Data Quality Management in Supply Chains

Gartner has a new case study on unlocking value from data 🚀 🌐 A leading oil and lubricants company faced challenges with a complex data landscape. 📊 This included multiple data sources, inconsistent reporting, and poor data quality. 📈 To address these issues, they implemented a modern data and analytics strategy. 🎯 The Solution 🌊 Harmonized Data Architecture: Established a unified logical data model aligned with a global data lake 🌎 Data Quality and Governance: Introduced a global data quality management strategy across operations 🔄 Data Value Chain: Enabled seamless data lineage for automated normalization 📊 The Results 🕒 Efficiency: Reduced human effort by over 90% 📈 Data Quality: Improved through local governance based on global KPIs. 🚀 Scalability: Enhanced ability to introduce new technologies quickly. 🌟 By harmonizing data architecture and focusing on quality, organizations can unlock value delivery and achieve a data-driven culture. 💪 This approach improves decision-making, operational efficiency, and competitiveness. Their work continues as the core team paves the way for further maturing its data lake, global data model, and data management and #analytics capabilities. Gartner clients who subscribe to our AI and Emerging Technologies topic in Digital Supply Chain Value Realization should check out the 🔗 link in the comments to read the full: ℹ️ Case Study: Data & Analytics Intelligence to Unlock Value Delivery From my colleagues Christian Titze and Leonard Ammerer. Great work on this case study, gentlemen.

🚛💡 How do you spot revenue leaks, fix logistics delays, and keep customers happy—all through data? That’s exactly what I explored in my latest 4-page Supply Chain & Logistics Report. I wanted to go beyond dashboards and uncover insights companies can act on. Hey 👋 #datafam I'm thrilled to share this 4-page report on supply chain and logistics I built, I started this project by first understanding the dataset I was working on then proceed to drafting project objectives which you could check it out here 🔗: https://lnkd.in/dQ_2sSUd This report is structured into 4 pages which are: Sales and Demand overview, Inventory ad and Production, Logistics and delivery then Quality Control and Efficiency. Here’s the breakdown: 📍 Page 1 – Sales & Demand → Identified top revenue drivers and seasonal demand shifts. ✅ Recommendation: Focus resources on high-demand products, reposition low-performers. 📍 Page 2 – Inventory & Production → Found stockouts in fast-movers and excess in low-demand items. ✅ Recommendation: Use forecasting + JIT practices to balance supply and demand. 📍 Page 3 – Logistics & Delivery → Tracked delivery delays and cost inefficiencies in certain routes. ✅ Recommendation: Optimize routes, renegotiate carrier costs, and use hybrid shipping. 📍 Page 4 – Quality & Efficiency → Calculated hidden revenue loss from defective products. ✅ Recommendation: Improve early-stage quality checks and automate inspections. 💡 Why this matters: These aren’t just numbers. They’re business decisions waiting to be made—cutting costs, saving time, and boosting customer trust. 👉 If you’re in supply chain, logistics, or retail, you’ll recognize these challenges. This is how data analytics transforms them into growth opportunities. Tool: Excel,Power Query,DAX, Power Pivot #DataAnalytics #BusinessIntelligence #Supplychain #Logistics #Dataviz

Supply Chain Control Tower (SCCT) What is SCCT? It is a centralized platform or system that provides end-to-end visibility, control, and management of a company’s supply chain operations. It acts as a command center, aggregating and analyzing data from various sources across the supply chain, such as orders, inventory, shipments, suppliers, and external events. Features Some of the specific features of SCCT are:   Data management A control tower uses cloud, IoT, and AI technologies to process and integrate the data and ensure its quality and accuracy. Business intelligence and analytics A control tower analyzes and visualizes the data to provide insights into the current and future state of the supply chain, such as demand and supply patterns, risks and disruptions, performance, and improvement opportunities. Performance management A control tower measures and monitors the key performance indicators (KPIs) and metrics that evaluate the supply chain efficiency, effectiveness, and customer satisfaction, such as service levels, fill rates, lead times, costs, and emissions. Actionable insights A control tower offers recommendations and guidance for decision-making and action-taking based on wisdom and performance data. Process The orchestration process involves a few steps: 1. Build a supply chain network that connects suppliers, producers, distributors, wholesalers, and customers. 2. Establish a communication infrastructure that connects the SC partners. This includes software and systems for communication, collaboration, and data sharing. 3. Integrate data from suppliers, producers, distributors, wholesalers, and customers, into the supply chain management system. 4. Use analytics and machine learning to identify trends and patterns in the supply chain. 5. Set up rules and policies for supplier, product, and customer data management. 6. Establish supply chain controls to monitor the inventory and performance and trigger the supply chain orchestration processes. 7. Automate the supply chain orchestration process to synchronize the communication, data, and controls across the supply chain. 8. Monitor, measure, and adjust the supply chain orchestration process when needed. 9. Data quality and accuracy are crucial for SCCT. Only complete, updated, consistent, and correct data can lead to good decisions and actions, reducing the system’s value and user trust. Benefits - Improved decision-making - Reduced inefficiencies - Increased agility - Enhanced collaboration - Improved customer service - Reduced cost - Providing predictive and prescriptive analytics - Demand and supply insights - Risk and disruption insights - Performance and improvement insights Challenges - Lack of clarity on the span of control - Resistance to change - Questions on actual data ownership - Required talent - Ambivalence on a build vs buy decision - Inability to identify the right technology requirements Source: https://lnkd.in/ddtzB_Uz

One of the biggest challenges leaders face today is not necessarily the complexities of their supply chain or unexpected disruptions.  But it is often leveraging data.  You can have the best talent assembled to reveal opportunities and waste with your supply chain partners, but it goes much further than that. It’s critical that you have a team to qualify and administer the data.  Before making strategic decisions based on data, supply chain leaders need to understand the qualification process and standards that are in place to validate the data coming from all partners. Making assumptions and not conducting enough diligence can be costly. An example where not qualifying the data can get you in trouble is determining which ports have the capacity to handle inbound volume. If you don’t have all the correct data available, a container could be shipped to a port that’s too congested to receive it. The container would need to be rerouted, causing delays and additional costs. Having your data scrubbed, managed, and qualified with your supply chain partners is critical.