Supporting Material is available for this work. For more information, follow the link from
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Table of Contents
Assessing Public Policy Impact on the Sustainable Growth Rate of New Ventures
Kuau Garsson, The California State University, Chico
& Tufts University
Graduate School of Arts & Sciences
Medford, MA 02155
kuaug79@ yahoo.com
Jeff Trailer, Ph.D. The California State University, Chico
College of Business
Chico, CA 95929-0031
Office: 530-898-6570
Fax: 530-898-5501
jtrailer@csuchico.edu
Abstract
The growth of firms is fundamentally based on self-reinforcing feedback loops,
one of the most important of which involves cash flow. When profit margin is positive,
sales generate cash, which may then be reinvested to finance the operating cash cycle.
We analyze simulations of a sustainable growth model of new ventures to assess the
importance of taxes, and regulatory costs in determining growth. The results suggest that
new ventures are particularly vulnerable to public policy effects, since their working
capital resource levels are minimal, and they have few options to raise external funds
necessary to fuel their initial operating cash cycles. Clearly, this has potential
consequences in terms of gaining competitive advantage from experience effects, word of
mouth, scale economies, etc. The results of this work suggest that system dynamics
modeling may provide public policy makers a cost effective means to meet the spirit of
the U.S. Regulatory Flexibility Act.
Contact author: Jeff Trailer, Ph.D.
Assessing Public Policy Impact on the Sustainable Growth Rate of New Ventures
Introduction
The buzz of the stock market bubble of the 1990s has subsided in recent years and
many companies are struggling to survive. This paper analyzes the classical growth of
the firm and hopes to explain certain important variables in finding a level of long-term
sustainable growth for a firm of any size. We use a sustainable growth model utilizing
Cash Conversion Cycles, Cash Required, and Profit Margin to instantiate our data.
Although there is substantial research on the economic impact of income taxation
on labor and wages, “there is a paucity of comparable information regarding the impact
of income taxation on entrepreneurial enterprises” (Holtz-Eakin & Rosen, 2001: 1). The
brevity of research on sustainable growth within academia seems to imply that
controlling production function variables has long been determined much more important
for economic advancement (Matsuyama, 1999). Our study will look at the impacts of
public policy in terms of taxation and regulation costs (i.e. infringements on market
efficiency).
Historically, success rates for new ventures have been abysmal, as “only 41.4% of
new enterprises reach an age greater than five years” (Forrester, 1963: 1). In a more
recent study, the success rate has remained about the same as, “66 percent fail within six
years” (DeCastro et al. 1999). In recent decades, many improvements in modeling firms’
growth patterns have taken place, with Jay Forrester providing us with the first such
model in Jndustrial Dynamics (Forrester, 1961). In this paper, we will analyze
simulations of a new venture. Our focus will be to verify or eliminate the importance of
taxes, and regulatory costs in determining growth, through a self-financed, sustainable
growth model.
Self-Financing Sustainable Growth Model
A company’s sustainable growth rate depends on three factors, the length of time
in the firm’s Cash Conversion Cycle (CCC), the amount of Cash Required (CR) for each
Operating Cash Cycle (OCC), and the magnitude of the Profit Margin (PM), or the
amount of cash generated from each dollar of sales (Churchill and Mullins, 2001).
The first self-financing, sustainable growth rate factor, CCC, represents the
average total amount of time cash is consumed in the firm’s operations; from the
purchase of material from suppliers, to carrying inventory, to collection of credit sales.
The longer this cycle, the longer cash is tied up, and the slower the rate at which cash
may be invested for growth. The maximum length of this cycle is called Operating Cash
Cycle (OCC) and is determined by the sum of days required for carrying inventory and
the days required for collection of accounts receivable. The calculation of OCC days
required may be represented as:
Operating Cash Cycle (in days) = (Accounts Receivable/(Sales/365))+(Inventory/(Cost of
Sales/365))
The average Cash Conversion Cycle (CCC) will be shorter than the OCC by the
average days of accounts payable. Thus, the calculation of CCC days required may be
represented as:
Cash Conversion Cycle (in days) = OCC — (Accounts Payable/(Cost of Sales/365))
The second self-financed, sustainable growth rate factor, Cash Required (CR),
represents the average amount of cash required to finance one CCC. Cash required is a
function of the magnitude of the firm’s costs; cost of sales and operating expenses. As
the firm finds ways to reduce costs, per dollar of sales, a lower amount of cash is required
to finance each operating cash cycle. The lower the amount of cash required for each
cycle, the greater the growth rate for a given level of investment cash available. If it is
assumed operating expenses are paid out uniformly throughout the cycle, then the cash
required for each operating cash cycle may be represented as:
Cash Required (for each OCC) = (Cost of Sales/Sales)*(CCC/OCC)) + ((OCC*.5)/OCC)
The third self-financed, sustainable growth rate factor, Profit Margin (PM),
represents the cash generated per sales dollar, or the efficiency with which potential
reinvestment dollars are generated on each dollar of sales. The greater the earnings per
dollar of sales, the greater the reinvestment amount, and the greater the self-financeable
growth rate. The profit margin may be calculated as:
Profit Margin = Net profit after tax/Sales
The Self-Financeable Growth (SFG) rate for one Operating Cash Cycle (OCC)
may then be approximated as:
SFG rate for each OCC= PM/CR
The annual SFG rate is obtained from the product of the SFG rate for each cycle
and the number of cycles in the year:
Annual SFG rate = (PM/CR)*(365/OCC)
This rate may be compounded to obtain an annual rate:
Compounded annual SFG rate = (1+ (PM/CR)) (365/OCC) -1
Basically then, expanding operations generates cash, which may then finance a
larger operating cycle, which expands operations; resulting in a self-reinforcing feedback
cycle.
y Expanding
Cash 4+) Operations
+
However, as operations expand, the cash required to finance the operating cycle
grows as well. This creates a balancing feedback effect on growth. Thus the firm will
grow only when the cash increases at the same or greater rate than the cash required.
Ls Koa
Cash Cash Required for
ce Each Operating Cycle
ay,
The dominant variable affecting the magnitude of cash generated from operations
is the profit margin. Given the dynamics of compounded returns, ceteris paribus, small
changes in profit margin will invoke large changes in the growth experienced by the firm.
The longer the time period observed, the greater the impact of profit margin on growth. It
is important to note that the exponential growth pattern is not necessarily due to
increasing profit margin as the firm grows (Murphy, Trailer and Hill, 1996), but rather to
the self-reinforcing feedback cycle of increasing investment in the operating cash cycle
that occurs even when the profit margin is unchanging.
Given the exponential nature of this relationship between profit margin and
growth, the short-run impact may be small even though the very-long-run impact will be
so strong that the firm will ultimately be blocked by constraints other than cash flow
issues. The availability of adequate property, plant and equipment are not typically the
dominant limit to growth; rather the availability of management talent (Schumpeter,
1951; Penrose, 1959; Packer, 1963), negative feedback effects of delivery delay
(Forrester, 1961; 1978), capacity-acquisition policy (Nord, 1963), and service quality
erosion (Oliva et al. 2003, Sterman, 2000) limit growth. Even when these latter
constraints are resolved, the growth rate will ultimately be limited by the market
saturation (Smith, 1776, Sterman, 2000). Thus, profit margin may be expected to have a
significant impact on potential, maximum, sustainable growth rates. However, actual
growth rates should be expected to be lower than potential growth rates when the time
period covers many years, or decades.
The Self-Financed, Sustainable Growth Model
In general, it is expected that policy makers are susceptible to problems with
decision making when the decisions are embedded in multi-loop nonlinear feedback
systems, because the human mind is not structured in a manner that accommodates such
complexity (Forrester, 1971). Public policy effects on the growth of new venture firms
are especially associated with such complex systems. The growth of firms is
fundamentally determined by nonlinear cost and revenue functions, each with their own,
multiple, dynamics inputs, many of which include delays in their impact. System
dynamic models offer a means of effectively overcoming such problems of complexity
(Sterman, 2000). Thus, to more effectively investigate the impacts of public policy on
new venture growth, we built a dynamic simulation. The model is a system of nonlinear
differential equations describing:
00) One competitor that sells in a competitive market; the firm represents only
one of many producers and it is assumed that the output of any one firm is
not sufficient to alter the market price.
(ii) The market will purchase as many units as this firm can produce, but will
pay only a single (commodity) price.
(iii) | Nonlinear cost structure, reflecting the interaction of fixed and variable
costs.
(iv) | A delayed, nonlinear impact on the sustainable growth rate from accounts
receivable.
(v) A delayed, nonlinear impact on the sustainable growth rate from accounts
payable.
(vi) | A delayed, nonlinear impact on the sustainable growth rate from cost of
sales.
(vii) A fixed cost impact on the sustainable growth rate from operating
expense.
(viii) A variable expense impact on the sustainable growth rate from sales tax.
(ix) A fixed expense impact on the sustainable growth rate from federal
regulation costs.
Model Structure
The model variables and their interactions are based on existing formulations of
self-financable, sustainable growth rates (Churchill and Mullins, 2001).
The model is comprised of five sectors:
o Cash
o Accounts Receivable
o Accounts Payable
o Labor
o Inventory
The Cash Sector
Cash is generated by sales and consumed by operating expenses. Cash from sales is
reduced by both sales tax and credit sales. Collections on credit sales generate cash. Any
cash accumulated determines the budget for the next weekly order from suppliers.
Cell pric
Collections
Credit sales
percentage
Sales:
Cells sold : Operating Expenses
Sales tax rats
Operating cash cash consumed
generated x
Budget.
The Accounts Receivable Sector
Accounts receivable is generated by credit sales and depleted by collections.
& Bra . Accounts Z >
Receivable
Credit Sales goollecnons
Weeks to collect
Sales: Credit sales
percentage
The Accounts Payable Sector
Accounts payable is generated by orders from suppliers and depleted by payments to
suppliers. The order decision, in terms of the size of the order, is determined by the
budget relative to the cost of the material.
Budget
Order decision Weeks to pay
OND SSP Accounts + ay
Order rate |_Pay {mg payments
Material cost gl
Cell
The Labor Sector
Labor is generated by the rate at which new hires can be recruited and trained, and
is depleted via attrition. The hiring rate decision includes both the anticipated attrition
rate, as well as the delay to the labor pool resulting from time in training.
Weeks to hire
Desired labor level
a $e Labor eer)
Hiring rate ye rate a a rate
Average duration of
employment
Desired New Weeks to train
Hires
The Inventory Sector
Inventory is generated by the rate at which new new orders are placed and
subsequently received from suppliers, and the production rate. Inventory is depleted by
sales. Accumulated sales determine the installed base, which is expected to influence
productivity due to economies of experience.
Order decision
Cost of Labor
Piece rate
Orders :
Placed >) Material Inventory ge! Installed
Purchasing rate Receiving Rate Production Rate Shipping Rate |_Base
Weeks to ees Economies of Weeks to ship.
experience
Desired labor leveld——__\y Weeks to produce
Productivity
The complete model is illustrated below, showing the linkages between the 5
sectors.
10
The Self-financed Sustainable Growth Model
Accounts or
¥ Receivable] Collections
Credit Sales
Cell price }
. Weeks to collect
Sales Credit sales
percentage :
sii Operating Expenses
Cells sold Sales tax rat
Ape} Cash ee)
Operating cash cash consumed
Weeks to pay
a
iin——. sig
Order decision
SS pgs Accounts
' Payabl
Order rate ay 5 payments.
Weeks toreceive. Cell ‘Cost of Labor
‘\ Piece rate”
ra oe Installed
r >| Material >) inventor >|
Purchasing rate |_Placed |" Receivin, B.
g Rate Production Rate| Shipping Rate
ae Saw, en
Economies oF Weeks to ship”,
Weeks to hire experience
‘Desired labor levet#—__ Weeks to produce
Productivity
Base
ge| New Hires
Labor
ee rate ea rate
Average duration of
employment
Hiring rate
Detained Weeks to train
Hires
New hire wage rate
‘Training wages
Growth Dynamics
Our growth dynamics model was used to test whether changes in public policy
would have a “Substantial” impact on the growth of new firms. In creating the model,
the aspects of growth we considered important for public policy, were sales and jobs.
Thus, our conclusions will focus primarily on these two variables.
11
A benefit of system dynamics modeling is that the impact of change in a single
variable can be isolated for assessment. In this case, a baseline self-financeable growth
(SFG) pattern was generated to serve as a control for isolating the impact of alternative
public policies. In this model, it is important to note that the growth of the firm is
limited only by internally generated funds. That is, it is assumed that the firm can sell as
many units as can be produced, and the physical plant provides sufficient capacity for any
production level, over the two year period studied. Also, labor and material are always
available, although with a delay. Thus, the model generates a best-case or maximum
potential growth of the new venture.
The simulation time was
elected to be two years because it is generally the most
restricted, for the entrepreneur, in obtaining external funding for growth. Bank managers
we interviewed stated that they were reluctant to lend to firms with less than three years
of documented operations. Thus, growth for the first couple of years is primarily
dependent on the founders’ own investment, and internally generated cash.
The public policies we studied were changes in the sales tax rate, and federal
regulation costs. In the following sections, data is presented that show how changes in
these policies affect the potential growth rate of a new venture.
Growth Dynamics: Impact of changes in the sales tax rate.
We simulated four sales tax scenarios. Sales tax was 7% in the baseline and we
compared it to: a sales tax increase of 1% (total tax of 8%); a decrease of 1% (total tax of
6%); and the elimination of sales tax (total tax of 0%). Because the latter scenario had
such a strong impact, the results for that scenario are reported separately, last.
12
Because sales tax is typically applied only to final sales, the model assumes the
product is sold to the end consumer. Additionally, the model assumes the product is of
an industry standard and sold internationally, and so the price is set by the market.
Accordingly, to be competitive, the firm must pay the sales tax out of the given market
price. These assumptions are intended to illustrate the comparative advantage associated
with competing counties, states, or nations’ sales tax policies.
The accumulating resource variables were selected for presentation in his section
to illustrate the general dynamics of new venture growth. To observe the dynamics of the
rate variables, the interested reader is referred to the accompanying Vensim model.
Cash. The entrepreneur launches their business with $10,000 in available cash.
The cash performance is presented in figure 1. The entrepreneur runs out of cash in
weeks six and seven, and accordingly requires a cash infusion of about $1,000 for week
six and $200 for week seven. We assume the entrepreneur uses a personal revolving
credit line, likely a credit card, to prevent insolvency. This seems consistent with the
SBA report that about 50% of small businesses use credit card debt. This negative cash
flow occurs because there initially are no sales to cover the costs of work-in-process.
Eventually, sales occur and cash is available for reinvestment in material and labor. The
erratic pattern in cash is entirely due to the firm’s own internal structure of delayed
feedback effects on the ordering decision.
13
Cash
400,000
299,500
199,000
98,500
-2,000
0 8 16 24 32 40 48 56 64 72 80 88 96 104
Time (week)
Cash : Sales tax change from 7 to 6 percent dollars
Cash : Sales tax change from 7 to 8 percent dollars
Cash : Baseline Sustainable Growth Rate dollars
Sales. The rate of sales exhibits the nonlinear growth pattern typical in a new
product life cycle. The seemingly erratic changes are in fact completely deterministic,
not random, effects of the multiple, internal feedback loops. The impact of changes in
the sales tax rate are clearly visible in the sales figure: the increase of 1% to 8% reduces
the comparative sales per week almost 25%, at the end of the second year of operations,
from $150,335 to $113,087; the decrease of 1% to 6% increases the sales per week more
than 30% at the end of the second year of operations, from $150,335 to $196,549.
The implication for the competitiveness of firms is that higher sales tax rates
reduce profit margin, which reduces cash available to be reinvested for growth. If
competing firms face similar constraints in terms of credit sales, and suppliers’ credit
terms, new ventures operating in regions with relatively high sales tax will experience
normal exponential patterns of growth, but at a slower rate. Ultimately, it has potential
consequences in terms of gaining competitive advantage from experience effects, word of
14
mouth, scale economies, etc. Eventually, when the product market matures, the firms
with slower growth rates will be eliminated from the market by their larger counterparts,
as the larger firms achieve cost and market power advantages. The slower growth and
ultimate elimination of these firms has consequences for the employment rate for the
region.
Sales
200,000
150,000
100,000
50,000
0 8 16 24 32 40 48 56 64 72 80 88
Time (week)
: Sales tax change from 7 to 8 percent -—————————_ dl
Sales tax change from 7 to 6 percent doll.
s : Baseline Sustainable Growth Rate do ll
96 104
ars/week
ars/week
ars/week
The advantage of operating in a region with no sales tax is illustrated in the figure
below. The sales per week at the end of year two is $943,647 in the region with no sales
tax, versus sales of $150,335 in the baseline SGR. This is a 600% increase in rate of
sales.
15
Sales
1M
750,000
500,000
250,000
0
0 8 16 24 32 40 48 56 64 72 80 88 9%
Time (week)
Sales : Sales tax change from 7 to zero percent dollars/week
Sales : Sales tax change from 7 to 8 percent dollars/week
Sales : Sales tax change from 7 to 6 percent dollars/week
Sales : Baseline Sustainable Growth Rate dollars/week
Labor. The alternative sales tax policies have very little impact on job growth
over the first year of operations. The firm grows from one employee to five under each
policy. By the end of the second year, however, the typical nonlinear growth pattern is
apparent as small initial differences have large consequences. By the end of the second
year the baseline SGR has employed 87 people. Operating under the higher sales tax
created only 66 jobs (25% less), and operating under the lower sales tax rate the firm
created 99 jobs (14% greater).
16
Labor
100
75
50
25
0 8 16 24 32 40 48 56 64 72 80 88 96 104
Time (week)
Labor : Sales tax change from 7 to 6 percent People
Labor : Sales tax change from 7 to 8 pereent————————— People
Labor : Baseline Sustainable Growth Rat People
The impact of operating in a region with no sales tax is illustrated in the figure
below. The employment at the end of year two is 560 in the region with no sales tax,
versus the employment of 87 people in the baseline SGR. This is a more than 649%
greater rate of job creation.
Labor
600
450
300
0 8 16 24 32 40 48 56 64 72 80 88 96 104
Time (week)
Labor : Sales tax change from 7 to zero percent People
Labor : Sales tax change from 7 to 8 percent People
Labor : Sales tax change from 7 to 6 percent People
Labor : Baseline Sustainable Growth Rate People
17
Installed Base. The cumulative sales are reflected in the Installed Base figure.
Under the baseline SGR, cumulative sales by the end of the second year are 14,571 units,
versus 10,845 units (25% less) under the higher sales tax, and 18,659 (28% greater) under
the lower sales tax, and 70,853 (486% greater) with an absence of sales tax.
Installed Base
80,000
60,000
40,000
0 8 16 24 #32 40 48 56 64 72 80 88 96 104
Time (week)
Installed Bas
: Sales tax change from 7 to zero percent Cells
Sales tax change from 7 to 8 percent Cells
Sales tax change from 7 to 6 percent Cells
aseline Sustainable Growth Rate Cells
Installed Bas
Growth Dynamics: Impact of changes in the costs of regulation.
Four regulation scenarios were simulated. Regulation compliance costs were
treated as a fixed expense in these scenarios. Regulation costs are not always fixed,
however the effect of variable expenses is captured in the preceding section, so the
simulations in this section are intended to illustrate the general growth dynamics
associated with fixed expense impacts.
18
In the baseline SGR, there exist no regulation costs, so the SGR is the same as in
the previous section. Those alternative regulation compliance scenarios reflect the actual,
average cost(s) for firms with less than twenty employees for: environmental costs
($3,328 annually), tax compliance ($1,202 annually), workplace ($829 annually) and all
regulation ($6,975 annually; equals the sum of the previous categories and includes
economic costs) compliance costs (Crain and Hopkins, 2001). In the model, regulation
expense is assumed to be paid out evenly throughout the year, so the annual cost is
divided by 52 weeks and added to weekly operating expenses.
Sales. As illustrated in the sales figure below, the impact of regulation expense
on sales is potentially significant. The baseline SGR, in sales per week, was $150,335.
The SGR including workplace regulation costs was $160,783, an improvement of 7%.
This positive impact is only an artifact of the cyclic patterns of growth. The overall
impact is negative, but only slightly. The SGR including tax compliance costs was
$136,996, an impairment of 9%. The SGR including environmental regulation
compliance costs was $91,211 a 39% decline. The SGR including all regulation costs
was $9,171, a 94% decline. The latter indicates the potential significance of regulation
costs on firm growth. In this case, the profit margin is almost entirely eliminated, and so
there exists virtually no cash to reinvest into the firm.
19
Sales
200,000
150,000
100,000
50,000
0 8 16 24 32 40 48 56 64 72 80 88 96 104
Time (week)
orkplace compliance cost of 829 per year. ————————. dollars/week
‘ax compliance cost of 1202 per year dollars/week
vironmental regulation cost of 3328 per yy ————————————_ dllars/week
ederal regulation cost of 6975 per year dollars/week
s : Baseline Sustainable Growth Rate dollars/week
Labor. The Labor figure below, illustrates that the impact of regulation expense
on employment is potentially significant, as noted with sales previously. The baseline
SGR, in the number of accumulated jobs, was 87. The number of jobs created when
including workplace regulation costs was 86, a decrease of only 1%. The number of jobs
when including tax compliance costs was 80, an impairment of 8%. The number of jobs
when including environmental regulation compliance costs was 51, a 41% decrease. The
number of jobs when including all regulation costs was only 6, a 93% decrease. The
latter indicates the potential significance of regulation costs on job growth. As mentioned
above, in this case, the profit margin is almost entirely eliminated, and so there exists
virtually no cash to reinvest into the firm, so production fails to grow.
20
Labor
100
75
50
25
0 =
0 8 16 24 32 40 48 56 64 72 80 88 96 104
Time (week)
Labor : Workplace compliance cost of 829 per year People
Labor : Tax compliance cost of 1202 per year People
Labor : Environmental regulation cost of 3328 per yr People
Labor : Federal regulation cost of 6975 per year People
Labor : Baseline Sustainable Growth Rate People
Conclusion
The growth of firms is fundamentally based on self-reinforcing feedback loops,
one of the most important of which involves cash flow. When profit margin is positive,
sales generate cash, and this cash can be reinvested to finance the operating cash cycle.
As more cash becomes available, more material and labor may be employed in each
cycle, generating more cash, allowing greater investment, etc. Consequently, in the
absence of limits to growth, the growth dynamics of the firm are compounded returns,
and ceteris paribus, small changes in profit margin will invoke large changes in the
growth experienced by the firm. The longer the time period observed, the greater the
impact of profit margin on growth. The results of these simulations, involving both
variable and fixed expense impacts from public policy, illustrate the exponential nature of
21
the relationship between profit margin and growth; the short-run impact may be small
and hardly noticed, but the long-run impact may be quite strong.
Both types of public policy, sales tax and regulation compliance costs, had
significant impacts on the sustainable growth rate of the model firm. The results suggest
that new ventures are particularly vulnerable to public policy effects, since their working
capital resource levels are minimal, and they have few options to raise external funds
necessary to fuel their initial operating cash cycles. Clearly, this has significant
detrimental consequences in terms of gaining a competitive advantage from experience
effects, word of mouth, scale economies, etc. Eventually, when the product market
matures, the firms with slower growth rates will be eliminated from the market by their
larger counterparts, as the larger firms will have achieved cost and market power
advantages. The slower growth and ultimate elimination of these firms has consequences
for the employment rate for the region. Thus, the results seem to suggest that, in general,
public policies should strive to avoid placing costs on new ventures for the first two to
three years of operations. The exponential growth patterns will generate sales and jobs
after the first couple of years, which may subsequently offset the initial public revenue
lost.
The results of this work suggest that system dynamics modeling may provide
public policy makers a cost effective means to meet the spirit of the U.S. Regulatory
Flexibility Act. “The RFA requires agencies to review their regulatory proposals and
determine if any new rule is likely to have a “significant economic impact on a
substantial number of small entities.” If such impact is likely to occur, the RFA then
requires the agencies to prepare and make available for public comment an “initial
22
regulatory flexibility analysis.” (Whitmore and Walthall, 2001:40). System dynamics
modeling isolates single variables for assessment. In this case, a baseline self-financeable
growth (SFG) pattern was generated to serve as a control for isolating the impact of
alternative public policies. This would seem to be, at least partially, a significant
solution to the problem of assessing potential impact.
Compliance with RFA currently seems to be a problem. “Jn monitoring agencies’
compliance with the law over the years as RFA mandates, the Office of Advocacy
(Advocacy) found that federal agencies, more often than not, failed to conduct the
analyses mandated by the RFA.” (Whitmore and Walthall, 2001:41). Thus, we hope our
results may provide a path of opportunity for public policy makers.
Acknowledgement: The authors would like to thank Nina Tsoi for her help, and
the anonymous reviewers for their thoughtful feedback.
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Appendix A
Model Variable Definitions
Accounts payable: Calculated as the total accumulation of Order rate less Supplier
payments. The initial value is set at zero dollars. Units in dollars.
Accounts receivable: Calculated as the total accumulation of Credit sales less
Collections. The initial value is set to zero. Units in dollars.
Attrition rate: Calculated as Labor divided by Average duration of employment. Units in
people/week.
Average duration of employment: Constant at 100. Units in weeks.
Budget: Calculated as the maximum of (Cash / Weeks to decide) - cash consumed, or
zero. Units in dollars/week.
Cash: Calculated as the total accumulation of Operating cash generated less Cash
consumed. The initial value was set to $10,000.00 dollars. The argument for the
initial amount is that the SBA reports that half of all new ventures in the U.S. are
started with ten thousand dollars or less. Units in dollars.
Cash consumed: Calculated as Supplier payments + Cost of labor + Operating expenses
+ Training wages. This is intended to capture the reduction in cash due to
operating expenses and the delayed expenses of cost of sales. Units in
dollars/week.
Cell price: Constant at $200.00 per Cell. Units in dollars/cell.
Cells sold: Calculated as Shipping rate per week. Units in cells/week.
Collections: Calculated as Accounts receivable divided by Weeks to collect. Units in
dollars/week.
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Cost of labor: Calculated as Piece rate multiplied by Production rate. Units in
dollars/week.
Credit sales: Calculated as Sales * Credit sales percentage. Units in dollars/week.
Credit sales percentage: Constant at 20% of Sales. Units were dimensionless.
Desired labor level: Calculated as INTEGER (Material / Productivity). Units in people.
Economies of experience: Constant at 1. Units in cells.
Inventory: Calculated as the total accumulation of Production rate less Shipping rate.
The initial value was set to zero. Units in cells.
Hiring rate: Calculated as the maximum of (Desired labor level — Labor - New Hires) /
Weeks to hire, or zero. This is intended to prevent a negative hiring rate. Units in
people/week.
Installed base: Calculated as the accumulated Shipping rate. Units in cells.
Labor: Calculated as the total accumulation of Training rate less Attrition rate. Units in
people.
Material: Calculated as the total accumulation of Receiving rate less Production rate.
The initial value was set to zero cells. Units in cells.
Material cost per cell: Constant at $90.00. Units in dollars/cell.
New hires: Calculated as the total accumulation of Hiring rate less Training rate. The
initial level was set to zero people. Units in people.
New hire wage rate: Constant at $300.00. Units in dollars/ (week*People).
Order rate: Calculated as Order decision multiplied by Material cost per Cell. Units in
dollars/week.
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Operating cash generated: Calculated as Sales * (1 - Credit sales percentage)) +
Collections - (Sales * Sales tax rate). This is intended to capture the reduction in
sales-generated cash due to credit sales, and sales tax payments. Units in
dollars/week.
Operating expenses: Constant at $400.00 per week. Units in dollars/week.
Order decision: Calculated as IF THEN ELSE (Budget < $10,000, INTEGER (Budget /
Material cost per Cell)/2, INTEGER (Budget / Material cost per Cell)). Units in
cells/week.
Orders placed: Calculated as the total accumulation of Purchasing rate less Receiving
rate. The initial value was set to 20. Units in cells.
Piece rate: Constant at $50.00 per Cell. Units in dollars/cell.
Production rate: Calculated as the minimum of Material / Weeks to produce or (Labor *
Productivity) | Weeks to produce ). Intended to limit production to the level
dictated by the average productivity of labor. Units in cells/week.
Productivity: Calculated as 20+LN ((Installed Base / Economies of experience) +1).
This is intended to capture a learning curve effect on productivity. Units in
cells/people.
Purchasing rate: Calculated as the maximum of Order decision, or zero. Units in
dollars/week.
Receiving rate: Calculated as Orders placed divided by Weeks to receive. Units in
cells/week.
Sales: Calculated as Cell price * Cells sold. A cell is a high technology, dynamic,
random access memory chip. These Cells conform to industry standard
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specifications, and are typically used by consumers in a wide range of hand-held
electronic devices. Units in dollars/week.
Sales tax rate: Constant at 7% of Sales, unless otherwise specified. Units are
dimensionless.
Shipping rate: Calculated as Inventory divided by Weeks to ship. Units in cells/week.
Supplier payments: Calculated as Accounts payable divided by Weeks to pay. Units in
dollars/week.
Training rate: Calculated as New hires divided by Weeks to train. Units in people/week.
Training wages: Calculated as New hires multiplied by New hire wage rate. Units in
dollars/week.
Weeks to collect: Constant at four weeks. Units in weeks.
Weeks to decide: Constant at one week. Units in weeks.
Weeks to hire: Constant at one week. Units in weeks.
Weeks to pay: Constant at three weeks. Units in weeks.
Weeks to produce: Constant at three weeks. Units in weeks.
Weeks to receive: Constant at two weeks. Units in weeks.
Weeks to ship: Constant at one week. Units in weeks.
Weeks to train: Constant at two weeks. Units in weeks.
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