There has never been a more complex time to work in supply chain. The combination of globalization, rising consumer expectations, and the boom of e-commerce is pushing supply chains to their limit. Old processes that included paper trails and excel spread sheets are no longer able to keep up with modern day supply chain demands. One thing we have more access to today than ever before is abundant data. Companies need to see the value in data analytics and how it can drive strategic decision making, risk management, cost savings and make day to day work easier for supply chain professionals.
What are Data Analytics and How Can They Help Supply Chains?
Data analytics helps individuals and organizations make sense of data. This typically involves analyzing raw data for trends and insights. More companies are recognizing the importance of data analytics in everyday practices. 60% of companies around the world use data analytics to drive process and cost-efficiency (MicroStrategy, 2020).
Let’s toss supply chains into the mix. There are 3 primary types of data prevalent in enterprise supply chain data.
Transactional Data: which refers to business events, and supporting daily operations of an organization. Example: Anything that requires a transaction such as sourcing, purchasing, sales orders etc.
Analytical Data: Refers to business performance. This will have the greatest impact on performance, decision making, reporting and analysis. Example: analysing historical trends for forecasting.
Master Data: refers to key business entities that are used by several groups and stored in different data systems across the organization. Example: if a company has an ERP in place to house all their data.
Successful supply chains incorporate the 3 primary data sets mentioned above for a more holistic view of their supply chain. We believe analytics should compile data from multiple applications into one central location to drive improved strategic decision-making. Our customers like to think of their supply chain analytics as a control tower that provides real-time information across multiple areas of their supply chain, allowing them to make fast, reliable decisions when faced with abrupt supply chain disruptions.
Why Are Supply Chain Analytics Important?
The supply chain industry is facing a major challenge with clean data. Organizations have access to more data than ever before, but it’s not centralized or governed, and users do not know how to make sense of it. Individuals struggle to understand what types of data they have access to, where to find it, and if its’ complete or standardized across the business.
Having a supply chain analytics platform in place will help organizations have one central location for all their data. This will eliminate time spent on data scrubbing, vetting and harmonizing and allow for time to do more purposeful work geared towards solving problems and improving performance outcomes.
Manual Processes
Supply chain analytics is also being used to drive more sustainable ESG outcomes. For example, The Owl is using analytics to track and manage plastic waste through packaging. By quantifying how much waste is being produced, companies can regulate it and look for alternative sustainable packaging options.
Lastly, supply chain analytics can help businesses of all sizes keep track of their spend. It adds a layer of visibility to the end-to-end supply chain by identifying inefficiencies that are often overlooked due to lack of awareness.
It is no secret that supply chains are subject to abrupt disruptions and in many cases, leaders must make fast decisions. This is where data analytics can help. It is an essential risk management tool that provides leaders with the information and visibility to make more confident strategic decisions to help achieve their business goals.
To begin making improvements to your data foundation, and overall supply chain visibility, The Owl has put together a 4 Step Plan to Improve Your Data Quality.