Best Practices for Using Data to Optimize Your Supply Chain

When a company is data-driven, it makes strategic decisions based on data analysis and interpretation rather than mere intuition. A data-driven approach to supply chain management is the key to building a strong supply chain, one that’s efficient, resilient, and that can easily adapt to changing business conditions.  

How exactly you can best incorporate data and analytics to optimize your supply chain depends on several factors, but these best practices should help you get started:     

1. Build a Data-Driven Culture

Transitioning to a data-driven approach requires a cultural change where leadership views data as valuable, creates greater awareness of what it means to be data-driven, and develops and communicates a well-defined strategy with buy-in from all levels of the organization.  

2. Identify Priority Business Use Cases

The good news is that there are a lot of opportunities to use supply chain analytics to optimize your supply chain across sourcing, processing, and distribution of goods. But you’ll have to start somewhere and should prioritize opportunities that will generate the greatest benefits for your business and that are solvable with the types of data and skills available in your organization.  

3. Define Success Criteria

After you’ve decided which use cases will add the most value, you’ll need to define what your business hopes to achieve and the key performance indicators (KPIs) you’ll use to continuously measure your progress. Your KPIs might track things such as manufacturing downtime, labor costs, and on-time delivery.  

4. Invest in a Data Platform

You’ll need a solution that includes integration, management, and analytics and that supports real-time insights into what’s happening across your supply chain. The platform will also need to be highly scalable to accommodate what can be massive amounts of supply chain data.  

5. Use Advanced Analytics

Artificial intelligence techniques such as machine learning power predictive analytics to identify patterns and trends in data. Insights help manufacturers optimize various aspects of the supply chain, including inventory levels, procurement, transportation routes, and many other activities. Artificial intelligence uncovers insights that can allow manufacturers to improve their bottom line and provide better customer service.  

6. Collaborate With Suppliers and Partners

Sharing data and insights can help develop strategies aimed at improving supply chain efficiency and developing innovative products and services.  

7. Train and Educate Employees

The more your teams know about advanced analytics techniques, especially artificial intelligence, and how to use and interpret data, the more value you can derive from your supply chain data. Plus, with demand for analytics skills far exceeding supply, manufacturers will need to make full use of the talent pool they already have.  

Learn More

Hopefully, you’ve found these best practices for using data to optimize your supply chain useful and actionable. Here’s my recommended reading list if you’d like to learn more about data-driven business and technologies:   

• The Power of Real-Time Supply Chain Analytics 
• How Can the Actian Data Platform Help Me Deliver Real-Time Data Analytics?
• How to Maximize Business Value with Real-Time Data Analytics 

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Actian Data Platform delivers, including proven data integration, data management and data analytics. She brings a 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware.