To some, it may seem as though the fruits of automation couldn’t get any better. The symphony of a self-orchestrated supply chain is quite a thing to behold. So when we envisage tomorrow’s supply chain, we think of it as being leaner, faster, and fully automated. But what technologies will take us there?
Predictive analytics has been anticipated as the top choice for strategic supply chain management. It’s even been said “predictive models combined with new-age fleets could lead to zero fulfilment time”.
There are often many different parts and stages to the supply chain, so how do you know if predictive analytics will help you, or if you’re ready for it? If you find there is a lot of information sharing between different parts in your supply chain, or even if you’d like to be able to foresee the future more clearly, predicative analytics may be the solution for you.
Let’s take a look at how to predict future events through data successfully.
What is it and why is it so special?
When supply chain professional’s get together to discuss predicting consumer behaviour through analytics, it’s easy to see why they would get gooey eyed, and possibly even hear Frank Sinatra’s I love you baby softly play in the background.
Utilising all the data collected within your supply chain doesn’t just help you make better decisions on the fly, it will pool together vast amounts of information to predict future events.
Predictive models stem from advanced analytics, and it uses techniques such as data mining and statistics modelling to unravel potential future scenarios. It looks for patterns from transactional data and could give you insights into the ebb and flow of your inventory management needs.
Amazon is trumping the rest when it comes to predicting consumer orders, as they’ve recently patented their very own anticipatory shipping algorithm. Essentially they are able to use historic transactional data from customers to predict when they will re-purchase items like toilet paper or tissues. Forbes even described this algorithm as pure genius so it’s definitely something worth keeping an eye on.
Being able to foretell future events will totally transform the way organisations manage their cargo and warehouses, and if you can combine predictive capabilities with other new technologies such as drone delivery, you’ll be off to a great start to achieving a self-orchestrated supply chain.
However, there is still quite a way to go for many businesses, especially the European manufacturing industry. Dale Benton of Supply Chain Digital reported that there is an over reliance on historic data, and an under reliance on dynamic and data-driven models.
“More organisations are driving their supply chains forward by looking in the rear-view mirror, rather than looking at the road ahead. It is not just that there’s an over reliance on historic data, it is quite possible that organisations are being driven along the wrong road altogether,” states Hans-Georg Kaltenbrunner, vice president manufacturing industry strategy, EMEA at JDA.
So that brings us to our next point, how do you know it’s for you or if your supply chain is ready to predict future events?
You found success? Great, now keep re-visiting and re-evaluating your model
In an article written by data-guru William Vorhies, posted in Data Science Central, Bill states that successfully implementing predictive models into the supply chain really comes down to an organisations ability to forecast accurately.
William writes: “From a predictive analytics perspective, about 90% of the problem is forecasting, starting with the demand forecast and letting that trickle back through the process to procurement and logistics planning.”
But that’s only just the beginning. In order for your predictions to continually remain accurate, you have to continuously update and incorporate new time frames and events into your strategy.
Essentially, the process of using advanced analytics should never stop. What worked for you last year or even five years ago certainly may not be good for you next year or in ten years.
So you may be thinking, great, my predictive analytics model has just gone live! But the work is not yet over.
You’ll need to closely monitor its accuracy and performance over time as predictive models tends to degrade over time. A new infusion of energy is required from time to time to keep that model continually updated. Below are a few tips to help you easily revisit and re-evaluate your predictive models.
Visualise your data. It will change the way you analyse
Visualising your data will save you time when you want to generate reports and graphs on the fruits of your analysis.
Essentially it can help you go from this:
The best part of visualisation is that, if you’re keeping your analysis up to date, the data is dynamically updated and you can verify new models against historical ones.
One great thing about data visualisation as well is that it’s a very effective way to communicate to the team.
Develop customised applications to automate monitoring
We all know it’s no fun staring at numerous spread sheets and reports and hoping we’ll find something new and exciting. The Industrial Internet of Things has created vast amounts of data, but the challenge really is making sense of it. Automating the process of monitoring your data will save you time and will reduce the occurrence of human error.
A few things to remember before building bespoke applications for your data monitoring:
- Data generated and monitored should align with business objectives
- Simple is always better – overly complex models may degrade the quality of your predictions
- Always start with good data – make sure your data is relevant and accurate
We hope you found this post on predictive analytics insightful. If you liked it register for an Exhibition Pass to CeMAT AUSTRALIA to learn first hand about the latest technologies to help you with supply chain automation.