Supply-chain inefficiencies are having a huge impact the world round. In the U.K. alone, they cost businesses at least US$1.9 billion annually in lost productivity.
The causes of friction are numerous, and manufacturers are continually looking to employ lean management tools to eradicate them. If manufacturing is delayed because of inefficiencies, including time spent waiting for assets, there will be a huge ripple effect on the supply chain, including an increase in costs and long-term damage to customer and supplier relationships.
We know from first-hand experience that employees spend many hours simply waiting for tools, assets and materials so that they can move on to the next manufacturing stage. Time is often lost tracking down assets, waiting for information on the condition they’re in, recalling them, and servicing or replacing them.
The internet of things (IoT) and adoption of advanced analytics are playing a growing role in manufacturing and wider supply-chain operations. A recent report by DHL revealed how advances in IoT, big data, artificial intelligence, cloud computing, and digital reality technologies have fueled the rise of digital twins within logistics — a technique by which users can interact with digital equivalents of the physical world.
The report is encouraging, as IoT has the potential to solve a number of manufacturing and supply-chain challenges. Sensors can potentially be placed on every tool, part and asset within a manufacturing supply chain, creating operational visibility and providing insights that can significantly improve performance and reduce inefficiencies. This is the vision behind Supply Chain 4.0, but it has yet to take hold in every manufacturing operation.
Those familiar with the market know that change is slow, especially because of the perceived complexity around digital systems, the prevalence of legacy equipment, and difficulty of integrating new systems. A substantial investment in skills and systems is required to bring about real change.
Miscommunication and wasted manufacturing time represent an ongoing headache for managers. There’s a real imperative for the manufacturing sector to consider how much time is spent waiting across operations, why it’s happening, how much it’s costing, and whom it’s impacting.
Problems with individual manufacturers arise when there’s a perception that time and resource are being wasted. For example, Asset X is never proactively returned, or charged, after use by Person Y. This means that all subsequent users must first locate and then charge the asset before it can be redeployed effectively, creating multiple process inefficiencies along the way. Similarly, there might be multiple times when assets have been taken on unplanned routes through a facility, or sent offsite for calibration. In these situations, time and money can be saved if teams know for certain when an asset has arrived at its destination or has been returned to the facility.
Asset intelligence, a recognized subset within the broader industrial IoT, is a process by which sensors are placed on assets, enabling supporting software to pinpoint their location. Depending on the nature of manufacturing operations or supply chains, this could involve indoor or outdoor tracking. Outdoor or offsite tracking relies on GPS technology, and can identify the location of an asset anywhere in the world.
The approach is gaining traction because it extends operational visibility to all parts of a manufacturing organization, and then outwards — back to suppliers and forward to end-user customers. By definition, it supports the fundamental approach of cellular manufacturing, where waste is eliminated as far as possible, and adds value to other fast-moving industries where the rapid, flexible redeployment of assets is demanded. In these situations, dynamic location and related intelligence can reduce risk, increase speed and save money.
Asset intelligence can also prevent the scrapping of high value assets due to lost paperwork, and alert engineers before an issue has even had a chance to arise. It offers minute-by-minute location, environmental and usage data, which can help pre-empt mechanical issues and increase an asset’s lifespan.
This approach gives a hint of how the manufacturing sector can progress. Manufacturers must move away from private, red-brick, production-led facilities, to becoming open, accountable, collaborative and customer-centric businesses. Digital transformation provides an opportunity for businesses to add services to their offering in cases where GPS tracking extends beyond the manufacturing lifecycle and acts as a built-in capability within a finished product. One leading engineering company integrates GPS-enabled tracking within large engines, allowing them to be monitored throughout their lifetime to predict when failure may occur, or when maintenance work is needed. This approach opens up new revenue streams, and deepens customer relationships beyond the traditional selling of hardware assets.
While asset intelligence is still very young, the future will be driven by advanced analytics, machine learning and prescriptive insights, all made available across core enterprise systems.
Integration of IoT systems can seem a daunting prospect, especially where supply chains are complex and multinational. Success occurs when a manufacturer has properly assessed why it’s undertaking such an initiative, and how it meets specific operational objectives.
Most organizations, having adopted IoT to support the location of assets, begin to consider where the technology can take them next. Once an asset can be remotely located, the next question might be how environmental conditions, such as temperature, affect an asset’s properties or performance. By asking these questions, manufacturers can operate far more intelligently and efficiently further down the supply chain and beyond.
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