Managing Variation in Demand: Lessons from the UK National Health Service

Walley, Paul; Silvester, Kate; Steyn, Richard

doi:10.1097/00115514-200609000-00007

Cited by 51 publications

(53 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using the data of 2005, the variance/mean ratio ranges from 0.658 to 1.759 for the 24 wards, indicating at least that the number of scheduled arrivals is highly variable. This fact is rather remarkable (2005) and also reported in other studies (Walley et al 2006). Most professionals would expect the elective patient flow to be more steady.…”

Section: Scheduled Admissionssupporting

confidence: 57%

Dimensioning hospital wards using the Erlang loss model

Bekker²,

et al. 2009

View full text Add to dashboard Cite

How many beds must be allocated to a specific clinical ward to meet production targets? When budgets get tight, what are the effects of downsizing a nursing unit? These questions are often discussed by medical professionals, hospital consultants, and managers. In these discussions the occupancy rate is of great importance and often used as an input parameter. Most hospitals use the same target occupancy rate for all wards, often 85%. Sometimes an exception is made for critical care and intensive care units. In this paper we demonstrate that this equity assumption is unrealistic and that it might result in an excessive number of refused admissions, particularly for smaller units. Queuing theory is used to quantify this impact. We developed a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units. We validated this model with hospital data over the years [2004][2005][2006]. Finally, we demonstrate the efficiency of merging departments.

show abstract

Section: Scheduled Admissionssupporting

confidence: 57%

Dimensioning hospital wards using the Erlang loss model

Bekker²,

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Service process time variability can mean delays that lead to (re)prioritisation of the offered services. Capacity constraints can also cause queues since at higher levels of capacity utilisation the likelihood of service failure increases (Walley et al, 2006). Thus queues in services equate to inventory in manufacturing supply chains, and both are used to buffer against demand uncertainty.…”

Section: A Comparison Of Manufacturing and Service Supply Chainsmentioning

confidence: 99%

A comparative study of manufacturing and service sector supply chain integration via the uncertainty circle model

Aitken

Childerhouse

Deakins

et al. 2016

The International Journal of Logistics Management

View full text Add to dashboard Cite

Purpose -The purpose of this research is to contrast manufacturing and service sector supply chains in order to develop an audit methodology capable of providing objective, cross-sector comparisons of supply chain integration performance. Design/Methodology/approach -A robust, site-based multi-method supply chain diagnostic for detecting manufacturing supply chain system uncertainty (QSAM) was modified for the service sector so as to yield the (normalised) supply chain integration performances of 109 separate organisations. Findings -Service supply chains do not always exhibit the unique attributes that effectively bar manufacturing supply chain best practice from being adopted (and vice versa). The research confirms the value of utilising an uncertainty circle model lens, which makes objective cross-sector comparisons of supply chain performance possible. Originality/value -Combining the uncertainty circle model with the QSAM approach yields performance vectors that can be used to align estimates of value stream 'health' irrespective of the organisational setting. This can assist identification and transfer of appropriate best practice even between firms located in dissimilar industry sectors and settings.

show abstract

“…A failure to distinguish between the two can lead to the view that queuing is due simply to a lack of capacity (in terms of beds, facilities, diagnostics, nurses or doctors). 39 This can be illustrated by the use of escalators and elevators in department stores. The elevators are more likely to cause queues in a surge situation, such as when the stores need to be evacuated, whereas escalators are more forgiving in such circumstances.…”

Section: Improving Patient Flowmentioning

confidence: 99%