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Simulation of alternative agricultural marketing systems
Agricultural Systems, Vol. 51, No. 4, pp. 395406, 1996 Copyright 0 1996 Published by Elsevier Science Ltd Printed in Great Britain. All rights reserved 0308-521X/96 $15.00+ .OO ELSEVIER
0308-521X(95)00066-6
Simulation of Alternative Agricultural Marketing Systems’ Darrell W. Donahue,a Robert S. Sowellb & Neal M. Bengtsonasc “Department hDepartment
of Bio-Resource Engineering, University of Maine, Orono, ME 04469, USA of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA ‘Department of Business Administration, North Carolina State University, Raleigh, NC 27695, USA (Received
13 March
1995; revised version received accepted 30 October 1995)
8 October
1995;
ABSTRACT A study of the current US flue-cured2 tobacco marketing system was performed to determine the need for a new system. Computer simulation models of 13 alternative marketing systems were developed. The throughput, kg of tobacco processed in a 40-hour period, was measured. Maximum throughput predicted by the 13 models varied from 298508 kg to 1347472 kg per 40-hour period. A fractional factorial experimental design was used to determine significant model factors. Statistical models were employed to isolate important factors and dtflerences among the marketing systems studied. Two marketing systems are recommended to the US flue-cured tobacco industry for further analysis. Copyright 0 1996 Elsevier Science Ltd
INTRODUCTION Flue-cured tobacco is a major agricultural commodity in the southeastern states of Florida, Georgia, South Carolina, North Carolina, and Virginia.
‘Paper No. BAE-92-06 of the Journal Series of the Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, North Carolina 27695-7625. The use of trade names in this publication does not imply endorsement by the North Carolina Agricultural Research Service of the products named, nor criticism of similar ones not mentioned. 2Flue-cured is a method of curing tobacco using controlled temperature and humidity. 395
396
D. W. Donahue, R. S. Sowell, N. M. Bengtson
For example in North Carolina, tobacco represents approximately 20% of farm-gate sales and contributes approximately US $7.5 bn annually to the state’s economy. It is essential that the US explore all possible means of reducing its cost of producing and marketing tobacco if it is to remain competitive. Previous research dealing with computer modelling of tobacco marketing systems is limited. Graves et al. (see Graves, 1969; Graves & Forrest, 1973a, b; Forrest & Graves, 1976; Graves & Sowell, 1976) developed techniques to mechanize receiving and handling sheets of US flue-cured tobacco in the warehouse. This work led to the current system practices of using conveyor systems, fork trucks, and electric carts with trailers. The latter study also showed that a mechanized sales system, in which conveying lines move tobacco through the warehouse, resulted in a 50% reduction in labor compared to conventional warehouses. New studies of US flue-cured tobacco marketing suggest that the equipment and methods currently being used lead to many inefficiencies in the system. Alternative tobacco marketing systems are investigated in this study by the use of computer simulation in an effort to identify lower cost systems. The US flue-cured tobacco marketing system In the current US flue-cured marketing system, tobacco leaves are harvested in the field and put in bulk barns for curing. After curing, the leaves are removed from the barns and placed on burlap sheets that are tied to become 113-136 kg packages, referred to as sheets. The sheets are transported to a warehouse for marketing. At the warehouse, the sheets are unloaded onto conveyors, transported to a weigh station, weighed, and moved to the warehouse floor where they are positioned in rows in preparation for sale. With the purchasers walking past the displayed sheets of leaf, leaf is sold in an ascending bid auction. Once sold, the sheets are retied and moved to another location to await load-out and transport to the purchaser’s processing facility. Other flue-cured tobacco marketing systems Flue-cured tobacco marketing systems operate in other countries. The most prominent auction-based systems are in Canada and Zimbabwe. These systems differ fundamentally from the US system in the way leaf tobacco is packaged and handled before, during, and after the sale. These systems have taken advantage of new technologies, thereby reducing costs.
Alternative agricultural marketing systems
397
In-warehouse marketing costs for the Canadian system was 1.44% for 1994.3 In Zimbabwe, the in-warehouse marketing costs for producers of flue-cured tobacco were approximately 2% of the market value of the crop in 1990.” A portion of the lower costs in these systems is attributed to economies of scale. These economies are realized by using more efficient materials handling methods and increasing the amount of tobacco sold at each market location. For comparison purposes, the US producer’s in-warehouse marketing costs were approximately 2.7%.
MODEL DEVELOPMENT In this study, simulation models were developed to describe functioning of alternative proposed market centers5 and studied using discrete-event computer simulation techniques. Generic unloading, selling and load-out routines were developed, and more detailed models of unloading, selling and load-out were implemented using these generic routines. The primary routines of the models accommodate the packaging (including forming of sales package) of tobacco at the market center. The simulation models were written using the FORTRAN language and SLAM II (Pritsker, 1986). Throughput, kg of tobacco processed per 40-hour period, from each of these models, was evaluated. In the computer simulation study, there were five basic models. Models 1 and 2 had four variations each, Models 3 and 4 had two variations each, and one implementation of Model 5; thus, there is a set of 13 models depicting alternative marketing systems (see Donahue, 1992). The descriptions of the alternative marketing systems and their computer model implementations follow. These computer models assume that stateof-the-art materials handling equipment and computer technology is used. A straight-line throughput conveyor system is implemented in the basic models -- these are referred to as Model 1, Model 2, Model 3, and Model 4. Model 1 models the mechanized sale concept developed by Davis (1968). In this model, see Fig. 1, leaf-tobacco is delivered in loose-leaf form to a single-line unloading station. Upon unloading, the leaf travels along a conveyor to a packaging station. Packages of approximately 454 kg are formed. During the packaging operation, a sample of leaf is 3Linsay D. Secretary and Manager, Ontario Flue-cured Tobacco Growers Marketing Board. bay 1995. 4At the time of writing current costs from Zimbabwe cannot be obtained. 5The term market center is used to distinguish these proposed systems from the currently used term warehouse.
D. W. Donahue, R. S. Sowell, N. M. Bengtson
398
Model 1
unload
unitize
grade
auction
load-out
sale
I-vehicle
Notemt to scale
Fig. 1. Model 1: Schematic of leaf-tobacco marketing process.
drawn for grading. The package then moves along a conveyor to a station where the sample is graded. When grading is completed, the package and its sample are conveyed to the sales station. An opportunity for buyersale-reject is included. After the auction sale is completed, the package moves to a station to await load-out. Three variations of Model 1 are accomplished by rearrangements of the packaging, grading, and selling stations. Model 2 expands the unload lines to three to feed the system more rapidly. The three unloading lines remain the same in all variations of Model 2. A schematic of Model 2 is presented in Fig. 2. Models 1 and 2 and their variations provide these services: unload, package, sample, grade, auction, weighing at some point, and load-out (Models 1C and 2C have no packaging operation). Model 3 uses one unload conveyor feeding the packaging line, and samples the leaf for grading before it is packaged. When the leaf is packaged, the package and its sample are taken from the conveyor and placed in sale rows. The packages are sold each day for an effective 6-hour sale. A schematic of Model 3 is shown in Fig. 3. Model 4 is the same as Model 3 except that it has three unload lines. Model 4 is shown in Fig. 4. Models 3 and 4 and their variations provide these services: unload, grade, package, auction, weighing at some point, and load-out.
399
Alternative agricultural marketing systems
Model 2
flow * c r c
grade
unitize
unload
auction
load out
sale
I-vehicle
NotemA to scale
Fig. 2. Model 2: Schematic
of leaf-tobacco
marketing
process.
Model 3
flow b
unitize
unload
grade
Note:not
wvehicle
Fig. 3. Model 3: Schematic
load out
auction sale floor
of leaf-tobacco
marketing
process.
to scale
D. W. Donahue, R. S. SoweN, N. M. Bengtson
400
Model 4 flow t
m/
m
\
II
unload
unitize
mvehicle
grade
load out
auction sale floor
Note:not to scale
Fig. 4. Model 4: Schematic of leaf-tobacco marketing process.
Model 5 was developed based on an idea similar to the marketing of fresh produce, *6 thereby reducing the number of times leaf tobacco is handled by eliminating the package. Tobacco arrives on a vehicle at the gross-weigh/grade-station of the marketing facility. Gross weight of the vehicle conveying the load is obtained and a leaf sample is drawn for grading. After the leaf grade is determined, the vehicle moves with the sample to a grandstand-type station where the load is sold as the vehicle moves through this station. At the terminal end of the sales station, the bids and purchase price are digitally displayed. If the purchase price is accepted, the vehicle moves to the load-out station where simultaneous unloading/loading occurs with a suction system. The vehicle then moves to a tare-weigh station to obtain a net weight. If the price is not accepted, the load of leaf remains on the vehicle, and it leaves the system to return at a later time. A flow schematic of Model 5 is shown in Fig. 5. Model 5 provides these services: a gross weigh, grade, auction, unload at some point, and a tare weigh.
%uggs, C. W. Agricultural Engineer - North Carolina State University, Carolina. Personal communications. September 1990.
Raleigh, North
Alternative agricultural marketing systems
401
Model 5
-I
auction sale arena gross weigh flow
and grade
C?
’
-1
i
tare weigh
,I
load-out area
Noknot
mvehicle
Fig. 5. Model 5: Schematic
SIMULATION
of leaf-tobacco
marketing
to scale
process.
EXPERIMENTS
After the models were verified a set of experiments was designed to evaluate them. The factors that were determined to affect model performance are summarized in Table 1. Industry experts were asked to provide maximum and minimum data points for each of these model factors.’ Once the maximum and minimum values for each factor were determined, a natural logarithm transformation was performed to find a mode point for each pair of data points (Lawless, 1982). The maximum, minimum, and modal points provide the basis for the experimental design. A fractional factorial experimental design is used to study the main effects and interactions of the factors involved in each model. By fixing factors at the levels described above, the random effects are removed and the resulting simulations are deterministic. A one-half fraction factorial design was used in all the models. In a onehalf fractional factorial design, confounding of higher order interactions with lower order interactions occurs. It was assumed that interactions above the fourth level contribute little to the confounding effect. At the end of each simulation run a results file was created. Statistical analyses were performed using these results files. ‘Price, T. United States Raleigh, North Carolina.
Department of Agriculture - Agricultural Personal communication. August 1991.
Marketing
Service,
402
D. W. Donahue, R. S. Sowell, N. M. Bengtson
TABLE 1
Summary of factors and models which are affected by each factor, ‘x’ indicates that the factor affects the model Model
Factor l,IA,lB, 2,2A,2B Amount of tobacco on vehicle Interarrival times of vehicle Unload rate Grading rate Selling rate Load-out rate Packaging Packaging set-up/unload Floor set-up rate Weigh/grade on vehicle” Travel to grandstand areaa Travel through sales arena” Travel to load-out area0 Travel to tare weigh” Tare weigh”
1 c,2c
3,3A,4,4A
5
X
X
X
X X X X X X X
X X
X X
X X
X
X X
X X X X X
“In Model 5, the same variable name has different meaning and value than in the other models.
Statistical analysis of each simulation model throughput was performed. A stepwise regression of simulation model throughput against the fractional factorial design was performed. This regression helps to identify the factors and interactions that have major effects on each simulation throughput. Based on the stepwise regression, an adjusted model Y*value is calculated for each factor and interaction as they enter the stepwise regression. In all stepwise regression models the cut-off level Y*was above 0.98.
RESULTS
AND DISCUSSION
Throughput analysis Simulated throughputs varied from model to model and within model. Table 2 shows the minimum, maximum, average, standard deviation, and group of the throughput of each model. The maximum model throughput is an important consideration because it directly affects market center profitability and marketing cost reductions for the entire industry.
Alternative agricultural marketing systems
Model
throughput:
Model
number
TABLE 2 of runs, minimum, maximum, and group reported
Factorslruns
S/129 S/129 8/129 8/129 8/129 8/129 8/129 8/129 91257 91257 91257 91257 91257
average,
standard
deviation,
Throughput in kg per 40-hour period Minimum
1 1A 1B 1c 2 2A 2B 2c 3 3A 4 4A 5 .____
403
Maximum
22 938 22 938 22 938 22 938 22 938 68431 22 938 22 938 19497 20 644 19497 61932 22 555 -____
“STD, standard deviation. bGroups are formed by calculating intervals have same letter.
396 649 396 649 396 649 305 177 338 042 395 289 394 836 460112 352 127 363 331 298 508 368 933 1347 472
confidence
Average
STD”
Group’
122073 121626 115363 141685 110271 159 238 115363 170 954 114691 120 298 90 225 131552 275 585
87431 87 042 75 543 90 869 81781 113 120 75 543 111809 75411 78 539 62 400 85 852 371579
A A A B A A A C A A B A D
interval
point
estimate,
overlapping
Because higher throughput translates to higher market center profitability, it is assumed that operators would attempt to operate a marketing system at the highest throughput possible. The maximum throughput values exhibited here are theoretically attainable under normal operating conditions. Therefore, only the maximum throughput, called throughput from this point forward, from each model is discussed in the analysis below. For comparison purposes, the average warehouse in the current marketing system handles roughly 147 386 kg of tobacco per 40-hour period. The maximum any current system handles is 453 494 kg per 40-hour period. This current maximum is the minimum throughput any alternative system should exhibit to be considered as a practical replacement for the current system. This current system value is used for comparison with the alternatives as they are discussed below. Table 2 shows that Models 1, 1A, lB, 2, 2A, 2B, 3, 3A, and 4A all exhibit similar throughput. These models are grouped together based on a confidence interval point estimate. Looking at the result file statistics, these models all have high waiting times related to the packaging operation. The ‘bottleneck effect’ of the packaging operation seems to be the limiting factor of throughput.
404
D. W. Donahue, A. S. Sowell, N. M. Bengtson
TABLE 3
Stepwise linear regression results: model, main factors, interaction Model
1A 1B 1c 2 2A 2B 2c 3 3A 4 4A 5
terms, and adjusted r2
Main factors”
Interaction term9
Adjusted r2
518 518 5/8 4/8 5/8 618 6/8 418 719 719 719 619 519
251120 25/120 491120 1l/120 261120 551120 48/120 1 l/120 561247 561241 511247 951247 261247
0.993 0.993 0.984 0.996 0.998 0.998 0.984 0.990 0.994 0.994 0.997 0.997 0.999
“Main factors brought in during stepwise model/total main factors of model. ‘Interactions brought into stepwise model/total number of interactions in model.
Models 1C and 4 display comparable throughput. The throughput for Model 1C is low because the load-out process is limiting. Model 4 is constrained by the packaging operation. Model 2C exhibits throughput that is similar to the maximum throughputs found in the current system. Recall that Model 2C has no packaging operation. The grading operation is the limiting process for this model. Throughput of 1347 472 kg in Model 5, is much higher than the current system throughputs. The vacuum suction load-out operation is the limiting process of this model. The throughputs exhibited in Model 2C and Model 5 are much higher because state-of-the-art materials handling equipment reduces the number of times tobacco is handled. Throughput can be increased dramatically by reducing the number of handlings and the associated time of handling. Statistical analysis The results of the stepwise regression are summarized in Table 3. The stepwise regression process is helpful when doing factor screening and design of experiments. The analysis of model throughput discussed above is important to understand the significance of each factor and its influence on the particular model. As shown in Table 3, all models require a small number of factors and interactions to explain a significant amount of model variation. The low number of factors and interactions yields a high adjusted model r* value which indicates a large significance value associated with some factors.
Alternative agricultural marketing systems
405
TABLE 4 Summary of the stepwise regression: ‘X’ indicates those main factors that entered stepwise model, ‘0’ indicates those main factors from original model that do not have significant affect on throughput Factor
Model I --_
Amount of tobacco on vehicle Interarrival times of vehicle Unload rate Grading rate Selling rate Load-out rate Packaging Packaging set-up/unload Floor set-up rate Weigh/grade on vehicle0 Travel to grandstand areaa Travel through sales arena” Travel to load-out area” Travel to tare weigh” Tare weigh”
IA IB IC
2
2A 2B 2C
xxxx xxxx xxxx 0.0. ?? ooooooo FD!X
xxxx xxxx xxxx @X.0
xxx
x
;B;X x
x
3
3A
x x xxxxx xxx. xexx OX.0 0.0.. x x x x x x
4
4.4
5
x
x
x
x x x
x x x : X 0 :
Table 4 summarizes the most significant factors found during the stepwise regression. As expected, the vehicle load amount and interarrival times of vehicles were highly significant in most of the models. The unload rate was significant in all models where unloading occurred except for Model 4C. The packaging set-up time and packaging rate were also highly significant in those models having a packaging operation (Models lC, 2C, and 5 do not have packaging operations). SUMMARY
AND CONCLUSIONS
Thirteen computer simulation models, describing alternative methods for marketing US flue-cured tobacco, were developed, and experiments for factor screening performed. Some variation in model throughput was explained by use of the factorial design experiments. Statistical analysis of simulation model throughput was performed to identify model variation. Stepwise regression showed that most of the variation, at least 98% in the model throughput, is explained by a small number of factors and interactions. The packaging operation in some of the systems modelled is a limiting process evidenced by low throughput and by file statistics. This is exhibited in Models 1, 1A, lB, lC, 2, 2A, 2B, 3, 3A, 4, and 4A because these have similar maximum throughputs.
406
D. W. Donahue, R. S. Sowell, N. M. Bengtson
Two models, 2C and 5, represent the best alternatives, based on throughput analysis, to the present system of marketing US flue-cured tobacco. A study of the engineering economics and the economic potential of the marketing systems modelled here will help to identify further the best alternative systems. This research has demonstrated that alternatives to the current US flue-cured tobacco marketing systems are practical and economically feasible. Tobacco is a product that has a market in the US and abroad. The world tobacco economy is becoming more flexible (Babcock & Johnson, 1989). A flexible market requires participants to reduce costs to become more competitive. The information provided by this research will prove useful in identifying areas of cost reduction in the US flue-cured tobacco industry. REFERENCES Babcock, B. A. & Johnson P. R. (1989). Standards applicable to flue-cured leaf from start of on-farm preparation for sale until time to ‘break the sale.’ Proceedings of the US Tobacco Marketing System: A Symposium, ed. J. Paxton Marshall. Myrtle Beach, South Carolina, pp. 78-87. Davis, R. B. Jr. (1968). A conveyor system for flue-cured tobacco auction centers. Presented at 22nd Tobacco Workers Conference. Louisville, Kentucky. Donahue, D. W. (1992). Computer simulation of alternative handling and marketing methods for US flue-cured tobacco. Unpublished PhD Thesis, Biological and Agricultural Engineering Department, North Carolina State University, Raleigh, North Carolina. Forrest, K. R. & Graves, A. H. (1976). New Equipment and Methods for Breaking Tobacco Sales. USDA-ARS publication No. ARS-S-87. Graves, A. H. (1969). Mechanized receiving of untied tobacco delivered in sheets to auction warehouses. Tobacco Science, 13,85-9. Graves, A. H. & Forrest, K. R. (1973a). Scheduling the Arrival of Tobacco at Tobacco Warehouses. USDA-ARS publication No. ARS-S- 12. Graves, A. H. & Forrest, K. R. (1973b). Improving the Receiving of Tobacco at Auction Warehouses. USDA-ARS publication No. ARS-S-28. Graves, A. H. & Sowell, R. S. (1976). Mechanized sales of untied tobacco delivered in sheets to auction warehouses. Tobacco Science, 20, 67-70. Lawless, J. F. (1982). Statistical Models and Methods for Lifetime Data. Wiley Publishers, New York, pp. 17-19. North Carolina Department of Agriculture - Agricultural Statistics Division (1994). North Carolina 1994 Agricultural Statistics, No. 174, ed. D. D. Watson. Raleigh, North Carolina. Pritsker, A. B. (1986). Introduction to Simulation and SLAM ZZ. Systems Publishing Corporation, West Lafayette, Indiana. Statistical Analysis System (1990). SAS Language: Reference, Version 6, 1st Edn. SAS Institute Incorporated, Cary, North Carolina.
0308-521X(95)00066-6
Simulation of Alternative Agricultural Marketing Systems’ Darrell W. Donahue,a Robert S. Sowellb & Neal M. Bengtsonasc “Department hDepartment
of Bio-Resource Engineering, University of Maine, Orono, ME 04469, USA of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA ‘Department of Business Administration, North Carolina State University, Raleigh, NC 27695, USA (Received
13 March
1995; revised version received accepted 30 October 1995)
8 October
1995;
ABSTRACT A study of the current US flue-cured2 tobacco marketing system was performed to determine the need for a new system. Computer simulation models of 13 alternative marketing systems were developed. The throughput, kg of tobacco processed in a 40-hour period, was measured. Maximum throughput predicted by the 13 models varied from 298508 kg to 1347472 kg per 40-hour period. A fractional factorial experimental design was used to determine significant model factors. Statistical models were employed to isolate important factors and dtflerences among the marketing systems studied. Two marketing systems are recommended to the US flue-cured tobacco industry for further analysis. Copyright 0 1996 Elsevier Science Ltd
INTRODUCTION Flue-cured tobacco is a major agricultural commodity in the southeastern states of Florida, Georgia, South Carolina, North Carolina, and Virginia.
‘Paper No. BAE-92-06 of the Journal Series of the Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, North Carolina 27695-7625. The use of trade names in this publication does not imply endorsement by the North Carolina Agricultural Research Service of the products named, nor criticism of similar ones not mentioned. 2Flue-cured is a method of curing tobacco using controlled temperature and humidity. 395
396
D. W. Donahue, R. S. Sowell, N. M. Bengtson
For example in North Carolina, tobacco represents approximately 20% of farm-gate sales and contributes approximately US $7.5 bn annually to the state’s economy. It is essential that the US explore all possible means of reducing its cost of producing and marketing tobacco if it is to remain competitive. Previous research dealing with computer modelling of tobacco marketing systems is limited. Graves et al. (see Graves, 1969; Graves & Forrest, 1973a, b; Forrest & Graves, 1976; Graves & Sowell, 1976) developed techniques to mechanize receiving and handling sheets of US flue-cured tobacco in the warehouse. This work led to the current system practices of using conveyor systems, fork trucks, and electric carts with trailers. The latter study also showed that a mechanized sales system, in which conveying lines move tobacco through the warehouse, resulted in a 50% reduction in labor compared to conventional warehouses. New studies of US flue-cured tobacco marketing suggest that the equipment and methods currently being used lead to many inefficiencies in the system. Alternative tobacco marketing systems are investigated in this study by the use of computer simulation in an effort to identify lower cost systems. The US flue-cured tobacco marketing system In the current US flue-cured marketing system, tobacco leaves are harvested in the field and put in bulk barns for curing. After curing, the leaves are removed from the barns and placed on burlap sheets that are tied to become 113-136 kg packages, referred to as sheets. The sheets are transported to a warehouse for marketing. At the warehouse, the sheets are unloaded onto conveyors, transported to a weigh station, weighed, and moved to the warehouse floor where they are positioned in rows in preparation for sale. With the purchasers walking past the displayed sheets of leaf, leaf is sold in an ascending bid auction. Once sold, the sheets are retied and moved to another location to await load-out and transport to the purchaser’s processing facility. Other flue-cured tobacco marketing systems Flue-cured tobacco marketing systems operate in other countries. The most prominent auction-based systems are in Canada and Zimbabwe. These systems differ fundamentally from the US system in the way leaf tobacco is packaged and handled before, during, and after the sale. These systems have taken advantage of new technologies, thereby reducing costs.
Alternative agricultural marketing systems
397
In-warehouse marketing costs for the Canadian system was 1.44% for 1994.3 In Zimbabwe, the in-warehouse marketing costs for producers of flue-cured tobacco were approximately 2% of the market value of the crop in 1990.” A portion of the lower costs in these systems is attributed to economies of scale. These economies are realized by using more efficient materials handling methods and increasing the amount of tobacco sold at each market location. For comparison purposes, the US producer’s in-warehouse marketing costs were approximately 2.7%.
MODEL DEVELOPMENT In this study, simulation models were developed to describe functioning of alternative proposed market centers5 and studied using discrete-event computer simulation techniques. Generic unloading, selling and load-out routines were developed, and more detailed models of unloading, selling and load-out were implemented using these generic routines. The primary routines of the models accommodate the packaging (including forming of sales package) of tobacco at the market center. The simulation models were written using the FORTRAN language and SLAM II (Pritsker, 1986). Throughput, kg of tobacco processed per 40-hour period, from each of these models, was evaluated. In the computer simulation study, there were five basic models. Models 1 and 2 had four variations each, Models 3 and 4 had two variations each, and one implementation of Model 5; thus, there is a set of 13 models depicting alternative marketing systems (see Donahue, 1992). The descriptions of the alternative marketing systems and their computer model implementations follow. These computer models assume that stateof-the-art materials handling equipment and computer technology is used. A straight-line throughput conveyor system is implemented in the basic models -- these are referred to as Model 1, Model 2, Model 3, and Model 4. Model 1 models the mechanized sale concept developed by Davis (1968). In this model, see Fig. 1, leaf-tobacco is delivered in loose-leaf form to a single-line unloading station. Upon unloading, the leaf travels along a conveyor to a packaging station. Packages of approximately 454 kg are formed. During the packaging operation, a sample of leaf is 3Linsay D. Secretary and Manager, Ontario Flue-cured Tobacco Growers Marketing Board. bay 1995. 4At the time of writing current costs from Zimbabwe cannot be obtained. 5The term market center is used to distinguish these proposed systems from the currently used term warehouse.
D. W. Donahue, R. S. Sowell, N. M. Bengtson
398
Model 1
unload
unitize
grade
auction
load-out
sale
I-vehicle
Notemt to scale
Fig. 1. Model 1: Schematic of leaf-tobacco marketing process.
drawn for grading. The package then moves along a conveyor to a station where the sample is graded. When grading is completed, the package and its sample are conveyed to the sales station. An opportunity for buyersale-reject is included. After the auction sale is completed, the package moves to a station to await load-out. Three variations of Model 1 are accomplished by rearrangements of the packaging, grading, and selling stations. Model 2 expands the unload lines to three to feed the system more rapidly. The three unloading lines remain the same in all variations of Model 2. A schematic of Model 2 is presented in Fig. 2. Models 1 and 2 and their variations provide these services: unload, package, sample, grade, auction, weighing at some point, and load-out (Models 1C and 2C have no packaging operation). Model 3 uses one unload conveyor feeding the packaging line, and samples the leaf for grading before it is packaged. When the leaf is packaged, the package and its sample are taken from the conveyor and placed in sale rows. The packages are sold each day for an effective 6-hour sale. A schematic of Model 3 is shown in Fig. 3. Model 4 is the same as Model 3 except that it has three unload lines. Model 4 is shown in Fig. 4. Models 3 and 4 and their variations provide these services: unload, grade, package, auction, weighing at some point, and load-out.
399
Alternative agricultural marketing systems
Model 2
flow * c r c
grade
unitize
unload
auction
load out
sale
I-vehicle
NotemA to scale
Fig. 2. Model 2: Schematic
of leaf-tobacco
marketing
process.
Model 3
flow b
unitize
unload
grade
Note:not
wvehicle
Fig. 3. Model 3: Schematic
load out
auction sale floor
of leaf-tobacco
marketing
process.
to scale
D. W. Donahue, R. S. SoweN, N. M. Bengtson
400
Model 4 flow t
m/
m
\
II
unload
unitize
mvehicle
grade
load out
auction sale floor
Note:not to scale
Fig. 4. Model 4: Schematic of leaf-tobacco marketing process.
Model 5 was developed based on an idea similar to the marketing of fresh produce, *6 thereby reducing the number of times leaf tobacco is handled by eliminating the package. Tobacco arrives on a vehicle at the gross-weigh/grade-station of the marketing facility. Gross weight of the vehicle conveying the load is obtained and a leaf sample is drawn for grading. After the leaf grade is determined, the vehicle moves with the sample to a grandstand-type station where the load is sold as the vehicle moves through this station. At the terminal end of the sales station, the bids and purchase price are digitally displayed. If the purchase price is accepted, the vehicle moves to the load-out station where simultaneous unloading/loading occurs with a suction system. The vehicle then moves to a tare-weigh station to obtain a net weight. If the price is not accepted, the load of leaf remains on the vehicle, and it leaves the system to return at a later time. A flow schematic of Model 5 is shown in Fig. 5. Model 5 provides these services: a gross weigh, grade, auction, unload at some point, and a tare weigh.
%uggs, C. W. Agricultural Engineer - North Carolina State University, Carolina. Personal communications. September 1990.
Raleigh, North
Alternative agricultural marketing systems
401
Model 5
-I
auction sale arena gross weigh flow
and grade
C?
’
-1
i
tare weigh
,I
load-out area
Noknot
mvehicle
Fig. 5. Model 5: Schematic
SIMULATION
of leaf-tobacco
marketing
to scale
process.
EXPERIMENTS
After the models were verified a set of experiments was designed to evaluate them. The factors that were determined to affect model performance are summarized in Table 1. Industry experts were asked to provide maximum and minimum data points for each of these model factors.’ Once the maximum and minimum values for each factor were determined, a natural logarithm transformation was performed to find a mode point for each pair of data points (Lawless, 1982). The maximum, minimum, and modal points provide the basis for the experimental design. A fractional factorial experimental design is used to study the main effects and interactions of the factors involved in each model. By fixing factors at the levels described above, the random effects are removed and the resulting simulations are deterministic. A one-half fraction factorial design was used in all the models. In a onehalf fractional factorial design, confounding of higher order interactions with lower order interactions occurs. It was assumed that interactions above the fourth level contribute little to the confounding effect. At the end of each simulation run a results file was created. Statistical analyses were performed using these results files. ‘Price, T. United States Raleigh, North Carolina.
Department of Agriculture - Agricultural Personal communication. August 1991.
Marketing
Service,
402
D. W. Donahue, R. S. Sowell, N. M. Bengtson
TABLE 1
Summary of factors and models which are affected by each factor, ‘x’ indicates that the factor affects the model Model
Factor l,IA,lB, 2,2A,2B Amount of tobacco on vehicle Interarrival times of vehicle Unload rate Grading rate Selling rate Load-out rate Packaging Packaging set-up/unload Floor set-up rate Weigh/grade on vehicle” Travel to grandstand areaa Travel through sales arena” Travel to load-out area0 Travel to tare weigh” Tare weigh”
1 c,2c
3,3A,4,4A
5
X
X
X
X X X X X X X
X X
X X
X X
X
X X
X X X X X
“In Model 5, the same variable name has different meaning and value than in the other models.
Statistical analysis of each simulation model throughput was performed. A stepwise regression of simulation model throughput against the fractional factorial design was performed. This regression helps to identify the factors and interactions that have major effects on each simulation throughput. Based on the stepwise regression, an adjusted model Y*value is calculated for each factor and interaction as they enter the stepwise regression. In all stepwise regression models the cut-off level Y*was above 0.98.
RESULTS
AND DISCUSSION
Throughput analysis Simulated throughputs varied from model to model and within model. Table 2 shows the minimum, maximum, average, standard deviation, and group of the throughput of each model. The maximum model throughput is an important consideration because it directly affects market center profitability and marketing cost reductions for the entire industry.
Alternative agricultural marketing systems
Model
throughput:
Model
number
TABLE 2 of runs, minimum, maximum, and group reported
Factorslruns
S/129 S/129 8/129 8/129 8/129 8/129 8/129 8/129 91257 91257 91257 91257 91257
average,
standard
deviation,
Throughput in kg per 40-hour period Minimum
1 1A 1B 1c 2 2A 2B 2c 3 3A 4 4A 5 .____
403
Maximum
22 938 22 938 22 938 22 938 22 938 68431 22 938 22 938 19497 20 644 19497 61932 22 555 -____
“STD, standard deviation. bGroups are formed by calculating intervals have same letter.
396 649 396 649 396 649 305 177 338 042 395 289 394 836 460112 352 127 363 331 298 508 368 933 1347 472
confidence
Average
STD”
Group’
122073 121626 115363 141685 110271 159 238 115363 170 954 114691 120 298 90 225 131552 275 585
87431 87 042 75 543 90 869 81781 113 120 75 543 111809 75411 78 539 62 400 85 852 371579
A A A B A A A C A A B A D
interval
point
estimate,
overlapping
Because higher throughput translates to higher market center profitability, it is assumed that operators would attempt to operate a marketing system at the highest throughput possible. The maximum throughput values exhibited here are theoretically attainable under normal operating conditions. Therefore, only the maximum throughput, called throughput from this point forward, from each model is discussed in the analysis below. For comparison purposes, the average warehouse in the current marketing system handles roughly 147 386 kg of tobacco per 40-hour period. The maximum any current system handles is 453 494 kg per 40-hour period. This current maximum is the minimum throughput any alternative system should exhibit to be considered as a practical replacement for the current system. This current system value is used for comparison with the alternatives as they are discussed below. Table 2 shows that Models 1, 1A, lB, 2, 2A, 2B, 3, 3A, and 4A all exhibit similar throughput. These models are grouped together based on a confidence interval point estimate. Looking at the result file statistics, these models all have high waiting times related to the packaging operation. The ‘bottleneck effect’ of the packaging operation seems to be the limiting factor of throughput.
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D. W. Donahue, A. S. Sowell, N. M. Bengtson
TABLE 3
Stepwise linear regression results: model, main factors, interaction Model
1A 1B 1c 2 2A 2B 2c 3 3A 4 4A 5
terms, and adjusted r2
Main factors”
Interaction term9
Adjusted r2
518 518 5/8 4/8 5/8 618 6/8 418 719 719 719 619 519
251120 25/120 491120 1l/120 261120 551120 48/120 1 l/120 561247 561241 511247 951247 261247
0.993 0.993 0.984 0.996 0.998 0.998 0.984 0.990 0.994 0.994 0.997 0.997 0.999
“Main factors brought in during stepwise model/total main factors of model. ‘Interactions brought into stepwise model/total number of interactions in model.
Models 1C and 4 display comparable throughput. The throughput for Model 1C is low because the load-out process is limiting. Model 4 is constrained by the packaging operation. Model 2C exhibits throughput that is similar to the maximum throughputs found in the current system. Recall that Model 2C has no packaging operation. The grading operation is the limiting process for this model. Throughput of 1347 472 kg in Model 5, is much higher than the current system throughputs. The vacuum suction load-out operation is the limiting process of this model. The throughputs exhibited in Model 2C and Model 5 are much higher because state-of-the-art materials handling equipment reduces the number of times tobacco is handled. Throughput can be increased dramatically by reducing the number of handlings and the associated time of handling. Statistical analysis The results of the stepwise regression are summarized in Table 3. The stepwise regression process is helpful when doing factor screening and design of experiments. The analysis of model throughput discussed above is important to understand the significance of each factor and its influence on the particular model. As shown in Table 3, all models require a small number of factors and interactions to explain a significant amount of model variation. The low number of factors and interactions yields a high adjusted model r* value which indicates a large significance value associated with some factors.
Alternative agricultural marketing systems
405
TABLE 4 Summary of the stepwise regression: ‘X’ indicates those main factors that entered stepwise model, ‘0’ indicates those main factors from original model that do not have significant affect on throughput Factor
Model I --_
Amount of tobacco on vehicle Interarrival times of vehicle Unload rate Grading rate Selling rate Load-out rate Packaging Packaging set-up/unload Floor set-up rate Weigh/grade on vehicle0 Travel to grandstand areaa Travel through sales arena” Travel to load-out area” Travel to tare weigh” Tare weigh”
IA IB IC
2
2A 2B 2C
xxxx xxxx xxxx 0.0. ?? ooooooo FD!X
xxxx xxxx xxxx @X.0
xxx
x
;B;X x
x
3
3A
x x xxxxx xxx. xexx OX.0 0.0.. x x x x x x
4
4.4
5
x
x
x
x x x
x x x : X 0 :
Table 4 summarizes the most significant factors found during the stepwise regression. As expected, the vehicle load amount and interarrival times of vehicles were highly significant in most of the models. The unload rate was significant in all models where unloading occurred except for Model 4C. The packaging set-up time and packaging rate were also highly significant in those models having a packaging operation (Models lC, 2C, and 5 do not have packaging operations). SUMMARY
AND CONCLUSIONS
Thirteen computer simulation models, describing alternative methods for marketing US flue-cured tobacco, were developed, and experiments for factor screening performed. Some variation in model throughput was explained by use of the factorial design experiments. Statistical analysis of simulation model throughput was performed to identify model variation. Stepwise regression showed that most of the variation, at least 98% in the model throughput, is explained by a small number of factors and interactions. The packaging operation in some of the systems modelled is a limiting process evidenced by low throughput and by file statistics. This is exhibited in Models 1, 1A, lB, lC, 2, 2A, 2B, 3, 3A, 4, and 4A because these have similar maximum throughputs.
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D. W. Donahue, R. S. Sowell, N. M. Bengtson
Two models, 2C and 5, represent the best alternatives, based on throughput analysis, to the present system of marketing US flue-cured tobacco. A study of the engineering economics and the economic potential of the marketing systems modelled here will help to identify further the best alternative systems. This research has demonstrated that alternatives to the current US flue-cured tobacco marketing systems are practical and economically feasible. Tobacco is a product that has a market in the US and abroad. The world tobacco economy is becoming more flexible (Babcock & Johnson, 1989). A flexible market requires participants to reduce costs to become more competitive. The information provided by this research will prove useful in identifying areas of cost reduction in the US flue-cured tobacco industry. REFERENCES Babcock, B. A. & Johnson P. R. (1989). Standards applicable to flue-cured leaf from start of on-farm preparation for sale until time to ‘break the sale.’ Proceedings of the US Tobacco Marketing System: A Symposium, ed. J. Paxton Marshall. Myrtle Beach, South Carolina, pp. 78-87. Davis, R. B. Jr. (1968). A conveyor system for flue-cured tobacco auction centers. Presented at 22nd Tobacco Workers Conference. Louisville, Kentucky. Donahue, D. W. (1992). Computer simulation of alternative handling and marketing methods for US flue-cured tobacco. Unpublished PhD Thesis, Biological and Agricultural Engineering Department, North Carolina State University, Raleigh, North Carolina. Forrest, K. R. & Graves, A. H. (1976). New Equipment and Methods for Breaking Tobacco Sales. USDA-ARS publication No. ARS-S-87. Graves, A. H. (1969). Mechanized receiving of untied tobacco delivered in sheets to auction warehouses. Tobacco Science, 13,85-9. Graves, A. H. & Forrest, K. R. (1973a). Scheduling the Arrival of Tobacco at Tobacco Warehouses. USDA-ARS publication No. ARS-S- 12. Graves, A. H. & Forrest, K. R. (1973b). Improving the Receiving of Tobacco at Auction Warehouses. USDA-ARS publication No. ARS-S-28. Graves, A. H. & Sowell, R. S. (1976). Mechanized sales of untied tobacco delivered in sheets to auction warehouses. Tobacco Science, 20, 67-70. Lawless, J. F. (1982). Statistical Models and Methods for Lifetime Data. Wiley Publishers, New York, pp. 17-19. North Carolina Department of Agriculture - Agricultural Statistics Division (1994). North Carolina 1994 Agricultural Statistics, No. 174, ed. D. D. Watson. Raleigh, North Carolina. Pritsker, A. B. (1986). Introduction to Simulation and SLAM ZZ. Systems Publishing Corporation, West Lafayette, Indiana. Statistical Analysis System (1990). SAS Language: Reference, Version 6, 1st Edn. SAS Institute Incorporated, Cary, North Carolina.