Space Utilization

Storage space for hospital supplies is nearly always tightly constrained, especially at and near point of use locations such as nursing, ICU and operating room areas. And yet, these spaces are typically clogged with rarely and never used (just-in-case) items, leaving inadequate space for fast-moving items.

Space constraints for fast-moving items can generate very high handling costs as a result of frequent orders and low unit of measure order quantities.

Optimizing supplies storage, however, is a complex task. Most items have multiple package sizes and form factors, several stacking and shelving alternatives, and often two or more bin-type options. These must be quantified and accommodated in any space optimizing model.

User needs are also critical. Frequently-used items should be placed in mid-level shelves to minimize bending and reaching. Items typically picked together (admission kits, for example) should be located together.

Rarely-used but critical, just-in-case items can often be located in shared space on a floor instead of being duplicated in several patient care storage locations on the same floor. This simple change can free up significant and often costly inventory capital and valuable storage space.

How much of your supplies storage space is utilized efficiently? How much supplies storage space do you actually have? How much costly clinician labor is consumed by inefficient storage? Not many hospitals have any idea of their supplies storage utilization.

Space Use Optimization

A likely extension to our simulation models is a space use optimization capability. The mechanics for this are not especially difficult but getting data to support them is a major task. A really major task: You have to measure each item in its various units of measure and make notes on stacking and orientation options and local preferences.

You also have to measure cabinets and shelving so the model can test various filling alternatives. You have to have data on bin configuration alternatives such as partitioned bins and rotating bins. Big job.

You will probably also need to have floor space data so the model can trade off costs of various floor space alternatives. This requires an estimate of the alternative use of floor space blocks. For example, if a nursing unit could reduce supply chain costs with additional stocking floor space, what is the value of that space in its best alternative use (e.g., patient room or equipment storage).

Sharing Space Data

It seems pointless having each hospital develop item space data for the large number of common items. Each hospital could do just a part of this for the benefit of all. Space use optimization data collection as a joint effort involving a number of hospitals appears to be a sensible approach.

Practice:   1. Data Requirements

In the preceding pages, we examined some of the main issues encountered in our supply chain work. At this point, we will shift to practice... Typical Data Requirements ...

To return to the main topic list ...

 

Space Use Inefficiency

So much of hospital space is directly involved in critically needed revenue generation. Revenues drive survival.

Storage space is necessary but is often constrained arbitrarily. Space is allocated first to revenue-generating areas, with revenue-support areas being compacted as much as possible.

There is rarely a quantitative way to assess space use tradeoffs. Patient care space is almost automatically favored, despite our finding that space-constrained storage can be very costly.

Space use management should really look at the full cost picture, with revenue potential included.