Waste in business is bad. It negatively affects your bottom line and can mean you’re using up to 20-30% of your revenue on inefficient and badly managed processes. Since the advent of robust data analytics tools, businesses everywhere have been striving to become more efficient in everything they do and identify numerous improvements to every process. This need to improve efficiency and business processes is keenly felt by the logistics industry – for several very good reasons.
As global business has become more connected and more complex, the business of logistics has followed suit. Companies must now deliver from and to more places, taking into account more variables and facing more pressure than before. Environmental footprint pressures, volatile global markets, changing rates of supply and demand and an increased complexity of logistics have all had an impact. Too often, the processes they are using were not designed with these pressures or challenges in mind. The result is huge inefficiencies.
The logistics industry has no shortage of complex and data-heavy processes that can be improved by the implementation of data analytics. So, let’s get down to business.
Step 1: Define Your Process
The first step to get done is to fully define the specific processes that need to be improved, whether that be the supply chain or inventory management. As these processes often evolve with a business over time and are constantly changing and adapting to new global developments it is unlikely that there will be a clear and detailed picture of exactly what is involved. So that’s the best place to start.
Using the example of a supply chain, we would need to consider all the inputs into that: suppliers, customers, third-party shippers, freight prices, market volatility etc. All of these inputs, outputs and stages will have an effect on each other, and this is a good time to find out if any of these data sources aren’t being monitored. To uncover as many insights as possible, gather as much data as possible.
Step 2: Define Your Measurements
Given that the nature of many of these processes is incredibly complex, the variety of measurements needed to accurately record and analyse them is also complex. There is no blanket solution – each input, output and mini-process will likely have a specific measurement, or measurements, that need tracking.
When looking at route optimisation, we would start by recording transit time, average delays, routes taken, weight of packages for example. There’d be points to consider first though. Primarily, is the data you collect from these sources going to have any meaningful impact on process improvement? An analysis of current Key Performance Indicators (KPIs) and targets can be a good foundation point.
Step 3: Conduct a Thorough Analysis
A thorough analysis of the processes that have been defined and measured should utilise both descriptive and predictive analytics. A descriptive analysis will focus more on past data. It will highlight trends and patterns and any anomalies in your datasets – this can be great for casting light on previously unnoticed issues or bottlenecks in processes.
To really get ahead of the curve, logistics companies should also be putting their efforts into a predictive approach to their data. Utilising technologies such as Artificial Intelligence (AI) and Machine Learning, a robust data analytics tool will be able to predict future process models and suggest improvements based on a set of assumptions. This is a fantastic method for fireproofing processes against events such as extreme weather, or significant changes in supply and demand. In an industry so susceptible to changes in global conditions, logistics stands to hugely benefit from this.
Step 4: Implement Changes and Monitor
The logical conclusion to the analysis of business processes is to implement the changes identified. Changes made as a result of descriptive analysis should enable immediate benefits to be gained. Those changes implemented because of predictive, may not be able to have an effect straight away, and very often they will take a role as contingency changes.
As we have discussed, the nature of the logistics industry is that it is very dependent on a huge variety of constantly changing inputs and global conditions. Processes evolve constantly, and so should always be monitored. Modern analytics tool will enable real-time analysis of data, so that insights can be extracted and acted upon immediately – eliminating any lag times.
Businesses depend on processes. But few depend so heavily on ones with so many variables, and that produce such a significant amount of high-quality data. This is what makes the logistics industry so well set for process-improvement via the medium of data analytics. The data is there, the processes are inefficient and there are huge gains to be made. All that remains is for a robust approach to data analysis to be adopted. The increasing accessibility of modern analytics tools and global adoption of the cloud means this is easier than many think.
