Most of what we do today is in anticipation of the world of tomorrow. The average business leader in any sector hears regularly about technology like artificial intelligence (AI), natural language processing (NLP), and machine learning (ML). All of these revolve around and are made useful by data.
And what sector has more data than the supply chain?
The reality is, the worlds of supply chain and transportation logistics have been slow to the plate when it comes to the art of using data. Leaders need to catch on to the urgency of adopting sophisticated data analytics practices if they hope to achieve efficiency.
Recently featured in Logistics Business, this article explores the two key benefits and four core types of data analytics that are going to make a difference. Read on to learn more.
Analytics at Scale (No More Guessing!)
Data analytics is no longer relegated to the world of “what-ifs” and “nice to have but who has time?” As digitization has taken hold in earnest, we now have unrivaled data sets at our fingertips. With sophisticated freight audit processes and mature transportation spend management practices, leaders in transportation logistics technology can now see more than ever before.
In short, data (done right) can tell a real story, unlocking powerful insights that shape strategic decision-making.
Drill Down to the Root Cause of any Issue
In the past, leaders in this space operated on gut instinct, standard operating procedures, and well-honed practices to address problematic symptoms. They made educated guesses about market dynamics and forecast with moderate certainty. That generally nebulous approach isn’t necessary anymore. Why? You guessed it: data.
Data clears the line of sight, pulling all of the historical and real time information to uncover the root cause of major and minor issues. Global transportation executives who have bought into comprehensive data analytics processes, and invested in these capabilities, are a leg ahead. The efficiencies of tomorrow rely on our ability to quickly see and remedy root cause level issues.
Supply Chain Efficiency Relies on 4 Types of Data Analytics
If we have a hope of overcoming the unparalleled disruption of the last few years, we must implement better data analytics practices.
Here’s what leaders need to pay attention to:
1. Descriptive Analytics
Descriptive analytics provide foundational understanding. What are your key performance indicators? Benchmarks? Year on year, month on month, even week on week data?
These are all of the black and white details of your business — goods, carriers, routes, modes, etc. — against timelines. They provide the baseline for all further data analytics practices.
2. Diagnostic Analytics
It’s great to know what happened, and how it compares to what’s happening now, but you must interpret it. In other words: why did that happen? What did it mean?
This gets to the variables inherent in any occurrence or operational outcome, and can form practices of analysis that yield strategic learnings and inform new goal-setting and more.
Some examples of key measurement strategies implemented in this type of data analytics include carrier scorecards, functional performance metrics, and cost analysis.
Warning — If at any point in evolving your data practices you uncover bad, unstructured, chaotic data: stop and fix it. Read The High Cost of Bad Data in Supply Chain Management to learn why.
3. Predictive Analytics
Understanding the baseline against which to measure performance, then interpreting performance-related dynamics, are levels 101 and 102 of data analytics. 103 is predictive analytics.
Some examples of data in this type of analysis include GPS tracking, weather data, traffic data, and more.
There is a measurable impact of external data on planning and validation. The race is on to more accurately forecast what will happen in any given market in any given timeline. A robust, accurate set of data — skillfully interpreted — can support an elite level of foresight.
4. Prescriptive Analytics
What may happen, based on what you know and can reasonably expect, should lead to the 104 level of data analytics: prescription. Any world class supply chain or logistics operation should have fully adopted a Logistics Control Tower strategy. This includes external and internal data of the highest quality.
After a certain duration of time, this data can be used for complex modeling and offer up prescriptive analysis.
Improving Supply Chain Efficiencies With Data Analytics
There are steps that leaders can take today to be poised for success tomorrow, especially when it comes to data.
Here is where to start:
- Start where you are. That may sound obvious, but a careful and accurate analysis of your company’s current “stage” in these four types of data analytics is invaluable.
- Progress to what’s next. The data analytics types listed above are progressive, and you should be making strides toward greater proficiencies and mature insights.
- Find the right partners. Data scientists and data tools — some of which are provided by Trax — are irreplaceable in the journey toward using data to inform strategic decisions.
Using data well and wisely is a long-term play, and there are no shortcuts. But there can also be no delay. Wherever your business is starting, you must push forward into progressive mastery of using data analytics for supply chain efficiencies, because it is capable of achieving that, and so much more.
Read next: head here to read about Big Data Analytics in Supply Chain Management.
