### 9-i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUC TION .............................................................................................................. 9-1

9.2 SHIPMENTS MODEL ....................................................................................................... 9-2

9.2.1 Shipments Model Equations ................................................................................... 9-2

9.2.2 Stock Events............................................................................................................ 9-3

9.2.2.1 Replacing Equipment ............................................................................... 9-3

9.2.2.2 New Equipment ....................................................................................... 9-3

9.3 DATA INPUTS .................................................................................................................. 9-4

9.3.1 Historical Shipments ............................................................................................... 9-4

9.3.2 Historical Shipments and Projected Building Stock Additions .............................. 9-5

9.3.3 Building Space and Market Saturation ................................................................... 9-6

9.3.3.1 Floor Space: New Construction ............................................................... 9-6

9.3.3.2 Market Shipments .................................................................................... 9-7

9.4 RESULTS ........................................................................................................................... 9-7

REFERENCES .......................................................................................................................... 9-10

LIST OF TABLES

Table 9.3.1 AEO2011 Forecast of Commercial Floor Space Additions by Building Type ......... 9-6 Table 9.3.2 ACIM Equipment Shipments, Percent by Equipment Class .................................... 9-6 Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice Makers by Building Type, 2016

− 2045 (Base Case) .............................................................. 9-8 Table 9.4.2 Forecast Shipments of Automatic Commercial Ice Makers, 2016

− 2045, by Equipment Type (Base Case)

................................ ............................................... 9-9

LIST OF FIGURES

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model ............................................. 9-19-1 

### CHAPTER 9 . SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Automatic commercial ice maker shipments are key inputs to the national energy savings,

net present value

, and manufacturer impact analys e s. This chapter describes the U.S. Department of Energy ’s (DOE ’s ) methodology for estimating automatic commercial ice maker shipments and annual equipment stocks for 2010 through 2045.

DOE’s shipments model predicts automatic commercial ice

- making (ACIM) equipment stock flows as a function of year and age. H istorical shipment data for the 21 primary ACIM equipment classes under consideration are key inputs to the shipments model. Figure 9.1.1 outlines the structure of the shipments model.

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model

The model assumes that, in each year, the existing stock of automatic commercial ice makers either ages by 1 year or fails, and that failed equipment is replaced. It also assumes that new equipment can be installed in new commercial floor space, and old equipment can be removed through demolitions (not shown in the diagram).

Section 9.2 presents the mathematical formulation of the model ; section 9.3 describes the data input to the model; and section 9.4 presents the results for the base

- case energy conservation
standard -level scenario and discusses the development of higher standard scenarios. Initial Shipments to
New Construction Shipments Projections

Initial Replacement Shipments Shipments to
Construction NEMS Commercial
Construction Forecast

Initial Stock Total Shipments by Equipment Class Percent Shipments by Equipment Class

Replacement

Shipments

Estimated Lifetime of

ACIM Equipment

Stock by Equipment
C lass9-2 

### 9.2 SHIPMENTS MODEL

DOE examined 21 equipment classes using the shipments model. DOE assumed that
there is no interdependence between the shipments of the various equipment classes, so the equations are applicable to each equipment class independently

.

As described in sections 9.3.1 and 9.3.2 , DOE collected historical automatic commercial
ice maker shipment data from 1994 through 2010. Using the historical d ata aggregated over 8-1⁄2-year periods, DOE developed a multi-year series of estimates of ACIM equipment stocks.

DOE used th e estimates of existing stock and the expected life of the equipment as a basis for calculating the portion of historical shipments that replace d existing stock

. With estimates of historical stock, total shipments, and replacement shipments, DOE developed a stock accounting model to track equipment stocks through time.

To estimate historic shipments for new installations

, DOE subtracted the estimated replacement sales from historical total sales

. DOE then combined the estimated historical sales for new installations with indexes of historical and forecasted construction activity in seven building types to estimate future shipments for new installations. DOE forecasted new installations as a function of new construction of floor s pace in seven building types: health care

, lodging, foodservice, retail

, education , food sales, and office.

DOE then appli ed th is stock accounting model across the 2016 to 2045 forecast period by adding new shipments and replacement shipments, and accounting for depreciated stock that was either replaced or demolished ( i.e., removed permanently from the stock). In doing so, DOE proj ected sales of new and replacement equipment by the year sold for the forecast period . In the final step, DOE allocated sales in each year to the 21 equipment classes in proportion to their
relative historical sales

.

9.2.1 Shipments Model Equations

DOE formulated the s hipments model equations to update the stock from year t to year
t+1 as a function of age a

.

As discussed in chapter 8 of the preliminary technical support document ( preliminary TSD)

, DOE estimates that the average lifetime of a unit is 8-1⁄2 years

. DOE assume s that all units
receive normal repairs that do not affect the lifetime of the equipment. The stock of age a in a given year t is represented by

U (t

,a) Eq. 9.1

W here:

U

( t,a ) = stock of age a in a given year t

,

a = age of stock (year) , and

t = year.9-3 

### The shipments of new stock in a given year is Uship(t) . By definition, the age of the equipment is zero in the year that it is shipped, so that Uship(t) = U(t ,0)

.

9.2.2 Stock Events

In the transition from year t to year t +1 , t hree things could happen to the stock of
automatic commercial ice makers :

1. existing equipment could fail, or be removed during a building renovation and be replaced

,
2. existing equipment could be retired permanently, generally in a building demolition or renovation during which the business type(s) situated in the building changes, or

3. the stock could simply age by 1 year.

In the shipments model, DOE did not consider early replacements ( i.e. , existing equipment that is replaced before it fails ), and DOE assumed all failed equipment wa s replaced. The following sections present the equations used to represent each possible event.

9.2.2.1 Replacing Equipment

DOE determine d the probability that ACIM equi pment of age a from stock U 0 

### will fail or will be replaced using a Weibull survival distribution function . Th e Weibull function describes a series of probabilities of equipment failure in a given year, starting with a minimum lifetime of
about 4 years and ending with a maximum lifetime of about 13 year s. The distribution produces an average time of equipment failure of roughly 8- 1⁄2 years, and result s in 88 percent of stock being replaced in the 7- to 10- year lifetime range, and 99 percent replaced between 6 and 11

years . D OE defined the quantities of replaced equipment as follows:

UB (t,a) = PB 0 

### (a) × U 0 

### (t,a) Eq. 9.2

W here:

UB ( t,a ) = quantity of units of age a in year t replaced

, PB 0 

### ( a ) = probability that stock of existing units of age a will fail or will be replaced

, U

0 

### ( t,a ) = original stock of units of age a in year t ( i.e., the probability of failure is applied against

the original new stock installed in the historical year

, yielding an age a in year t , not against the remaining stock)

,

a = age of stock (years) , and

t = year.

9.2.2.2 New Equipment

Available information suggest s that the purchase of new equipment for use in new buildings is driven by the rate of new construction in proportion to new floor space.9-4 

### By definition , for each type of building:

EFS (t +1) = EFS (t ) + NFS (t )

- DFS (t ) Eq. 9.3

W here:

EFS(t ) = existing floor space in year t ( ft 2 

### ), NFS(t) = new floor space added in year t ( ft 2 

### ), and

DFS (t) = floor space demolished in year t ( ft 2 

### ).

The number of units installed in new buildings is:

UN( t ) = UN(t -1 ) × NFS (t )/ NFS( t-1) Eq. 9.4

W here:

UN(t) = the number of units installed in new buildings in year t

.

DOE has little information on the variation in the market saturation of automatic
commercial ice makers by building type or over time. Therefore, in the model, DOE assumed
that the purchase of new equipment is driven by the construction of new floor space , and that
failed or removed equipment is replaced on a one- for

- one basis.

9.3 DATA INPUTS

9.3.1 Historical Shipments

Historical shipments data came from two main sources. T he Air -Conditioning, Heating, and Refrigeration Institute (AHRI) provided automatic commercial ice maker shipment data for 2010, which provided a distribution of shipments by equipment class . AHRI also provided a 10- year history of total shipments. Additional ly

, DOE collected the U.S. Census Bureau’s

C urrent Industrial R eports (CIR) data for the 1973 to 2009 period, including total i ce maker shipments. DOE reviewed the total shipments in the AHRI and CIR data, and noted that the CIR

reported shipments were consistently higher than the AHRI-reported shipments. DOE considered the possibility that these discrepancies we re associated with net exports. However, the CIR data
did not present export information at a level of aggregation making it possible to identif y ice
maker exports. In the e xecutive s ummary of this preliminary TSD , DOE requests input to aid in understanding the differences between the AHRI and CIR shipments data.

DOE identified one source with identifiable export information, the North American

As sociation of Food Equipment Manufacturers ( NAFEM ) . NAFEM data for two recent calendar years (2007 and 2008) showed approxima tely 20 percent of total ice maker shipments associated with food service equipment as exports. Applying a 20 percent export factor to the CIR shipments data brought the CIR data into approximate agreement with the AHRI data. For the preliminary analysis, DOE relied on t he CIR shipment values, reduced 20 percent for exports.9-5 

### Using adjusted CIR data DOE create d a rolling estimate of tot al existing stock by aggregating historical shipments across 8-1⁄2 - year historical period s. DOE u s ed the CIR data to estimate a time series of shipments and total stock for 1994 to 2006. For each year, using shipments, stock, and the estimated 8 -1⁄2- year life of the equipment, DOE estimates that , on average, 14 percent of shipments were for new construction and the remainder for replacement of existing stock.

DOE then used t he split between of shipments to new buildings and shipments for stock replacement to d erive new shipments using the 2009 historical shipments. The 2009 shipments to
new stock w ere then linked to 2009 and future building construction estimates to forecast shipments for new installations over a 2010–2045 forecast period. Starting with the 2009 stock estimates, adding shipments and subtracting estimated stock retirements, DOE also developed a forecast of surviving stock for the 2010–2045 period.

To identify appropriate building construction estimates for future growth in the forecast
step, DOE distributed the estimated total 2009 existing ice maker stock across building types using distributions from a 2009 commercial refrigeration equipment report from Navigant Consulting, Inc

. 1 

### Thus divided, the projections of new construction activity for ea ch building type derived from National Energy Modeling System (NEMS) input files was used to project shipments for each building type. a 

### Ice maker shipment and stock projections were then aggregated across building types by year, and the totals ultimately were distributed across all 21 equipment classes using distributions derived from the AHRI 2010 2 

### data.

9.3.2 Historical Shipments and Projected Building Stock Additions

DOE converted the estimated 20 10 shipment data in each equipment class to percent ages of total shipped units of automatic commercial ice makers for use in the shipments model. This
establish ed the ACIM equipment market share attributed to each equipment class for a base, historical data year.

DOE developed the forecast from the base historical data using projections of building
construction derived from the 2011 Annual Energy Outlook (

AEO 2011 ). 3 

### Table 9.3.1 shows the forecast square footage of new construction used to scale annual new ACIM equipment shipments. As the data in Table 9.3.1 show, forecast square-footage additions to the building
stock s vary from year to year , with the first few years exhibiting lower levels of growth due to the lingering impacts of the current U.S. economic recession. The forecast ACIM equipment shipments therefore show some variability as well, tracking the forecast floor space additions.

DOE used t he growth rates over the last 10 years of the AEO 2011 forecast (2026

– 2035) to extrapolate the AEO forecast through the year 2045 to develop the full 30- year forecast needed
for the n ational i mpacts a nalysis (see preliminary TSD chapter 10)

.

a For some types of consumer products and commercial equipment, specific classes of equipment are often linked to specific types of buildings. Hence, the step of dividing the equipment stock among building types captures crucial
information for the forecast of specific equipment classes. In the case of ACIM, DOE is currently unaware of such specific linkages between types of buildings and equipment classes. However, because building stocks for each of the seven building types are projected to grow at varying rates, projecting shipments by building type enables DOE to capture as much information as possible from the NEMS commercial building construction projections.9-6 

### Table 9.3.1 AEO2011 F orecast of Commercial Floor Space Additions by Building Type

Year New Construction of Commercial Floor Space million ft 2

Health

Care Lodging Foodservice Retail Education Food Sales Office

2011 69.194 113.946 39.32 0 406.927 313.378 28.162 205.734

2016 71.093 170.671 51.604 506.012 244.8 00 37.18 0 333.037

2020 72.744 168.989 53.026 514.581 261.367 38.159 402.214

2025 74.954 179.904 57.487 564.551 256.838 41.384 386.741

2030 76.834 184.135 59.748 599.171 273.84 0 42.946 388.644

2035 79.432 191.012 62.723 635.403 289.617 45.032 392.928

Annual

Growth Factor, 2026

- 2035 0.58% 0.60% 0.88% 1.19% 1.21% 0.85% 0.16%

Source: U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2011 , data file kdbout.



### DOE used the AH RI shipments data to derive the percentage of units shipped for each of
the 21 primary equipment classes

. Table 9.3.2 shows the fraction of the units shipped for each of
the 21 equipment classes.

Table 9.3.2 ACIM Equipment Shipments, Percent by Equipment Class

Equipment Class Percentage of Shipments Equipment Class Percentage of Shipments

IMH

- W

- Small

- B 4.540% IMH

- W

- Small

- C 0.780%

IMH

- W

- Med

- B 2.919% IMH

- W

- Large

- C 0.076%

IMH

- W

- Large

- B 0.461% IMH

- A

- Small

- C 4.447%

IMH

- A

- Small

- B 27.075% IMH

- A

- Large

- C 0.160%

IMH

- A

- Large

- B 16.137% RCU

- Small

- C 1.216%

RCU

- Small

- B 5.428% RCU

- Large

- C 0.477%

RCU

- Large

- B 6.077% SCU

- W

- Small

- C 0.000%

SCU

- W

- Small

- B 0.679% SCU

- W

- Large

- C 0.149%

SCU

- W

- Large

- B 0.216% SCU

- A

- Small

- C 4.611%

SCU

- A

- Small

- B 13.853% SCU

- A

- Large

- C 4.139%

SCU

- A

- Large

- B 6.560%



### 9.3.3 Building Space and Market Saturation

The amount of commercial floor space is the main driver for automatic commercial ice
maker shipments and is one of the basic inputs to the shipments model. As discussed in
section 9.2.2 , the model divides commercial floor space i nto new construction floor space ( NFS ) and existing floor space ( EFS ).

For this analysis, “ commerci al square footage with automatic commercial ice

- making equipment” refers to both the new and the existing stock of buildings.

9.3.3.1 Floor Space : New Construction

DOE used the projected floor space construction after the year 2009 from the NEMS projection underlying AEO2011

. DOE extracted annual estimates of new floor space additions
from an AEO2011 data file (kdbout) for the 2009 through 2035 period. As stated in section 9.3.2

,9-7 

### DOE used the last 10 years of the AEO forecast to develop growth rates used to extend the
forecast to 2045.

DOE projecte d t he shipments of ice makers for new buildings by multiplying the estimated shipments to new buildings in 2009 by future ind ices of new construction for each building type. DOE developed the index for each building type by dividing floor space projections in each future year by t he prior year’s floor space projection

.

As discussed in section 9.3.1 , future shipments for new buildings were factored into the stock accounting model. Future shipments to replace existing stock were developed using the
Weibull function described in section 9.2.2.1

.

9.3.3.2 Market Sh ipments

DOE combined the shipments for new buildings and replacement of existing stock to yield total shipments in year t. DOE used total shipments t o estimate shipments for each ACIM equipment class y , MKTSHIP(t,y) , as shown with Eq. 9.5

. Because DOE assumed that the percent of shipments in each class wa s constant over time and the lifetime of each class was the

same , the ratio of shipments of a specific equipment class indicate s the market share for that
class

, MKT(y)

. DOE assume d the MKT(y) for a particular equipment class to be a constant value equal to the percent historical shipments to that class. Table 9.3.2 reports t hese percentages

.

MKTSHIP ( t

, y ) = M KT ( y ) × ( UN ( t ) + UB ( t ) ) Eq. 9.5

W here:

MKTSHIP (t,y) = total market shipments of equipment class y in year t

, MKT(y) = percentage of total market shipments in equipment class y (%)

, UN(t) = total shipments for new construction in year t

, and

UB (t) = total replacement shipments in year t

.

9.4 RESULTS

Table 9.4.1 shows DOE’s shipments forecast for the ACIM equ ipment by type of commercial building.

Table 9.4.2 shows DOE’s shipments forecast for th e ACIM equipment classes at the
base

- case energy consumption level (or Level 1 ) by equipment type.9-8 

### Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice

Makers by Building Type , 2016

− 2045 (Base Case)

Building

Sector Number of Units (for selected years, and cumulative)

2016 2020 2025 2030 2035 2040 2045 Cumulative (2016 to

2045)

Health Care

– New 11,800 12,074 12,441 12,753 13,184 14,160 15,120 392,180

– Replacement * 90,163 78,604 100,308 97,093 106,814 112,089 114,283 2,987,182 Lodging

– New 8,317 8,236 8,767 8,974 9,309 9,928 10,542 274,548

– Replacement* 50,861 44,240 56,999 56,216 61,839 66,529 67,715 1,727,652

Food Service

– New 4,937 5,073 5,500 5,716 6,001 6,393 6,791 173,677

– Replacement * 32,366 28,186 36,470 35,571 39,521 42,081 43,265 1,099,160

Retail

– New 2,859 2,908 3,190 3,386 3,591 3,843 4,104 102,628

– Replacement * 20,807 18,102 23,127 22,506 24,804 26,401 27,007 694,632

Education

– New 1,610 1,719 1,689 1,801 1,904 1,987 2,078 54,878

– Replacement* 18,495 16,080 19,779 19,120 20,406 21,367 21,317 582,985

Food Sales

– New 1,417 1,454 1,577 1,637 1,716 1,827 1,939 49,716

– Replacement * 9,247 8,053 10,426 10,168 11,301 12,033 12,372 314,225

Office

– New 1,424 1,720 1,654 1,662 1,680 1,784 1,879 51,282

– Replacement* 9,247 8,026 10,053 10,451 11,077 12,279 12,235 313,669

Total

– New 32,365 33,184 34,819 35,928 37,386 39,921 42,453 1,098,909

– Replacement * 231,187 201,292 257,162 251,126 275,762 292,779 298,195 7,719,505

– Total 263,552 234,476 291,981 287,055 313,147 332,700 340,648 8,818,414 * Replacement includes equipment replaced from original stock (installed before standards) and the standards

- level equipment replaced in subsequent years.9-9 

### Table 9.4.2 Forecast Shipments o f Automatic Commercial Ice Makers , 2016

− 2045, by Equipment Type (Base Case)

Equipment Class Number of Units by Equipment Class (for selected years, and cumulative)

2016 2020 2025 2030 2035 2040 2045 Cumulative (2016
– 2045)

IMH

- W

- Small

- B 11,966 10,646 13,257 13,033 14,218 15,106 15,467 400,387

IMH

- W

- Med

- B 7,693 6,844 8,522 8,379 9,140 9,711 9,943 257,392

IMH

- W

- Large

- B 1,215 1,081 1,347 1,324 1,444 1,534 1,571 40,669

IMH

- A

- Small

- B 71,357 63,484 79,054 77,720 84,785 90,079 92,230 2,387,589

IMH

- A

- Large

- B 42,530 37,838 47,118 46,323 50,533 53,689 54,971 1,423,049

RCU

- Small

- B 14,306 12,728 15,849 15,582 16,998 18,060 18,491 478,681

RCU

- Large

- B 16,016 14,249 17,743 17,444 19,029 20,218 20,701 535,879

SCU

- W

- Small

- B 1,789 1,592 1,982 1,949 2,126 2,258 2,312 59,862

SCU

- W

- Large

- B 570 507 632 621 678 720 737 19,084

SCU

- A

- Small

- B 36,510 32,482 40,448 39,765 43,380 46,089 47,190 1,221,605

SCU

- A

- Large

- B 17,290 15,382 19,155 18,831 20,543 21,826 22,347 578,506

IMH

- W

- Small

- C 2,056 1,829 2,277 2,239 2,442 2,595 2,657 68,779

IMH

- W

- Large

- C 200 178 221 218 237 252 258 6,688

IMH

- A

- Small

- C 11,721 10,428 12,985 12,766 13,926 14,796 15,149 392,177

IMH

- A

- Large

- C 422 376 468 460 502 533 546 14,136

RCU

- Small

- C 3,204 2,851 3,550 3,490 3,807 4,045 4,142 107,219

RCU

- Large

- C 1,256 1,118 1,392 1,368 1,492 1,586 1,624 42,029

SCU

- W

- Small

- C * 0 0 0 0 0 0 0 0

SCU

- W

- Large

- C 392 348 434 427 465 494 506 13,103

SCU

- A

- Small

- C 12,151 10,811 13,462 13,235 14,438 15,340 15,706 406,585

SCU

- A

- Large

- C 10,908 9,705 12,085 11,881 12,961 13,770 14,099 364,992

Total 263,552 234,476 291,981 287,055 313,147 332,700 340,648 8,818,414

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Data available to DOE shows there are no existing SCU- Water

- Small

- Continuous products available, so this class is not currently defined in the models.



### E quipment purchase price increases associated with lower energy consumption levels
could cause shipments to drop relative to the base case. DOE acknowledges that the shipment projections under the standards cases could be lower than those in the base case because the higher installed costs could cause some customers to forego or delay discretionary equipment purchases. DOE calculated an average impact factor of -0.625, -6.25 percent for a 10 -per cent price increase, based on an average of input received during manufacturer interviews. This information was compiled after DOE completed the work underlying this preliminary TSD. DOE will consider performing sensitivity analyses during the notice of pr oposed rulemaking (NOPR) stage of the rulemaking to analyze the impact of higher prices on shipment. In the executive summary of this preliminary TSD, DOE requests comment on the potential impacts on future shipments in standards- case scenarios.9- 10 

### REFERENCES

1. Navigant Consulting, Inc., Energy Savings Potential and R&D Opportunities for
Commercial Refrigeration. 2008. Prepared for the U.S. Department of Energy, Washington, D.C.

2. Air -Conditioning, Heating and Refrigerator Institute. 2010 shipments data submittal. February 2011.

3. DOE. 2010. Annual Energy Outlook 2010. U.S. Department of Energy, Energy Information Administration. Washington, D.C. DOE/EIA-0383(2010).