A recent Gartner article states, “Significant disruptions over the last two years have reinforced the need for supply chain resilience and agility.” To resolve pain points and manage costs in uncertain times, supply chain professionals are turning to sustainable data quality initiatives. By applying data management best practices across product inventory, distribution, retailers, and more, leaders are actually achieving a more authoritative view of their supply chain despite siloes and business disruptions. Whether looking to stay ahead of a fluctuating economy, manage multiple vendor access, or maximize production, data quality management feeds accurate data into the business and redefines how organizations view their supply chain.
“The intersection of data management with supply chain activities becomes critical to the extended enterprise’s expanding role in future ecosystem development as supply chain management evolves to a networked function from a legacy linear process.”
- HFS Report
Why the Supply Chain Needs Better Data Quality Management
Supply chain visibility issues caused by siloed and dirty data negatively impact inventory and distribution planning. These disparate data sources quickly balloon and cause bottlenecks in the journey of your product from inception to delivery. The lack of visibility into logistical and supply trends bars organizations from staying ahead of disruptions, preventing third parties and distributors from actually seeing inventory and moving it in a timely fashion. Not to mention, duplicate suppliers or vendors in the same database create invoicing and payment issues.
Timely access to clean, consistent, and accurate data is essential for better supply chain performance and product commercialization and innovation. Organizations are realizing a more agile, flexible supply chain, improved inventory management, operational efficiency (especially concerning automating manual and repetitive tasks), and sustainability efforts.
The Impact of High-Quality Data on the Supply Chain
There are many sources of data in the Supply Chain—more so than in many other business processes: ERP and SCM Systems, suppliers’ systems, vendors, customers, etc. Prioritizing the quality of data in the supply chain can improve visibility and transparency into data issues and why they persist.
If data quality isn’t monitored and addressed, it can cause a number of business ramifications:
- Delays in product time-to-market
- Increased forecasting errors
- Production or procurement of incorrect orders or stock
- Cross-sell/upsell opportunity disruption
- High data management and admin costs
Customers have become accustomed to the quick and seamless delivery of products and services. Siloed and dirty data slows and reduces that visibility and causes additional disruptions, preventing third parties and distributors from actually seeing inventory and moving it in a timely fashion. With data ops and cleansing, companies can regain logistical controls and deliver superior customer experiences.
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Driving Business Goals with Data Quality
Syniti delivers data management solutions as a cloud-based, unified platform powered by the Syniti Knowledge Platform.
- Procure-to-Pay process (P2P) (procurement): slow invoice processing time, reduced accuracy, high costs per invoice, lack of visibility, and data retrieval. Syniti helps you leverage your data to recover working capital, reduce P&L costs, and manage procurement supply chain risks. Vendor and material data are key company assets. Improve your Procure-to-Pay performance with actionable insights from your data.
- Order-to-cash: Recover working capital and reduce revenue erosion with Data Jumpstart. Customer and material data are key company assets. Improve your O2C performance and your customer experience by prioritizing data improvements.
- Material Management: Understand how data can help ensure OEE and plant uptime while recovering working capital and expense costs. Your Spare Parts and related data are valuable company assets. Keep your plant and equipment running on time and on budget.
How Three Companies are Transforming the Supply Chain with Data
1. Company: Eli Lilly
The 10th largest pharmaceutical company in the world, Eli Lilly, has experienced massive growth in both inventory and revenue. But whereas the company had grown up, the data management process still lagged behind. Material master data was the key to Eli Lilly’s supply chain integrity, and their governance process needed to become more sustainable and reliable. Syniti offered a robust solution that allowed Eli Lilly to not only improve the entire data stewardship process but automate much of the manual processes currently in the material master. Change requests and orders could be validated and submitted within the Syniti solution, automating collaboration and removing any duplicate data entry or tasks.
“Supply chain data governance at Eli Lilly is different because of Syniti. Throughout our SAP implementation, we understand the importance of maintaining reliable master data. Syniti has enabled us to do it with optimal quality and efficiency. Syniti is a key part of our supply chain data governance roadmap.”
- Processed over 4,200 global requests in five months
- Improved workflow-based process resulted in an efficiency gain of more than 25% at the local data steward level
- An 85% reduction in staffing and a savings of nearly $750,000 annually
- Improved cycle time for master data completion by more than 67% compared to the previous process
- Annual procedure deviations post-implementation were reduced from 4-6 to zero
Read the full case study
2. Company: Graybar
Industry: Electrical Supply & Wholesale Distribution
Graybar, a specialist in supply chain management services and leading North American distributor of high-quality components, is moving their legacy ERP systems to mySAP ERP, turning to Syniti’s rules-based solution to orchestrate the data migration and put in place new applications to maintain clean data.
“The ability to run ‘what if’ scenarios is significant in our business. For example, if a supplier increases their prices by 3%, we need to determine how this will affect our inventory value. And with such large inventory numbers, you can understand the need to uncover any anomalies before we apply calculations of that sort across all our stock.”
Results / Quick Wins:
- Moved legacy systems to mySAP ERP on time and on budget using Syniti data migration strategy
- Intelligent approval routing decreases processing time for change requests from 12,000 suppliers currently using the system, in addition to automating much of the business process
- Ongoing automated data quality checks and enforced data governance rules ensure only clean data enters the ERP system as employees populate it
Read the full case study
3. Company: Fortune 100 Global Aerospace Manufacturing Enterprise
Industry: Transportation and Air
Easier for an engineer to design a brand-new part for an aircraft than it was to determine if the part from another aircraft would suffice. Data quality best practices like cleansing, matching, and linking were also performed across spare parts systems to provide engineers with the time-sensitive data needed to make design decisions.
“Those costs accelerate throughout the lifetime of the aircraft’s maintenance and cause extra havoc when trying to reconcile throughout systems. Reconciliation required consolidation and integration of all the engineering and parts data across engineering, MRO, and spare parts systems into one place.” - Steward Bond, Research Director at IDC,
Watch the full interview with Stewart Bond, Research Director at IDC, in our webinar ‘Connecting Data Quality to the Business Bottom Line’ here.
With data quality issues hampering business-critical supply chain management processes, visibility into where the issues lie is the first step to correcting the underlying flaws. These days, organizations need to pivot and adapt quickly and need an approach designed to identify data quality leakages in business processes quickly.
Learn how to stay ahead of your organization’s Material Management and Supply Chain data challenges. Contact us to learn more.