Cost Data Analysis for the Food Iindustry

Journal of Food Engineering 67 (2005) 289–299 www.elsevier.com/locate/jfoodeng

Cost data analysis for the food industry A.Z. Marouli, Z.B. Maroulis

*

School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece Received 24 October 2003; accepted 23 April 2004

Abstract

A systematic analysis of published food industries cost data is used (a) to estimate the appropriate factor models for rapid cost estimation in food plant design, and (b) to reveal the particular food industry characteristics for supporting various techno-economic studies.  2004 Elsevier Ltd. All rights reserved. Keywords: Fixed capital cost; Equipment cost; Raw materials cost; Utilities cost; Annual operating cost; Food factories; Food plant design; Cost estimation; Labor cost; Land cost; Product cost; Manpower; Buildings construction cost; Plant capacity

1. Introduction

Cost data are crucial crucial in plant design. design. The most significant magnitudes concerning the cost estimation of an industry are: cost C fx  the fixed capital cost C fx in $, which is paid during the installation installation period, and  the annual operating cost C op op in $/yr, which is paid during the operation. Both Both fixed fixed capi capita tall and and annu annual al oper operat atin ing g cost cost estimates are also important in project evaluation, product pricin pricing, g, proces processs optimiz optimizati ation on and other other techno techno-economic studies. Based on process design principles, the raw materials and utilities costs are obtained obtained from material material and energy balanc balances, es, while while the purcha purchased sed equipm equipment ent cost cost can be based based on equipm equipment ent sizing sizing proced procedure ures. s. On the other other hand hand labor labor cost cost can be estimat estimated ed from from an intelli intelligen gentt

*

Corresponding author. E-mail address: [email protected] [email protected] (Z.B. Maroulis). URL: URL: http://users.ntua.gr/maroulis.

0260-8774/$ - see front matter  2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2004.04.031

study of the equipment flowsheet, paying attention to the kind of equipments. Rapid fixed capital cost estimation can be based on the purchased equipment cost by using appropriate factors (factor methods). These factors are characteristics of the industrial sector considered, e.g., chemical, pharmaceutical, mineral, food industry. It is the purpose of the present paper to estimate the corresp correspond onding ing factors factors for the food food industr industry y by fitting fitting simplifie simplified d factor factor models models to publish published ed data data retriev retrieved ed from existing food factories. Furthermore, the present paper aims to reveal the basic characteristics of the food industr industry y concer concernin ning g the cost cost distrib distributi ution on to variou variouss resources.

2. Cost estimating models

Fixed Fixed capita capitall cost cost estima estimates tes are often often based based on an estimate of the purchased equipment cost (C ( C eq eq) of the major maj or equipm equipment ent items items require required d for the process, process, the other costs being estimated as factors of the purchased equipment cost. The accuracy of this type of estimate depends on the reliability of the factor values.

290

A.Z. Marouli, Z.B. Maroulis / Journal of Food Engineering 67 (2005) 289–299

Nomenclature

B c C cv C eq C fx C labor C me C mu C op C misc e

building area (m2) product cost ($/kg) civil works cost (M$) purchased equipment cost (M$) fixed capital cost (M$) labor cost (M$/yr) mechanical and electrical works cost (M$/yr) raw materials and utilities cost (M$) annual operating cost (M$/yr) miscellaneous cost (M$/yr) capital recovery factor (–)

F f cv f L f labor f me f mu f misc L M

The factorial method of fixed capital cost estimation (C fx) is expressed by the equation: C fx ¼ f L C eq

Table 1 Cost items definition C eq

ð1Þ

where f L is called the ‘‘Lang factor’’ due to the early work of Lang (Sinnott, 1996). The Lang factor depends on the type of industry. For chemical processes the following values are often used (Sinnott, 1996): 3:10 for predominantly solids processing plants f L ¼ 4:70 for predominantly fluids processing plants

3:60 for a mixed fluidssolids processing plants

ð2Þ For food industries the Lang factor has a lower value because of the higher equipment cost (Maroulis & Saravacos, 2003): f L ¼ 1 :60 for food plants

annual production capacity (1 Gg= 106 kg) (Gg/yr) civil works cost factor (–) Lang factor (–) labor cost factor (–) mechanical and electrical works cost factor (–) raw materials and utilities cost factor (–) supplement cost factor (–) land area (m2) number of employees (persons)

ð3Þ

To make a more accurate estimate, the cost factors that are compounded into the Lang factor are considered individually. The cost items that are incurred in the construction of a plant, in addition to the purchased cost of equipment, can be arranged into the following two categories:

 Civil works cost (C cv), including site improvements, buildings and structures.  Mechanical and electrical works cost (C me), including equipment installation, piping, instrumentation and controls, electrical equipment, engineering and supervision. The above division is selected for the purpose of the present analysis, which is appropriate for the available data. As the definitions of these terms given by various authors are generally different, Table 1 is introduced to make clear the definitions used. More sophisticated

(A) Total capital (I) Fixed capital Purchased equipment Installation Piping Instrumentation and control Electrical Buildings Yard improvement Land Engineering Contingency

C me

C cv

* * * * * * * *

(II) Working capital C mu (B) Total production cost (I) Operating cost (direct) Materials Utilities Labor Miscellaneous

C labor

C misc

* * * *

(II) Overheads (indirect) Administrative Sales General expenses

divisions of the capital fixed cost are considered in the literature (Clark, 1997; Peters & Timmerhaus, 1991). Based on the above cost division, the detailed factorial method can be expressed by the following equations: C fx ¼ C eq þ C cv þ C me

ð4Þ

C cv ¼ f cv C eq

ð5Þ

C me ¼ f meC eq

ð6Þ


where C eq C cv C me f cv f me

291

The effect of production capacity (F ) is examined in the following:

purchased equipment cost civil works cost mechanical and electrical works cost civil works cost factor mechanical and electrical works cost factor

Purchased equipment cost (C eq) Annual operating cost (C op) Product cost (c) Product cost is defined as follows: c ¼ ðeC fx þ C op Þ= F

The above model is equivalent to the following: C fx ¼ ð1 þ f cv þ f me ÞC eq

ð7Þ

In a similar way, the annual operating cost can be estimated based on the raw materials and utilities cost (C mu), using appropriate factors. The raw materials and utilities cost can be calculated accurately by material and energy balances. In food plants, the cost of packaging materials is important (Clark, 1997) and it should be included in the raw materials. The one-factor method for the annual operating cost (C op) is expressed by the equation: C op ¼ f op C mu

ð8Þ

where f op is the operating cost factor. The detailed factorial method for the annual operating cost (C op) is summarized by the following equations: C op ¼ C mu þ C labor þ C misc

ð9Þ

C labor ¼ f laborC mu

ð10Þ

C misc ¼ f misc C mu

ð11Þ

where C op C mu C labor C misc f labor f misc

annual operating cost raw materials and utilities cost labor cost miscellaneous cost (maintenance, repairs, royalties and patents) labor cost factor miscellaneous cost factor

The above model is equivalent to the following: C op ¼ ð1 þ f labor þ f misc ÞC mu

ð12Þ

ð13Þ

where the annual production capacity F the capital recovery factor e

(b) Requirements in employees, building and land versus production capacity (F ) Employees (M ) Buildings (B ) Land (L) (c) Interrelation between the various fixed capital cost items The effect of the purchased equipment cost ( C eq) is examined in the following: Fixed capital cost (C fx) Civil works cost (C cv) Mechanical and electrical works cost (C me) (d) Other interesting cost-related relationships Annual operating cost (C op) versus annual materials and utilities cost (C mu) Annual materials and utilities cost to purchased equipment cost (C mu/C eq) ratio Average labor rate (C labor/M ) Average building construction cost (C cv/B ) The results of the above analysis are very useful in various techno-economic studies such as plant design, project evaluation, process optimization, product pricing.

4. Data and methods 3. The food industry cost radiography

The term ‘‘food industry cost radiography’’ means the particular characteristics of the cost distribution to its components and the relationships which express that allocation. These characteristics could be grouped into the following categories: (a) Effect of the production capacity on the various cost items

The appropriate data for the present analysis have been retrieved from the work of Bartholomai (1987). Forty one existing factories (Table 2) are described in a uniform format concerning the process, the equipment and the cost data. Most of the food plants refer to economic conditions in the United States and Western Europe in the year 1986. Cost data refer to both fixed capital (Table 3) and annual operating (Table 4) costs in US dollars ($).

292


Table 2 Food factories data: product capacity, annual operating time (Bartholomai, 1987) No.

Plant

Product

1 2 3 4 5 6 7 8 9

Fruits and vegetables Apple Processing Plant Community Cannery for Education Fruit Puree Plant Multi-purpose Fruit Processing Line Orange Juice Concentrated Plant Baby Food Line Tomato Paste Plant Frozen Vegetable Plant Mushroom Farm

Apple products Fruit jams Fruit puree Fruit jams Concentrated juice Baby food Tomato paste Frozen vegetables Mushrooms

4000 4800 6000 4800 3200 10,000 5000 4400 80

1000 2000 2000 2000 2000 2000 2000 2000 2000

10 11 12 13 14 15 16 17

Dairy and egg products Mozzarella Cheese Plant Blue Cheese Plant Dairy Plant Modular Dairy Plant Milk Powder Plant Dried Whole Egg Plant Yoghourt Plant Ice cream Plant

Mozzarella cheese Blue cheese Milk products Milk products Skim milk powder Egg powder Yogurt Ice cream

1875 5440 43,500 5000 12,000 500 25,000 4000

3000 2560 2400 2000 7200 2000 3125 2000

18 19

Cereals and grains Parboiled Pice Plant Corn Starch Plant

Parboiled rice Corn starch

36,000 42,000

7200 7920

20 21 22

Pasta and Tofu Pasta Plant Plant Precooked Lasagna Plant Tofu Line

Pasta Lasagna Tofu

3575 3420 2600

5500 5700 2000

23 24

Fermentation Baker s Yeast Plant Vinegar Plant

Dry yeast Vinegar

8200 2500

7200 7200

25 26 27

Extruded products and snacks Quenelles Plant Tortilla Chip Plant Corn Snacks Plant

Quenelles (dumpling) Tortilla chips Corn snacks

1200 1750 500

2000 3500 2000

28 29 30 31 32 33

Seafoods and meats Catfish Processing Plant Shrimp Processing Plant Surimi Plant Cattle Slaughterhouse Coextruded Sausage Plant Protein Recovery Plant

Frozen fish Frozen shrimp Seafood Slaughter products Sausages Protein

3350 375 20,100 28,800 1600 23,750

2000 1500 3528 1800 1600 4000

34 35

Fats and oils Soybean Oil Extraction Plant Vegetable Oil Refinery

Soybean oil Cooking oil

300,000 10,800

7200 6000

36 37 38

Baked products Pan Bread Bakery Arabic Bread Bakery Half-backed Frozen Baguette Bakery

White bread Arabic bread Frozen bread

11,000 10,608 2160

5000 6240 4000

39 40 41

Beverages Sea Water Desilination Plant Fruit Juice Plant Soymilk Line

Water Fruit juice Soymilk

3,000,000 4000 2000

7200 2000 2000



Capacity (Mg/yr)

Operating time (h/yr)

293

A.Z. Marouli, Z.B. Maroulis / Journal of Food Engineering 67 (2005) 289–299 Table 3 Food factories data: fixed capital cost (Bartholomai, 1987) No.

Plant

Fixed capital cost ($) Equipment

Mech. & Electr.

Civil

Total

1 2 3 4 5 6 7 8 9


1,963,000 103,817 767,000 401,600 1,016,500 200,000 1,042,085 803,820 41,000

313,500 52,183 183,000

646,760 48,000 150,000

391,000

650,000

324,915 96,180 355,00

470,000 450,000 40,000

2,923,260 204,000 1,100,000 401,600 2,057,500 200,000 1,837,000 1,350,000 116,500

10 11 12 13 14 15 16 17


340,750 2,860,000 6,600,000 760,200 2,700,000 1,302,500 3,236,600 1,330,000

65,850 843,000 4,620,000 427,700 715,000 314,700 878,000 650,000

435,000 390,000 2,310,000 22,000 585,000 670,000 713,000 535,000

841,600 4,093,000 13,530,000 1,209,900 4,000,000 2,287,200 4,827,600 2,515,000

18 19


888,000 13,680,000

326,500 6,842,000

165,000 9,776,000

1,379,500 30,298,000

20 21 22


1,714,000 2,211,800 350,000

374,000 590,400

265,000 458,800

2,353,000 3,261,000 350,000

23 24


9,776,000 498,700

7,286,000 136,300

9,488,000 115,000

26,550,000 750,000

25 26 27


476,000 1,250,000 122,425

409,000 153,000 37,575

100,000 285,000 150,000

985,000 1,688,000 310,000

28 29 30 31 32 33


1,011,803 191,700 5 899 600 930,000 1,000,000 1,900,000

588,197 59,300 1,300,400 355,000 410,000 311,000

800,000 180,000 2,800,000 2,375,000 590,000 460,000

2,400,000 431,000 10,000,000 3,660,000 2,000,000 2,671,000

34 35


5,920,000 1,320,000

1,480,000 524,000

17,500,000 515,000

24,900,000 2,359,000

36 37 38


1,719,600 659,150 1,188,850

448,400 228,100 390,150

635,000 384,500 374,000

2,803,000 1,271,750 1,953,000

39 40 41

Beverages Sea Water Desilination Plant Fruit Juice Plant SoymilkLine

8,043,000 497,000 910,000

8,009,000 95,000

2,381,000 217,000

18,433,000 809,000 910,000



294


Table 4 Food factories data: annual operating cost (Bartholomai, 1987) No.

Plant

Annual operating ($/yr) Materials

1 2 3 4 5 6 7 8 9


10 11 12 13 14 15 16 17


18 19


20 21 22


23 24

Utilities

Labor

Suppl.

Total

5,855,640

904,000

618,000

505,000

852,000

7,100,000 1,850,000 78,000

59,200 762,000 362,000 26,600 790,000 307,920 8000

216,000 24,000 166,000 30,000 1,280,000 340,000 54,000

57,000 24,000 100,000 65,000 75,000 25,000 16,500

7,882,640 0 1,184,200 810,000 5,873,000 121,600 9,245,000 2,522,920 156,500

1,700,000 1,520,000 7,000,000 760,000 13,210,000 3,589,520 11,750,000 1,074,860

51,600 157,560 893,400 48,325 1,088,000 587,250 465,375 143,500

86,000 304,000 1,058,000 118,000 98,000 178,000 246,000 271,000

10,000 30,000 300,000 23,800 160,000 83,000 111,700 31,000

1,847,600 2,011,560 9,251,400 950,125 14,556,000 4,437,770 12,573,075 1,520,360

7,191,220

146,200 3,817,440

138,400 664,000

5000 469,140

289,600 12,141,800

1,364,000 1,330,100

147,000 384,180

170,000 198,000

14,500 92,751

1,695,500 2,005,031


961,400 257,275

1,123,950 18,585

262,000 90,000

106,000 8630

2,453,350 374,490

25 26 27


720,000 955,500 123,800

35,600 239,750 10,000

102,000 178,000 82,000

25,000 62,000 200

882,600 1,435,250 216,000

28 29 30 31 32 33


11,237,680 581,250 8,262,640 28,260,000 167,500 0

141,000 97,500 949,810 468,000 70,500 1,440,000

286,000 110,000 1,626,690 1,292,000 78,000 132,000

374,000 38,000 1,332,490 122,000 25,000 383,000

12,038,680 826,750 12,171,630 30,142,000 341,000 1,955,000

34 35


72,000,000 7,305,000

3,698,000 252,000

1,080,000 322,000

1,018,000 62,250

77,796,000 7,941,250

36 37 38


3,302,000 2,733,627 826,944

332,100 76,196 188,800

213,600 169,000 148,000

55,000 30,000 13,000

3,902,700 3,008,823 1,176,744

39 40 41


8,589,440 1,669,600

0 1,465,000

8,403,840 96,800

100,000 86,000

85,600 21,800



5,245,000

295

A.Z. Marouli, Z.B. Maroulis / Journal of Food Engineering 67 (2005) 289–299 Table 5 Food factories data: requirements in buildings, yard and employees B ( artholomai, 1987) No.

Buildings (m2)

Plant

Yard (m2)

Employees (persons) UO

1 2 3 4 5 6 7 8 9


10 11 12 13 14 15 16 17


18 19


20 21 22


23 24


25 26 27


28 29 30 31 32 33


QCT

FM

PM

3

1

2 1 2 1 1

1

2

1 2 2

3 3

1 1 1

2 1

1 1 1

14 5 31 4 16 2 29 50 10

2 6 20 4 17

1 4 13 2 1 1 3 6

1 2 5 1 1 1 1 3

1 2 9 1 1 1 3 1

1 1 1 1 1 1 1 1

8 31 115 14 10 24 36 28

3000 6000

10,000 30,000

1500 11,300 7000 0 1500 1200 8500 1700

10,000 30,000 20,000 1500 0 3000 20,000 5000

600 2400

2000 20,000

2 18

8 3

2 21

2 6

1 4

1 1

16 53

2000 1150

10,000 4000

9 12 4

2 1 2

2 1 1

3 1 1

2 3 1

1 1 1

19 19 10

8000 375

40,000 2000

34 2

25 1

4 2

5 1

3 1

1 1

72 8

350 950 500

2000 4000 2000

5 3 1

13 2

1 3 1

1 1 1

1 3 1

1 1 1

9 24 7

1200 600 11,500 5200 1000 1000

10,000 2000 20,000 30,000 5000 3000

8 84 54 2

27 2 30 115 1 4

1 1 10 5 1 4

1 1 4 2 1

1 1 8 3 1 2

1 1 1 1 1 1

31 14 137 180 7 11

34 35


2500 1000

40,000 10,000

28 16

6 9

13 8

3 1

9 2

2 1

61 37

36 37 38


2800 1000 1056

10,000 4000 6000

18 4 8

3 2 2

1

19 2

2 2 2

1 1 1

25 28 15

39 40 41


110 724

3000 5000

4 1

20 2 1

1 1

1 1

1 1

1 1 1

21 10 6

2 87 7

24

10 1 2 1 2

2 22

3

Total

5000 800 2000 0 5000 0

1 20 43 6

2

M

2000 150 500 100 2000 300



5 2 24 3

SO

1 1

UO: unskilled operators, SO: skilled operators, M: mechanics, QCT: quality control technicians, FM: foreman, PM: plant manager.

296


The original data were doubled to take into account the inflation. Based on the Marshall and Swift Equipment Cost Index and the Consumer Price Index, this assumption seems to be logical for the 20 years period from 1985 to 2005. Furthermore, the requirements in employees, buildings and yards are presented (Table 5). Among the 41 plants summarized in Tables 2–5 four are concerning process lines (Nos. 4, 6, 22, and 41), one farm cultivation (No. 9) and one education plant (No. 2). These data are not taken into account. Thus, 35 existing factories are considered in the present analysis. Simple power models of the following form are used to describe the relationship between any two magnitudes (X ,Y ) throughout this paper. Y ¼ fX n

100

milk products

seafood

yogurt

) 10 $ M (

blue cheese

q e

lasagna

C

t s o c t n e m p i u q e d e s a h c r u P

skim milk powder

slaughter products

apple products

white bread protein ice cream cooking oil pasta frozen fish tomato paste frozen bread

concentrated juice sausages tortilla chips

milk products arabic bread quenelles (dumplings)frozen vegetables fruit puree mozzarella cheese

fruit juice vinegar

1

frozen shrimp corn snacks

ð14Þ

Eq. (14) was fitted to the appropriate data for all the cases described in Sections 2 and 3 above. When the estimated value of parameter n was near to unity a linear model was refitted. All the regressions were presented graphically in appropriate figures. In addition to the best fitting curve two more curves are presented: the double of the best fitting curve (maximum) and the half of the best fitting curve (minimum). The data lying significantly outside of this region were considered as outliers and were excluded from the regression. It should be noted that, in most cases, the estimated model parameters were very sensitive to the points considered in the regression analysis. Thus, no strictly statistical criteria but rather visual observations were used. In addition, rounded values of the parameter estimates were used.

C eq = 1.80 F 1/2

0.1 0.1

1

10

Fig. 1. Purchased equipment cost versus plant capacity for various food industries. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

100

slaughter products

skim milk powder frozen fish

corn starch cooking oil

) r y / $ M (

white bread fr ozen vegetables

p o

t s o c g n i t a r e p o l a u n n A

milk products

apple products

10

C

Eleven figures summarize the results of the present analysis. In each figure the literature data are presented along with the resulting fitting equation. Fig. 1 presents the purchased equipment cost versus the production capacity for various food industries. These data are described very well by the equation C eq =1.80F 1/2. It must be noted that the 27 from the 35 factories examined are lying in the range between the minimum and maximum characteristic curves C eq =0.90F 1/2 and C eq =3.60F 1/2 respectively. Thus, these equations can be used for preliminary purchased equipment cost estimation for a new food industry. Similar results can be concluded from Fig. 2, which presents the annual operating cost versus the production capacity for various food industries. In this case, the number of industries inside the min/max range is less 20 from the total 35, which means that the annual operating cost estimation is of less accuracy than the case of purchased equipment cost.

seafood yogurt tomato paste

concentrated juice

5. Results and discussion

100

Plant capacity F (Gg/yr)

lasagna mozzarella cheese tortilla chips

pasta

blue cheese

protein

fruit juice

ice cream frozen bread

frozen shrimp

arabic bread dry yeast

quenelles (dumplings)

fruit puree

milk products

1 sausages

vinegar

corn snacks

C op = 2.00 F

3/4

0.1 0. 1

1

10

100


Fig. 2. Annual operating cost versus plant capacity for various food industries. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

Fig. 3 is a very good picture of the various food products cost. Data are very scarce due to the variability of

297

A.Z. Marouli, Z.B. Maroulis / Journal of Food Engineering 67 (2005) 289–299 10

100000 frozen fish

frozen shrimp concentrated juice tomato paste apple products skim milk powder mozzarella cheese


yogurt

10000

dry yeast lasagna

sea food

blue cheese

slaughter products

tortilla chips

cooking oil sea food

) g k / $ (

frozen bread

frozen vegetables pasta

corn snacks

c

t s o c t c u d o r P

m (

blue cheese

frozen vegetables

s g n i d l i u B

fruit juice corn starch

white bread

slaughter products

B

ice cream

sausages

1

milk products

)

2

yogurt

white bread corn starch

concentrated juice

arabic bread

apple products

frozen fish

egg powder

milk products

vinegar

cooking oil

lasagna frozen bread

sausages

1000

skim milk powder

ice cream

mozzarella cheese

milk products fruit puree

soybean oil

pasta

protein arabic bread

tortilla chips

frozen shrimp

fruit juice corn snacks

vinegar

protein

c =4.00 F -1/ 2

fruit puree

quenelles(dumplings)

1/2

B = 800 F 100

0.1 0. 1

1

10

0. 1

100

1

10

100



Fig. 3. Product cost versus plant capacity for various food industries. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

Fig. 5. Effect of plant capacity on the required buildings. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

1000 100000

L = 5000 F

1/3

blue cheese dry yeast

slaughter products

soybean oil

slaughter products

sea food

sea food

yogurt

corn starch

100

milk products

dry yeast milk products

) s n o s r e p (

frozen vegetables

)

2

L d n a L

white bread

frozen fish

m (

mozzarella cheese 10000

pasta

apple products fruit juice

sausages

blue cheese

cooking oil

ice cream tomato paste lasagna pasta concentrated juice apple products

egg powder frozen shrimp

yogurt

fruit puree

tortilla chips

s e e y o l p m E

cooking oil

concentrated juice frozen bread

frozen fish

M

corn starch

soybean oil

frozen vegetables

arabic bread white bread

parboiled rice

frozen bread

ice cream tortilla chips

quenelles (dumplings) mozzarella cheese

10

arabic bread lasagna

egg powder

sausages

protein

skim milk powder fruit juice

protein

vinegar

corn snacks

frozen shrimp vinegar

corn snacks

fruit puree

parboiled rice


M = 10 F

1/2

skim milk powder milk products 1 1

0.1 0.1

1

10

1

10

100

100

Plant capacity F (Gg/yr) Plant capacity F (Gg/yr)

Fig. 4. Effect of plant capacity on the required land. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

the raw materials cost. The effect of production capacity on the product cost is characterized by a negative

Fig. 6. Effect of plant capacity on the required number of employees. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

sign which verifies that massive production results in products of lower cost. These data can be used for

298


100

100

corn starch dry yeast

water water

milk products

dry yeast corn starch

sea food 10

milk products

) $ M (

10

e m

yogurt skim milk powder

) $ M (

C t s o c s k r o w l a c i r t c e l e d n a l a c i n a h c e M

blue cheese

lasagna white bread apple products ice cream protein frozen fish pasta concentrated juice egg powder sausages frozen bread tomato paste tortilla chips parboiled rice frozen vegetables arabic bread milk products quenelles (dumplings) fruit puree

x f

C t s o c l a t i p a c

d e x i F

mozzarella cheese vinegar fruit juice 1 frozen shrimp corn snacks

soybean oil sea food blue cheese ice cream

yogurt

skim milk powder frozen fish lasagna cooking oil milk products sausages white bread quenelles (dumplings) frozen bread pasta slaughter products egg powder protein parboiled rice tomato paste apple products arabic bread

1

vinegar

fruit puree tortilla chips

frozen vegetables

fruit juice mozzarella cheese frozen shrimp

0.1

corn snacks

C fx = 1.80 C eq

C me =

0.1

0.35 C eq

0.01 0.1

1

10

0.1

100

1

10

100

Equipment cost C eq (M$)


Fig. 7. Relationship between fixed capital cost and equipment cost. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

Fig. 9. Relationship between mechanical and electrical works cost and equipment cost. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

1000

100

soybean oil

dry yeast

corn starch soybean oil 100

10 sea food slaughter products

slaughter products water

) r y / $ M (

milk products

) $ M (

frozen fish

v c

concentrated juice

C

t s o c s k r o w l i v i C

frozen vegetables

1

mozzarella cheese arabic bread

skim milk powder frozen fish corn starch tomato paste water cooking oil concentrated juice

C

egg powder apple products yogurt white bread sausages skim milk powder ice cream cooking oil lasagna tomato patse protein blue cheese frozen bread tortilla chips pasta

t s o c g n i t a r e p o l a u n n A

fruit juice frozen shrimp corn snacks

yogurt

seafood milk product s apple products

p o

parboiled rice fruit puree vinegar quenelles (dumplings)

10

egg powder white bread arabic bread frozen vegetables dry yeast mozzarella cheese lasagna protein blue cheese ice cream fruit juice pasta tortilla chips fruit puree frozen bread quenelles (dumplings) milk products frozen shrimp

1

0.1

sausages

vinegar

parboiled rice corn snacks

C cv =

0.45

C op = 1.10 C mu

C eq

0.1

0.01 0.1

1

10

100


0.1

1

10

10 0

100 0

Raw materials and utilities cost C mu ( M$ / y r )

Fig. 8. Relationship between civil works cost and equipment cost. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

Fig. 10. Relationship between annual operating cost and raw materials and utilities cost. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

preliminary product pricing, applying the cost-plus-fairprofit approach (Holland & Wilkinson, 1997).

Figs. 4–6 reveal the needs in land, buildings and employees versus plant capacity for various food indus-

299


1.10 suggests that the largest component of the annual operating cost is the raw materials and utilities cost, which can be accurately estimated through materials and energy balances. Fig. 11 reveals a linear relationship between the annual operating cost and the purchased equipment cost. The slope is estimated equal to 2.50, which means that the annual operating cost for a typical food industry is about 2.5 times the purchased equipment cost. The average labor rate cost for the examined factories is estimated as the slope of the annual labor cost versus the number of employees. The corresponding graph is not presented here due to space limitation. The resulting average labor rate cost is estimated about 20,000 $/year. Similarly, the average building construction cost for the examined factories is estimated as the slope of the civil works cost versus the buildings area. The resulting average building construction cost is estimated about 600 $/m2.

100

skim milk powder yogurt

sea food

corn starch

tomato paste milk products apple products

cooking oil

concentrated juice 10

egg powder

) r y / $ M ( p

white bread arabic bread frozen vegetables

o

C t s o c g n i t a r e p o l a u n n A

lasagna

mozzarella cheese fruit juice fruit puree frozen shrimp

protein pasta ice cream tortilla chips frozen bread

blue cheese

milk products quenelles (dumplings)

1 vinegar

corn snacks

C op = 2.50 C eq

0.1 0. 1

1

10

100


Fig. 11. Relationship between raw materials and utilities cost and purchased equipment cost. Points represent actual data; lines depict (a) the double of the best fitting curve, (b) the best fitting curve, and (c) the half of the best fitting curve, respectively from the top.

tries. The trend in employees and in buildings is visible contrary to the trend in land requirements, which is confusing. In any case, these data could be used for preliminary estimation. For example, the requirements of a new food plant of production capacity 1 Gg/yr are estimated: land 5000 m 2, buildings 800 m2, and number of employees 10. Fig. 7 is a plot of the fixed capital cost versus purchased equipment cost. The strong linear relationship is obvious. The slope is the Lang factor, according to Eq. (1). The estimated value 1.80 is a little higher than the value proposed by Maroulis and Saravacos (2003) , but it is still lower than the values used in the chemical industries. In modern food processing plants the Lang factor may be higher, e.g. f L =2, due to the cost of process control and automation. Figs. 8 and 9 are plots of the civil works cost and mechanical and electrical works cost versus purchased equipment cost. Linear dependences are also obtained. The slopes estimate the corresponded factors, defined by Eqs. (5) and (6). Fig. 10 is a plot of the annual operating cost versus raw materials and utilities cost. The strong linear relationship is obvious. The slope is the annual operating cost factor according to Eq. (8). The estimated value

6. Conclusion

A systematic analysis of the existing food factories cost data can lead to useful results concerning the particular characteristics of the food industry and to reveal simple equations for rapid preliminary cost estimations needed in various techno-economic studies.

Acknowledgment

The authors are grateful to professor GD Saravacos for his valuable suggestions.

References Bartholomai, A. (1987). Food factories: processes, equipment, costs . New York: VCH Publishers. Clark, J. P. (1997). Cost and profitability estimation. In K. J. Valentas E. Rotstein & R. P. Singh (Eds.), Handbook of food engineering practice. New York: CRC. Holland, F. A., & Wilkinson, J. K. (1997). In R. J. Perry & J. H. Green (Eds.), Perry s chemical engineers handbook (7th ed.). New York: McGraw-Hill. Maroulis, Z. B., & Saravacos, G. D. (2003). Food process design . New York: Marcel Dekker. Peters, M. S., & Timmerhaus, K. D. (1991). Plant design and economics for chemical engineers (4th ed.). New York: McGraw-Hill. Sinnott, R. K. (1996). Chemical process design. In J. M. Coulson & J. F. Richarson (Eds.). Chemical engineering (vol. 6). London: Butterworth–Heinemann. 



Cost Data Analysis for the Food Iindustry

Recommend Documents