Predictive Analytics in Supply Chain Execution
Managing time, vehicles, and human resources are key supply chain execution tools to build a successful transport chain. Many factors can influence the operational efficiency, and managers often struggle to take them all into account at the planning stage. Businesses need to adjust to dynamic customer behaviors, make prompt on-demand deliveries, work with multiple shipment schedules, and choose the most convenient routes in regards to weather and traffic conditions.
To foresee crises and keep track of resources, businesses use predictive analytics in logistics industry. According to the Council of Supply Chain Management Professionals, more than 90% of logistics companies indicate that data collection is crucial to their activities. In this article, we will dive deeper to predictive analytics for transportation industry and see how it influences business.
What’s predictive analytics
Predictive analytics in logistics is a method of applying big data where past data is used to predict future events and track recurring patterns, route execution analytics. The historical information is collected via predictive modeling, machine learning (ML), and data mining. Data mining algorithms collect, structure, and analyze the data while ML derives specific insights.
The purpose of supply chain predictive analytics is to provide teams with tangible data and actionable insights. With such algorithms, companies can plan months and years, and correct these plans automatically according to real-time changes. It’s an ever-changing schedule that adapts to the environment.
How to collect data for predictive analytics
Predictive analytics for logistics combines multiple data collection methods. It’s a combination of many research and data organization methods, applied step-by-step. Let’s take a look at the universal predictive analytics procedure.
- Identify the purpose of technology use. Transportation managers have to define the project that will use the technology. The next step is evaluating business objectives, time constraints, deliverables, and events that need to be predicted. Each of the deliverables has a dataset that describes its past and current state.
- Start data mining. Data mining is performed in data warehouses — central data storage. The logistics provider connects mining tools to the warehouse and identifies logical relationships between information. Data science algorithms group data into clusters based on their shared patterns.
- Analyze the collected data. Collected and grouped information is inspected, cleaned of redundant fragments, and transported into clear reports. The raw data is processed and turned into insights.
- Using statistics. Statistical models are applied to create a hypothesis and test them, using data that was collected and processed during previous steps.
- Modeling scenarios. Statistical models and insights are used to create accurate prediction models and detect likely events, sometimes with the assistance of Artificial Intelligence. The software determines what’s likely to occur in regards to detected past and current patterns.
- Deploying the insights into decision making. Models and ideas are delivered to decision-makers and team members who make data-driven conclusions. Usually, this entails making changes to schedules, revising picked routes, and managing employees. Often, predictive analytics software uses automation to adjust schedules or routes — the team only revises the changes and makes strategic decisions.
- Monitoring the results. The team checks the results, brought by adjusted processes, with what was expected in the original estimate. If events, predicted by the software, indeed occurred, it’s proof that the process was executed correctly.
Supply chain managers can calculate performance improvements after using predictive software. If the data was used to optimize logistics efficiency, you could calculate how much time and resources were saved in the process. If a crisis was averted, calculate the cost of the issue in case it would’ve occurred. This will provide you with a clear picture of your investments’ efficiency and define how efficiently your team uses predictive analytics.
Advantages of predictive analytics
Predictive analytics in transportation is a powerful tool that helps to organize the process, avert problematic issues, and allow forecasting in supply chain management. The transportation process is influenced by multiple independent factors, from order details to vehicles’ maintenance, and predictive analytics helps to connect all these dots by offering multiple advantages.
Increased chain transparency
A predictive analytics supply chain combination keeps all parties engaged in the logistics process, updated on their progress. Logistics providers can avoid technical issues with vehicles, pick better routes, and take delays into account. Vendors can access detailed schedules of future shipments, which can change real-time if an unexpected pattern is observed. End customers receive prompt orders and know where exactly their packages are and when they will arrive.
Improved transportation management systems
By integrating predictive analytics supply chain logistics into their TMS, logistic companies can predict transportation disruption, and edit schedules according to weather changes and road conditions. They can also increase their efficiency by adapting to customers’ behavioral patterns, different logistics channels, taking into account seasonal habits, and most commonly purchased items.
Predictive analytics in supply chain management is often used with predictive maintenance — the type of maintenance where logistic teams are alerted about potential vehicle damage before it occurs. They can repair technical issues at the early stages when they are easier and cheaper to fix. Also, it prevents downtime and delays — companies don’t have to disrupt their work with unexpected machinery fails.
Manufacturers need to make prompt decisions on which product to deliver and in what quantity. Instead of basing their operations on hunches, they can forecast which items will be ordered next and how to deliver them. The companies can adapt to demand fluctuations during seasonal sales and holidays.
Examples of the use of predictive analytics in the supply chain
Leading companies in the logistics industry widely use predictive analytics technology. Let’s consider the examples of predictive analytics in supply chain, real-life applications of this technology from leading supply chain providers.
DHL Supply Chain
In 2018, DHL announced that the company would invest $350 million in innovating its logistics operations. One such innovation was MySupplyChain. Custom software that uses data mining, machine learning, and statistics to predict changes in DHL operations. With this tool, the company aims to deliver a better experience to partners and end-customers, as well as make workflow easier for their employees.
The biggest shipping company in the world, Maersk Line, was among the first providers to use predictive analytics for the supply chain. The corporation uses predictive tools to decide which ships are damaged or wasteful. Predictive analytics helps figure out the ultimate container position and save space on the ship by taking into account the type of order, weather conditions, route, and other details.
The company implements anticipatory shipping — the software analysis customers buying habits in a particular location to see what, when, and where a particular customer will buy. When a client makes a purchase, it will be sent to the closest hub. The company predicts the number of required delivery drivers, shipments, and the shortest routes.
The company introduced a Decision Support Tool in 2016 to each of its warehouses. The software analyzes all fulfilled shipments, identifies the shortest routes, edits daily schedules, and monitors employees’ efficiency. The company also partners with predictive maintenance providers via its Industrial Data Space.
DB Schenker’s partners are in charge of monitoring the technical state of their vehicles, maintaining them, and repairing any damage.
Supply chain management predictive analytics offers many unique opportunities for logistics providers. Data-driven forecasting solutions transform decision-making in the supply chain and optimize day-to-day operations. Major leaders are actively adopting predictive algorithms into their workflows, but still, many supply chain chief officers admit that their digital transformation isn’t active enough. How can a business use of predictive analytics in supply chain, and start implementing a predictive analytics process into their supply chains?
The first step is to hire a qualified team of data scientists and software developers who know the specific transport predictive insights of logistics. Hiring an in-house team takes time and is too expensive for smaller providers — you can invite an outsourcing expert to build a predictive platform for you.
Amconsoft team specializes in logistics and supply chain innovations — we’ve built dozens of solutions for traffic prediction, inventory management, planning, and order processing. Contact our data experts to build a predictive analytics solution for you.