Supply chain data is one of the most critical assets for manufacturers today. With the promise of advanced enterprise systems, many companies expect smooth data handling and analytics.
However, the reality is starkly different: despite heavy investments in these systems, Excel remains the primary tool for making sense of this data. This reliance on Excel highlights the deep-rooted challenges in data cleansing, integration, and consolidation that modern manufacturers face.
The Reality of Manufacturing Analytics: Excel Still Dominates
In many organizations, this data is often analyzed using Excel, even when companies have paid for ERPs and Supply Chain Planning Tools.
But why is this the case?
Data is Siloed in Multiple Systems that Don’t Talk to Each Other.
In manufacturing, data is often siloed across different systems—ERP, APS (Advanced Planning Systems), MES (Manufacturing Execution Systems)—making integration a significant challenge.
– Data Silos: Each system houses different sets of data that don’t easily integrate with one another. For instance, companies using both an ERP and Planning Tool often still rely on Excel because their data remains fragmented and difficult to combine.
– ERP Vendors and Analytics: Most ERP vendors, focus primarily on their core offerings (financial reporting, supply chain planning) rather than advanced analytics. This results in a lack of tools designed to easily unify supply chain data from multiple systems.
The Myth of ERP as the Ultimate Supply Chain Solution
Many manufacturers assume their ERP system contains all the data needed for analytics. This misconception causes serious gaps in how they approach data integration.
– ERP Limitations: ERP systems are excellent for handling specific operational data, but they struggle to manage the broader scope of data needed for advanced analytics. This often forces companies to supplement ERP data with external tools like Excel.
– Data Lifecycles: ERPs aren’t built to manage the full lifecycle of supply chain data across planning, execution, and reporting. As a result, data required for supply chain analytics often ends up fragmented, with companies scrambling to unify and cleanse it before analysis.
Challenges in Supply Chain Data Standardization
Dealing with relevant data is no easy task, especially when trying to integrate it across various systems.
Here are some of the key challenges:
– Data Cleansing: One of the biggest hurdles is cleaning relevant data, which often has discrepancies in format and structure. For example, companies spend an enormous amount of time reconciling data from different systems before they can even begin analyzing it.
– Data Standardization and Consolidation: Data from multiple sources (ERP, MES, CRM) must be standardized into a common format. However, these systems don’t naturally align, causing issues with data integrity.
– Data Mapping and Integration: Bridging the gap between different systems requires complex data mapping and custom integrations. Often, companies must build bespoke adaptors to ensure the seamless flow of supply chain data, which is a resource-intensive process.
Supply Chain Data Integration Solutions
Even with advanced ERP systems, data remains difficult to manage and analyze effectively.
Companies need to:
1) Develop a Strong Data Strategy: Focus on implementing clear data governance processes to ensure that data is cleansed, integrated, and standardized properly.
2) Invest in Specialized Analytics Tools: Using third-party tools like The Owl Solutions can help streamline supply chain data analysis beyond the capabilities of standard ERP systems.
3) Close the Planning-Execution Gap: Integrating real-time execution data with planning tools will provide a more comprehensive and faster understanding of the entire supply chain.
The challenges around supply chain data will persist until companies take proactive steps to address these integration and cleansing issues. By adopting the right tools and focusing on robust data management strategies, manufacturers can finally unlock the full potential of their data and move beyond the limitations of Excel.
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