Predictive maintenance is a methodology that uses AI (Artificial Intelligence) and ML (Machine Learning). The main goal is to forecast as well as avert unplanned downtime in business operations. Did you know that the global predictive maintenance market is expected to be worth an impressive 64.3 billion dollars by 2030?

What is Predictive Maintenance?

In this model, components and machinery have condition-monitoring sensors placed in them. The sensors send data to a centralized system. This lets the staff know which parts are about to fail or need to be replaced. Thus, maintenance is done only when needed. Normally repairs are done only when the equipment fails. Here the existing component has to be discarded and a new component installed. This is costly as well as time consuming. Also, there is downtime that results in delays. The delay may annoy clients and lead them to switch to competitors. Another type of maintenance is preventative maintenance.  Here equipment is serviced or repaired as per pre-defined schedules. The disadvantage of this approach is that if the equipment is working fine, unnecessary maintenance is carried out. This results in unnecessary wastage of money, effort, and time. The business operations are unnecessarily stopped in order to carry out the preventative maintenance. Though predictive maintenance entails a major investment in buying different sensors and installing them, in the long run this results in a good ROI (Return on Investment).

How Predictive Maintenance Helps Logistics Companies

Logistics organizations usually have a fleet of ships or trucks. Sensors to detect parameters such as temperature, pressure, vibrations, and humidity are placed in each truck. The physical condition of equipment is tracked in real-time with the help of sensors as well as data analytics. Historical as well as current data is employed to forecast when maintenance is required enabling proactive actions. A CMMS (computerized maintenance management system) tool is utilized to manage all the maintenance activities.

Sophisticated algorithms as well as machine learning models analyze the collected data. The respective models can trace patterns, anomalies as well as trends in the data. The system delivers predictive insights leveraging the analysis. Predictive maintenance also results in better inventory planning. Spare parts for the equipment are procured based on future demand. This eliminates overstocking of spare parts which results in wasted expenditure and occupation of real estate for storage. Understocking means that fresh stocks have to be ordered which results in delays. Delay in maintenance results in unacceptable downtime as well as financial and reputational damages.

Nowadays cloud technology is used to store data for predictive maintenance. This trend is because of the scalability and high storage capacity of the cloud. Also, cloud providers offer data backup as well as disaster recovery services. This trend is likely to become more popular in the future.

Advantages of Using Predictive Maintenance

• Superior Safety

As per PWC predictive maintenance in manufacturing can bring down safety, health as well as environmental risks by 14%. Accidents at the workplace are decreased leading to peace of mind among technicians.

• Targeted Maintenance

Predictive Maintenance provides clear insights into which particular component needs maintenance. Thus, the technicians can implement targeted maintenance.

• Superior Productivity

Time, effort, and money are saved thanks to enhanced efficiency. Thanks to minimum downtime as well as superior maintenance, the equipment performs at optimum levels. Enhanced utilization of labor as well as enhancing the lifespan of equipment results in ramped up productivity.

• Minimum Maintenance Expenses

As per a study predictive maintenance can save around 40% over reactive maintenance. As manufacturers are aware of potential failures well ahead, maintenance expenses are slashed by 18 to 25%.

• Minimized Downtime

As per a PWC report, predictive maintenance can raise uptime by 9%. Also, it increases the lifespan of aging assets by a significant 20%.

• Superior Supply Chain Visibility

Organizations experience better visibility of shipment status. Expenses related to delayed shipments are minimized.

Leading Data Engineering tools in the Logistics sector

The tools include machine learning tools, data lakes, data warehouses as well as data pipelines. The former enables maximum benefit of existing data. Organizations experience ramped up productivity, happy clients as well as more profits.

• Machine Learning Tools

Thanks to these tools companies can detect anomalies as well as perform predictive analytics. As a result, risks are managed well and operational efficiency is ramped up.

• Data Lakes

Here raw data is stored in respective native formats till required. This step is important for conducting effective data analysis.

• Data Warehouses

This refers to the centralized storage system for relevant data. As a result, data can be conveniently accessed as well as analyzed.

• Data Pipelines

Thanks to them data can be efficiently gathered, stored as well as processed. As a result, organizations can implement data-driven decisions.

The Role of Data Engineering

Data engineering refers to crafting systems that gather, store as well as analyze vast quantities of data. Predictive models are machine learning methodologies that predict future behavior with the help of historical data. Data is preprocessed to verify whether it is accurate as well as consistent. This is necessary for correct predictions. The predictive model is trained on historical data. The performance of the model is determined for factors including accuracy and recall. The models are specifically designed to harness historical patterns as well as present trends to predict future demand with higher preciseness with the passage of time. Now we know how data engineering enhances predictive maintenance in logistics.

The Role of Predictive Analytics in Logistics

This includes leveraging machine learning as well as statistical algorithms to efficiently analyze historical data. Next patterns are traced in the data. Thanks to this step businesses can accurately predict future demand, disruptions in the supply chain as well as operational efficiencies.

Thanks to predictive analytics organizations can implement a proactive approach instead of a reactive approach. The benefits include

• Superior risk mitigation
• Superior client experience
• Better overall efficiency
• Efficient allocation of resources

The Need for Educating Staff in using Predictive Maintenance in Logistics

Many times, the existing staff in logistics companies are used to traditional systems and processes to do maintenance. They may have the fear that adopting predictive maintenance technology will result in them losing their jobs. Some may not trust the technology such as in terms of performance and reliability. There may exist a skills gap in scenarios where employees have little or no knowledge of predictive maintenance technology. Adequate training should be provided to all involved to boost their competence as well as confidence.

Compatibility and Integration Issues

One challenge of predictive maintenance is that the existing infrastructure may not be compatible with the sensors. New equipment that is compatible with the sensors may need to be ordered. Sometimes sensors may need to be integrated with the equipment. The integration process may cost time, money, and effort. Rest assured when the twin challenges of compatibility and integration are addressed the business operations will be smooth.

With expertise and experience in both logistics and predictive maintenance, CoffeeBeans is well positioned to integrate both as per your organization’s specific needs as well as expectations. We have a team of professionals who know the challenges that implementation of predictive maintenance in logistics involves.  We specialize in implementing predictive maintenance in logistics using data engineering techniques. Reach out to us at [email protected] to know how we can help you meet your unique requirements as well as expectations.

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