If the vision of Industry 4.0 is to be realised, enterprises must step further into the realms of digitalisation. A critical element of this evolution is the move from traditional supply chains towards a connected, smart and highly efficient supply chain ecosystem. I4.0 is bringing down walls.
Traditionally, supply chains contain a series of largely siloed steps taken throughout product development, manufacturing, distribution and finally into the hands of the customer. Digitalisation brings down those walls. Making the supply chain a transparent ecosystem involves all its key factors, from suppliers and transporters of raw materials all the way up to the end user.
Many manufacturers must deal with a significant quantity of manual, paper-based processes when managing the supply chain. This creates a complex web of procedures and actions, which can be easily confused or lost entirely. Moving from paper to glass and digitalising the tracking and monitoring process creates digital threads that cannot be lost or altered.
While many major organisations run computerised enterprise resource planning (ERP) and supply chain management software, including digital shipping notices and radio-frequency identification (RFID) scanning systems, most have only limited insight into where their products are at any given movement.
Production may be recorded digitally but the moment it moves along the supply chain, a PDF created for shipping becomes no more than a software copy of a printout. Its digital number may tell you where the package is heading, but not what’s inside the box and in the ocean of data that could be available, only a drop of it is actually visible.
Blockchain is no longer confined to the worlds of cryptocurrency and finance. In fact, blockchain will support the global movement and tracking of $2 trillion (USD) worth of goods and services annually by 2023, according to research by Gartner. By implementing blockchains, companies gain a real-time digital ledger of the actions and movements for all participants in their supply chain network.
“The final frontier of business analytics is prescriptive analysis that allows software to not only analyse issues, but also suggest next steps for the supply chain manager.”
Blockchain could also help keep products and consumers safe. In 2018, a woman was charged after needles were found in strawberries in Australia, sparking a nationwide crisis and the need for emergency recalls, which caused a major blow to the multimillion-dollar industry.
Using blockchain, a food processing plant can keep track of raw materials, from the farm all the way to supermarket shelves. This means that if a contamination scandal broke out, the source could be traced back to the specific farm or factory where it began. By improving traceability along the supply chain, loss due to recalls is prevented and the risk to public safety is massively reduced.
Imagine putting a crystal ball to your supply chain and asking it how much of a certain product you should create. Predicative analytics could help plant managers know exactly what they need to produce to meet demand and how to satisfy downstream customers.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. The technology has been a part of our everyday lives for some time. Take credit card monitoring, for example. Machines learn our regular credit card activity over time so when a suspicious transaction outside of our normal purchasing behaviour is noted, the credit card company immediately puts all account activity on hold.
In order to do this, data is key. With more data available than ever before, predictive algorithms can help streamline the supply chain and efficient forecasting can predict future demand based on past events and prevailing trends. Both over and understocking result in loss. If too much of a product is produced, it will result in waste. If not enough is made to meet demand, it leads to unfulfilled orders that could damage future customer relationships.
With an accurate and detailed picture of demand, predictive technologies such as machine learning and cloud-based inventory management eliminate overstocking and enable warehouses to work together to manage inventory.
The final frontier of business analytics is prescriptive analysis that allows software to not only analyse issues, but also suggest next steps for the supply chain manager. By taking advantage of predictions, prescriptive analytics attempts to quantify the effect of future decisions in order to advise on possible outcomes before those choices are actually made.
Predictions can also extend to plant maintenance. Manufactures often stockpile industrial parts, particularly those that risk becoming obsolete, to arm themselves against the threat of breakdown. This large amount of ‘just-in-case’ extra equipment takes up valuable factory space and may not be required for years to come.
Predictive analytics can be used to monitor the condition of all operating equipment in a factory, so that if equipment isn’t performing to its optimum capacity, a plant manager can receive an automatic alert. This increase in transparency means that, in many cases, the problem can be rectified without disrupting the entire production process.
If smart sensors on a motor can, for example, detect and monitor an issue such as overheating and adjust accordingly, this could prolong the component’s overall lifespan. If fixing existing equipment isn’t an option, the smart factory could order a replacement part from a reputable parts supplier, such as EU Automation, without any human involvement at all. This would eliminate the need for manufacturers to stock spare parts in their own facilities and optimises the management of equipment health.
If businesses are to delve deeper into digitalisation, the walls of their supply chains need to be broken down. By making the most of smart technologies, manufacturers can have a better grasp on their stock during every step of the chain. This will not only determine how much of a product should be created, but where it should go and whether the manufacturing process is working as effectively as it should.