In a fast-paced distribution hub, efficiency, timeliness and accuracy are everything. Every delayed order, misplaced item, or inventory error can impact customer satisfaction or SLA(Service Level Agreement). This is where data analytics transforms operations into a competitive advantage using a tool like Power BI.
Attached below is the sample of the project in a Fast-Moving Consumer Goods (FCMG) distribution centre that I and my team worked upon and the results.
Key findings
‣ North region has the best SLA performance (28.9% for Retail, 24.4% for Wholesale), East region has the worst SLA performance (16.7% for Retail, 20.3% for Wholesale).
‣ Retail and Wholesale customers have similar SLA compliance rates, but Wholesale is slightly better (23.1% vs. 21.7%). Processing times are also a bit lower for Wholesale. This suggests that operational processes for Wholesale may be more streamlined, or that order profiles differ in a way that makes SLA easier to achieve.
‣ Across all regions that which includes dates of each month, shows that orders that miss the SLA have much higher average total order processing times (about 140–145 minutes) compared to those that meet the SLA (about 84–87 minutes).This pattern is consistent, indicating that long processing times are the primary driver of SLA failures, not regional differences.
‣ Orders that did not meet SLA have a noticeably higher average cost to serve (45.35) compared to those that did (40.19). This shows that operational delays or inefficiencies that cause SLA misses also drive-up costs.
while the cost to serve varies slightly varies across different regions and dimensions, when I looked in further into all regions through the months of the year, I discovered that there is an interception in the month of August between SLA met and not met, as well as a major deep in the month of April, when it comes to SLA compliance, this shows stability, but then again, it needs to be investigated, to know how sufficient and sustainable it could be, I guess it was caused by external influence factor.
‣ The most common return reason is “Quality Issue,” followed by “Incorrect Item.” And when linked to SLA compliance that is met fewer returns are made(has a performing relationship with SLA), for example fewer returns occurred when orders met SLA targets—only 16 of 78 quality-related returns complied with SLAs, while damaged goods had the highest SLA compliance (23 out of 73). This points to potential issues in quality control and order accuracy.
‣ I discovered a pattern suggesting that SLA performance and customer return rates are not directly linked, meaning that meeting SLAs does not necessarily result in fewer returns, Orders that meet SLA have a higher return rate (20.58%) than those that don’t meet SLA (18.38%) in this dataset.
‣ Return rates is inversely proportional to total order distribution—regions with fewer orders tend to have higher return rates. And for Inventory SKU: for example, SKU026 has a return rate of 27.6%, which is significantly higher than others. This warrants further investigation and improvement efforts with the quality assurance team.
Recommendations:
To improve efficiency, we must eliminate bottlenecks in our transaction processes. Since SLA misses nearly double total order time, and it influences other factors as well, even small improvements can have a big impact. We need to streamline picking, packing, and shipping while strengthening communication between quality assurance and inventory teams for better sustainability.
Implementing real-time tracking through Power BI will help us identify potential delays early and improve SLA compliance.
Additionally, standardising best practices by learning from top-performing teams, upskilling through training, and partnering with innovative logistics firms will drive long-term efficiency.
Outcomes to gain from recommendation
1️⃣ #SmartInventory 📊
Real-time tracking keeps bestsellers in stock ⏳, slashing backorders & boosting customer satisfaction. #SupplyChain #DataDriven
2️⃣ #FasterDeliveries ⚡
AI-powered route optimization finds the quickest paths 🗺️, cutting delays & ensuring lightning-fast shipping. #LogisticsTech #LastMile
3️⃣ #PersonalizedCX ❤️
Data reveals buying habits—like favourite delivery times 🕒—so hubs tailor promotions & subscriptions. #CustomerFirst #eCommerce
4️⃣ #Zero Waste ♻️
Spot overstocked items 📉, cut storage costs, and boost sustainability—winning eco-conscious shoppers. #LeanSupplyChain #CostSavings
5️⃣ #PredictiveSolutions 🔍
ML detects issues (like return spikes 📈) before they blow up, turning risks into trust-building wins. #AIinLogistics #ProactiveOps
The Bottom Line: Data brings about happier customers. Ready to transform your hub? #DataAnalytics #FulfillmentTech
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