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S w 906D06 WILKINS, A ZURN COMPANY: DEMAND FORECASTING Professors Carol Prahinski and Eric O. Olsen prepared this case solely to provide material for...

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1. Comment on the current forecasting method.
2. Could you do it better?

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Jennifer

Wilkins--Case_for_Session_7.pdf

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906D06 WILKINS, A ZURN COMPANY: DEMAND FORECASTING Professors Carol Prahinski and Eric O. Olsen prepared this case solely to provide material for class discussion.
The authors do not intend to illustrate either effective or ineffective handling of a managerial situation. The
authors may have disguised certain names and other identifying information to protect confidentiality.
Ivey Management Services prohibits any form of reproduction, storage or transmittal without its written
permission. This material is not covered under authorization from CanCopy or any reproduction rights
organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey
Management Services, c/o Richard Ivey School of Business, The University of Western Ontario, London,
Ontario, Canada, N6A 3K7; phone (519) 661-3208; fax (519) 661-3882; e-mail [email protected]
Copyright © 2005, Ivey Management Services Version: (A) 2006-09-13 On Monday, January 10, 2005, as Bernie Barge, the newly promoted inventory
manager at the Wilkins plant in Paso Robles, California, prepared for the
forecasting meeting scheduled for the following day, he wondered if he could find
an easier and possibly more reliable means of forecasting the sales demand.
BACKGROUND Wilkins Regulator Company had built its strength on high-quality products for the
plumbing, municipal waterworks, fire production and irrigation customer markets,
ranging from water pressure reducing valves and backflow preventers to anti-scald
shower valves. The general plumbing customer market represented approximately
half of its sales revenue and the irrigation customer market represented
approximately a quarter of its sales revenue. Chris Connors, the plant’s general
manager and Barge’s supervisor, had targeted the fire protection and municipal
waterworks customers as opportunities for growth.
Zurn Industries acquired Wilkins in 1971. In 1998, Zurn merged with U.S.
Industries Bath & Plumbing Products Co., and changed its name to Jacuzzi Brands
in 2003. From the most recent Jacuzzi Brands 2004 Annual Report, Barge read:
Demand for our products is primarily driven by new home starts,
remodeling and construction activity. Accordingly, many external
factors affect our business including weather and the impact of the Page 2 9B06D006 broader economy on our end markets. Weather is an important
variable for us as it significantly impacts construction. Spring and
summer months in the U.S. and Europe represent the main
construction season for . . . commercial and industrial markets. As
a result, sales in our bath products and plumbing products segments
increase significantly in our third and fourth fiscal quarters as
compared to the first two quarters of our fiscal year. The autumn
and winter months generally impede construction and installation
activity.
Our plumbing products business is dependent upon commercial and
institutional construction activities and is, therefore, affected by
macroeconomic factors, such as the unemployment rate and the
availability of financing. Despite the cyclical nature of the U.S.
commercial and institutional construction market, which
experienced declines in revenue of approximately 14 per cent in
fiscal 2002, approximately six per cent in fiscal 2003 and a slight
rebound in fiscal 2004, sales of our commercial and institutional
products have continued to grow at a rate that exceeds that of the
industry. We have achieved this growth through favorable pricing,
product innovation and targeted marketing programs.
Connors provided Barge with additional insight into the complexities involved in
forecasting demand:
There are lots of uncontrollables. Uncontrollables included the
weather, competitors’ product introductions and our own product
introduction. Sometimes, we cannibalize our own sales —
intentionally — and, sometimes, unintentionally. Other influences
on the demand include marketing strategies, such as price
promotions and, in the irrigation market segment, an early-buy
program that encourages customers to place their orders in the early
spring.
CURRENT FORECASTING PROCESS: THE FORECAST MASTER Each quarter, Connors and Rick Fields, the sales/marketing manager, developed
the quarterly demand forecasts for each product family. Barge, in his newly
created position, would also participate in the forecast development. Based on
their knowledge of industry trends, competitive strategies and sales history, they
would estimate the sales for the next five or six quarters. Barge commented:
Rather than forecast the total quarterly sales volume for a product
family, we forecast the average anticipated sales per week for the
quarter for each product family. We have about 25 different Page 3 9B06D006 product families and each product family has what we call a
planning bill. To start the process, however, we start with what we
call the forecast master. The forecast master is a spreadsheet that
lists the average weekly sales history for each product family by
quarter and year since 1999. For each product family, we divide
the total quarter’s actual sales by 13 weeks per quarter to determine
the average weekly sales per quarter. Then, we plug in our
expected demand for the next five or six quarters. These numbers
represent our best estimate. This information is then used to
calculate the average dollars per unit and average gross profit per
unit, which is used by our accounting and finance group to develop
various budgets.
A portion of last quarter’s forecast master is shown in Exhibit 1 for two product
families: Pressure Vacuum Breakers (PVBs) and Fire Valves. PVBs were a type of
backflow prevention device, which was designed to prevent the reverse flow of
water and other substances into the water source. PVBs were used predominantly
by the irrigation market segment.
Fire valves, a type of pressure reducing valve, were designed to reduce or regulate
water pressure in residential, commercial and industrial applications. In addition
to having just signed on a new customer, Connors anticipated high growth in the
fire valve market since Wilkins was introducing a number of new product
extensions designed to increase market share. One such product extension was the
development of fixed-setting fire valves. Wilkins’ current fire valves had
adjustable settings, which were set by the installer. Some regulatory agencies,
however, were concerned about improper installation or modification to the valves
and were now requiring fixed setting valves to improve safety.
CURRENT FORECASTING PROCESS: THE PLANNING BILL Each product family had its own planning bill. Barge described the planning bill:
There are five important components to each planning bill. First,
the planning bill contains the sales history for each product within
the family. We have quarterly sales history that goes back to 1989.
If I dig into the old files, I can go back even further. Second, for
the last four quarters, the planning bill calculates the average
number of units sold within that product family each day within
each quarter. For example, for our first fiscal quarter of 2005,
which started on October 1, 2004, we sold 48,159 units within the
PVB product family [as shown in Exhibit 2]. Since the quarter had
58 days, the planning bill will calculate that we sold a daily average
of 830 units. We will also calculate the average daily sales for the
last four quarters; for the PVBs, it was 1,205. Page 4 9B06D006 Third, the planning bill contains our projection on the average daily
sales for that family that we think we will sell in the next 12
months. This number came from the forecast master and is one of
the key determinants of the forecast by product. With the PVB
product family, for example, we think our sales will have a
moderate growth rate predominantly due to industry growth and
some problems at one of our competitor’s manufacturing facilities.
Last quarter, we forecasted that we would sell an average of 1,400
units each business day in the next 12 months [as shown in the far
right column of Exhibit 3].
Fourth, we disaggregate the family forecast into each product based
on the per cent of sales of the product family. To do this, we first
calculate the proportion of unit sales that each product currently
represents within the family. We call this the “raw per cent.”
Then, we try to forecast the percent of family sales that the product
will represent in the future, which we call the “planning bill per
cent.” It can get pretty complicated. If we have new products, we
have to factor in the effect that they may have on our existing
products. Plus, with new products, we also have to project growth
without any historical data.
The fifth key piece of information in the planning bill is the
calculation of the annual sales forecast for each product within the
family. We use a couple of key pieces of information: The
planning bill per cent is multiplied by 250 days in a year and by the
daily sales forecast for the family, which are 1,400 units in this
situation.
The sales history for a select group of products is shown in Exhibit 2. The
planning bills for the PVBs and fire valves, as of October 2004, which was the
beginning of the 2005 fiscal year, are shown in Exhibit 3 and 4, respectively.
FORECASTING PERFORMANCE When contemplating the forecast accuracy, Barge said, “I don’t have a clue on
how well we have been doing. I think we are doing OK at the aggregate level, but
we probably have some swings in our accuracy level at the individual product
level.” For the first quarter of 2005, Connors and Fields had forecasting sales of
53,560 PVB units and 559 five valve units. According to Exhibit 2, actual sales
were 48,159 PVB units and 580 fire valve units. Page 5 9B06D006 IMPLEMENTATION CONCERNS Barge wondered if he could use statistical forecasting methods to ease the
forecasting process and perhaps improve the reliability of the sales forecast. If
Barge was going to recommend a new forecasting method, he considered how he
should gain buy-in from Connors and other managers at the plant. He knew that
Connors considered it important to use judgment in developing the sales forecast.
For example, if Connors believed that the industry was entering a mild recession,
he wanted to make sure the demand forecast reflected the anticipated economic
downturn.
Barge also wondered how to incorporate the occasional price promotions that were
used to sell off excessive finished goods inventory. He knew that if management
reduced the price, Wilkins was going to sell more units and be more competitive.
Barge frequently joked that the fire valves were leading economic indicators.
Although he said it jokingly, he wondered if there was some truth in it. Since the
product was used in new construction, an increase in product sales would indicate
that the construction industry was in an upswing. To help determine the demand
forecasts, he wondered if he could use the United States economic information,
such as the unemployment rate data (see Exhibit 5), the bank prime loan rates (see
Exhibit 6) or the number of new housing starts (see Exhibit 7). Barge knew that
less than one per cent of the PVB sales were outside the United States and he
didn’t remember any fire valves having been sold outside of the United States.
Finally, he wondered how to forecast new products, such as the new fixed-pressure
fire valves. Although he could use the historical sales of the adjustable-pressure
fire valves, both he and Connors believed that the new fixed-pressure valves would
have dramatic growth, which Barge did not think could be captured by the
historical sales data of the older products.
As Barge reflected on his preparation for tomorrow’s meeting, he wondered what
he should recommend to Connors and how to address any potential
implementation concerns. The Wilkins series of cases is dedicated to Dr. Michael F. Pohlen, Professor Emeritus of Operations
Management, Alfred Lerner College of Business, University of Delaware. Page 6 9B06D006 Exhibit 1
FORECAST MASTER AS OF OCTOBER 7, 2004
WILKINS REGULATOR DIVISION UNIT SALES PER WEEK BY QUARTER
FORECAST BEGINNING QI 2005
HISTORY THROUGH Q IV 2004
REVISED OCTOBER 7, 2004
Final
FILE: FCSTMASTER
PROD
LINE FISCAL
YEAR QI Q II Q III Q IV YEAR
TOTAL PVB 1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010 1788
2097
2116
2352
2721
3029
4120 3748
4008
3523
4092
4449
5786
7480 5115
5532
5921
6824
7184
9451
9341 3167
3123
3374
3968
4531
4231
5983 179,634
191,880
194,136
224,072
245,506
292,465
350,012 FIRE
VALVE 1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010 36
47
21
36
42
22
43 43
32
45
38
27
26
51 18
34
19
28
25
28
51 13
24
38
43
11
29
51 1,432
1,777
1,608
1,886
1,357
1,371
2,550 Note: Numbers in bold font represent actual weekly unit sales, averaged over the quarter.
Source: Company files. Page 7 9B06D006 Exhibit 2
SALES HISTORY FROM THE PLANNING BILLS
(as of January 10, 2005) Year Qtr PVB
12-720 2001
2001
2001
2001
2002
2002
2002
2002
2003
2003
2003
2003
2004
2004
2004
2004
2005 1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1 2108
2516
2954
2224
1788
2512
3540
3666
3546
5596
5948
4892
4311
6637
7823
6652
6235 PVB
34-720 6716
11510
18438
10456
9464
12942
21640
11650
11270
15322
19424
13310
10214
20983
27492
19535
28020 PVB
1-720 10688
21688
40814
19510
9824
21848
48936
24640
12400
23692
47904
26990
13851
31507
66644
17337
11163 Note: Data reflects total units sold within the fiscal quarter
Source: Wilkins Plant Data PVB
34-420 PVB
1-420 7158
7034
9026
7254
6968
9156
8134
6958
5208
6806
10446
7960
7397
9043
11233
7496
384 842
3050
5736
4414
2536
6740
6454
4676
2948
6424
9666
5754
3609
7049
9676
3976
2357 Total Fire
Valve
Z2105 Fire
Valve
Z3000 Fire
Valve
Z3000IL Fire
Valve
Z3004 27512
45798
76968
43858
30580
53198
88704
51590
35372
57840
93388
58906
39382
75219
122868
54996
48159 122
139
54
78
18
224
138
193
124
112
25
37
79
71
68
54
85 77
240
138
275
301
133
175
251
213
150
64
57
108
105
151
114
199 1
0
3
36
-35
0
0
10
2
3
139
3
49
52
2
84
140 20
144
25
59
48
35
15
50
32
46
26
18
23
26
62
44
66 Fire
Valve
Z3004IL 50
67
32
49
132
103
42
63
170
40
77
22
26
90
77
86
90 Total 270
590
252
497
464
495
370
567
541
351
331
137
285
344
360
382
580 Page 8 9B06D006 Exhibit 3
PLANNING BILL FOR PVBS
(as of October 3, 2004)
WILKINS DIVISION
PLANNING BILL: PVB
Revised 10/03/04 cc: Steve, Jim, Cyd, Neal, Terri, Ed, Lisa QI
UNIT
SALES
09/28/03
01/04/04 Q II
UNIT
SALES
01/04/04
04/03/04 Q III
UNIT
SALES
04/04/04
07/03/04 Q IV
UNIT
SALES
07/04/04
10/02/04 12-720
34-720
1-720
34-420
1-420 4311
10214
13851
7397
3609 6637
20983
31507
9043
7049 7823
27492
66644
11233
9676 TOTAL
DAYS
UNIT/DAY 39382
64
615 75219
65
1157 122868
64
1920 PART NO TOTAL
DAYS
UNIT/DAY Source: Wilkins Plant Data. FORECAST
12-Month
DEMAND
1400/DAY RAW
% PLAN
BILL
% 6652
19535
17337
7496
3976 8.7%
26.7%
44.2%
12.0%
8.3% 10.6%
25.1%
44.1%
12.0%
8.2% 37100
87850
154350
42000
28700 54996
63
873 100.0% 100.0 350000
250
1400 292465
256
1142 350000
250
1400 Page 9 9B06D006 Exhibit 4
PLANNING BILL FOR FIRE VALVES
(as of October 12, 2004)
WILKINS DIVISION
PLANNING BILL: Fire Valves
Revised 10/12/04 cc: Jim, Cyd, Steve, Ed, Terri, Brad, Pete, Lisa QI
UNIT
SALES
09/28/03
01/04/04 Q II
UNIT
SALES
01/04/04
04/03/04 Q III
UNIT
SALES
04/04/04
07/03/04 Q IV
UNIT
SALES
07/04/04
10/02/04 Z2105
Z3000
Z3000IL
Z3004
Z3004IL 79
108
49
23
26 71
105
52
26
90 68
151
2
62
77 TOTAL
DAYS
UNIT/DAY 285
64
4 344
65
5 360
64
6 PART NO TOTAL
DAYS
UNIT/DAY Source: Company files. FORECAST
12-Month
DEMAND
10.2/DAY RAW
% PLAN
BILL
% 54
114
84
44
86 19.8%
34.9%
13.6%
11.3%
20.4% 21.2%
38.8%
11.8%
8.2%
20.0% 540
990
300
210
510 382
63
6 100.0% 100.0 2,550
250
10.2 1371
256
5 2550
250
10.2 Page 10 9B06D006 Exhibit 5
UNEMPLOYMENT RATE IN THE UNITED STATES
Unemployment
Rate (%)
3.90
4.23
4.40
4.83
5.53
5.70
5.83
5.73
5.87
5.83
6.13
6.13
5.87
5.67
5.57
5.43
5.43 Oct-Dec 2000
Jan-Mar 2001
Apr-Jun 2001
Jul-Sep 2001
Oct-Dec 2001
Jan-Mar 2002
Apr-Jun 2002
Jul-Sep 2002
Oct-Dec 2002
Jan-Mar 2003
Apr-Jun 2003
Jul-Sep 2003
Oct-Dec 2003
Jan-Mar 2004
Apr-Jun 2004
Jul-Sep 2004
Oct-Dec 2004
United States Unemployment Rate Oct-04 Jul-04 Apr-04 Jan-04 Oct-03 Jul-03 Apr-03 Jan-03 Oct-02 Jul-02 Apr-02 Jan-02 Oct-01 Jul-01 Apr-01 Jan-01 Oct-00 6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0 Seasonal Adjusted Unemployment Rate Note: Data reflects the seasonal adjusted unemployment rate for people 16 years and over. The quarterly data represents
the unweighted average of the monthly data.
Source: U.S. Department of Labor, Bureau of Labor Statistics. Page 11 9B06D006 Exhibit 6
BANK PRIME LOAN RATE IN THE UNITED STATES
Bank Prime
Loan Rate (%)
9.50
8.62
7.34
6.57
5.16
4.75
4.75
4.75
4.45
4.25
4.24
4.00
4.00
4.00
4.00
4.42
4.94 Oct-Dec 2000
Jan-Mar 2001
Apr-Jun 2001
Jul-Sep 2001
Oct-Dec 2001
Jan-Mar 2002
Apr-Jun 2002
Jul-Sep 2002
Oct-Dec 2002
Jan-Mar 2003
Apr-Jun 2003
Jul-Sep 2003
Oct-Dec 2003
Jan-Mar 2004
Apr-Jun 2004
Jul-Sep 2004
Oct-Dec 2004
Rate of Interest in Money and Capital Markets 10.00
9.00
8.00
7.00
6.00
5.00
4.00
Oct-04 Jul-04 Apr-04 Jan-04 Oct-03 Jul-03 Apr-03 Jan-03 Oct-02 Jul-02 Apr-02 Jan-02 Oct-01 Jul-01 Apr-01 Jan-01 Oct-00 3.00 Bank Prime Rate Note: Figures are based on the rate posted by majority of top 25 (by assets in domestic offices) insured U.S.-chartered
commercial banks. Prime is one of several base rates used by banks to price short-term business loans. Not seasonally
adjusted. Quarterly figures are based on the unweighted average of the monthly rates, which include each calendar day in
the month.
Source: Federal Reserve Statistical Release, H.15, Selected Interest Rates. Page 12 9B06D006 Exhibit 7
NEW PRIVATELY OWNED HOUSING UNITS STARTED IN THE UNITED STATES
(in 000s)
Single-Unit
Housing Starts
275
274
374
341
285
293
386
361
319
304
406
412
377
345
456
440
370 Oct-Dec 2000
Jan-Mar 2001
Apr-Jun 2001
Jul-Sep 2001
Oct-Dec 2001
Jan-Mar 2002
Apr-Jun 2002
Jul-Sep 2002
Oct-Dec 2002
Jan-Mar 2003
Apr-Jun 2003
Jul-Sep 2003
Oct-Dec 2003
Jan-Mar 2004
Apr-Jun 2004
Jul-Sep 2004
Oct-Dec 2004 Multi-Unit
Housing Starts
82
74
87
88
81
76
89
98
84
71
84
99
95
80
84
92
90 Total
Housing Starts
357
348
461
429
366
369
475
459
403
375
490
511
472
425
540
532
460 New Privately Owned Housing United Started 600
500
400
300
200
100 Single Units Multi-Units Source: U.S. Census Bureau: Manufacturing, Mining and Construction Statistics Total Oct-04 Jul-04 Apr-04 Jan-04 Oct-03 Jul-03 Apr-03 Jan-03 Oct-02 Jul-02 Apr-02 Jan-02 Oct-01 Jul-01 Apr-01 Jan-01 Oct-00 0

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