Predictive analysis is a powerful tool used by businesses to improve their operations, which is why predictive analytics is essential in the workplace. Essentially, it helps simplify business insights while playing a pivotal role in fast-tracking a company’s road to success.

Whether you need a forecast of the next day’s sales or want to determine which branch or distribution centre has the highest demand at a specific time, predictive analytics tools give you the head start you need for insightful decision-making.

Sit tight as we show you how predictive analysis works, its crucial role in thriving supply chain and fleet operations, and how you can implement this powerful tool in your business.

  In this article:

  • Familiarise yourself with different types of data analytics and their uses
  • Take a closer look at predictive analytics in supply chain and fleet companies
  • Learn about the three steps of predictive analytics technology
  • Understand the benefits of predictive analysis in the supply chain and fleet industries
  • Know why predictive analysis is a must for your business
  • Discover how Cartrack’s technology can solve your business’s predictive analytics needs

The different types of data analytics

Examining data to identify trends, gain insights, and answer questions is what underlines data analytics. However, there is more than one type, each with its benefits.

Here’s everything you need to know about the different types of data analytics and where predictive analytics fits into the bigger picture.

‍Descriptive analytics: What happened?

So, let’s say you want to look at your fleet drivers’ productivity over the past business quarter. By enabling a descriptive analysis process, you can gain valuable insights that support smart decisions for improving future driver productivity. Descriptive analytics is a great starting point if you want to transform data into a powerful tool that provides actionable insights.

Also, being able to use basic statistical software like Google Charts or Microsoft Excel to identify different trends and relationships makes descriptive analytics an accessible, user-friendly tool.

‍Diagnostic analytics: Why did this happen?

Most fleet managers would want to determine what’s causing the premature ageing of fleet vehicles. After using descriptive analytics to identify trends related to untimely vehicle ageing, diagnostic analytics is the logical next step for pinpointing why these trends exist. 

For example, insights on harsh-braking activity can indicate why tyres are prematurely worn out, giving fleet managers a solid basis for solving the issue and saving on costs for premature tyre replacements.

Concepts closely linked to diagnostics analysis like hypothesis testing, diagnostic regression analysis, correlation, and causation make the process more complex than descriptive analytics, but using algorithms and basic stats software like Excel makes the process more straightforward.

‍Prescriptive analytics: What next?

Want to take some guesswork out of budgeting for your fleet? Prescriptive analytics is an ideal tool for getting data-driven answers to those “what if” questions that may be keeping you up at night. Conducting prescriptive analysis allows your business to look at crucial factors like future revenue and maximise your budget by determining the best course of action. And the more data you have available, the better the results you’ll get from the process.

Machine-learning algorithms are a popular way to efficiently plough through large amounts of data and achieve refined, accurate results. By simply using phrases containing the words ïf” and “else,” as part of the analysis process, you can start gaining valuable recommendations based on your specific business needs.

Predictive analytics: What may happen in the future?

One of the most challenging tasks for businesses is identifying the best course of action. In both fleet management and supply chain businesses, it’s critical to ensure your vehicles stay on the road as much as possible. By gathering a range of data, the predictive analysis process helps you forecast future events that may contribute to vehicle downtime so you can develop innovative strategies to avoid them.

There are different predictive analytics tools you can use to get the insights you need. Regression analysis, for example, determines the relationship between two variables, also known as single linear regression. In comparison, multiple regression refers to three or more variables. Ultimately, predictive analysis helps your business formulate solid, data-driven strategies and drives intelligent choices by supporting proactive decision-making.

‍The Place for predictive analytics in the fleet management & supply chain industries

If you agree with the expression: “why fix something that’s not broken,” predictive analytics will change how you think about running a business. The new and improved version evolved from “Why fix something that’s not broken” to “Why wait for something to break to start fixing it?” This updated perspective is more of a proactive approach that helps you really get behind the steering wheel of your business.

In the supply chain industry, predictive analytics supports many smart approaches, including determining ideal inventory levels, which in turn help minimise stock while still satisfying demand. For fleet-based companies, predictive analysis helps support proactive vehicle maintenance, enabling you to schedule preventive checkups. In both these cases, you’ll be able to make significant savings by cutting unnecessary costs.

What’s more, the use of predictive analytics tools in supply chain businesses has risen significantly, with the 2020 MHI Annual Industry Survey reporting a growth from 17% in 2017 to 30% in 2019 — a jaw-dropping 76% increase in only two years.

More and more companies are looking toward adopting this transformative technology, with 57% of companies not yet using predictive analytics planning on making this proactive change in the next five years.

So, if you want to find out how predictive analytics can be applied to improve performance in an organisation, you’re in the right place as we’re about to unpack crucial details about how this tool is used in the fleet and supply chain industries.

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What is predictive maintenance for fleet management?

With statistics, algorithms, data, machine learning, and even AI, predictive analytics can anticipate future fleet management challenges, including safety, maintenance costs, problematic driver behaviour, fuel expenditure, and more. 

Instead of picking up the pieces after a workplace mishap, having this powerful data at your fingertips enables innovative and proactive decision-making that helps you foresee and prevent these types of crises. Here are a few examples of how data-driven decision-making with predictive analytics can improve your business:

  • Fleet managers can proactively identify potentially dangerous driving behaviour in their fleets, helping them to address it promptly and even use these insights to improve new driver training. 
  • Getting accurate fuel usage insights is the key to identifying the main contributors to fuel wastage. Using this data to implement tailor-made solutions will help you save significantly on fuel costs.
  • Collecting corporate data and ownership costs helps fleet managers reduce manual overhead and optimise workforce utilisation for the most efficient customer service.
  • With accurate and detailed fleet data, that ensures your fleet vehicles are maintained and in tip-top shape, putting you in control.

How can predictive analytics be applied to improve performance in a supply chain organisation?

A fleet is also a significant component of a supply chain business’s optimal functioning; however, there are even more ways predictive analytics can make a notable contribution to providing valuable insights. 

With access to detailed data analysis from different areas of business like orders, invoices, and delivery notes, supply chain businesses can make helpful predictions not only for their fleet but also for other crucial aspects of supply chain operations:

  • Predictive analytics help supply chain companies one-up their competitors with more innovative risk management. A constant supply of accurate data helps businesses identify possible bottlenecks in their supply process that may negatively affect overall efficiency. Effectively mitigating this risk helps streamline the entire process, leading to greater savings and customer satisfaction.
  • Zooming in on customer service and satisfaction reveals even more benefits. Collecting a constant stream of data showing how customers interact and show preferences makes it easier to decode your customers’ minds and how to keep them coming back for more. 
  • Knowing the demand at locations you supply products to is a massive help in ensuring you don’t over or under-deliver at any time. Predictive analysis tools take things up a notch by helping supply chain managers forecast demand for the most efficient stock provision and delivery strategies.

Considering how the adoption of predictive analytics technology in businesses is growing, little room for doubt remains regarding its efficiency.

‍Three Crucial Steps Behind the Success of Predictive Analysis Technology

What better way to understand predictive analytics than through its intricate functioning? The process can be broken up into three crucial steps that show how it makes its magic for fleet and supply chain ventures. Here’s an outline of how the process works:

  1. Collection: To get the proverbial ball rolling, IoT devices need to capture the available telematics data. The more data captured, the more comprehensive and helpful future insights will be. 
  2. Transmission: Then, after the data is collected, it’s shared with analytics software to make sure it’s sorted into smaller and more specific categories. 
  3. Processing & analysis: Finally, now that the data is ready for processing, it is analysed to optimise its usefulness. This final process allows the data to reveal implicit and explicit dependencies between relevant events, followed by solid future insights that help you manage your business.

How these steps take your supply chain business to the next level

When it comes to your business, each step that constitutes practical predictive analysis resonates with developing functional strategies and optimising your operations.

  • Data collection helps you to define areas of improvement more broadly. Think about it. Without a range of data, analysing all areas of your supply chain’s effectiveness and where improvements need to be made will be a lot harder. 
  • Data transmission helps you connect the dots by enabling an effective sorting process. Knowing that data is shared with the correct analytics software, you can rest assured that all information will be standardised and correctly processed, taking a lot of tedious work off your plate. 
  • The final step, data processing and analysis, allows you to build predictive analytics models that actually work. Nobody can make a 100% accurate future prediction, but using carefully sorted and processed data will bring you pretty close to it.

It’s as easy as that — start creating guidelines and implementing changes directly linked to improved efficiency and reap the rewards. The sooner you start, the more data you’ll have over time to finetune your processes and set them up for ultimate success.

Take Steps

Five critical reasons why you can’t afford to overlook predictive analytics for your supply chain business
Here’s why, if you haven’t invested in predictive analysis technology for your business yet, there’s no time like the present to get what you deserve from your business ventures.

Less spending

Cutting unnecessary costs should be a top priority in all industries as it increases ROI and allows you to put that money toward better use. Having comprehensive insights at your fingertips can help you lower supply chain costs, identify any inefficiencies in the production process, and minimise waste, reducing spending across the board.

Demand forecasting

Imagine how effectively you can streamline your business if you have a solid idea of what your customers want and when they want it. Forecasted customer demand is one of the highly functional contributions that predictive analytics brings to your business, helping you act before an increase in demand occurs.

Optimised pricing

Product demand not only helps you make sure you have enough stock en route to designated locations. It can act as an expert guideline on how to optimise prices for maximum profitability. Also known as predictive pricing, it involves dynamically adjusting the pricing of products according to their demand forecast, applicable exchange rates, and inflation. Additionally, predictive pricing helps control the element of human error by lowering the risk of pricing mistakes.

Smart inventory management

If you’re in the supply chain business, inventory management and resource planning are crucial components that affect how much you can save by preventing unnecessary waste. Predictive analytics help establish an ideal inventory level for each destination, helping you meet demand with minimum spending. It’s the best tool to help you decide if keeping the bulk of your stock centrally or regionally is more profitable.

Maximised customer purchase potential

With the ability to forecast what customers want to buy in the future, your business can magically stock what they need when they need it, which is one of the critical points to customer satisfaction. You can even take things further by accurately recommending products and providing individualised pricing based on your customer data, ensuring you retain ecstatic customers who keep coming back.

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