By Shashi Jina Supply Chain Management Research Director SAPInsider
In our continuing series on supply chain visibility and the emergence of enterprise control towers, we exam the importance of data.
The emergence of big data, artificial intelligence (AI), and machine learning (ML) have opened new possibilities for using real-time data to improve and drive greater visibility in the digital age. As a result, there is a need for solid solutions that leverage these technologies for data aggregation and visualization, and alerts and escalations that form the new digital enterprise supply chain control tower.
What Is a Supply Chain Control Tower?
A Supply Chain Control Tower is a visual dashboard of data, KPI metrics, and events across the enterprise supply chain, identifying problems and impacts of events to better predict issues and provide recommendations to address the problems.
In most supply chain organizations, there are several opportunities to improve existing processes. These include:
- The process to collate and report at an enterprise level is considered cumbersome, time-consuming, and requires considerable hours of manual work.
- Processes are often highly “siloed” and are not scalable across different functional groups, divisions, or other organizations.
- Many of the existing dashboards and reports are very limited; providing limited visibility and analysis capabilities to drive immediate actions.
- Timely reporting is a critical requirement for many organizations. Unfortunately, existing processes often take too long for the dashboard to drive timely decision-making.
- Dashboards are limited in providing drill-down, parameter-driven diagnostics, and analytics to drive process improvements.
The Improvement Opportunity
Critical questions to ask:
- Are the dollars being spent on the current process, as outlined above, bringing value to the organization? • Or, is there an opportunity to leverage technology to deliver a more practical approach to generating the necessary information and reporting needed to increase productivity while reducing the cost of manual processes?
- Can you report on data in real-time, producing leading indicators versus reports that were as lagging and siloed across the enterprise?
An enterprise supply chain control tower provides a solid analytics framework, integrating and automating data from the multiple ERPs and siloed data sources on a user-friendly ML- and AI-based platform. It enables organizations to understand and measure supply chain performance, process variations, and drive sustainable and step-change improvement.
Why Is Data Important?
One of the fundamental principles of an effective enterprise supply chain control tower is the quality and timely availability of data used to drive decision-making. The challenge faced by most organizations with multiple systems is providing data that can be merged and aggregated to make effective decisions. By connecting numerous data elements and converting them into usable information, organizations can perform meaningful analysis. However, the gaps and duplicates in the data, combined with the volume, make manually “connecting the data” very difficult — leading to limited leverage of available data. Additionally, in many cases, data from multiple ERP systems and locally developed legacy systems increased the volume and the variety of data and sources. Also, data is often created under different contexts, purposes, and requirements. This unprecedented data volume and variety presents challenges, including overlaps and gaps.
Automating this process, aligned with some AI/ML-driven processes, would significantly impact the decision cycles. This is often referred to as data curation and will be covered in a separate article.
All organizations in the digital age recognize the importance of data. Robust processes and frameworks need to be in place to address data quality issues to drive practical data usability, enabling data discovery and retrieval, maintain quality, add value, and provide for re-use over time. The core impact of this is to allow more complete and high-quality data-driven models for knowledge organizations.
Some of the critical elements of this framework include:
- Organizing data, identifying common data across systems, including missing or dirty data
- Developing relationships and correlations between data streams, and converting multiple data sources into useable information.
- Machine Learning, Learning trends, correlating and developing ”new” relationships.
- Data quality focusing on accessibility, availability, completeness, consistency, accuracy, and trustworthiness
- Driving user behaviors with alerts.
How Is the Data Used?
Good, quality data fuels effective supply chain control towers. They present data visually on dashboards, utilize business rules for KPI metrics reporting and events across the enterprise supply chain identifying problems and impacts of events, predict issues, and provide recommendations to address the issues. By analyzing trending data and linking these data elements to performance metrics, we can perform effective root-cause analysis to enable the following:
- End-to-end visibility of the supply chain process and all of its constituent parts (events)
- Establish performance thresholds (i.e., timeliness, quality, etc.) for process events
- Process owners have visibility of exceptions, with automated alerts when something is out of tolerance.
- Downstream activities can be expedited when necessary before any process delays affect the customer.
- Supervisors/managers have visibility of their processes and receive escalation alerts at a predetermined threshold.
- Capability to measure processes/sub-processes drives continuous process improvement and analysis activities.
What Should SAPinsiders Consider for the Future
Based on SAPinsider community insights and trends on the current landscape, organizations should adopt the supply chain control towers framework that supports their overall strong supply chain strategy. They should consider this a typical supply chain solution and a competitive weapon to drive agile, responsive supply chains. They should consider technologies that support these strategies in the form of big data, AI/ML, predictive analytics, and data visualization. Build your supply chain control towers with the following considerations:
Understand the current state of the data. A critical first step is to understand where your data resides. The data may be fragmented across many systems, and you must develop a comprehensive view of this fragmented landscape.
Determine key data elements required. Understand what data elements are fundamental to the enterprise supply chain control tower (e.g., order transaction history, customer and vendor delivery performance, supply chain network data, and additional data elements that drive the decision process within the control towers).
Leverage the business rules library. While the data forms the foundation of your enterprise supply chain control tower solution, the business rules library is the brain behind a differentiated analytics solution. It is imperative that you capture all unique nuances of your business, whenever you can, in the form of business rules in this component. Leveraging expert knowledge of your people is essential in this initiative.