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Museum storage space estimations: In theory and practice
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PREVENTIVE CONSERVATION
ICOM-CC
17th Triennial Conference
2014 Melbourne
SIMON LAMBERT*
Canadian Conservation Institute
Ottawa ON, Canada
simon.lambert@pch.gc.ca
TANIA MOTTUS
Canadian Conservation Institute
Ottawa ON, Canada
tania.mottus@pch.gc.ca
*Author for correspondence
KEYWORDS: storage, facility planning, space,
preventive conservation, overcrowding, storage
reorganization, museums, collections
ABSTRACT
Storage space is a major concern for most museums
all over the world, yet practical guidance to
assess current space usage or future space needs
may not be entirely adapted to the needs of nonspecialized
users. Six published storage space estimation
methods are reviewed, compared and
tested on a sample storage area, revealing that
further guidance to elucidate key concepts and
reduce uncertainty is needed.
Museum storage space estimations:
In theory and practice
INTRODUCTION
Worldwide, most museums struggle with storage space; over 60 percent claim
that space is insufficient to store their collection, and 25 percent claim that
it is difficult or impossible to circulate within storage due to overcrowding
(ICCROM-UNESCO 2011). Well-managed storage areas require a holistic
approach of which space planning is only one element (ICCROM-UNESCO
2011–2014). Yet, overcrowding can aggravate existing risks or generate
new ones: damage through handling, undetected pest infestations, theft or
misplacement of objects, or ineffective emergency response. Overcrowding
may also diminish a museum’s capacity to use collections for the benefit of
society through learning, interpretation, exhibitions and research.
Effective space management requires planning. As shown by the Canadian
Conservation Institute’s (CCI) workshops on storage reorganization,
estimating storage space is not straightforward. Museums with reduced
resources who wish to assess space efficiency or understand the remaining
‘useful life’of their space still feel that they must rely on external assessments.
Some do-it-yourself guidance exists, but not all of it is easily accessible
or in a ready-to-use format.
This article explores the process of storage space estimation from the
perspective of the end user, in this case a staff member without access to
outside expertise. Six published methods intended for mixed collections
are reviewed and compared by test subjects on a sample storage area
containing 257 objects. Perceptions on space estimation were also collected
by way of an online survey (224 respondents from 50 countries). This
study aims to define key parameters for the development of additional
tools for small- and medium-sized museums.
WHY ARE MUSEUMS ESTIMATING STORAGE SPACE?
Whether for internal purposes or for a larger project, the motives and
objectives of museum storage space estimations are many, and may focus
on an existing storage area, or look into the future at a new yet-to-bedefined
space (Figure 1).
PUBLISHED STORAGE SPACE ESTIMATION METHODS, IN THEORY
Defining storage space requirements is a complex task because it involves
translating into physical space certain factors that are inherently difficult to
MUSEUM STORAGE SPACE ESTIMATIONS:
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quantify, such as projected collection growth and requirements for collections
access, use and preventive conservation. While museums may be using
different methods (e.g. collections database) to calculate space requirements,
this article focuses on published space estimation methods, which will be
categorized as approximate (methodsA, B, C, D) or precise (methods E, F).
A – Basic order-of-magnitude
This method is part of the “Planning for Collections Storage” chapter in
the Lord Cultural Resources’Manual of Museum Planning. In this chapter,
order-of-magnitude calculations are described as being sufficient for
preliminary planning documents, but not to convince a governing board
to launch into a large capital project. This method requires the user to
adjust the existing floor space to reduce overcrowding and for the possible
replacement of fixed shelving with compact shelving. Five- and ten-year
floor space projections are made based on a historic annual growth factor
(Maximea 2012).
B – Guesstimating storage space
This method was developed as an alternative to an object-by-object
approach (specifically, to method E below), to produce quick answers
required for funding bids and options appraisals. It is presented as useful
for ‘large collections, where most of the objects are relatively small, the
sort one person can lift unaided’at the ‘feasibility stage in planning storage’
(Chapman 1998, 34, 42). This method begins with a visual estimation of
the collection volume, which is adjusted for overcrowding. This volume
is then converted into current and future floor space requirements for
fixed shelving, compact shelving and open storage, taking into account a
storage room lifetime and collection growth factor, and safe collections
access. The degree to which the space will accommodate the collection is
then determined by dividing each floor space requirement (fixed, compact,
open – either current or future) by the total floor space available.
C – RE-ORG
This method is one element in a much larger methodology for storage
reorganization – RE-ORG (www.re-org.info ) – developed as a self-teaching
tool for small museums by the International Centre for the Study of the
Preservation and Restoration of Cultural Property (ICCROM) in partnership
with the United Nations Educational, Scientific and Cultural Organization
(UNESCO).The space estimation method is divided into four steps, beginning
with an inventory of existing storage units, followed by a shelf-by-shelf (or
unit-by-unit) visual estimation of fullness, overcrowding and room height
usage, then by a calculation of effective use of floor space by storage units,
and finally, by an overall calculation of the percentage of the fullness of
storage when including overflow objects stored on the floor and extra
unused units that may be available. Collection growth is considered, but
only in the following phase of the methodology.
D – US National Park Service Conserve O Grams
Two guides or Conserve O Grams were developed by the United States
National Park Service (NPS) focusing on the needs of NPS-operated
Figure 1
According to 127 respondents working for or in
small to large museums: (a) the main reason for
estimating space on a recent project; (b) the top
question they were trying to answer
MUSEUM STORAGE SPACE ESTIMATIONS:
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interpretation centres and museums. The first guide, entitled Determining
museum storage equipment needs (NPS 1997a), is a stepwise method
to define storage unit needs using a list of different unit types that are
associated to typical numbers of objects (of various types) they can contain.
Using this calibration tool, collection numbers can be converted into unit
numbers. The second guide, entitled Determining museum storage space
requirements (NPS 1997b), focuses on creating a space-efficient and
access-friendly layout using cut-outs of the previously identified unit types
and numbers (to scale); this method can be used to evaluate whether the
units identified in the first guide would fit in a space of known dimensions.
E – Estimating space for ethnographic collections
This method was developed for training purposes in the context of the
Prevention in Museums inAfrica (PREMA) international courses organized
by ICCROM throughout the 1990s. It was designed for large ethnographic
collections numbering thousands or hundreds of thousands of objects,
where precision is said to be more critical and ‘well-documented and
credible estimate of the additional space needs’ are required. The method
is presented as ‘accurate and fast’ (Walston and Bertram 1992, 137). The
starting point is to measure the footprint of each object (or groupings
of similar objects) using decimetre (dm) increments and grouping them
in height categories. The footprints are then augmented by a ‘buffering
coefficient’ and a collection expansion estimate. Adding the augmented
footprints produces a total shelf space requirement (dm2
) per object group,
per height category. Shelf space requirements are then multiplied by heights
to calculate the total collection volume. Further calculations are suggested
to convert volumes into storage units and floor space requirements. The
inherent biases of this method and their impact on the accuracy of the
results have been reviewed elsewhere (Païn 2009).
F – Detailed calculation method (Lord)
This method is part of the “Planning for Collections Storage” chapter in
the Lord Cultural Resources’Manual of Museum Planning. It is presented
as a more complete and credible alternative to an ‘order-of-magnitude’
calculation that can account for the ‘storage problems that may have
built up over many decades’ and overcome the challenges of estimating
space in storage areas that are ‘so crammed and inaccessible’. Among the
three detailed calculations presented, the ‘more practical’ approach uses
in-house knowledge to develop size categories tailored to the collection
categories, much as in method E. The total object footprint is converted
into storage unit footprints by using a ‘stacking factor’, and a 60 percent
circulation factor is applied to determine how much floor space would be
required when using fixed shelving; this figure is then adjusted to show the
floor space required to store the same collection using compact shelving
(Maximea 2012, 277–279).
PUBLISHED STORAGE SPACE ESTIMATION METHODS, IN PRACTICE
Each method was applied, one by one, on a storage area (41 m2
) containing
257 objects (mostly agricultural and domestic items of various sizes)
made available by the City of Ottawa’s Shared Museum Resources (SMR)
MUSEUM STORAGE SPACE ESTIMATIONS:
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(Figures 2–3). The testing team included the authors and an SMR staff
member. The testers had no working familiarity with the methods, with the
exception of the main author who helped develop the RE-ORG method.
The aim of this exercise was to better understand the possibilities offered
by each method and to identify obstacles and areas of uncertainty in their
application. It was not designed to make definitive statements about which
method provides the most accurate results.
An initial review of each method with the testing team revealed that
additional guidance would be required since some methods lacked clear
steps to follow and did not define technical terms (A, F); one did not
include sample tables for data recording (B). Hence, a one-page ‘cheat
sheet’ of sequential steps and data tables was created for each method.
This may have contributed to improving the precision of the results, but
it was a necessary step to ensure that all testing could be concluded in
one day. Each team member worked independently to minimize biases in
opinions and results. A standard annual collection growth rate of 2 percent
and time span of 10 years were used when required. Overall, the testing
team found that each method had desirable features, but few were easily
applied in their published form. The results are summarized in Table 1.
DISCUSSION
Opening the black box: Improving transparency
This exercise confirms that personal interpretations of terminology, and
perceptions of what constitute problematic or acceptable situations (e.g. is
it too crowded or is there still room for more?) are so varied that ensuring
reproducibility, consistency, and thus the credibility of results, becomes
a major challenge – particularly for novice users. Personal experience,
knowledge, and even personality can have a significant impact on the results.
Some methods provided explicit instructions on how to derive a ‘buffering
factor’for objects (E), how to calculate overcrowding (C), or benchmarks
on what constitutes ‘safe access’ to collections (C). Yet, in most cases, a
number of ‘black box’decisions (decisions with known inputs and outputs
but for which the thought process is unclear) are solicited, in particular
for ‘overcrowding’, ‘compaction’, ‘stacking’, ‘circulation space’, ‘historic
annual growth’, and even to define ‘groups of similar objects’.
Table 2 shows that the inherent variability of these methods has led to
some serious discrepancies between test subjects when using the same
method (F), and between methods when measuring the same quantity (A,
B, F). The extent to which human perception influences results, even when
measuring something tangible (a collection and a storage space), is a clear
indication that each process should be made more explicit and transparent,
so those who later interpret the results understand how the calculations
were made, thus enabling them to qualify their conclusions accordingly.
Improving transparency could be approached in different ways: one is
through careful documentation of thought processes and paths not taken,
as has been suggested for recording conservation decisions with decision
diagrams (Michalski and Rossi-Doria 2011); another is to guide users by
Figure 2
Overview of the case study storage area, Ottawa
Shared Museum Resources
Figure 3
Test subjects applying a precise space estimation
method
MUSEUM STORAGE SPACE ESTIMATIONS:
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Table 1
Summary of the review of space estimation methods, highlighting potential uses, strengths and
weaknesses for each
ApproximateorPrecise
Possible uses
Strengths Weaknesses
Currentfloorspaceneeds
Futurefloorspaceneeds
Currentfullness(%)
Collectionfittinginaspace
Storageunitrequirements
Storagesystemcomparison
Developingastoragelayout
A-Basicorder-of-magnitude
Approximate
✔
✔
✔
✔
• Speed.
• Adequate for mixed collection.
• Answers key questions.
• Ability to see benefits of compact
storage.
• Calculation table is a useful feature.
• ‘Compaction factor’ is not defined.
• Calculations in table not
sufficiently explicit.
• No benchmarks to assess
suitability (i.e. for overcrowding).
• Unusable floor space and overflow
objects stored on floor not
accounted for.
• Would require a step-by-step
approach.
B-Guesstimatingstoragespace
Approximate
✔
✔
✔
✔
✔
• Speed.
• Simplicity of the calculations.
• Ability to compare various storage
options side-by-side (fixed,
compacted, open).
• Accounts for annual growth.
• Works well for a mixed collection.
• Access and circulation requirements
are made explicit and integrated
into calculations.
• Method to account for
overcrowding is not provided.
• Schematic diagrams of density
of various storage options per
footprint are of little use if no
benchmarks are provided as to
what constitutes good or bad
density.
• Unusable floor space and overflow
objects stored on floor not
accounted for.
C-RE-ORG
Approximate
✔
✔
✔
• Useful supporting materials (forms,
case studies, examples, guidelines).
• Good compromise between
approximate and precise methods.
• Reasonable confidence in the
reliability of results.
• Provides explicit benchmarks for
good access (maximum 2 or 3
objects moved to retrieve another).
• Overcrowding calculations are
explicit.
• Available online in English, French
and Spanish.
• Fragmentation of method in
different worksheets makes it
difficult to focus on estimating
space only (if not embarking
on a full storage reorganization
project).
• Scattered worksheets make it
difficult to print a package to work
in storage.
• Does not immediately account
for collection growth (only later in
Phase 2).
• Method to handle overflowobjects
stored on flooris confusing.
D-NationalParkService
Approximate
✔
✔
✔
✔
✔
• Simplicity; clarity of instructions.
• Inclusion of typical storage unit
capacities per object type; useful
when there are no units.
• Hands-on and low-tech approach;
visual and requires few calculations.
• Appropriate level for non-specialists.
• Available online.
• Does not account for collection
growth.
• Refers to ‘typical’ storage units that
may not be available everywhere.
• No definition or benchmarks
provided for ‘safe movement’,
‘adequate access’, while precise
aisle widths are provided.
E-Ethnographic.collections
Precise
✔
✔
✔
✔
• Clarity of methodology; illustrative
examples.
• Takes into consideration oversized
objects that have unique storage
requirements.
• Factoring in a set ‘buffering’ factor
for safe access to objects removes
any guessing of whether each
object has enough circulation
space or is too crowded.
• Time consuming.
• Best suited for a well-organized,
homogeneous collection
(determining “object categories” is
difficult otherwise).
• Difficult to determine where to
draw the line with object category
specificity.
F-Detailedcalculation
Precise
✔
✔
• Presumably produces reliable,
accurate results.
• Time consuming.
• Best suited for a well-organized,
homogeneous collection
(determining “object categories” is
difficult otherwise).
• No definition provided for
‘stacking factor’.
• Would require a step-by-step
approach.
• No use for some gathered
information (e.g. object height,
% total objects).
MUSEUM STORAGE SPACE ESTIMATIONS:
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providing methodologies, benchmarks or decision calibration tools to
ensure that the results are derived from similar thought processes.
To be, or not to be, precise
Commenting on the accuracy of these methods (how the results obtained
compare to a ‘correct’value) is complicated by the compounded errors that
originate from human perception. That said, considering the small size of
the SMR storage area and that the space was not especially overcrowded
(every object visible and easily retrievable, aisles mostly uncluttered),
some 10-year projections seemed more plausible (A, B) than others (E, F).
In addition to human perception errors, it has been demonstrated that
some of the inaccuracy in precise methods originates from variables that
cannot be controlled, e.g. the objects’ size (the smaller the better), the
variability in object sizes (the more varied the better), or the last digit in
the dimensions of single objects (the closer to 1 and the farthest from 9,
the better); these variables influence the under- or overestimation of space
requirements significantly (Païn 2009). Perhaps as a security blanket, some
Table 2
Summary of results for the space estimation exercise. Asterisks (*) indicate results expressing the
same quantities (space need in 10 years assuming compact storage is installed). Relative standard
deviation is an indication of the degree of variance between test subjects. Method D was excluded
from the exercise as it is not calculation based
Subject 1 Subject 2 Subject 3
Relative
standard
deviation
Average
time per
object
Observations about the
variance in results
A
Basic order-of-magnitude
*10-year projection,
compacted (m2
)
33.5 m2
29.9 m2
35.2 m2
3% 9 s Discrepancies due to the
different figures used for
‘overcrowding’ (0%, 20%, 30%)
and ‘compaction factor’ (32%,
50%, 46%). Subjects perceived
safe access and gained floor
space through compaction
differently.
B
Guesstimating storage
space
*10-year projection,
compacted (m2
)
16.2 m2
18.0 m2
14.0 m2
4% 9 s Discrepancies originate from
‘overcrowding’ that had
to be mentally included in
the calculated ‘collection
volume’ (27 m3
, 30 m3
, 23 m3
).
Subjects perceived safe access
differently.
C
RE-ORG
Fullness of storage, now (%)
44.0% 40.0% 58.0% 7% 11 s Discrepancies originate from
the step that requires users to
imagine overflow objects on the
floor as though they were inside
units to determine the impact
on the overall fullness. Subjects’
mental mapping was different.
E
Ethnographic collections
10-year projection, fixed
shelving (m2
)
14.4 m2
9.5 m2
8.6 m2
10% 34 s Discrepancies partly originate
from degree of approximation
used when defining ‘categories
of similar objects’. Subjects
used 9, 22, and 32 categories,
respectively. Total number of
objects numbers also varied
between subjects.
F
Detailed calculation
*10-year projection,
compacted (m2
)
17.5 m2
326.8 m2
835.2 m2
35% 36 s Discrepancies partly originate
from degree of approximation
used when defining ‘categories
of similar objects’: 9, 22, and
32 categories respectively,
but mostly due to confusion
over the term ‘stacking factor’
(not defined in the method
description).
MUSEUM STORAGE SPACE ESTIMATIONS:
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approximate methods included disclaimers that more precise calculations
should be used for larger projects (A) or for purposes beyond the feasibility
stage (B). This perception was echoed by the respondents to our survey,
particularly when estimations would have external uses, or would be tied
to a budget. But do the precise methods really yield more accurate results?
An important question for smaller institutions is whether an approximate
method could be good enough (sufficient confidence in the data) to support
a project, or to convince decision makers.
Realistically, it will not always be possible to use a precise method for
all projects because of time, money and staff constraints. If objects have
excellent documentation that includes measurements for all objects, it may
be feasible, for example, to estimate storage unit requirements by using
a spreadsheet and dividing the collection volume by volume available
in a single unit. However, in many institutions object documentation
is often incomplete (ICCROM-UNESCO 2011). This was the case in
the SMR storage area, where dimensions had not always been recorded
consistently in the past and the collection database could not be used to
automate calculations.
Although using a database may work in some cases with objects of regular
and easily predictable dimensions, in other cases this is not possible, as
these rarely (if ever) indicate whether objects are stored on their side or
upright, whether they have enclosures or mounts and of what size, or
whether the current storage conditions are acceptable. For segments of
the collection where dimensions are fairly regular, it may be possible to
extrapolate based on defined volumes, but for peculiar objects (heavy,
large, awkwardly shaped), object-by-object or precise methods may be
needed. Rare are the projects that would not require a hybrid method.
Assumptions
With the exception of method C, it is significant that most methods assume
that storage areas are reasonably well organized, as guidance is seldom
provided on how to account for overcrowding, objects that do not belong
to the collection, or overflow objects on the floor (A, B, E, F). Even one
method (D) – introduced as being useful for situations where objects are
stored ‘haphazardly in a variety of places: on the floor, on top of file
cabinets, on multipurpose shelving and in cabinets with non-museum
items’ or in an overcrowded space – begins by defining unit needs based
on known object quantities and sizes, which may be difficult to define
in a disorganized storage area that likely has incomplete documentation
(NPS 1997b). Given the poor state of museum storage worldwide, it
is surprising that overcrowding is not taken into consideration more in
estimation methods.
A recurring assumption in some methods (A, B, F) and in the general
literature on storage planning is that a consultant will be hired later in
the process (Verner Johnson and Horgan 1979, Bordass 1996). In larger
or better-funded projects, it may be possible to conduct a first ‘quick and
dirty’ estimation and then commission a specialist (conservator, shelving
contractor, architect firm) to find solutions for specific spaces or to define
the requirements for a new space. However, for many small- to medium-
MUSEUM STORAGE SPACE ESTIMATIONS:
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sized museums, this will not be possible. The ‘quick and dirty’estimation
might be the only one, so a better tool is needed for them.
Making the best of what exists
Regardless of their current shortcomings, some general advice can be
provided to reduce uncertainty and improve consistency when applying
the existing published methods:
•	 Calibrate visual estimations. When working with a colleague on a
method that requires shelf-by-shelf or unit-by-unit visual estimations
(C), or an overall estimation of overcrowding (A, B), assess the first
few storage units together so that each team member has the same
conception of fullness, e.g. what ’80 percent full’ or ‘150 percent full’
means to both people.
•	 Develop working benchmarks. Define some rules-of-thumb that will be
used throughout the process to decide what constitutes ‘overcrowded’,
‘enough space for access’, or a decent category of objects of similar
size. Present these to colleagues to see if they make sense to them.
•	 Document the process and be transparent. Clearly define and disclose
the context, the specific questions to be answered, and the boundaries:
the working assumptions (e.g. objects will remain in this space), what
is included or excluded from the calculations (e.g. objects on the floor,
non-collection items), and how any ‘black box’ decisions that can be
potentially influenced by human judgment were made.
•	 Double check precision. When using approximate methods that can
be applied reasonably quickly, ask a colleague to do his or her own
assessment and compare your results.
•	 Double check accuracy. Make a drawing to scale by hand or by using a
free program such as SketchUp (www.sketchup.com ), or use the cut-out
system proposed in method D, to see what the results mean in concrete
terms. Do they still make sense?
CONCLUSION
Based on this review, what is currently available in the published literature
on storage space estimation still requires more information and tools
to assist users in reducing the level of uncertainty in their calculations.
Eliminating uncertainty altogether is not a realistic goal, especially given
the variability in project structure and scale, operational and collection
requirements, and the sometimes-irregular physical characteristics of spaces.
Eliminating the influence of personal perceptions is probably not a realistic
goal either. Further developments should aim to provide unambiguous,
stepwise instructions that help to make decisions based on clearly defined
criteria and benchmarks, and should elicit careful documentation and
transparency from its users. A useful space estimation method should be
able to provide a meaningful estimate to avoid those unfortunate events
when collections are brought into a space for which the requirements have
been largely underestimated, or where a large acquisition is made on the
assumption that the storage area can still accommodate it.
MUSEUM STORAGE SPACE ESTIMATIONS:
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In most countries, given the current trend in the museum sector towards
increasing workloads and fewer staff, access to tools that provide quick,
reasonably reliable and credible results is desperately needed. Because
there seems to be a general lack of support for storage-related activities
(ICCROM-UNESCO 2011), a useful space estimation method would
also help museum staff highlight the urgency of the situation to senior
managers or the governing body.
The CCI, in collaboration with ICCROM and other partners, is developing
new guidance to support project-based, distance-learning activities on storage
reorganization as part of a wider initiative called RE-ORG International.
This guidance will include a simple tool for estimating storage space that
combines the most useful elements of each method (available online in
2016 at http://re-org.iccrom.org ).
ACKNOWLEDGEMENTS
The authors thank the staff of the City of Ottawa’s Shared Museum Resources
for their collaboration, in particular Emily Greenlaw. Thanks also to Fiona
Graham, Joe Gerber, Vivien Chapman, Irene Karsten, Jane Sirois, Julie
Stevenson and Catherine Antomarchi.
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How to cite this article:
Lambert, S. and T. Mottus. 2014. Museum storage
space estimations: In theory and practice. In
ICOM-CC 17th Triennial Conference Preprints,
Melbourne, 15–19 September 2014, ed. J. Bridgland,
art. 1503, 9 pp. Paris: International Council of
Museums.
(ISBN 978-92-9012-410-8)
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