Supply Chain & Chemical Data Operations
COMPLETED1. The Situation: The Nomenclature Barrier
The Context: In the pharmaceutical supply chain, speed is critical. At Kreative Organics, the Business Development team needed to rapidly identify gaps in the global market for specific chemical intermediates.
The Gap: The industry suffers from severe data fragmentation. A single molecule might be listed under a Trade Name, an IUPAC name, or a generic identifier across different global databases.
- The Chemical Problem: Mismatched CAS or common chemical name fomats (Chemical Abstracts Service) led to missed market opportunities.
- The Operational Problem: Teams used to spent 70% of their time manually cross-referencing safety data sheets (SDS) and trade logs rather than analyzing market strategy.
2. The Task: Operationalizing Data
I was assigned as the Technical Project Manager to lead the Digital Transformation of this workflow. My objective was not just “software,” but Process Engineering: creating a robust, standardized pipeline that could translate ambiguous market data into actionable chemical intelligence.
Key Objectives:
- Standardization: Define a logic to resolve chemical synonym conflicts and establish a “Single Source of Truth.”
- Reliability: Ensure the tool was robust enough for non-technical staff to run independently.
- Market Speed: Compress the timeline from “Data Gathering” to “Sales Action.”
3. The Action: Engineering the Process
I operated as the Technical Lead, bridging the gap between chemical domain knowledge and technical execution.
A. Defining the “Chemical Logic” (Strategy)
I spearheaded the standardization algorithm. Unlike a generic approach, I understood the nuances of chemical naming conventions. I defined the rulesets for mapping inconsistent trade names to verified CAS or common chemical Names, ensuring accuracy in the output.
B. Containerizing the Workflow (Docker)
To ensure operational continuity, I packaged the solution using Docker. This wasn’t just about code; it was about Process Reliability. By containerizing the environment, I ensured that the standardization engine ran identically on every machine, immune to local configuration errors—critical for a regulated industrial environment.
C. Visualizing Market Gaps (Tableau)
I developed Strategic Dashboards in Tableau that mapped global trade volume. This transformed raw rows of data into stratergic insights, allowing the sales team to navigate and spot the under-served regions for specific chemical classes.
(Ambiguous Naming)"/]:::input subgraph "Standardization Engine" direction TB Logic["Chemical Logic Definition
(CAS Name Mapping)"]:::chemistry Docker["Docker Containerization
(Process Reliability)"]:::ops end subgraph "Business Intelligence" Viz["Tableau Market Insights
(Supply/Demand Gaps)"]:::ops end Result[/"Qualified Sales Targets"/]:::output %% Flow with System State Labels (The "CAD" Look) Raw -- "Unstructured CSV" --> Logic Logic -- "Mapping Rules" --> Docker Docker -- "Cleaned Dataset" --> Viz Viz -- "Actionable Insights" --> Result
4. The Result
- Operational Efficiency: Slashed market research time by 70%, effectively automating the “grunt work” of data collection.
- Data Integrity: Achieved a 90% accuracy uplift in chemical identification, virtually eliminating errors caused by ambiguous naming confusion.
