As organisations become increasingly interconnected, global supply chains also grow far more complex and vulnerable.
That means when chaos strikes – whether in the form of natural disasters, political upheaval, cyberattacks or market uncertainty – these events impact both suppliers and customers.
Martin Clothier, Technical Director at Columbus UK, explains why the need for businesses to ‘expect the unexpected’ has never been greater in these volatile times, and how using predictive analytics will be key to effectively react to unforeseen challenges.
In the UK, Brexit has prompted businesses of all sizes to weigh up how any number of potential outcomes may impact their operations, and how they can effectively identify and manage threats to their business. In fact, recent research by Vuealta reveals that uncertainty over Brexit has resulted in more disruption to supply chains in the last five years than natural disasters and cyberattacks combined.
But is there a realistic opportunity here for businesses to not only react to events outside of their control, but use them to gain a competitive edge?
First – ask smarter questions of your data
It all comes down to the ability to use predictive analytics to manage supply chain risk. For instance, businesses can leverage machine learning techniques, such as regression analysis, which analyses historical demand to help predict product sales, allowing them to adjust production accordingly.
In 2019 and beyond, techniques in predictive analytics have advanced to such a point that they can combine traditional analytics with other external sources – including labour, weather, exchange rates or commodities markets, to allow you to ask much more intelligent and business defining questions of your data. If your business is reliant on migrant labour in your workforce, for example, you can now run scenarios based on labour costs and other data to determine the impact to the organisation should cross-border activity be negatively affected by Brexit.
Second – understand potential supply chain threats
Let’s look at another scenario and look across the entire supply chain. We’re seeing an increase in extreme weather around the world, and meteorological data is proving a novel data source that can help inform business decisions alongside transactional data. Every enterprise resource planning (ERP) system has a vendor database which can be tagged with a geographical location, and this enables businesses to start to question the likelihood of weather events – such as a typhoon hitting Asia in the next six months and how it will impact their supply chain.
You don’t need to be a data scientist to extract value from your data either. Any business decision-maker can now ask natural language questions such as: ‘What’s the average order quantity of this product in March?’ and get those answers back in a timely fashion. It is also possible to leverage an existing ERP system to create map-based visualisations, and tools such as Microsoft PowerBI mean this ‘single source of truth’ is readily available to any stakeholder across the business. It’s even possible to build a supply chain dashboard, which identifies any risk in the supply chain and classifies the risks to a traffic light system depending on their severity.
Third – extract insights from a single data repository
Having a modern data repository that can sit in the cloud and act as a data hub between your different applications is the third significant pillar of effective predictive analytics.
As more organisations implement both their ERP and customer relationship management (CRM) solutions in the cloud, they require a single repository for both transactional data and aggregated analytics data, which can be used to extract actionable insights.
Take the Microsoft Power Platform as an example. At Columbus we now provide customers with a common data model that sits between cloud applications. The customer can use this central data hub not only for reports, but to make data available to employees in the field that previously wasn’t easily accessible. The customer is able, through the platform, to create their own PowerApps that expose this data.
Getting ahead of the game
Machine learning is like a digital crystal ball for businesses – it can help predict the future, while artificial intelligence (AI) gives business decision-makers the ability to ask smart questions in a natural language and unlock data insights. Increased accessibility of data enables the worker on the shop floor, in the warehouse or out on road, to have the data visibility they require to make relevant, informed business decisions.
It’s impossible for businesses to see into the future but using predictive analytics is the next best thing. Advanced technologies such as machine learning and AI enable businesses to react to uncontrollable threats in the most effective ways possible – which could be the difference in ensuring future success and gaining a competitive advantage.
About the Author
Martin Clothier, Technical Director at Columbus UK. Over 20 years experience in managing the Technical Aspects of ERP in the manufacturing sector. Worked on 10+ AX implementation projects from version 4.0 onwards. Currently focusing on ERP in the cloud.
Featured image: ©barks
