The Stanford Supply Chain Department is responsible for non-labor spend, contract management, and item and implant procurement for the Stanford hospital system including Stanford Health Care (Adult), ValleyCare, and Lucile Packard Children’s Hospital. The organization participates in a number of cost savings initiatives with physicians in order to reduce spend, ranging from vendor consolidation, contract negotiation, and value-based selection of new cost-effective medical technologies, such as for Spine surgeries. To successfully drive these initiatives, Supply Chain depends on reliable and accessible data to perform the relevant analytics with its available tools. However, these systems have not been thoroughly investigated for the construction of a standardized playbook for cost savings: one that generalizes to all of the enterprise’s departments and units. Our team was tasked with generating a comprehensive data mapping of all the databases, analytical tools, and external benchmarking data services that Stanford uses. This data mapping will help with different goals such as establishing a source of truth for item price, increasing PO accuracy, easily finding and tracking spend per item/department, and being able to present surgeons with an accurate cost-per-case after every surgery. By interviewing over 50 internal and external employees and stakeholders we were able to generate a detailed data mapping and a comprehensive documentation of the many analytical tools and databases used regularly by Supply Chain.

Specific Aims:

1. Interview all relevant stakeholders (Supply Chain, IT, Analytics, Physicians, External Vendors, Executives, etc.) to understand exactly what tools and data sources are being used and what each department needs as far as data analytics and reports go to inform their key decisions
2. Create an extensive a detailed spreadsheet of all analytical tools and databases currently in use, their advantages and disadvantages, and exactly from where in the system they receive data and how that data is currently being used
3. Generate a detailed data mapping of the current system that clearly demonstrates how data moves throughout the system and from where the different analytical tools and reports pull data
4. Present our findings and recommendations for process and system improvements, workflow standardization, and tools documentation

Key Conclusions:

1. There is not an established source of truth for some data points (price, usage, etc.) that all employees can use when analyzing data
2. Having two different ERPs (Enterprise Resource Planning) systems for Stanford Adult and LPCH causes increased process steps for employees when trying to perform analysis on the entire system as a whole
3. Mistrust of data integrity and accuracy system wide
4. Inefficient process flows for data uploads and updates causes time delays and more room for error
5. Confusion among Stanford employees and external vendors as to the exact type of data being sent to benchmarking vendors which can lead to inaccurate spend analysis
6. Lack of standardized work processes and analytics tools’ training leads to different analytical methods and different results

Next Steps:

1. Gather data to quantify issues (exactly how many POs have been incorrect, how often does this happen, how much money is this costing?)
2. Use the data mapping and our recommendations to start targeting inefficient processes and streamline them
3. Increase standardization and onboarding training for new employees
4. Establish sources of truth for different data points to which all relevant employees have access

We would love to acknowledge our project sponsors, Amanda Chawla and Dr. Kevin Shea, for their excellent guidance and leadership that made the success of our project possible.