Case studies

Irish Blood Transfusion Service

Maximising efficiencies in the Irish Blood Transfusion Service (IBTS)

A great challenge for companies is how to manage costs while at the same time optimising valuable staff resources. In an effort to cut costs or allocate workload appropriately, organisations may be tempted to make staff adjustments and even cuts without sufficient information on their impact – on the organisation, on the employees and on the customers.

The Irish Blood Transfusion Service wanted to optimise their resources, to maximise efficiency and to allocate the right mix of staff to their day shift and night shift. Recognising that the day shift was under considerable pressure to prepare and deliver the majority of orders – how could they manage the distribution of work more successfully?

Optimising work-load distribution

They approached StaffBalance to gain a factual understanding of exactly how workload should be distributed between the night and day teams for optimum efficiency. StaffBalance used business modelling techniques – one of the most powerful and most realistic decision-making tools available to evaluate the distribution of tasks between the teams. By re-distributing workload IBTS were able to progress changes in work practices to reduce overtime requirements in the department.

Business modelling comes into play

The first step is to determine the staffing levels were required to run their operation successfully. Business modelling was used to build a profile of how both the day shift and night shift staff spent their time. The modelling process took an entirely realistic view of the IBTS staff capacity – both day and night shift teams into account. As part of the
analysis, annual leave, sick leave, secondments and so on were stripped out. Once an authentic staff capacity level was identified, the next step was to work out the staffing time and unit cost for every process and product.

“IBTS found the StaffBalance approach to quantifying workload and its distribution excellent. The opportunities identified will lead to improved service for Irish hospitals while simultaneously improving IBTS efficiency.”
Paddy Bowler, Director of Operations,

Irish Blood Transfusion Service

Analysis revealed opportunities for the night shift team Analysis of data collected from the employees of IBTS showed that there were windows of opportunity in the allocation of work for the night shift team. While the day shift was engaged in preparing, processing and administering orders, the night shift was not allocated its fair share of tasks. Of 6,500 orders processed daily, 4,300 were fulfilled by the day shift team. Here was an opportunity for the night shift to engage more in order preparation, ready for the peak ordering times during the day.

StaffBalance established the numbers of staff required to provide 24/7 cover in the Distribution and Issue laboratory areas. It confirmed that the staffing levels were sufficient to provide cover and were sufficient for the workload. The value for the IBTS was in identifying the unequal distribution of workload between day and night shifts. StaffBalance proposed various tasks that could be more evenly distributed between the shifts.
StaffBalance identified where processes were duplicated in a much greater than expected number of distributions by analysing electronic records and date and time stamping the activities. This provided the IBTS with tasks that could be re-engineered to increase efficiency in processing orders. All orders for blood were received from hospitals by telephone. The volume of telephone orders was analysed confirming the need for an electronic ordering system to facilitate intelligent batching of standard orders. This process automation that would eventually save the IBTS a considerable amount in terms of administration and delivery costs leading to significant reduction in delivery costs.

A factual basis for task allocation

StaffBalance provided IBTS with the factual data they needed to help calibrate their resources structure. Once the Staff Capacity model was implemented, the team had greater  certainty because task allocations among the teams were now based on facts, not estimates.

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