With performance being a major concern, companies are turning to artificial intelligence to enhance their business and change the way they manage their supply chain. To streamline logistics, organizations must address complex problems throughout the value chain.
AI takes into account factors that traditional supply chain solutions cannot track or quantify over time. By implementing end-to-end supply chain solutions, companies can collect and analyze large quantities of data in order to initiate those complex processes. This blog post will explore how AI powered supply chains enable real-time decision-making to optimize asset tracking, inventory management, and service networks.
Asset Tracking & End-to-End Visibility
For asset intensive industries like manufacturing, where assets are the backbone of the business, having high performing and reliable assets is critical. Delayed product deliveries, product theft or displacement, and damaged inventory can have a significant impact on the overall operations.
Traditionally barcodes and RFID technologies are used to address this issue by offering a solution that tracks the lifecycle of assets. But in today’s world of speed and reduced error there are limitations.
- It takes up a lot of time. Labelling all of your assets, across multiple locations with barcodes is a time consuming and expensive process. Employees often spend more time trying to locate equipment or update records leading to a higher inventory turnaround time.
- There is a higher chance of human error. If an employee fails to scan the barcode or incorrectly scans the barcode, there is no record of the asset. If an asset has been stolen or misplaced, there is no easy way to remotely identify its whereabouts.
- It lacks the contextual information required to form a holistic view of the assets lifecycle. Asset records are limited to static product descriptions and do not include diagnostic or usage performance data.
Artificial Intelligence changes that. IoT sensors combined with real-time monitoring connects multiple stakeholders, processes and assets across the value chain into a single view. Real-time monitoring can help manufacturers check product issues at a granular level, from location to usage and diagnostics. Using machine learning and predictive analytics, manufacturers can take corrective actions and prevent damage before the asset reaches its destination.
With IoT-enabled devices and AI, organizations can track products from ‘floor to store’ and beyond. Knowing the exact path a shipment takes, and when it reaches a certain point not only helps businesses stay on schedule with their work, but it also facilitates demand forecasting.
Planning & Inventory management
Demand forecasting forms an essential component of the supply chain process, and is responsible for almost all supply chain related decisions:
- Strategic planning: from budgeting to profit margins,
- Push processes: such as raw material planning and inbound logistics,
- Pull processes: packaging, distribution, and outbound logistics.
While it is crucial to organizations, demand forecasting is also one of the most difficult aspects of supply chain management. The traditional supply chain uses a number of methods to forecast demand. It can be as pragmatic as asking channel partners and sales teams for their own estimates, to using more advanced statistical methods.
These methods work – to a certain extent – but they fail to address one of the most challenging aspects of managing the supply chain: how to accurately predict when assets will require maintenance and how this will influence the production demands. Until recently, supply chain managers had no choice but to follow a time-based maintenance schedule.
Machine learning shifts this paradigm. Because of the way it can analyze large data sets from various sources and extract actionable insights, ML is effectively transforming supply chain management. With the right IoT analytics tool, industry players can now quickly identify parts that require immediate replacement or assets at risk of failure, and unlock predictive maintenance programs.
Algorithms take into account internal and external factors that influence demand and weren’t known before. For instance, they will leverage asset failure rates to determine spare parts replacements. This improves both demand forecasting accuracy and planning optimization, while drastically reducing inventory costs.
Thanks to AI, supply chains now have the contextual intelligence they need to optimize their internal processes. However, companies still rely heavily on external suppliers.
Services & Dealer Networks
Manufacturing companies rely heavily on external suppliers for components, distribution, and aftermarket services. For that reason, they typically find themselves investing a lot of time creating feedback loops to identify machine failures and more generally, inefficiencies in the value chain.
These organizations have elaborate systems and processes in place because they often deal with mission critical equipment. Supply chain monitoring becomes highly intricate considering the amount of manpower required to oversee inspections, installations, repairs, and preventive maintenance programs.
The issue is then to find an efficient and timely way to gather and share information between manufacturers, dealer networks, and end-users. Striking a balanced feedback loop with dealer networks is essential for manufacturers to provide a consistent experience for end users. Without a constant flow of information, communication gaps may cause discrepancies in equipment quality.
To close those gaps, organizations have started turning to AI and end-to-end supply chain solutions to create a consistent service experience. By exchanging real-time data with dealer networks, manufacturing companies can have a better pulse on the status of their equipment. Tracking asset states over time through critical sensor readings allow organizations to empower dealer networks with condition-based monitoring and automated reports. As a result, equipment failures can be solved earlier as preventive maintenance data is shared with all relevant stakeholders.
Pattern identification will not only improve supplier quality management, but also extend the life of connected assets through the usage data that is collected. By analyzing machine data, organizations are able to determine which factors (internal and external) influence the machine’s performance. AI and ML technologies help organizations identify which equipment has the most faults and alerts, what the trends are over time, how this varies across the value chain, and so on.
By ensuring the operational effectiveness of equipment, organizations have a more accurate measure of overall asset health, a key metric many manufacturers and supply chain operators rely on.
To keep up with the increasing volume of data in supply chains, companies need to adopt more sophisticated processing solutions. End-to-end supply chain solutions provide organizations with the ability to take action on insights at a granular level through advanced analytics technologies.
When equipped with an AI-enabled supply chain, these organizations can perform complex functions in real time and obtain accurate analysis results. These results are then used to enhance decision-making around asset tracking, inventory management, and service networks – ultimately impacting a company’s bottom line.
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