Predictive Analytics has been a very popular term for some time and we will probably use it more and more often. Its use is able to help in production activities and more. It is also important for the supply chain.
Let’s start with the definition – predictive analytics means using statistical data and algorithms to determine the probability of future results on the basis of this historical information. In fact, it is about transforming the industry from human decision-making to data-based decision-making. And there are plenty of them.
In practice, predictive analytics is all about the ability to determine what may happen in the future. Someone will say, it’s obvious, they’ve been trying to do it for years. Yes, but specialists argue that this is no longer the proverbial ‘reading the tea leaves’. Only modern methods of collecting information, its vast amount and analytical methods make it possible to predict with satisfactory accuracy.
The increasing availability of computers and more user-friendly software is also contributing to this. Thanks to them, predictive analyses are no longer only the domain of mathematicians, but can also be used by business analysts.
Predictive Analytics helps to predict failures
Transport company heads and logistics operators use predictive analytics to help solve problems, but also to explore new opportunities.
The list of typical applications is quite long and includes the following:
– detection of fraud (e.g. online),
– better execution of individual processes,
– inventory forecasting and resource management,
– price optimisation.
As a result, the aim is, on the one hand, to increase efficiency and, on the other, to minimise risk.
All these features also apply to the supply chain. This includes, for example, anticipating equipment failure and mitigating safety and reliability risks, which is particularly important in the case of automated warehouses and autonomous transport systems.
Kamil Słowik from Mecalux stressed during the recent Smart Warehouse conference that also modern warehouse programs (e.g. WMS) have this kind of functionality.
“They help predict warehouse failures and the WMS is able to react to scenarios, e.g. when there is a power outage in the warehouse. More and more companies appreciate this type of solutions because they are associated with optimisation of work,” said Kamil Słowik.
In the case of automated warehouses and autonomous means of transport, so-called predictive maintenance is therefore particularly important, helping to detect possible failures before they occur and before they shut down the system. This allows for a minimum of possible interruptions in the operation of the equipment. Mikołaj Ruta from WDX also highlights this feature of automated solutions. Based on the data, it is possible to predict what will happen in the future (predictive maintenance). This is particularly important for the smooth operation of the warehouse.
Predictive analytics will optimise routes and resource consumption
According to specialists from Transmetrics, thanks to predictive analytics, logistics operators and carriers can plan actions based on data on customer demand and purchase behaviours (supply and demand forecasts) weeks or even months in advance. This optimises the use of resources in logistics. Thanks to the analytic tools used in transport management software (TMS), it is possible to forecast potential threats and adjust their actions accordingly.
Predictive analytics is also useful for “last mile” delivery planning, optimising routes and schedules, eliminating bothersome traffic jams where possible, and helping to coordinate cargo transport on a shared basis.
Specialists point out that investing in solutions for predictive analytics is no longer an option, it has become a necessity.
Data selection critical to success
What can companies do to start implementing such solutions? According to Transmetrics specialists, in some cases, the first step may be to hire or appoint a dedicated person to manage the digital transformation of the company and build a supply chain based on information.
The second option is to work with a third party technology provider who will provide products and services for predictive analytics, tailored to the needs of the transport and logistics industry.
The key to using predictive analytics is to be aware of the problem that needs to be solved. The next step is to select the data, the analysis of which will help to solve the problem. In the case of the supply chain, these are usually data collected by various sensors and recorders. The obtained data should then be prepared for analysis, according to the previously defined “key”. The development of a forecasting model takes into account the nature and quantity of data collected.
Predictive modelling requires a team approach. You need people who have a clear understanding of a specific business problem to solve, who can prepare data for analysis and then provide the right analytical infrastructure to build and implement models.
In the next part, we will discuss the effects of using predictive analysis in the activities of leading logistics operators, including Amazon, Maersk and UPS.