Machine learning is providing insight and capability to supply chains in a way we could previously only dream of. Through machine learning, we are able to analyse and understand patterns which emerge, using algorithms to quickly utilise the most important data. Importantly, this process is also founded on continual improvement as the technology adapts. Patterns hold the key to business success in a vast number of ways. It can even revolutionise the way we do things. The fact that this occurs without manual input, using machine learning, is even more incredible.
Algorithms are created to understand subtle information held within data in order to accurately predict things. This will include elements from inventory levels, demand forecasting, supplier quality, production planning and more. Each time data is used, analysed and understood, the knowledge feeds back into the process to make it even more successful in the future.
Improved demand forecasting accuracy:
Supply chain management has always struggled when it comes to predicting future demand. It’s a challenge we are all familiar with. Up until now, we have had to become masters of statistics and modelling to try to gain a handle on this. With machine learning, this can be done faster, in a bigger way and more accurately. What’s more, it can also take in to account various factors which the human-based process simply can’t.
Lower freight costs, minimising supplier risk and improving supplier delivery:
Collaborative networks bring with them a range of benefits. With different parties needing to liaise with one another, each with varying capabilities and objectives, machine learning makes things more efficient and straight-forward. Through efficiencies, the entire supply chain can work together more cohesively.
Insights into how supply chain management can be improved:
Older technology was limited in the information it could provide to us. This isn’t to say it is completely redundant. However, using a combination of machine learning, human-led machine learning and skilled labour we are able to continuously improve supply chain management. Where performance issues occur, machine learning can help us to pinpoint the problem and improve.
Machine learning boosts pattern recognition which in turn enables physical labour to be used more effectively:
Machine learning has a huge range of capabilities. However, one area where it is particularly beneficial is in spotting patterns which skilled labour is unable to do because of the quantity of data required. Algorithms can make sense of things on a remarkable scale.
This also means that machine learning is ideal for automating a range of processes including quality control and inspections. It can pinpoint where physical problems in machinery or even the wider supply chain are occurring. Its use doesn’t stop here. It can also be used to ascertain what the best corrective process would be in order to get things working more efficiently once more. You effectively get real-time suggestions and recommendations.
Machine learning enables us to reduce inventory across the supply chain, reduce operating costs, and respond more quickly to customers:
Previously we would not have been able, easily, to simultaneously reduce inventory and increase our responsiveness to customers. Effectively, machine learning enables us to take a ‘control tower’ approach to every facet of supply chain management. This makes for a more collaborative approach enabling both warehousing and logistics to be markedly improved.
This streamlining of supply chain management prioritises customer needs whilst also reducing our costs and risk. In an arena of tighter margins, this is hugely welcome.
Forecasting demand, used for new products, is greatly improved with machine learning:
Forecasting demand has always relied to some degree on a crystal ball and a good understanding of statistics. However, no matter how advanced our statistical approaches have been, we are still somewhat limited by human capability.
A machine, on the other hand, can utilise and analyse data statistically in a way we can only touch on manually. These statistical models then prove immensely valuable, particularly in the realm of predicting demand for new products. Machine learning can use knowledge and data which we simply didn’t even recognise as being important.
The life of critical assets within the supply chain, such as machinery, vehicles, engines and warehouse equipment, is extended by the use of IoT:
By installing sensors on critical assets within the supply chain, the internet of things (IoT) combined with machine learning, can give insight in to potential future problems and downtime. Machine learning can analyse a range of data which indicates the health of machinery, such as vibrations or heat, and ensures that maintenance is scheduled with minimal impact on efficiency and cost-effectiveness.
Keeping an eye on suppliers and managing their quality management and compliance, in a hands-off way:
In supply chains, it is the nature of the beast that we are relying on a vast range of different suppliers for various different components, each one important to the final product. Maintaining a high and consistent level of quality across these different suppliers has traditionally proven to be difficult. Machine learning, along with blockchain technology, allows us to trace quality and consistency from one supplier to the next. This is less labour intensive than old-school practices.
Improvements in production planning and improving accuracy in factory scheduling through a process of analysis and optimisation:
For many manufacturers, it is common practice to work according to a model of build-to-order production. There have always been common problems with this practice. Machine learning makes it easier to make improvements here and reduce risk for manufacturers where customised products are concerned.
Visibility is bolstered by combining machine learning with a range of other technologies including IoT sensors, blockchain and real-time analysis:
Visibility is hunted across supply chains more and more as accountability is needed. Machine learning, along with other technological advancements such as the IoT, make it possible to collect and utilise data far more readily and reliably. This increases visibility and the accuracy of that visibility.
How are you using machine learning to transform your supply chain management?