Collecting and interpreting data is an essential part of supply chain management
In the past two years, I have worked with a number of organisations on their procurement processes and surgical and medical pathways. As much as these two areas of work differ from each other, for both it is essential to have good data to achieve the optimum results.
I am constantly surprised at how materials are managed in healthcare organisations. Staff I have met and discussed this with have commented that it requires an inordinate amount of time to manage stock, that they regularly run out of materials and have to expedite materials from suppliers when they do, and that they have too many of some items.
Staff involved in the surgical pathways I have worked with have made similar comments. They say patients never arrive in theatres on time and that theatres never start on time.
Getting good data
In trying to overcome these problems, collecting and interpreting data is key.
I have been to organisations where no one can tell me what they purchase, the quantities, their usage, how many times they run out each month or how they classify their materials by volume.
Yet in these very same organisations, there are policies in place that state that only individuals in management or finance can sign off purchases over the value of X pounds.
This would be fine, I suppose, if there were a process in place, or data that showed what was needed based on what was used, but this is rarely the case. Instead, decisions are made by the management or finance person based not on reality but on guesses.
I have also been to organisations where people tell me they never start surgeries on time because a key item or person is never there. This could on any given day be the surgeon, anaesthetist, patient, instruments or porter. There is rarely good data about what causes these delays. Therefore, decisions are usually based on subjective criteria.
But there are some examples of good data being used to inform decisions. One supply chain manager I know has an annual spend of nearly €1bn a year across 20 sites. Every month he has to report the number of line items he has run out of and where and when it occurred to the senior management team.
Over the course of this year, through the use of historical data and forecasting, he has reduced the number of line items he runs out of a month from more than 200 when he started the job in February to 70 in September, with a financial year-end target of zero.
Without good data on what they use, how much they use and where they use it, this would have been impossible.