Eliminate Data Silos, Reduce Waste and Increase Efficiency with Robust Analytics
Big data is impacting industries across the board. Supply chain leaders understand that in order to gain the visibility and insights needed to improve productivity and efficiency, they must be able to capture data in real time. The need to do so becomes even more critical as supply chains grow larger and more complex, and as leaders look to improve service via improved inventory management, reduced operating costs, reduced order-cycle time, and improved flexibility, all in the name of achieving lean operations. Supply chain leaders also must be in a position to become data-driven in order to meet the needs of consumers and B2B partners. Thus, better visibility and enhanced supply chain analytics are a must for organizations wanting to remain competitive.
The need to enhance supply chain analytics is clear. But organizations must be prepared to implement monitoring and controlling processes to make more informed decisions. Supply chain leaders also must assess their current use of technologies and tools, and be prepared to make changes to harness the power of big data to gain actionable insights. To make this happen, organizations should take the following actions to enhance the capabilities of their existing analytics solutions:
- Move to smarter logistics
- Pursue business intelligence initiatives
- Eliminate data silos
- Emphasize predictive and prescriptive analytics
1. Move to smarter logistics
Supply chains and logistics operations are more complex today than ever before. As companies expand across the globe, the number of nodes in the supply chain seems to grow exponentially. Yet leaders need to monitor those nodes closely, and increase visibility and transparency if they are to improve operations. Going lean means enhancing supply chain analytics by moving to smarter logistics. Organizations need to enhance basic metrics and reporting to access data that will improve performance and gain visibility into all supply chain activities.
Utilizing advanced analytics-driven control metrics gives leaders the ability to monitor critical events in real time; they also make it possible to monitor key performance indicators (KPIs) at multiple touchpoints. Combining these metrics with predictive analytics saves money and delivers ROI quickly as organizations have the knowledge needed to increase supply chain responsiveness and reduce costs while maintaining and even improving customer satisfaction.
2. Pursue business intelligence (BI) initiatives
If the shift to big data and analytics seems to be too much for your organization to handle immediately, start with business intelligence (BI) initiatives. It often is easier to get more buy-in into analytics and data-driven decision-making when supply chain managers can tie it into processes they already implement, such as using mobile devices and asset-tracking solutions paired with a computerized inventory control system.
Using BI tools for transportation and logistics, according to International Asset Systems COO and CTO Steve Dowse, provides:
- Detailed monitoring across the supply chain to identify delivery issues
- Better understanding of accessories' cost at a detail or summary level
- Detailed evaluation of vendor performance for on-time deliveries and work-order performance
- The ability to review work orders by motor carrier and the time between assigned and accepted to identify key partners
- The capacity to consolidate workload to better-performing receivers and improve equipment utilization
- The ability to drill down into shipment history for decision-making and continuous improvement
- Visibility into events impacting on-time delivery and asset utilization for improved decision-making and risk mitigation
- Knowledge of what drives cost and profit so managers can appropriately allocate resources and identify areas for improvement
- The ability to update strategy quickly to remain competitive
3. Eliminate data silos
Of course, the analytics your organization has access to only are as good as the data you put into them. If your data is spread out across different, siloed sources, enterprise applications, the cloud, data warehouses, data lakes, and various user databases and spreadsheets, you are not working with a complete picture, because your data is too segmented to gain complete visibility. Working with integrated software solutions is the key to unifying your data and gaining true visibility. Implementing a software solution that delivers accurate, real-time monitoring of data from all sources will give you the insights and analytics you need to improve operations.
Your integrated software solution should make use of cloud technology to connect data and data structures from all sources into a single view, and eliminate data silos. Every time your BI tools are deployed, you will be analyzing consistent metrics that will be available to users across your organization. In fact, your analytics will be up to date, accessible and consistent for everyone.
4. Emphasize predictive and prescriptive analytics
Organizations are accustomed to using applications for analyzing unstructured data in inventory management, forecasting and transportation logistics. Warehouses use digital cameras to monitor stock levels and unstructured data prompts alerts when it is time to restock. But forecasting with machine learning and BI tools can predict when restocking is needed. These principles are beginning to be applied to the supply chain as a whole with predictive analytics.
Organizations find that getting insights that are predictive and prescriptive gives them the information they need to improve operations and customer satisfaction. It is with predictive and prescriptive analytics that decisions can become more data-driven than ever. Indeed, advanced analytics are the most adept at analyzing real-time data to predict future scenarios and prescribe complex, profitable decisions at any time.
To remain competitive and create lean operations, supply chain leaders must make use of data and analytics to gain the visibility they need into their entire operation. Traditional systems that keep data in silos and that do not monitor operations in real time no longer are sufficient. To enhance supply chain analytics, organizations must move to smarter logistics, pursue business intelligence initiatives, eliminate data silos, and emphasize predictive and prescriptive analytics.