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Business Intelligence – Operation Excellence

Challenge

In the dynamic landscape of the life science industry, our client — a prominent data science company — has long stood as a bastion of innovation. Their commitment to delivering cutting-edge products and services to global leaders in healthcare demanded a meticulous approach to operational excellence and product quality. With an extensive portfolio of systems supporting data asset production, their emphasis on precision and industry standards was unparalleled.

However, our client recognised the imperative to amplify operational efficiencies further. An aim for greater visibility within their production process drove the ambition for potential automation opportunities. Simultaneously, there was a pressing need to rekindle innovation, increasing revenue through new ways of service and operations.

How could we infuse innovation into their operations while meeting the stringent demands of quality assurance and operational oversight?

Could enhanced visibility significantly enhance production efficiency and product quality?
How might insights derived from process analysis fuel revenue growth through service optimization?

Solution

Responding to the initial request for a dashboard to pinpoint production process anomalies, our focus on data visualisation aimed to illuminate system errors and efficiency bottlenecks. This transparency empowered our client and the team grew its confidence to innovate instantly. Now, our client can predict production time and recommend products to customers based on previous orders of similar clients.

BI Dashboards: A centralised dashboard channelled data from diverse sources, providing departmental users with updated, curated insights.
Automated Data Ingestion: Implementing toolsets facilitated seamless data loading, harnessing automated processes and cloud technology for database and file extracts.
Integrated Data Model: Our team connected siloed data sources, offering a unified data view encompassing over five critical production and delivery systems.
Machine Learning Integration: Leveraging this integrated data model, predictive analyses for delivery timelines and product recommendations for upselling opportunities became attainable.

Results

We led a transformative project to develop an operations control system aimed at streamlining workflows across five different production systems at a leading data science company within the life science industry. Focused on optimising operational efficiency and fostering innovation, Aiku collaborated closely with the service, operations, and quality assurance departments to elevate their technological infrastructure and drive unparalleled outcomes.

Enhanced Operational Visibility: Implementation of BI dashboards facilitated unprecedented transparency into production systems, enabling instant identification and resolution of errors and bottlenecks.
Unified Data Integration: Integration of over five disparate systems into a harmonized data model streamlined operations, fostering data-driven decision-making across departments.
AI-Driven Insights: Introduction of predictive models empowered the client to forecast production and delivery times while offering personalised product recommendations to foster revenue streams.

Tech Stack delivered

Strategy: Vision and goal setting with the sponsors and subject matter experts. Regular reviews with department leadership team to inform objectives of upcoming releases.
Product: Detailed the product roadmap and backlog. Maintained data on progress on Jira and all technical documentation on Confluence
Cloud: Leveraged Cloudera for job orchestration and scheduling for running the data and ML pipelines.
DevOps: Used programming languages: Python, R, SQL. Employed GitLab as the DevOps platform.
Data:
- Extraction and loading from various systems incl. Oracle, MySQL, MS SQL Server, Salesforce, SAP.
- Transformation and modelling with Pandas (Python) and tidyverse (R).
- Storing integrated data model in Datawarehouse on Exasol.Analytics:
Customized Dashboards for Quality Assurance and Service teams using PowerBI, MicroStrategy. Additionally, automated Excel reports generated from dataset extracts.
ML/AI: Used Caret (R) for the recommendation engine and Prophet (Python) for production time prediction.

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