Introduction
Implementing an End-to-End Automated Solution
Client
Logistics
Client since
Services
Solutions
Technologies
The problem
Unlocking Value from Logistics Data to Increase Operational Efficiency
In the fast-paced world of logistics, where vast volumes of data are generated daily, a data-driven approach is essential for optimal planning and operational efficiency. Recognizing the untapped potential within their data, our client, a prominent industry player, embarked on a journey of visual analysis. They identified emerging patterns and pinpointed noticeable outliers, which were primarily due to erroneous data input. By swiftly identifying and rectifying these inaccuracies data quality and consequently operational efficiency could be enhanced.
However, the sheer volume of product categories and varying levels of product groups made manual analysis impractical. To overcome this, the client sought an automated solution capable of defining the appropriate level of detail for analysis and detecting outliers across their extensive range of product categories. Their vision was to develop an algorithm that could scale this process efficiently and consistently.
How we solved it
Implementing an End-to-End Automated Solution
We kicked off the project by meeting with the business team, aiming to thoroughly understand their requirements and preferred interaction methods with the final solution. In parallel, we conducted an in-depth exploratory analysis to acquaint ourselves with the data’s scope and scale, achieving a holistic understanding.
In the subsequent phase, our team investigated multiple strategies to tackle the existing challenges. We established a method to determine the ideal granularity for our analysis by evaluating statistical similarities between subgroups to ascertain which could be aggregated. Within these groups, we assigned an outlier score to each data point using our developed machine learning algorithm designed for outlier detection.
Our methodology is transparent and interpretable. Rather than depending on a ‘black box’ model, we leveraged explainable machine learning techniques that allow for the visual representation of results. This approach demystifies the scoring process, clarifying the rationale behind each assigned score. Such openness not only feels intuitive but also enhances understanding and confidence in the algorithm.
To consolidate all components into an integrated system, we designed and implemented an architecture that automates the entire process and is able to score new instances in real time. To ensure the system’s durability and prepare for future changes, the architecture includes a module for retraining models with new or additional data. This design is also capable of handling high volumes of data, ensuring the solution remains robust and scalable.
Implementing an End-to-End Automated Solution
We kicked off the project by meeting with the business team, aiming to thoroughly understand their requirements and preferred interaction methods with the final solution. In parallel, we conducted an in-depth exploratory analysis to acquaint ourselves with the data’s scope and scale, achieving a holistic understanding.
In the subsequent phase, our team investigated multiple strategies to tackle the existing challenges. We established a method to determine the ideal granularity for our analysis by evaluating statistical similarities between subgroups to ascertain which could be aggregated. Within these groups, we assigned an outlier score to each data point using our developed machine learning algorithm designed for outlier detection.
Our methodology is transparent and interpretable. Rather than depending on a ‘black box’ model, we leveraged explainable machine learning techniques that allow for the visual representation of results. This approach demystifies the scoring process, clarifying the rationale behind each assigned score. Such openness not only feels intuitive but also enhances understanding and confidence in the algorithm.
To consolidate all components into an integrated system, we designed and implemented an architecture that automates the entire process and is able to score new instances in real time. To ensure the system’s durability and prepare for future changes, the architecture includes a module for retraining models with new or additional data. This design is also capable of handling high volumes of data, ensuring the solution remains robust and scalable.
The results
Enhanced Data Quality and New Service Offerings
Our hands-on approach enabled us to develop a production-ready algorithm within a short time frame. This industrialized set-up now automatically identifies outliers in both existing and incoming data. By immediately detecting anomalies and improving the data quality, operational efficiency can be enhanced across the logistics chain.
Moreover, this solution empowers our client to offer new “data check” services, opening up additional revenue streams and strengthening their market position. The success of this project demonstrates the value that can be gained by leveraging advanced analytics.
Key Learnings
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