas where predictive analytics tools are making their mark. There are plenty of companies that also have facilities where the potential events listed above could pose a threat. Facilities management needs to know their risks in advance, being alerted through preferred channels (SMS text, email, voice alerts) of all of their risks, how likely those risks are of occurring, and when they can be expected so they can prepare. Going a step further, these risks are based on user tolerance settings. Users can determine what risk scores warrant alert and which ones fail to meet the threshold. This kind of customization ensures leaders can focus their time on the highest priority issues and not waste time on risks that are less likely to occur or cause an interruption.
As good as this information is, the benefit comes when those risk factors and scores inform decisions up to two weeks prior to their estimated occurrence. Keep in mind, a risk at one location may not impact a different location. For this reason, it is important that the predictive analytics tool is customizable per site, per region, or per type. For instance, if a wildfire is threatening a facility in southern California but not at a facility 200 miles away, the system is intelligent enough to send alerts to management in southern California only.
Predictive analytics tools offer companies a powerful method for identifying and staying ahead of emerging and potential risks to their shipments and facilities. Manually gathering all of the big data required to accurately forecast potential issues and detail their possible impacts is inefficient and results in incomplete, often inaccurate insights. Today’s dynamic environment requires robust and sophisticated technology. Companies that utilize predictive analytics tools will establish themselves as leaders and gain a competitive advantage.
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