SMALL MANUFACTURER STRATEGIC DECISION
MAKING ASSISTANCE TOOL (SMSDM): A CASE
STUDY OF A SMALL OKLAHOMA
MANUFACTURER
By
WILLIAM D. ROBERTSON
Bachelor of Science in Agribusiness
Oklahoma State University
Stillwater, Oklahoma
2009
Submitted to the Faculty of the
Graduate College of the
Oklahoma State University
in partial fulfillment of
the requirements for
the Degree of
MASTER OF SCIENCE
July, 2011
ii
SMALL MANUFACTURER STRATEGIC DECISION
MAKING ASSISTANCE TOOL (SMSDM): A CASE
STUDY OF A SMALL OKLAHOMA
MANUFACTURER
Thesis Approved:
Dr. Daniel S. Tilley
Thesis Adviser
Dr. Rodney Holcomb
Dr. Paul Weckler
Dr. Mark E. Payton
Dean of the Graduate College
.
iii
ACKNOWLEDGMENTS
I wasn’t sure if graduate school would be a place that I could excel in, but with
the guidance of Dan Tilley, a little bit of hard work, and good friends to share the
experience with, I guess anything is possible. I want to thank Dan Tilley for not only
being my advisor but also, for giving the right advice when it was needed the most. I
would also like to thank him for the opportunity that he gave me to intern with the New
Product Development Center. It is quite possible that the knowledge and skills I have
obtained their will define me in my next carrier more so, than my graduate degree.
I also want to thank Rodney Holcomb and Paul Weckler for being on my graduate
committee. Without Rodney’s guidance in using Simetar© this thesis may have not been
possible. I would like to thank Kay Watson, Shea Pilgreen, and Bear Runyan for their
advice and guidance on this thesis and other things. Also, thank you Oklahoma State and
the Department of Agricultural Economics for the opportunity to earn my degree.
Finally, thank you Mom, Dad, Nathan, and Michelle for all your support through the
years. Without your guidance and encouragement none of this would have been possible.
iv
.
TABLE OF CONTENTS
Chapter Page
I. PROBLEM STATEMENT ......................................................................................1
Objectives ................................................................................................................3
II. CONCEPTUAL FRAMEWORK AND HYPOTHESES ....................................4
Methods and Procedures ..........................................................................................7
Basic SMSDM .........................................................................................................8
Advanced SMSDM ................................................................................................16
III.SMSDM CASE STUDY .......................................................................................21
IV. FINDINGS............................................................................................................29
SMSDM Case Study Simulations .........................................................................29
Simulation One ......................................................................................................30
Simulation Two ......................................................................................................32
Simulation Three ....................................................................................................34
Simulation Four .....................................................................................................36
Simulation Five ......................................................................................................38
Simulation Six ........................................................................................................40
V. CONCLUSIONS ..................................................................................................44
REFERENCES ...........................................................................................................48
v
LIST OF TABLES
Table Page
Table 1. Oklahoma Small Manufacturer Current and
Proposed Products Spring (2011) ...............................................................22
Table 2. Product Line A Yearly Summary Statistics .................................................25
Table 3. Product Line A Monthly Sales Summary Statistics.....................................25
Table 4. Product Line B Yearly Summary Statistics .................................................25
Table 5. Product Line B Monthly Sales Summary Statistics .....................................26
Table 6. Bureau of Labor Statistics Steel Producer Price Index
Monthly Summary Statistics ....................................................................... 27
Table 7. Bureau of Labor Statistics Steel Producer Price Index
Yearly Summary Statistics ...........................................................................27
Table 8. SMSDM Case Study Simulations ................................................................30
Table 9. Simulation One Product Lines A & B Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................31
Table 10. Summary Statistics Simulation One Monthly Cash Flows and
Annual Cash Flow Year One Projections ....................................................32
Table 11. Simulation Two Product Lines A, B, & C Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................33
Table 12. Summary Statistics Simulation Two Monthly Cash Flows and
Annual Cash Flow Year One Projections ...................................................33
Table 13. Simulation Three Product Lines A1, B, & C Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................35
Table 14. Summary Statistics Simulation Three Monthly Cash Flows and
Annual Cash Flow Year One Projections ...................................................35
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Table Page
Table 15. Simulation Four Product Lines A & B Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................37
Table 16. Summary Statistics Simulation Four Monthly Cash Flows and
Annual Cash Flow Year One Projections ...................................................38
Table 17. Simulation Five Product Lines A, B, & C Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................39
Table 18. Summary Statistics Simulation Five Monthly Cash Flows and
Annual Cash Flow Year One Projections ...................................................39
Table 19. Simulation Six Product Lines A1, B, & C Year 1
Monthly and Year 1 Annual Cash Flow Probabilities .................................40
Table 20. Summary Statistics Simulation Six Monthly Cash Flows and
Annual Cash Flow Year One Projections ...................................................41
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LIST OF FIGURES
Figure Page
Figure 1. Basic SMSDM ............................................................................................10
Figure 2. Triangle Distribution ..................................................................................17
Figure 3. Advanced SMSDM ....................................................................................20
Figure 4. Yearly Sales Cycle in Average Percent
Monthly Sales Product Line A ....................................................................22
Figure 5. Yearly Sales Cycle in Average Percent
Monthly Sales Product Line B ....................................................................23
Figure 6. Expected Yearly Sales Cycle in Average Percent
Monthly Sales Product Line C ....................................................................24
Figure 7. Yearly Steel Price Index Cycle...................................................................27
Figure 8. Advanced Case Study SMSDM .................................................................28
Figure 9. Steel Price Index .........................................................................................43
1
CHAPTER I
PROBLEM STATEMENT
Small businesses in the U.S. create over 50 percent of the domestic non-farm
GDP and are directly related to 60 to 80 percent of all new jobs created over the last ten
years (smallbusinessnotes.com, 2011). In Oklahoma, there are over 70,973 small
employers that account for 94.7 percent of the state’s total employees and 54 percent of
the state’s private sector employment. Of these firms over 3,600 are small manufacturers
which are defined as firms with 500 or fewer employees. These firms accounted for
around 142,000 jobs in Oklahoma in 2006 (SBA, 2009). In total, the manufacturing
sector in Oklahoma employed 9.6 percent of the state’s workforce and accounted for 10.4
percent of Oklahoma’s GDP (Scott, 2008).
Oklahoma has taken steps to help manufacturers stay competitive and continue to create
jobs. Several state and national agencies such as the Oklahoma Center for the
Advancement of Science and Technology (OCAST), National Institute of Standards and
Technology (NIST), and Oklahoma Department of Commerce (ODOC) fund associations
2
and centers such as the Oklahoma Manufacturing Alliance, Innovation to Enterprise
(i2E), Rural Enterprises of Oklahoma (REI), Robert M. Kerr Food &Agricultural
Products Center (FAPC), and the Oklahoma State University New Product Development
Center (NPDC). These organizations help manufacturers stay competitive by giving
advice as well as helping small manufacturers find efficient solutions to problems and
issues they face. These problems may range from engineering and design of new
products to grant writing assistance to obtain funding for projects (New Product
Development Center, 2011). The Oklahoma Manufacturing Alliance offers several
services to Oklahoma manufacturers such as company-wide assessments, technical
assistance, problem-solving resources, local manufacturing councils, business growth
services, lean manufacturing, and assistance in acquiring state incentives.
Even with assistance from these agencies, manufacturers must still make a
strategic decision to employ their advice. All of these firms make strategic decisions
about how their company is organized, how many are employed, how the company is
financed, and which products are manufactured and sold. Many small firms find it
difficult to make strategic decisions and to put in place a strategic plan to achieve the
goals of their business. Robinson and Pearce (1984) assert firms neglect informed
strategic decision making and planning because firm mangers lack 1) time; 2) knowledge
of how to get started in the process; 3) broad expertise that may be necessary to make an
informed decision and plan; and 4) openness or access to outside advisors. For these
reasons many managers have been known to do nothing or accept the first attractive
option instead of fully evaluating their possible alternatives (Robinson and Pearce, 1984).
3
It is important to give small manufacturers a financial analysis that will assist
them in the strategic decision making process and allow manufacturers to simulate the
financial consequences of business decisions. Simulation of the financial consequences
of business decisions will enable manufacturers to first evaluate their current business
situation and compare it to alternative simulated business scenarios before making a
decision. The goal is to help identify decisions that may lead to desirable outcomes with
acceptable risks. To achieve these objectives, the Excel® based program Simetar© is
used to conduct stochastic simulation of profitability and cash flow. Simulating a
stochastic model in Excel® is accomplished by drawing random values for each of the
random variables, letting Excel calculate the model’s equations for multiple iterations
(Richardson 2005).
Objectives
The primary objective of this thesis is to design an informative analytical financial
analysis for a small Oklahoma manufacturing firms that will assist in their strategic
planning and decision making processes. The specific objectives are to:
1. Determine the probability of a positive cash flow and profit for a small Oklahoma
manufacturer under different product mixes and production practice scenarios;
2. Analyze seasonal sales variability of a small Oklahoma manufacturer and
determine its effect on the firm’s monthly cash flow and profit given various
product mixes and employment strategies; and
3. Determine the importance of variability in prices of key inputs, primarily steel, on
cash flow and profit.
4
CHAPTER II
CONCEPTUAL FRAMEWORK AND HYPOTHESES
Budgeting-based economic-engineering analysis is used in this thesis to build a
model that represents a small manufacturer. The economic-engineering technique
described by French (1977) has four steps: 1) system description, 2) specification of
alternative production techniques, 3) estimation of the production input/output
relationships, and 4) synthesis of the cost function. System description is described as the
delineation of the firm, with the full specification of the firm’s nature and operations to
be performed. The specification of alternative production techniques allow for the
consideration of multiple production processes that are being considered by firm
managers. Estimation of the production function or the “building blocks” is the
combination of input/output or production relationships of various operating stages or
components (French 1977). These production relationships are the building and
equipment capacities and the associated input-output relationship for labor, energy, and
materials. Synthesis of the cost function can be performed by applying input prices to
5
the production relationships. Short- run cost functions are obtained by specification of a
set of production techniques and their capacities (French 1977).
The economic-engineering technique has been used to create budgets for
simulation models of firms to predict financial performance for many years. French
(1977) lists more than fifty applications. The development of the microcomputers and
associated spread sheet programs has made the modeling of equipment capacities, the
associated input/output relationships for labor, energy and materials much easier. Falk,
Tilley, and Schatzer used the packing simulation model PACKSIM which was based in
spread sheet software Lotus 1-2-3® in the late 1980’s. PACKSIM simulated the
financial performance of crop packing facilities and allowed the user to run many
different simulations in a short time. The PACKSIM users could change various price,
quantity, and volume scenarios in a simulated packing facility and see the effects of these
changes on profit and cash flow (Falk, Tilley and Schatzer 1987).
Kenkel and Holcomb (2005) have updated the idea into feasibility templates that
are based in the computer program Excel®. These feasibility templates work on many of
the same basic principles as PACKSIM, but are able to be adapted to cover many
different firm types. The templates allow both proposed new firms and existing firms to
conduct feasibility assessments. The feasibility temples can be used unmodified for basic
feasibility assessments or be modified for more advanced assessments (Kenkel and
Holcomb, 2005).
In this thesis a model to assist small manufacturers when evaluating strategic
decisions is created by modifying Kenkel and Holcomb’s (2005) existing feasibility
spreadsheet temple.
6
The foundation theory is that of a profit maximizing firm from neoclassical
microeconomics. Businesses interact with the market to determine pricing and demand
and then allocate resources to maximize net profits given capital, labor, and management
resources. In the long run, sustainability is the goal of the firm but in the short run the
firm’s goal is to maximize profit.
Economists use the term theory of the firm in its singular form, but there is no
single complex multipurpose theory of the firm that explains all firms’ actions and
strategies (Grant 1996). The resource-based view of the theory of the firm states that the
firm is a unique bundle of resources and capabilities, where the primary task of
management is to maximize value through the optimal deployment of existing resources
and capabilities, while developing the firm’s resource base for the future (Grant 1996).
Grant (1996) proposed there was also a knowledge-based theory of the firm. The
knowledge-based view relies on the fundamentals of the nature of firm’s coordination
within an organizational structure, the role of management, the allocation of decision
making, and the theory of innovation. Grant (1996) went on to state that, fundamental to
a knowledge-based theory of the firm is the assumption that the critical input in
production and primary source of value is knowledge.
When using the theory of the firm as a guideline to how a firm will react to
different possible production scenarios, the firm will have several alternative profitable
product mixes and production practices from which to choose. The firm will most likely
chose the product mix that best fits their long-term goals. The probability that the firm
will have a positive cash flow for the overall year is expected to be high, but the
probability for a positive cash flow may be lower for individual months.
7
This is particularly true for input supplying agribusinesses because of the seasonality of
agricultural production and sales.
The hypotheses are:
1. A budgeting-model-based, economic-engineering analysis to assist small
manufactures in strategic decision making is possible and, can be achieved by
modifying Kenkel and Holcomb’s (2005) existing feasibility spreadsheet
template.
2. The firm will have several profitable alternative product mixes and production
practices situations. The most likely situation to have the highest potential cash
flow will be a mix between existing products and new innovatively designed
products.
3. The probability that the firm will have a positive cash flow for the overall year is
expected to be high, but the probability of positive cash flow will be lower for
individual months.
4. Input price variability will increase monthly cash flow variation in this case study.
To test theses hypothesizes a case study of a small Oklahoma manufacturer is
conducted and probabilities for cash flows under different product mixes are calculated.
Methods and Procedures
The methods and procedures for building and simulating annual profitability,
monthly cash flows, and cash flow probabilities are presented for a small manufacturer
strategic decision making model (SMSDM) in this section. The section covers both the
basic version and advanced version of the SMSDM. The basic SMSDM version is an
Excel® spreadsheet that is derived from Kenkel and Holcomb’s existing feasibility
8
spreadsheet template (Kenkel and Holcomb 2005). Kenkel and Holcomb’s existing
feasibility template allows firms to simulate annual income, profit and cash flow of new
business ventures. The feasibility template provides firms with a 10-year annual income
and expense statement with annual cash flow projections for the proposed firm’s venture.
The feasibility template uses four base input pages: 1) the “Input” sheet is where capital
structure information, sales projections, and cost of goods sold data are entered; 2)
the“Deprecation” sheet is used for entry of plant and equipment information; 3. the
“Personal Expenses” sheet is used for employment information 4. the “Expense
Projections” where supplies and miscellaneous expenses are entered. The information
from the four input sheets is then used to calculate market projections, depreciation on
plant and equipment, and loan amortization (Kenkel and Holcomb 2005). From these
calculations annual projected incomes, expenses, and profits statements are created. The
feasibility template also gives firms a “Return on Investment” sheet which includes a
benefit/cost ratio, internal rate of return, the net present value, and the payback period for
the proposed venture (Kenkel and Holcomb 2005).
Basic SMSDM
The SMSDM basic version is based on Kenkel and Holcomb’s existing
feasibility template but uses a single input page rather than three separate pages. The
SMSDM basic version adds the ability for users to produce monthly cash flow
projections from expected monthly sales of up to fourteen products. The ability to
calculate probabilities for monthly cash flows and account for monthly product
inventories are also added
9
Kenkel and Holcomb’s existing feasibility spreadsheet template is modified to
better fit the needs of small manufacturers by: 1) expanding the standard feasibility
template to fourteen products with the option to use monthly sales data for each; 2)
adding the capability to do monthly cash flow and product inventories for the first year;
and 3) creating a single input page for all firm information.
Figure 1 illustrates the flow of information thought the basic SMSDM to small
manufactures for informed decision making. As shown in the Figure 1, monthly product
sales volume, per unit product pricing, unit inputs and materials per product, materials list
and pricing , personnel and salaries, building and equipment, and capital structure are
input on a single input page into the SMSDM. From the information on the input page,
calculations for market projections, depreciation on plant and equipment, variable costs
per unit of production, personnel expenses, and loan amortization are used to create
expense projections and projected incomes. These projections are the used to create
yearly cash flow projections for ten years and monthly cash flow projection for year one.
The probability of a negative cash flow for the months in year one are calculated. The
cash flow projections and probabilities may then be used by the firm to make informed
strategic decisions.
10
Figure 1. Basic SMSDM
Input Page
1. Monthly Product Sales Volume and Per Unit
Pricing
2. Unit Inputs and Materials Per Product
3. Materials List and Pricing
4. Personnel and Salaries
5. Capital Structure
6. Building and Equipment
7. Materials List and Pricing
Market
Projections
Per-Unit of
Production
Building and
Equipment
Costs
Depreciation
Loan
Amortization
Personnel
and Benefits
Expense Projections
Operation
Summary Monthly
Cash Flow
Projections
Basic Monthly Cash
Flow Probabilities
Strategic
Decisions made
based on Cash
Flow Projections
and Probabilities
Variable Cost
Pre-Unit
Production
11
In addition, Kenkel and Holcomb’s (2005) existing feasibility spreadsheet
template which calculates yearly cash flow projections. The ability to do monthly cash
flow projections for the first year was added. This was accomplished by using the
following methods and equations.
Profit before Tax
Profit before Tax ( ) is a profitability measure that looks at a company's
profits before the company has to pay corporate income tax. This measure deducts total
costs ( ) from gross sales (GS ) but it leaves out the payment of tax as shown in
equation (1).
(1) GS
After Tax Profit
After tax profit ( ) is the firms total monthly earnings, reflecting revenues
adjusted for costs of doing business, depreciation, interest, taxes and other expenses for
the given period. After tax profit ( ) is calculated by subtracting taxes (
) form
profit before tax ( ) as shown in equation (2).
(2)
Monthly Cash Flow
Cash flow refers to the relationship between money inflows and outflows in a
specific month. Monthly cash flow ( ) is the summation of after tax profit ( )
and depreciation ( ) with principle ( ) subtracted out for the given month as
shown in equation (3).
(3)
12
Gross Sales
Monthly gross sales (GSM) are the combined sales for all products in the given
month as shown in equation (4),
(4) GS Σ
P
where M is month, i is product, PiM is product price for product i in month M, and QiM is
quantity produced of product i in month M.
Variable Costs
Monthly production expense (PEM) is the total production expense (materials) for
all products built in the given month as shown in equation (5),
(5) Σ
E
where M is month, i is product, EiM is materials cost based on economic-engineering
calculations for product i in month M, and QiM is product i quantity for month M.
Labor ( ) is based on a fixed monthly employment as shown in equation (6),
(6) Σ
! "
where M is month, n is worker classification, SnM is worker salary for worker
classification n in month M, and WnM is number of workers with the classification of n in
the M month.
Monthly utilities expense (UM) is the total utility expense incurred by the firm in a
given month. Total variable cost (TVCM) includes the expenses that vary in direct
proportion to the quantity of the products produced. TVCM is a direct function of
production volume, rising when production increases and falling when production
volume decreases. TVCM for a given month can be calculated from the summation of the
13
total production expenses (PEM), labor expense ( ), and utilities expense (UM) as shown
in equation (7).
(7) # $
Fixed Costs
Monthly equipment and plant maintenance expense ( ) is calculated as a
percentage of the total dollar amount of both equipment and plant facilities. In this model
is set at a fixed amount per month based on a percentage of yearly total dollar
value as shown in equation (8),
(8) % % /12
where t is time period of a year, M is month, PRMm is percentage chosen for maintenance
costs by firm in the year t, and TPEt is total dollar amount of plant and equipment year t.
Monthly cost for insurance ( ) !
) and monthly property taxes ( ) is also calculated
as a percentage of total plant and equipment as shown in equation (9),
(9) ) !
% % /12
where t is year, M is month, PRIt is percentage chosen for insurance costs by firm for
year t, and TPEt is total dollar amount of plant and equipment year t as shown in equation
(10),
(10) % % /12
where t is time period of a year, M is month, PRTt is percentage chosen for property tax
for year t, and TPEt is total dollar amount of plant and equipment year t. Monthly
depreciation ( ) is calculated subject to building type (special propose or standard
buildings), equipment, and vehicles. Standard buildings are depreciated using thirty nine
year straight line deprecation while special propose building, equipment, and vehicles are
14
deprecated using the reducing balance method of deprecation. This method deprecates
items faster at the beginning and slower at the end of their life cycle as shown in equation
(11),
(11) %* %+, %- %. /12
where t is year, M is month, Dtb, deprecation buildings for year t, Dtsp is deprecation
special propose buildings for year t, Dte is depreciation on equipment for year t, and Dtv is
deprecation vehicles for year t. Monthly interest costs for loans ( ) ) is the total
interest charge in the given time period for working capital loans and percent of the firm
that is financed as shown in equation (12),
(12) ) " % % /12
where t is time period of a year, M is month, WCt is working capital loan interest for year
t, and FFt is firm financed loan interest for year t.
Total Fixed Costs ( ) for the given time period can be calculated from the
summation of the time periods monthly equipment and plant maintenance expense
( ), insurance ( ) !
), monthly property taxes ( ), monthly depreciation
( ), and monthly interest costs for loans ( ) ) as shown in equation (13).
(13) ) !
)
Other Cost
Total Other Cost (OCM) may include miscellaneous cost such as patents fees,
research and development expenses, and attorney fees.
Total Costs
Total Cost ( ) describes the total economic cost of production and is made up
of Total Variable Cost (TVCM), which varies according to the quantity of each product
15
produced and, includes inputs such as total Production Expenses (PEM), Labor expense
( ), and Utilities expense (UM). Total Cost ( ) also includes Total Fixed Costs
( ),which are independent of the quantity of a product produced and includes inputs
that cannot be varied in the short term, such as monthly equipment and plant maintenance
expense ( ), insurance ( ) !
), monthly property taxes ( ), monthly
depreciation ( ), and monthly interest costs for loans ( ) ). is calculated as
the summation of all cost for a given time period. Total costs includes Total Variable
Cost (TVCM), Total Fixed Costs ( ), and Total Other Cost (OCM) as shown in
equation (14).
(14) TVC OC
Equations (1) through (14) represent firms that manufacturer products to order
and have just in time inventory. If the firm uses straight line production and keeps a
standing inventory of products the flowing equations (15) and (16) are required in
addition to equations (1) through (14).
Inventory (INM) is the number of unsold good a firm has on hand in a give time
period as shown in equation (15),
(15) ) 3 45
where M is month, i is product, HM-1i is holdover from previous time period or being
inventory for month M for product i, and PMi is production of product for the month M of
product i. Gross sales is constrained by the inventory the firm has on hand for the given
time period as shown in equation (16). This equality shows that gross sales can only be
less than or equal to the firm’s inventory for the time period.
(16) 6! 7 )
16
To confirm the equation for monthly cash flow (MCFm), the outputs of the
monthly cash flow projections of the SMSDM were compared with the existing yearly
cash flow (YCFt) projections generated by Kenkel and Holcomb’s (2005) existing
feasibility spreadsheet template as shown in equation (17).
(17) Σ 9 %
5:
;5
where t is year, M=month, MCFM=monthly cash flow, and YCFt=yearly cash flow.
These comparisons show the equations were correct and did yield the same results.
Advanced SMSDM
The advanced SMSDM requires the Excel® based program Simetar©. Simetar©
allows the program to simulate monthly cash flows based on monthly sales and input
price data. The program also gives the advanced version of the SMSDM the ability to
perform risk analysis for monthly cash flows.
Simetar© was developed in 1997 at Texas A&M University by James W.
Richardson, Keith D. Schumann and Paul A. Feldman. The software was initially
developed to provide simulation and graphical analysis tools for conducting risk analysis
of policy changes on agribusinesses. Simetar© is a simulation language written for risk
analysts to provide a clear method for analyzing data, simulating the effects of risk, and
presenting results in the user friendly environment of Excel© (Richardson 2005). The
advanced version of the small manufacturer strategic decision making model uses
Simetar© for empirical and trianglular distribution tools and scholastic simulation
functions to evaluate financial risks and outcomes.
Figure 3 illustrates the flow of information thought the advanced SMSDM to
small manufacturers for informed decision making. As shown in the Figure 3, the
17
information such as monthly and yearly product sales volume data, per unit pricing,
product materials lists, input prices, personnel and salaries, capital structure, and building
and equipment are input into the SMSDM. From the information on the input page,
calculations for market projections, depreciation on plant and equipment, variable costs
per unit of production, personnel expenses, and loan amortization are used to simulate
monthly sales and monthly input prices. A trianglular distribution based on annual sales
data is used to simulate annual sales for products produced as shown in equation (16),
(16) <= >?@A =BC<=DEC=F> <= >?@A =>, FHA,Max
where Min is the minimum value for the distribution, Mode is the mode of the
distribution, and Max is the maximum value for the distribution as show in Figure 2
Triangle distributions work well in instances where there are little data available
(Richardson 2005).
Figure 2 Triangle Distribution
Mode
Min Max
Triangle Distribution
18
Monthly sales data is used to simulate the seasonal sales cycle for the products.
The simulated annual sales are then adjusted to a monthly sales volume to fit the seasonal
sales cycle as shown in equation (17),
(17) F>CL@M <FHENDC =>A ! @AB 9 !%
where M is month, i is product line, t is year, YTSti is yearly total sales generated from
triangle distribution, and MPmi is monthly percent sales based on products season sales
cycle. An empirical distribution based on annual input price data is used to simulate
annual average priced for inputs as shown in equation (18),
(18) OP=<=N @ =BC<=DEC=F> % !%, !
where t is yearly average price index, St is n sorted random values including min and max
from input price data and F(S) is cumulative probability for the S values, including the
end points of zero and one (Richardson 2005). Monthly input pricing data is used to
simulate the seasonal pricing cycle for inputs. In the SMSDM simulation, the current
annual prices for inputs are adjusted by the annual input price index generated by the
empirical distribution. The adjusted input price is then adjusted for the monthly price
cycle as shown in equation (19).
(19) F>CL@M >PEC <=NAB 9 ! !
Where M is month, YA is yearly annual price index generated, AASIP is actual annual
steel input price for current year, and PRSCM is percent change for monthly adjustment
form yearly steel cycle. These simulations and the information from the input page are
used to produce expense projections and projected income statements. These projections
19
are then used to create yearly cash flow projections for ten years and monthly cash flow
projection for year one. Probabilities are calculated with the program Simetar© for
positive monthly and year one annual cash flows. The cash flow projections and
probabilities may then be used by the firm to make informed strategic decisions.
20
Figure 3. Advanced SMSDM
Annual Market Projections Per-Unit
of Production Using Distribution
Based on Annual Sales Data for a
Small Manufacturer
Building and
Equipment
Costs
Loan Depreciation
Amortization
Personnel
and Benefits
Expense Projections
Monthly Variable Cost Per-Unit of
Production Using Distribution Annual
Average Input Price or Producer Price Index
Data
Operation
Summary
Monthly Cash
Flow Simulation
Simetar©
Strategic
Decisions
Made Based
on Cash Flow
Projections
and Risk
Seasonal Input Price Cycle Based Monthly
Data. Annual Input Price Adjusted for
Seasonal Input Price Cycle
Seasonal Product Sales Cycle based
on Monthly Product Sales Data.
Annual Market Projections Adjusted
for Seasonal Sales Cycle.
Risk Analysis for
Monthly Cash
Flow using
Simetar©
Input Page
1. Monthly Product Sales Volume Data and Per
Unit Pricing
2. Unit Inputs and Materials Per Product
3. Materials List and Pricing Data
4. Personnel and Salaries
5. Capital Structure
6. Building and Equipment
7. Materials List and Pricing
21
CHAPTER III
SMSDM CASE STUDY
The advanced version of SMSDM in conjunction with Simetar© was used
perform simulations for case study of a small Oklahoma manufacturer. The simulations
and data used in this thesis for the SMSDM case study are illustrative of a firm’s actual
situation but have been modified to protect the firm’s financial data. The case study firm
has three current product lines A, B, and C and one proposed product line A1 that would
potentially take the place of product line A. Product line A1 is a redesigned version
product line A that would require less labor per unit of output. Product line A consists of
six product models which are distinguished by a bulk capacity rating. Product lines B
and C both consist of one product model. Product line A1 is assumed to have the same
product line models as product line A. Product lines A, B and proposed product lines A1
are product lines built for the agricultural industry. Product line C is a product line for
school athletics and sports storage. For more information on case study product lines is
included in Table 1. Sales data received from the case study firm revealed that product
lines A and B have distinct seasonal sales patterns. Product line A has a sales pattern that
peaks in the winter months, declines greatly in the spring, is stagnant during the summer,
and greatly increases in the fall as show in Figure 4.
22
Figure 4. Yearly Sales Cycle in Average Percent Monthly Sales Product Line A
Product line B data showed that its sales peaked at the end of spring/beginning of
summer, declined during the summer, and peaked again at the end of summer/beginning
of fall and then declined during the winter months as shown in Figure 5.
Product line C had no data to establish an annual sales pattern. The case study
firm assumes that the majority of sales for product line C are during the summer months,
peaking in June and July with these months accounting for 40 percent of total sales for
23
product line C. These product lines sales patterns and assumptions were used in the
simulations that were conducted for the case study firm.
Case study assumptions are as follows:
1. Product line A1 is expected to use less labor and have the same yearly sales
cycle as product line A. Product line A1 is assumed to eliminate two
employment positions in manufacturing due to a more efficient product
design. It is also assumed that product line A1 will seamlessly replace
product line A and therefore follow the same sales patterns.
2. Product line C is expected to have a yearly sales cycle that peaks during the
months of June, July and August because of the summer shutdown periods of
schools that allow this product to be installed and not hinder operations during
school sessions. This is illustrated in Figure 6.
Figure 5. Yearly Sales Cycle in Average Percent Monthly Sales Product Line B
24
Figure 6. Expected Yearly Sales Cycle in Average Percent Monthly Sales Product
Line C
Monthly sales and price data for product lines A and B for 2008, 2009, and 2010,
from the case study firm, are used in the case study to simulate monthly sales. A triangle
distribution was used in the SMSDM to generate a yearly average sales total for product
lines A and B. To generate these annual totals, yearly sales data was used for these
product lines as shown in Tables 2 and 4. The generated yearly totals for each product
line included in the model. The yearly totals are adjusted by the yearly sales cycle for
their perspective product lines. This allowed the model to both simulate yearly sales
variability as well as hold true to each product’s lines yearly sales cycle. Tables 2 and 4
show yearly and Tables 3 and 5 show monthly sales cycles. Figures 4, 5, and 6. show
yearly sales cycles. Figure 8 is an illustration of how the case study information included
in this section flows thought the advanced SMSDM to small manufactures for informed
decision making.
25
26
Bureau of Labor Statistics (BLS) Producer Price Index (PPI)-Commodities (2004-
2010) data for steel products are used to simulate monthly steel prices in an empirical
distribution for the case study. Bureau of Labor Statistics (BLS) Producer Price Index
(PPI)-Commodities (2004-2010) data for steel products are used to simulate monthly
steel prices in an empirical distribution for the case study. The PPI measures the average
change over time in the selling prices received by domestic producers for their output.
The prices included in the PPI are from the first commercial transaction for products.
The data for the (PPI) is collected by a BLS survey via systematic sampling, from a
listing of all firms that file with the unemployment insurance system (Bureau of Labor
Statistics, 2011). An empirical distribution is used to generate yearly averages for the
producer price index for steel. These averages are based on annual data form Bureau of
Labor Statistics PPI-Commodities (2004-2010) for steel products. The yearly average
form the empirical distribution is then used to adjust the annual average price of all steel
inputs. Once adjusted the steel inputs prices are then used to generate monthly steel
price. This is accomplished by adjusting the new steel prices by the yearly steel price
index cycle as show in Figure 7. By using this method of adjusting steel price we are able
27
to capture both the variability in annual steel prices and as well as the seasonal price
cycle. Tables 6 and 7 have summary statistic for the PPI for steel adjusted to the base
year of 2010.
Figure 7. Yearly Steel Price Index Cycle
28
Figure 8. Advanced Case Study SMSDM
Market Projections Per-Unit of
Production using Triangle
Distribution based on Annual Sales
Data (2008-2010) for Small
Oklahoma Manufacturing Firm
Building and
Equipment
Costs
Loan Depreciation
Amortization
Personnel
and Benefits
Expense Projections
Monthly Variable Cost Per-Unit of
Production using Empirical Distribution
Based on BLS Producer Price Index Steel
Annual Averages (2004-2010)
Operation
Summary
Monthly Cash
Flow
Simulation
Strategic
Decisions
made Based
on Cash Flow
Projections
and Risk
Seasonal Steel Price Cycle based monthly
data from BLS Producer Price Index Steel
(2004-2010)
Seasonal Product Sales Cycle based
on monthly Sales Data (2008-2010)
for Small Oklahoma Manufacturing
Firm
Risk Analysis for
Monthly Cash
Flow using
Simetar©
Input Page
1. Monthly Product Sales Volume and Per Unit
Pricing
2. Unit Inputs and Materials Per Product
3. Materials List and Pricing
4. Personnel and Salaries
5. Capital Structure
6. Building and Equipment
7. Materials List and Pricing
29
CHAPTER IV
FINDINGS
The creation of an informative analytical tool for small Oklahoma manufacturing
firms to assist in their strategic planning and decision making processes was successfully
created by modifying Kenkel and Holcomb’s (2005) existing feasibility spreadsheet
template. Both the basic and advanced version of the SMSDM tool produces monthly
cash flow and yearly cash flow projections. Basic SMSDM allows the firm to do basic
cash flow projections based on limited information about monthly product sales volume
and price per-unit, personnel and salaries, capital structure, buildings and equipment, and
materials. and pricing information for inputs and outputs. The advanced SMSDM uses
the same information but adds the capabilities of Simetar©. Simetar© allows the user to
run simulations of the firms cash flows and calculate probabilities of having a positive
monthly cash flow as well as a positive cash flow for the year.
SMSDM Case Study Simulations
In simulations one through three the case study firm it is assumed that the firm
manufacturers products when orders are received and uses just in time inventory. With
this assumption the firm has no inventory of products or inputs. All products are built to
meet orders and parts are purchased as need to produce the ordered products. Simulations
30
four through six simulate assume that the case study firm produces the same amount of
each product each month and has an inventory of products which sales cannot exceed.
The assumptions for straight line production is that the firm can produce a set amount of
products in a given time period and the production of the products is not dependent on
sales. This allows the firm to build inventory in low sales months and meet demand in
high sales months by selling from accumulated inventory. Table 8 shows further
information on case study simulations one through six. For each simulation, probabilities
for a cash flow above $5,000, probabilities for a cash flow between $5,000 and $0.00,
and the probability of a cash flow bellow $0.00 were calculated and are presented in
stoplight graphs (Richardson et al., 2005). Summary statistics for each simulation are
also presented.
Simulation One
In simulation one simulate cash flows and year one annual cash flow for the case
study firm, are simulated as if the firm only produces product lines A and B, without
maintaining inventory. Simulation one results show that with both products lines A and
B in production the case study firm has a 100 percent probability of a positive annual
31
cash flow in the first year with an average year one annual cash flow of $106,684.23.
January, February, August, October, November and December were all found to have
100 percent probabilities of a positive cash flow above $5,000. March had a 5 percent
probability of a cash flow above $5,000, an 85 percent probability of a cash flow between
$0.00 and $5000, and a 10 percent probability chance of a negative cash flow. The
probability of a positive cash flow for September was 20 percent, 77 percent probability
of a cash flow between $0.00 and $5,000, and a 3 percent chance of a negative cash flow.
The months of April, June, and July have 100 percent probability of negative cash flows
with the month of May having a 99 percent probability of a negative cash flow and a 1
percent chance of a positive cash flow for the month as shown in Table 9.
The summary statistics in Table 10 show that if the case study firm were only to
produce product lines A and B they would require access to capital to operate during the
months of April, May, June, July, and possibly March and September. This capital may
be attained by retaining capital from more profitable months. As shown in Table 10 the
month of April could require up to $15,847.71 in capital to sustain the case study firm for
the month. The month of May $9,297.01, June $24,670.42, July $15,424.84, and finally
32
September could require up to $1,637.28 and March $2,811.03 in funds to continue
operations. In this simulation it would be important for the case study firm to have
access to readably available capital for the summer months. The cumulative cash flow for
the simulation is positive for the year and shows the firm can use accumulated capital to
sustain the firm in negative cash flow months.
Simulation Two
In simulation two, monthly cash flows and year one annual cash flow for the case
study firm is simulated if produced product lines A B and C are produced and no
inventory is maintained. In simulation two, the case study firm is projected to have a 100
percent probability of a positive annual cash flow in year one. The simulation projected
the highest annual cash flow at $214,976.51, an average annual cash flow of
$141,684.77, and a minimum annual cash flow of $56,507.10. The months of January,
February, August, October, November, and December are projected with a 100 percent
probability of a monthly cash flow above $5,000. The month of March has a 25 percent
33
probability of a cash flow above $5,000, a74 percent probability of a cash flow
between$0.00 and $5,000, and a 1 percent chance of a negative cash flow. May has a 38
percent probability of a cash flow between $0.00 and $5,000, and a 62 percent probability
of a negative cash flow. July was found to have a 10 percent probability of a cash flow
above $0.00 and, a 90 percent probability of a negative cash flow. April and June have a
100 percent probability for a negative cash flow as shown in Table 11. Simulation two
summary statistics show that the case study firm’s cash flow problems continue for the
months of April, May, June, and July. The lowest expected cash flow ($17,648.75) is for
the month of June as shown in Table 12.
34
Compared to simulation one, simulation two cash flows are improved because
sales of product C during the summer months generate revenue and employ labor when
product line A sales are low.
Simulation Three
Simulation three is used to simulate the monthly cash flows and year one annual
cash flow for the case study firm if product lines A1, B, and C are produced, with the
firm using just in time inventory. Results of this product mix out of the simulations using
just in time inventory is the most promising with the highest maximum annual cash flow
of $248,416.51. Simulation three also showed improvement in monthly cash flow
projections and probabilities. The months of January, February, August, October,
November, and December all were found to have a 100 percent probability of a cash flow
of least $5,000. The Months of March and September also improved with a 100 percent
probability of a positive cash flow. March was found to have an 80 percent probability of
a cash flow above $5,000 and 20 percent probability of a cash flow between $0.00 and
$5,000. September has a 93 percent probability of cash flow above $5,000 and only a 7
percent probability of a cash flow being lower. The months of May and July had mixed
outcomes with May having an 8 percent probability of a cash flow above $5,000, a 79
percent probability of the cash flow falling between $0.00 and $5,000, and 13 percent
chance of a negative cash flow. July was found to have a 49 percent probability of a cash
flow between $0.00 and $5,000 and a 51 percent probability of a negative cash flow as
shown in Table13. Again April and June with all simulations to this point have a 0
percent probability for a positive cash flow. The summary statistics in Table 14 show
that only the months of April and June will require the firm to acquire capital to continue
35
operation. April requiring an average capital infusion of $7,698.18 and June requires
$11,894.05 to keep manufacturing operations on going. This is an improvement from
simulation two which, on average, would need capital for the months of April, May,
June, and July.
Out of simulations one and two, simulation two shows that the case study firm has
the highest potential annual cash flow when producing all of the existing product lines.
Product line A is clearly the most important product line to the case study firm in making
36
a positive yearly cash flow. The weakness of product line A is the annual sales cycle that
the product follows. It leaves the firm’s cash flow vulnerable form March to September
as shown in Figure 4. Product line B is important to the firm during the months of
March, August, September, and October as shown in Figure 5. The addition of product
line C in simulation two improves the firm’s cash flow position. Product C has the
potential to add cash flow stability to the months of March, May, July, and September.
For product line C to greatly affect cash flow of the case study firm, output and sales of C
would have to be three times greater than what has been assumed. This may not be
feasible in the short run but may be a position the case study firm pursues in the long run
to continue to improve the cash flows for summer months.
Simulation Four
In simulation four, the monthly cash flows and year one annual cash flow for the
case study firm are simulated if product lines A and B are produced. In simulation 4, it is
assumed that the firm is using straight line production and is keeping an inventory of
products which the firm’s gross sales cannot exceed. Simulation four revealed the months
of January, February, August, October, November, and December all were projected to
have a 100 percent probability of a cash flow above $5,000. The months of April, May,
June, and July were found to have a 0 percent probability of a positive cash flow. The
month of March was found to have a 52 percent probability of a cash flow between $0.00
and $5,000 and a 47 percent chance of a negative cash flow. September has a 32 percent
probability of a monthly cash flow above $5,000, a 50 percent probability of a cash flow
between $0.00 and $5,000, and an 18 percent chance of a probability of a negative cash
flow as shown in Table 15. The annual cash flow for simulation four was found to have a
37
99 percent probability of positive cash flow above $5,000 and a 1 percent chance of a
negative cash flow. The lowest annual cash flow found by the simulation was
($19,125.25). When simulation four results are compared to simulation one, simulation
one yields better overall cash flows and cash flow probabilities. Simulation four’s
summary statistics does yield three months that show the potential to have a higher cash
flow then that of simulation one. These months as shown in Table 16 include October
with a maximum cash flow of $40,837.01, November with a maximum cash flow of
$43,925.88, and December with a maximum cash flow of $47,726.92. These cash flows
can be explained by the straight line production and the standing inventory strategy the
firm uses in this simulation. For these three months the firm has higher sales rates for
product line A as well as a larger standing inventory of the product that were produced
during the spring and summer months. This allows the firm to sell products that were
paid for in earlier months. This strategy causes the firm to have lower summer cash
flows than those found in simulation one.
38
Simulation Five
Simulation five is used to simulate the monthly cash flows and year one annual
cash flow for the case study firm if product lines A, B, and C are produced. Simulation
five also assumes the firm is using straight line production and keeping a standing
inventory of products which the firm’s gross sales cannot exceed. This simulation reveals
a 100 percent probability of an annual cash flow above $5,000, with the highest annual
projection of $152,683.22. The monthly cash flows for January, February, August,
October, November, and December were all found to have a 100 percent probability of a
cash flow above $5,000. The months of April, May, June, and July yielded a 0 percent
chance of a positive cash flow. March was found to have a 53 percent probability of cash
flow between $0.00 and $5,000 and a 47 percent chance of a negative cash flow.
September yielded a 33 percent chance of a cash flow above $5,000, a 50 percent chance
of a cash flow between $0.00 and $5,000, and a 17 percent chance of a negative cash
flow. Further cash flow probability information for case study five can the found in
Table 17.
39
The summary statistic shown in Table 18 show that the months of August,
September, October, November, and December all have higher maximum cash flows the
those of simulation two. The spring and summer months are again found to be lower
than those of simulation two. The lowest monthly cash flow of ($34,888.02) is found for
the month of June.
40
Simulation Six
In simulation six, the monthly cash flows and year one annual cash flow for the
case study firm if product lines A1, B, and C are produced. Simulation six also assumes
the firm is using straight line production and keeping an inventory of products which the
firm’s gross sales cannot exceed. Simulation six reveals a 100 percent probability of a
cash flow above $5,000 with the highest possible annual cash flow of $186,123.22. The
months of January, February, August, October, November, and December were all found
to have a 100 percent of a monthly cash flow above $5,000. April, June, and July yielded
0 percent probability of a positive monthly cash flow. The months of March, May, and
September were found to have mixed results. March had a 17 percent probability of a
cash flow above $5,000, a 70 percent chance of a cash flow between $0.00 and $5,000,
and a 12 percent chance of a negative cash flow.
May yielded an 8 percent probability of a cash flow above $0.00 and a92 percent
probability of a negative cash flow. The month of September was found to have a 63
41
percent probability of a cash flow above $5,000, a 36 percent probability of a cash flow
between $0.00 and $5,000, and a 3 percent chance of a negative cash flow as shown in
Table 19.
Summary statistics in Table 20 show that again the months of August, September,
October, November, and December have higher monthly cash flows then those of
simulation three. But again as all simulations’ using straight line production and a
standing inventory, simuation six still has a lower annual cash flow than simulation three.
Simulations One though Six all show a need for the case study firm to have
readably available capital for months that may not have a positive cash flow. These
months are most common in the late spring and summer. From the simulations we can
also conclude that the most profitable product mix for the case study firm is that of
Simulation’s three and six. The product lines A1, B, and C in a product mix give the firm
the highest possible annual cash flow and the least risk of a negative cash flow for all
months. These simulations made several assumptions about product line A1 that should
42
be considered by the case study firm. Since product line A1 replaces the case study firms
most important product line A, caution should be taken when considering any change.
When comparing the two production strategies, (build to order use of just in-time
inventory and straight line production with a standing inventory of products) the strategy
of just in time inventory is found to produce much better overall results. The simulations
using this strategy have a consistently high annual cash flow. The straight line
production strategy does show some promise to improve the cash flow of late summer
and fall months but these improvements are negated by the decrease in cash flow during
early and mid summer months.
Steel price variability increases variability in cash flow. Figure 9 shows that for
the last seven years steel prices have been trending upward at around 21 percent per year.
It is also true that the steel prices have a seasonal price cycle for the time period between
(2004-2010), that peaks during the months of June, July, August, and September as
shown in Figure 7. Since these months are prone for negative cash flows due to low
sales, the rise in steel prices compound the case study firm’s cash flow problems,
particularly when the buys steel for production during summer months when sales are
low. The firm can use buying tactics to limit the negative effects of seasonal price
increases of the summer months by buying steel inputs during the spring. This would
help lower the case study firms overall steel input cost as well as raise the probability of a
positive cash flow for the firm during the slower summer months
43
Figure 9. Steel Price Index
44
CHAPTER V
CONCLUSIONS
Small manufacturers are important to the state of Oklahoma’s economy and the
state of Oklahoma has taken steps to keep them competitive and retain manufacturing
jobs. The state of Oklahoma is currently achieving this through the funding of state
associations and centers such as the Oklahoma Manufacturing Alliance and the
Oklahoma State University New Product Development Center. These organizations help
manufactures stay competitive by giving advice as well with helping small manufacturers
find efficient solutions to problems and issues they face. Even with the assistance from
these agencies the manufacturers must still make a strategic decision to employ their
advice. Many of these firms will have to make strategic decisions that will change how
their company is organized or even which products they manufacture and sell. It is
important to give small manufacturers a tool that will assist them in the strategic decision
making process and allow manufacturers to simulate their business structure. Improved
strategies will enable manufacturers to first understand their current business situation
and simulate business scenarios before making a decision that may or may not lead to
desirable outcomes with a higher probability of success.
45
The primary objective of this thesis is to design an informative analytical tool for
small Oklahoma manufacturing firms that would assist in their strategic planning and
decision making processes.
The specific objectives were to:
1. To determine the probability of a positive cash flow for a small Oklahoma
manufacture firm under different product mixes and production practice
scenarios.
2. To analyze seasonal sales variability of a small Oklahoma manufacture and
determine its effect on the firms monthly cash flow given various product mixes.
3. To determine the importance of variability in prices of key inputs, primarily steel,
on cash flow.
To meet these objectives the small manufacturer strategic decision making model or
(SMSDM) was built by modifying and expanding on Kenkel and Holcomb’s existing
feasibility template, so to better fit the needs of small manufacturers. Two versions of the
SMSDM were created and demonstrated: the basic version for cash flow projections and
probabilities and the more advanced version used in this thesis. The advanced version
uses the Excel® based program Simetar© to run scholastic simulations for monthly cash
flows and gives the SMSDM the ability to calculate cash flow risk. We find that both the
basic and the advance versions of the SMSDM meet the overall objective of this thesis,
which was to assist small manufactures with an informative tool to aid in make strategic
decisions.
46
The SMSDM was used to simulate production practices and product mixes of a
small Oklahoma manufacturing firm. These simulations yielded information to the cash
flow cycle of the firm and probability’s of positive and negative cash flows. It was found
that the firm’s most profitable product mix of current products was a mix of product lines
A, B, and C with product line A being the most important. When the new product line A1
is produced in place of product line A the probabilities of positive cash flows for several
months of the year increase. If the assumption for product line A1 are found to hold true
then these simulations find that product line A should be replaced with product line A1.
The simulation also found that the firms had the highest potential annual cash flow when
using the strategy of just in time inventory.
Product sales data for the case study firm showed that the firm has a very distinct
sales pattern for product line A. The majority of sales for product line A are during the
fall and winter months. Product line B had a more stable sales cycle with increases in
sales during the end of spring and the beginning of fall. These sale cycles haven shown
to leave the case study firm vulnerable during the summer months to negative cash flows.
Product line C attempts to fill the void during these months but has not yet been capable
given its limited volume it has been in production. The case study firm may need to
explore other options for a summer product or think of expanding current product line
sales to new markets. This thesis finds that sales variability is the largest threat to the
firm’s cash flow.
Variability in steel prices does increase the variability in the case study firm’s
cash flow. For the last seven years steel prices have been trending upward at around 21
percent per year on average. Steel prices also have a seasonal price cycle that peaks
47
during the summer months that the case study firm is prone for negative cash flows due
to low sales. This rise in steel prices during the summer months compounds the case
study firm’s cash flow problems.
Further research recommended for future study should consider the inventory of
parts, seasonal variation in employment, and possible alternative employment strategies.
These topics are not directly addressed in this thesis but may hold important information
for Oklahoma manufacturers.
48
REFERENCES
"Bureau of Labor Statistics Data." Databases, Tables & Calculators by Subject. Web. 14
Apr. 2011. <http://data.bls.gov/pdq/SurveyOutputServlet?series_id=wpu101>.
Falk, Constance L., Daniel S. Tilley, R. Joe Schatzer (1987). “THE PACKING
SIMULATION MODEL.” Southern Journal of Agricultural Economics 24(2):
211-216
French, Ben C., (1977) “The Analysis of Productive Efficiency in Agricultural
Marketing: Models, methods, and Progress.” A Survey of Agricultural Economics
Literature 1: 94-170
Grant, Robert M. (1996). “TOWARD A KNOWLEDGE-BASED THEORY OF THE
FIRM.” Strategic Management Journal 17(Winter Special Issue): 109-122
How Important Are Small Businesses to the U.S. Economy?” Small Business Notes.
http://www.smallbusinessnotes.com/small-business-resources/how-important-are-small-
businesses-to-the-us-economy.html (accessed February 7, 2011)
Kenkel, Phil, Rodney Holcomb (2005). “Feasibility Templates for Value-Added
Manufacturing Businesses.” Journal of Food Distribution Research 36(1); 232-
235.
"New Product Development Center - Services." New Product Development Center -
Oklahoma State University. Web. 08 Apr. 2011.
<http://npdc.okstate.edu/services.htm>.
Richardson, James W., Keith Schumann, Paul Feldman (2005). “Simetar: Simulation for
Excel© To Analyze Risk©.” Simetar User Manual
Robinson, Richard B. JR., John A. Pearce II (1984). “Research Thrusts in Small Firm
Strategic Planning.” Academy of Management Review 9(1): 128-137.
SBA (2009). “Small Business Profile: Oklahoma.” U.S. Small Business Administration,
Office of Advocacy 1-2..
VITA
William D. Robertson
Candidate for the Degree of
Master of Science
Thesis: SMALL MANUFACTURER STRATEGIC DECISION MAKING
ASSISTANCE TOOL (SMSDM): A CASE STUDY OF A SMALL
OKLAHOMA MANUFACTURER
Major Field: Agricultural Economics
Biographical:
Personal Data: Born in Vinita, Oklahoma, on February 10, 1986, the son of Dale
and Cindy Robertson.
Education: Graduated from Jay High School, Jay, Oklahoma, May 2005;
received an Associate of Science degree in Agriculture form
Northeastern Oklahoma A&M College, Miami, Oklahoma, May 2007:
received a Bachelor of Science degree in Agribusiness from Oklahoma
State University, Stillwater, Oklahoma, May 2009; completed the
requirements for the Master of Science degree in Agricultural
Economics at Oklahoma State University in July 2011.
Experience: Graduate Research Assistant, Oklahoma State University
Department of Agricultural Economics, August 2010 to May 2011;
Business Analyst Intern, Oklahoma State University New Product
Development Center, May 2009 to August 2010;.
ADVISER’S APPROVAL: Daniel S. Tilley
Name: William D. Robertson Date of Degree: July, 2011
Institution: Oklahoma State University Location: Stillwater, Oklahoma
Title of Study: SMALL MANUFACTURER STRATEGIC DECISION MAKING
ASSISTANCE TOOL (SMSDM): A CASE STUDY OF A
SMALL OKLAHOMA MANUFACTURER
Pages in Study: 48 Candidate for the Degree of Master of Science
Major Field: Agricultural Economics
Scope and Method of Study: The propose was to design an informative analytical tool
for small Oklahoma manufacturing firms that would assist in their strategic
planning and decision making processes. The specific objectives were to: 1)
determine the probability of a positive cash flow and profit for a small Oklahoma
manufacturer under different product mixes and production practice scenarios; 2)
analyze seasonal sales variability of a small Oklahoma manufacture and
determine its effect on the manufacturer’s monthly cash flow and profit given
various product mixes; and 3) determine the importance of variability in prices of
key inputs, primarily steel, on cash flow and profit. Data was obtained for a small
Oklahoma manufacturer and the Bureau of Labor Statistics. The program
Simetar© was used to run cash flow simulations and project cash flow
probabilities for a case study.
Findings and Conclusions: An informative analytical tool for small Oklahoma
manufacturers was successfully created and used to run six simulations. Cash
flow projections for the case study show that the firm has a cash flow problem
during summer months of the year. The negative cash flow for the summer
months was found to be most directly related to variability in product sales. Steel
price variability did affect cash flows negatively during the summer months also
but was less of a factor than sales variability. The firm had the highest projected
annual cash flows when producing one newly designed product and two of the
standard products while manufacturing when products are ordered.