The post-pandemic demand for food, drink, chemicals and building materials has helped the industry recover from previous stagnation, with over two years of growth predicted.
However, many manufacturers across the country still face the burdensome issue of downtime. Factory equipment, personnel or supply chain problems continue to interfere with productivity goals, many of which are unforeseen. It’s a significant obstacle for organisations looking to expand their operations to meet current business targets and grow.
Business leaders and facility administrators continue to work in tandem to find methods to minimise downtime. As smart technology becomes an integral part of heavy industries, predictive analytics can be the solution.
What causes downtime in the manufacturing industry?
Manufacturing facilities often have complex equipment and intricate workflows to meet production and quality goals for their goods. They are a crucial requirement for the success of the operations.
However, as with any industry, manufacturing is subject to downtime due to malfunctions or interference with operational assets. It’s estimated to cost UK manufacturers around £180 billion per year, being a substantial hurdle to sector growth.
Narrowing the reasons behind downtime can help facilitate solutions to address the issues. Here are some of the leading causes:
- Equipment failure: Operational machinery and equipment that break down or develop faults are one of the most common causes of downtime in manufacturing. Leading organisations worldwide lose an estimated $864 billion a year due to equipment failure.
- Power outages: Most equipment and software in manufacturing facilities need sufficient energy to run optimally. Any interruptions to the power supply can cause lengthy downtime.
- Cybersecurity incidents: Many organisations use smart platforms connected to the internet to optimise their production line. However, they are susceptible to cyberattacks that can disrupt daily operations.
- Supply chain delays: Late deliveries, unhappy customers and raw material shortages are examples of issues that can cause delays and downtime. Consistently unstable supply chains can significantly impact productivity.
- Accidents and injuries: Human accidents and equipment malfunctions also contribute to the amount of downtime manufacturers experience annually. Health and safety hazards around facilities can also cause accidents that interfere with daily productivity.
- Personnel shortage: A lack of available personnel or poorly managed shifts can also cause downtime in manufacturing. When staffing is insufficient for tasks, it can affect output.
How predictive analytics can overcome manufacturing downtime
The rise of smart technology in today’s manufacturing environment can offer solutions that help address the lingering issue of downtime. Predictive analytics is an application that can help factory managers and supervisors be proactive in optimising the production line, taking action before any significant problems arise that could be costly or restrictive.
Platforms like Evocon demonstrate how predictive analytics can empower manufacturing teams to address downtime. Below are the main features that showcase how the application can help mitigate common factory obstacles.
- Extending equipment life: Keeping your existing equipment in service as long as possible can significantly reduce manufacturing costs. Predictive analytics can help extend the life of production machinery by signalling when they are likely to need maintenance.
- Early anomaly detection: The software provides managers and supervisors with a clear yet detailed view of the production line, alerting them to anomalies. Teams can take swift action to address issues, such as potential machine failures and impaired performance.
- Supply chain resiliency: Understanding the full scope of production is crucial to a successful operation. The intricate data supplied can help provide insight into supply chain resilience, reducing the time spent rectifying issues that cause delays.
- Resource optimisation: Facility leaders can use the platform to optimise the production line, ensuring equipment runs efficiently and minimising waste. Consistently analysing data can also help improve product quality and enhance production shifts.
- Improved efficiency and productivity: Predictive analytics provides teams with a complete view of factory performance, enabling employees to identify and address issues that could cause downtime quickly. Being more vigilant can help improve overall efficiency and productivity.
What manufacturing industries can benefit from predictive analytics?
Manufacturers can leverage the benefits of predictive analytics to help ease concerns with production lines and meeting daily quotas. Business leaders, floor managers and supervisors can use the comprehensive data to streamline operations in their respective industries.
Here are some of the sectors that can benefit from predictive analytics platforms:
- Pharmaceutical: Consistency and compliance are vital parts of production in the pharmaceutical industry. Predictive analytics can help ensure both by identifying anomalies in the production process and complying with local regulatory frameworks.
- Automobile: Car and vehicle manufacturers rely on heavy equipment to assemble the complex components. Predictive analytics can gauge when machinery is likely to have a fault or malfunction, signalling the maintenance team to take action before an incident.
- Packaging: The ever-increasing demand for packaging makes line efficiency and changeovers core to the manufacturing process. The platforms can help optimise machine performance and facilitate faster transitions between packaging runs.
- Food and beverage: Producing food and drinks can be particularly costly if not optimised for wastage and production efficiency. Real-time data from predictive analytics can help supervisors stay on top of the line, ensuring equipment operates optimally and reducing waste and downtime.
- Construction materials: The complexity of manufacturing construction materials often leads to machine errors, slowing production. Analytics applications can leverage data to improve planning and action.
- Aerospace: The industry faces significant costs if production delays occur. Predictive analytics can help manufacturers optimise their supply chains to minimise downtime and reduce bottlenecks.
Overcoming downtime with smart predictive analytics platforms
There is continuous pressure on the UK government to help find resource-efficient solutions to boost productivity in the manufacturing sector. The mounting calls highlight the need for more innovative action to address downtime across the majority of the country’s facilities.
Predictive analytics platforms can be a crucial component of reducing downtime for the manufacturing industry. Where there’s an intuitive dashboard displaying comprehensive data of machine status and operational efficiency throughout the factory, managers and supervisors can take proactive action, preventing issues before they become lengthy and costly.
Today, smart technology offers applications that paint a more coherent, actionable picture of complex operations. The manufacturing sector can significantly benefit from predictive analytics, helping ensure the leading causes of downtime are minimal.