A supply chain data silo is a non-integrated cache of stored data somewhere within the supply chain that negatively affects an organization’s ability to create net value, build competitive infrastructure, leverage logistics, synchronize supply and demand, and adequately measure performance.
Simply put, a supply chain data silo is a pocket of your supply chain in which data becomes obscured or difficult to access.
Causes & Overall Effects
Next, let's address how data silos develop.
In the current digital age of business, adaptation is the major key to survival and success. An organization will adopt technology in different departments including but not limited to:
digital calendars for scheduling
shipment tracking software
cloud-based information systems
and databases of vendors, suppliers, shippers, and customers.
When these many different solutions are purchased and developed without forethought or an overarching plan, the technologies do not integrate well with one another. Thus data becomes isolated and hidden away from essential uses. This is a data silo.
Logistics data originates from within the supply chain. When it is concealed within a data silo it creates bottlenecks in the supply chain and impedes data analytics from creating usable information for future business operations. This, in turn, makes it difficult for executives to manage processes and make better-informed business decisions.
Specific Effects on Visibility & Accuracy
In logistics, seeing where a shipment is and knowing what stage it is at in the shipping process is vital. However, when disparate systems are used from supplier to warehouse to shipper to customer, the data silo effect reduces its visibility. And as such, it will prevent awareness of where the product is within the system.
Lack of visibility among technological links in the supply chain creates insufficient and unreliable data. This alone makes it difficult to predict the number of goods or materials needed.
By their very nature, data silos are difficult to pinpoint.
Some signs of supply chain data silos include:
different departments reporting differing data for the same information
executives unable to find data about certain business operations
end-users of data sets identifying incomplete or out-of-date information
and unexpected IT costs in departments as they attempt to address and resolve a lack of data coming from other departments.
Benefits of Breaking Down Data Silos
At the primary planning stage of the process, it’s worthwhile to communicate the data silo problem to the entire organization and allocate resources to achieve the data integration goal. This would include funds to purchase technological solutions that will integrate and share data company-wide. It would also include finances to employ an IT and data management team to collaborate with managers and executives to determine where data silos are occurring.
The next stage would be to identify and purchase the automation or technology tools needed to create a unified data sharing and storage system. Data warehouses hold big data in a functional format for businesses. At this step, it is important to create assessments to measure the effectiveness of the data consolidation process which would include integrating data analytics and reporting applications to develop useful information from the now shareable data.
Lastly, a strategic maintenance plan is created. This establishes, develops, and maintains trust among departments within the company. This concept can flow outward, and ultimately, include sharing data with trusted supply chain partners.
There is an excellent return on investment for breaking down supply chain data silos. As more data is available at each link in the chain, visibility in the whole system increases. This allows for more productivity, more effective product flow management, and higher-quality customer service and satisfaction. The overall trust in the organization’s data will build and expand the horizons for improved business growth opportunities.