Building a Resilient Supply Chain with Advanced Predictive Analytics

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Global supply chains have made front-page news for all the wrong reasons in recent years. The pandemic shook the foundation of supply chain management, blockages in the Suez Canal cost businesses billions, and the global conflict undermined the stability of otherwise reliable trade routes. 

Responding to these changes is key if you want your firm to last in the long term. You cannot afford to be out of action for weeks on end when upstream suppliers falter and should be quick to respond to potential issues caused by geopolitical tensions. 

Rather than reacting to supply chain issues, adopt a proactive approach by harnessing the power of predictive analytics. Today’s predictive analytics tools can help you spot weaknesses, make strategic changes, and avoid costly errors. Predictive maintenance can keep your fleet on the road for longer and improve your overall operational efficiency, too. 

Understanding Predictive Maintenance

Predictive maintenance is a branch of predictive analytics that attempts to forecast faults and mechanical failures. This can be revolutionary if you’re used to responding to problems like dead motor batteries, electrical wiring issues, or worn-down fabrication units. Predictive analytics programs use data to identify these issues and bring them to your attention before a supplier or employee does. 

Read also: Technology’s Impact on the Supply Chain

These predictive maintenance programs rely on machine learning (ML) algorithms to crunch the numbers and learn from patterns. Investing in these ML programs is crucial, as ML programs can optimize your global logistics and improve your supply chain efficiency. Common uses of ML in supply chain management include:

Demand Prediction

These tools identify consumer trends and use historical data to identify patterns. This is crucial if you want to respond to seasonal surges in order volume.

Route optimization

Route optimization apps minimize energy waste and expedite delivery times. They give drivers the fastest route possible and reduce the risk of an accident while on the road. 

Fleet management software

These tools protect drivers and identify failing parts before your machine or vehicles break down. This is particularly important if you utilize Just-In-Time production models and need to minimize the amount of time products spend in the warehouse.  

These predictive analytics tools can be used in conjunction with predictive maintenance tools to improve the efficiency of your global business. 

For example, if you sell winter apparel in Australia, predictive analytics tools can prepare you for a surge of sales if temperatures are set to suddenly dip. You can then look towards predictive maintenance programs to ensure that your delivery vehicles are primed for increased use and will not break down while you’re trying to meet high demand. 

Anticipating Downtime

Predictive maintenance tools can’t prevent your equipment from breaking down. However, they can help you get ahead of faults and spot issues with your supply chain before an issue can arise. These tools can be used to justify your decision to replace or repair supply chain assets by improving your understanding of asset lifecycles. This is crucial, as all business assets go through four common stages, including: 

  • Acquisition,
  • Operation and maintenance,
  • Repair or replacement,
  • Disposal.

You can identify which stage of the product lifecycle your asset is in by utilizing data analytics to conduct an effective cost-benefit analysis. For example, if you have recently bought a used fleet of trucks, you can use AI-powered enterprise asset management (EAM) software to determine when the vehicles have outlived their usefulness. These EAM programs pull data directly from sensors that are connected to the Internet of Things (IoT) to read the vital signs of your assets. 

These insights can help you make pivotal calls that save you money and bolster the resilience of your supply chain. EAM programs help you evaluate asset performance and improve the veracity of your cost-benefit analysis, too. This data-driven approach to asset management will reduce downtime AI spreads throughout supply chain management, as EAM programs will be able to draw from larger data sets as your IoT expands. 

Additional Features

Predictive maintenance tools do more than tell you when a screw is loose or a clutch is worn out. The best predictive maintenance tools are all-in-one programs that give you on-the-go updates based on data points that are easily overlooked by human supply chain specialists. 

For example, if you work in manufacturing, AI-driven predictive maintenance tools can assess safety compliance at your place of work. By tapping into a range of visual surveillance systems, inventory management tools, and real-time performance metrics like temperature, pressure, and usage, AI can spot safety hazards and help managers remove faulty equipment before it can cause an accident. 

Predictive maintenance tools are particularly beneficial during times of high production when you cannot afford a breakdown. These tools work in tandem with your automated scheduling services and automatically reassign workers to different tasks if a fault has shut down a machine or workstation. This gives you additional time to replace or repair equipment, reduces the pressure that your staff feels during peak times, and improves your supply chain resilience. 

Conclusion 

Predictive maintenance tools should be a part of your wider supply chain management system. Predictive tools can spot faults and minimize downtime when something goes wrong. They can help you make better-informed decisions when a vehicle or machine breaks and innately improve safety standards at work. Just be sure to integrate predictive maintenance programs into your wider tech stack, as they work best when they have access to your wider EAM. 

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