Last week, we hosted an insightful session on „𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐄2𝐄 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐁𝐞𝐭𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲“, featuring our guest speaker, Oscar Diaz Marti. The discussion was packed with actionable insights, interactive brainstorming, and a compelling real-world example of AI’s impact on data quality.
🔍 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐁𝐫𝐞𝐚𝐤𝐨𝐮𝐭 𝐒𝐞𝐬𝐬𝐢𝐨𝐧
We began with a collaborative breakout session, where participants tackled 𝐝𝐚𝐭𝐚 𝐢𝐬𝐬𝐮𝐞𝐬 𝐢𝐧 𝐄2𝐄 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 & 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧.
1️⃣ 𝐂𝐨𝐦𝐦𝐨𝐧 𝐝𝐚𝐭𝐚 𝐢𝐬𝐬𝐮𝐞𝐬: Challenges included inconsistent naming standards, incorrect planning parameters, and lack of clarity and accessibility in datasets.
2️⃣ 𝐈𝐦𝐩𝐚𝐜𝐭𝐬 𝐨𝐟 𝐩𝐨𝐨𝐫 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲: From inaccurate reporting and operational inefficiencies to reduced customer service and financial strain, the consequences of data issues were far-reaching.
3️⃣ 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐧𝐞𝐞𝐝𝐬: Priorities identified were improving data transparency, aligning processes, and fostering better collaboration across functions.
4️⃣ 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐩𝐫𝐨𝐩𝐨𝐬𝐞𝐝: Clear data governance, implementing RACI frameworks, and adopting advanced tools were highlighted as key enablers for sustainable improvements in data quality.
🔍𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐎𝐬𝐜𝐚𝐫 𝐃𝐢𝐚𝐳 𝐌𝐚𝐫𝐭𝐢’𝐬 𝐏𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
Oscar shared a powerful real-world example of how AI-driven solutions are reshaping E2E planning:
💡 𝐖𝐡𝐲 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐞𝐫𝐫𝐨𝐫 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧?
Detects errors early to prevent costly decisions and operational setbacks.
Enhances productivity by integrating seamlessly with existing systems.
Enables frequent updates to inventory policies, master data, and sourcing strategies without compromising efficiency.
Provides a foundation for data-driven corrective actions backed by recurring analysis.
💡 𝐇𝐨𝐰 𝐀𝐈 𝐰𝐨𝐫𝐤𝐬:
Unbiased and non-rule-based: Delivers faster and more reliable results.
Involves 2–4 weeks of AI analysis followed by 1 week of user validation for impactful implementation.
📊 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝐝:
Identified errors facilitated root cause analysis and corrective actions, ultimately reducing inventory levels by 36% and enhanced by significant improvement in inventory health.
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