Today, many homes already have a selection of connected devices and appliances that help in daily tasks and provide different forms of entertainment. Additionally, other devices manage power consumption, control A/C and heating systems, and provide home security.
In the past few years, home wireless connectivity, mainly in the form of Wi-Fi routers connected to the internet, allowed many appliance manufacturers to add their products to the list of smart home devices, enabling new features and opportunities for consumers and businesses alike.
Those appliances provide advanced features for their users, such as programmability, remote operation, and reduced power consumption. They also collect invaluable information about the use of the units and their components. This trove of data is crucial for manufacturers to understand the real-world use of their products, provide on-time maintenance, and correct potential design shortcomings in future generations.
Preventive and predictive maintenance for better supply chain management
The last two years have been challenging for forecasting required spare parts and products. Since people spent more time at home, the use of many products dramatically changed, and the information manufacturers and service providers collected for many years before the pandemic became inaccurate, at least.
Additionally, the disruption of manufacturing and transportation created by the unforeseen needs of healthcare products and shipping routes affected the supply chain in a way never envisioned before.
The most challenging situation affecting manufacturers today is the shortage of components and spare parts. This is not only creating significant delays in manufacturing new products, but it is also creating bottlenecks in maintenance operations and repairs.
Many analysts agree, considering the situation of some markets crucial to production, that the shortage of many components and parts, especially semiconductors, could well continue until 2024.
Most manufacturers expect that their products will operate without interruption for several years. However, the different conditions and frequency of operation of many appliances and other home devices could significantly affect the estimated lifespan of those units.
Since many of the current household products connect to the internet, and some are embedded with sensors, manufacturers and service providers could use the information gathered from those to start predicting the upcoming maintenance needs.
For example, an air conditioning unit installed in a tropical place, where it is in use most of the year, could need maintenance or replacement much sooner than the same unit in a location when it is required only for a few weeks during the summer. A washing machine in a household of two will last longer than the same model in a house of a family of four, especially if they have small children. Additionally, other environmental conditions and social habits can also affect the usage and wear of devices, such as water quality, humidity, heat, or freezing weather.
With the information collected from onboard sensors and power usage, manufacturers and service providers can now use analytics and machine learning to forecast the need for maintenance, especially spare parts or replacement units.
Therefore, with the knowledge of immediate and future needs, components and spare parts can be sourced or produced in advance and shipped to the destinations where they’ll be needed.
Both manufacturers and service providers can streamline their supply chain management by getting advance notice of the products and components required.
Machine learning and intelligent manufacturing can improve new products and the availability of components
Using power analytics and machine learning, manufacturers can use the information collected by their connected devices to help them correct problems during manufacturing.
For example, suppose an oven or a coffee machine has repeated problems keeping the right temperature during operation. In that case, it could be that the thermostats used on those appliances are not the best for that application or are positioned in the wrong place.
For years, manufacturers had learnt about these problems from service providers when they requested spare parts to repair those appliances. Still, the information is incomplete, as it doesn’t usually identify the models affected and the usage conditions.
With the information collected from sensors, matched with usage conditions and other relevant factors, manufacturers can start looking for new parts for their products or redesign existing ones to avoid similar situations. This could also help manufacturers avoid potentially expensive recalls.
Knowledge of service issues and the conditions of usage of connected machines and devices also help to predict their supply and demand over time, allowing for a better understanding of the constraints of the existing supply chain.
Helping the environment with a circular economy
There is no doubt that the continuous manufacturing and replacement of billions of consumer and industrial products is devastating to the planet. Mining for raw materials and the energy needed for their transformation depletes natural resources and contributes to global climate change.
That’s why it is imperative that products and components are built to last longer and, when they need to be replaced, their parts and materials are given a second life.
While recycling has been going on for many years, it takes a long time for discarded parts to be classified, inspected, retrofitted, and put back in the supply chain. Many usable parts and materials are discarded in landfills or burned in power plants.
With the information collected from connected devices and advanced analytics, as seen above, it is possible to determine when a device, an appliance, or any other connected machine would need to be partially or entirely replaced.
Many parts are probably in perfect operating condition when an appliance or other device is replaced; those could serve as spare parts for other devices. Furthermore, when those parts are no longer usable, their components could be taken apart to manufacture other products.
With the data from the connected devices and predicting machine learning capabilities, manufacturers and service providers could forecast the upcoming availability of spare parts from replaced units and offer those parts a second life, thus reducing waste and the need for more raw materials.
Towards a new, digital supply chain
“If we leverage technology and information, we can create resiliency at far less financial burden in the organization and build a supply chain in the future,” says Jim Kilpatrick, Deloitte’s Global Supply Chain & Network Operations Leader.
The connectivity of billions of consumer devices could not only give users more possibilities and control over their appliances, but it can also help manufacturers and service providers improve their supply chain, provide better service and, most importantly, make better products and improve customer experience.