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Process Mining in Logistics: Top 3 Benefits & Challenges in '24

Process Mining in Logistics: Top 3 Benefits & Challenges in '24Process Mining in Logistics: Top 3 Benefits & Challenges in '24

In 2019, 55 % of the companies reported that they handle logistics processes manually which make it prone to human error that is costly, time and resource consuming, and reduces customer confidence and loyalty to the business. 1 Process mining in logistics aims to solve these problems. In process mining case studies, companies that applied the tool have stated that leveraging process mining in logistics has decreased their warehousing costs by 40 % and increased their on-time delivery by 18 %. 

What is process mining in logistics?

Process mining in logistics aims to optimize logistics operational efficiency and improve customer experience. Specifically, process mining tools identify bottlenecks like late deliveries and their root causes to prevent possible supply chain disruption.

It shows how to apply process mining in logistics and how it captures the stored data.
Figure 1: An application of process mining in logistics

What are the applications of process mining in logistics?

Recent process performance management tools based on process mining techniques analyze data extracted from external and internal sources. These tools provide a comprehensive operational excellence approach and predictive analytics. Process mining can be used for:

  1. Visualizations and monitoring: Leveraging process mining enables logistics teams to visualize processes and monitor operational performance:
    1. Data analytics models the process and provides suggestions about adjustments or best practices for the adequate performance of the customer’s supply chain.
    2. Process mining identifies the critical areas for improvement and generates data-driven recommendations. 
  2. Predictive analytics: Process mining technology allows to identify potential problems with predictive analytics:
    1. Process mining analyzes historical data to determine patterns and trends. It formulates possible directions in the future. Doing so, process mining predicts following events with a reasonable degree of certainty. 
    2. Supply chain managers benefit from these predictive analytics in demand forecasting. This enables them to optimize their warehousing costs, shipment requirements, and inventory strategies according to demand.    
  3. Identifying automation opportunities: Process mining enables logistics firms to identify processes that are manually executed, include repetitive tasks or do not comply with ideal process models. This way, companies can automate and enhance their IT systems, warehouse management system, supply chain management, enterprise resource planning and transportation planning.

What are the benefits of applying process mining to logistics?  

With process mining, logistics teams can: 

  • Understand logistics processes 
  • Improve on-time delivery: It’s been estimated that process mining decreases delivery time by 18%.
  • Save costs: 40 % in warehousing costs
  • Discover key regions where payment is on time
  • Benchmark and observe the amount of returned goods
  • Identify root causes for order changes with automated root cause analysis
  • Track business performance through process KPIs
  • Monitor lead times, which are the time between the initiation and completion of a process.

What are the challenges to apply process mining to logistics?

Real-time logistic data

Logistic teams need to use real-time data in order to manage sudden problems. For example, traffic is slow due to snowfall, or a hurricane is expected to reach a certain delivery area. The information for this event can be provided by sensors on the roads, from a weather website in the form of a RSS feed, from a logistics control tower, or even from social media. This data needs to aggregated in real time to derive more reliable or complete results. Businesses can leverage web crawling to extract relevant data about the target areas to input to their process mining tools in order to predict accurate results.

For more on process mining

Process mining is one of many technologies used today in hyperautomation. To explore hyperautomation and process mining use cases and applications in different business functions, feel free to read our in-depth articles:

If you are interested in understanding the process mining market and landscape, explore our data-driven article 20 Process Mining Statistics: Market Size, Adoption.

If you believe your business can benefit from process mining, feel free to check our data-driven list of process mining solutions.

And we can guide you through the process:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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