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QUALITY LEADERSHIP UNIVERSITY
UNIVERSITY OF LOUISVILLE - PANAMA
MASTER IN ENGINEERING MANAGEMENT

EM 613
OPERATIONS MANAGEMENT

GROUP PROYECT

“OPTIMIZATION OF HAM PRODUCTION PROCESS
IN PROCESSED MEAT INDUSTRY
ALIMENTOS CÁRNICOS DE PANAMÁ S.A.”

MEMBERS:
CRUZ, ESTEFANÍA Id. 8-831-2043
CRUZ, JOSÉ Id. 8-785-79
CHANIS, NICOLE Id. 8-837-172.
PALMA, MANUEL Id. 8-845-2054
IBAÑEZ, ROBERTO Id. PE-11-1044

PROFESSOR:
JOHN S. USHER, PhD, PE

JUNE 19, 2015.

1

INDEX
INTRODUCTION............................................................................................................................... 3
BUSINESS: ALIMENTOS CÁRNICOS DE PANAMÁ S.A. ....................................................... 3
Processed Meat Business within Grupo Nutresa ......................................................................... 3
Alimentos Cárnicos de Panamá S.A. .......................................................................................... 3
Goals: Process Optimization ....................................................................................................... 4
PROJECT OBJECTIVES ................................................................................................................... 4
General Objectives: ......................................................................................................................... 4
Specific Objectives: ........................................................................................................................ 4
PROJECT JUSTIFICATION .............................................................................................................. 5
PROCESS ANALYSIS ....................................................................................................................... 5
ACTUAL FLOWCHART OF COOKED HAM PRODUCTION PROCESS .................................... 6
ACTUAL LAYOUT AND FLOWLINES OF COOKED HAM PRODUCTION PROCESS ........... 9
PROBLEM#1: ICE MACHINE PRODUCTION PROBLEM ......................................................... 10
PROBLEM #2: EXISTING PROBLEM FROM THE COOLING CHAMBER #2 UNTIL THE
SLICING STAGE ............................................................................................................................. 14
HAM BARS SPECIFICATIONS: ................................................................................................ 14
HAM BARS ROTATION FROM COOLING CHAMBER #2 TO COOLING CHAMBER #3 . 14
BOTTLENECK PROBLEM IN COOLING CHAMBER #3 ....................................................... 15
STARVING PROBLEM IN THE SLICING PROCESS .............................................................. 18
PRODUCT LOSSES IN THE SLICING PROCESS OF THE HAM BARS ................................... 19
CONSIDERATIONS FOR THE SHEWHART CONTROL GRAPHICS ................................... 19
STATISTICAL STUDY OF PRODUCT LOSSES ...................................................................... 21
OVERWEIGHT IN FINAL PRODUCT ........................................................................................... 23
MONITORING DATA TABLE OF COOKED HAM WEIGHT IN MAY ................................. 24
FREQUENCY HISTOGRAM AND PROCESS OVERVIEW OF COOKED HAM WEIGHTS
SAMPLING IN MAY ................................................................................................................... 25
ALTERNATIVE TO IMPROVE THE COOLING PROCESS ........................................................ 28
STAGES OF FLOWCHART WITH IMPROVEMENTS PROPOSALS AND STATISTICAL
CONTROL ANALYSIS ................................................................................................................... 29
IMPACTS OF THE IMPROVEMENTS PROPOSED IN THE PROCESS..................................... 31
BIBLIOGRAFY ................................................................................................................................ 32

2

INTRODUCTION
BUSINESS: ALIMENTOS CÁRNICOS DE PANAMÁ S.A.
Processed Meat Business within Grupo Nutresa
Alimentos Cárnicos de Panamá is part of the Nutresa group. Produces and commercialize processed meats such as: sausages, hams, hamburgers, mortadella, and some others. We count with nine production plants distributed like this: 7 in Colombia, 1 in Panama, 1 in
Venezuela.
We are leaders in Colombia with 73.3 % market share, while in Panama reached 18%.
Alimentos Cárnicos de Panamá S.A.
Alimentos Cárnicos de Panamá is the merge from two businesses that already existed in
Panama and group Nutresa decides to buy: Blue Ribbon and Berard. These production plants are located in different parts in the country and have been running like this for three years with their own products and brands. But the meat business takes the decision to merge both
Panamanian production plants and change the name to Alimentos Cárnicos de Panamá S.A. about a year ago.
The production plant has suffered a series of changes such as:
 The Berard plant moved its equipment and products to the Blue Ribbon plant so there was an operational merge.
 Products with low sale were eliminated from the production.
 The focus was given to high sale products in the Panamanian market.
The Panamanian processed meat plant have 4 lines of fixed production in the market and one line of special products:
 Sausages
 Hams (cooked and smoked)
 Bars (mortadella and salami)
 Special Products (for Christmas and new years).
The leading brands are BLUE RIBBON and BERARD, but also Alimentos Cárnicos S.A. represents HORMEL and ARMOUR in Panama.

3

Goals: Process Optimization
The processed meat plant from Panama give report to the lowest earnings in the processed meat business, partly affected of all the changes it has suffered. In the meantime, there is process and equipment restructure in the plant to optimize the profitability of the business.
The product line with the most economic loss for the business in Panama is the cooked ham because of the variability in the process.

PROJECT OBJECTIVES
General Objectives:


Optimize ham production using operations management techniques.

Specific Objectives:




Identify the principal problems in ham production.
Propose improvements in ham production that reduce variation and improve the quality of the end product.
Evaluate the impact of the improvements proposed in ham production process.

4

PROJECT JUSTIFICATION
With respect to the following graphic, we can visualize why the main interest is the product line of sliced cooked hams, which represents the most reprocess. Inside of this line of sliced cooked hams we choose the one with the most production, which is Blue Ribbon cooked ham. Análisis de Kg Reprocesos por linea

4000

3508

3500
3000
2500
2000

1440
1132

1500
1000

294

500
0

jamon

Chorizo

Salchichas

Barras

Graphic #1: Tendency of reprocess per product line of production.

PROCESS ANALYSIS
The first step was to study the chosen production process. Then, after studying it, the principal causes that may cause variation in the several stages were determined. After this, the operational standard of the process in the production of a sliced ham was analyzed with the objective of comparing what the parameters of the process versus the operational reality indicate us.
In the next pages we will start with the flow of the process and the explanation of the problems. 5

ACTUAL FLOWCHART OF COOKED HAM PRODUCTION
PROCESS

DRY STORAGE
INGREDIENTS

COLD MEAT
STORAGE

DOSAGE

DOSAGE

BRINE
PREPARATION

MILLING

MIXED

REST OF THE
MIXED

INLAY

6

THERMAL
PROCESS:
COOKING

COOLING #1

UNMOLDING

COOLING #2

COOLING #3

7

SLICING

PACKING

FINISHED
PRODUCT
STORAGE

TRANSPORT TO
THE SALE
PLACE

8

ACTUAL LAYOUT AND FLOWLINES OF COOKED HAM PRODUCTION PROCESS

Figure #1: Plan view of areas of cooked ham process production

9

PROBLEM#1: ICE MACHINE PRODUCTION PROBLEM
The ham production consists of a mixture of certain ingredients that have to be measured for the product to have the correct consistency; these ingredients have to be available for the process to go on smoothly. The studied ham is the BR Cooked ham it is produced daily and its mixture is composed of 2376 kg of several ingredients (1169 meats, 500 water, 400 of ice,
318 dry ingredients) as seen in table n. Every day there are at least 3 different types of hams that have to be made and sometimes the ice machine can´t go on with the needed production, so the lack of ice introduce variation in the process.
Type of Ham
A
B
C
D
F
G
H

Condiments (Kg)
60,25
253,26
13,31
135,3
154,74
31,39
210,3

Water (L)
283
380
22
200
305
55
350

Ice (kg)
157
315
39
157
315
50
315

Total Mixture (kg)
500,25
948,26
74,31
492,3
774,74
136,39
875,3

BR Cooked Ham
J
K
L
M
N
O
P
Q
R
S
T

543,6
182,81
17,41
73,56
68,43
18,48
90
90
206,3
923,56
11,35
31,39

500
229
157
599,4
604,9
55
585
585
350
539
121,6
65

400
200
40,6
235
235
30
235
235
315
235
60
40

1443,6
611,81
215,01
907,96
908,33
103,48
910
910
871,3
1757,56
192,95
136,39

Table #1 - Ingredients needed per ham type.
This is a problem that has to be solved because the lack of an ingredient so common like ice cannot be the cause for stopping the ham production and this is what has been happening.
The actual ice machine has an approximate capacity of 90 kg of ice per hour and the orders of ice are made twice a week. Each order consists of 200 bags of ice that cost $2.75 if ice cube-shaped and $3.75 if flake ice. If the bag bought is of cube ice, then the production lags because of the ice grinding. If the company buys an ice cube bag and has to use workers to grind ice, this is a capacity loss in the overall work; also there is $1100 per week to pay for ordering the bags of ice. On the other side if the company buys flake ice bags they don’t have to grind but will have to pay an extra $1.00 that turns into $400 per week in regard to the ice
10

cube bag. There is no increase in the process efficiency as the company is paying $1500 for something that they could produce in the plant.
The proposed solution is to increase the capacity and reliability of the ice producing system adding another ice machine with more or the same capacity in parallel to add redundancy to the system. As we know in reliability analysis if there is only one component in the system and this component fails, then there is no backup for the system to continue working. In our case if the existing ice machine is out, the needed ice orders increase to approximately 5 orders per week giving us a payment per week of $3750. So if we add another ice machine to the system, now it has a backup in case an ice machine is damaged, since the other one can do the job and by doing these ice orders can be fixed at the current value. If we make a summary of the previous analysis we can study the requirement or not requirement of a new ice machine in terms of reliability, we can divide the decision in two scenarios, one with the failure of a unique ice machine (Scenario #1) and other with the failure of one of the two ice machines available (Scenario #2). This is shown in the following table:

Scenario
1
2

Bags to order Price of Ice Bags Total Capacity of the per week
Ordering
System per hour
1
1000
$3750
90 kg / hour
2
400
$1500
180 kg / hour (at least)
Table #2 – Scenarios for the failure of an ice machine

Machines

With the previous analysis we can conclude that with two machines there is no increase in ice orders because if a machine is out the other one does the job partially as the current system
(two orders of ice per week). Now if we analyze the system in terms of capacity it can be modeled the following way: =

=

If we calculate the actual ice requirements per week assuming the machine works 15 hours from Monday to Friday and 6 hours on Saturdays there are:
Current ice machine: 90kg/hour*[(5*15 h) + (6h)] = 7290 kg/week
Bags of ice: 400 bags/week*(20kg) = 8000 kg/week
Total ice required per week = 7290 + 8000 = 15290 kg /week
Then the machines needed to avoid ice bags ordering are: =

15290 /
= 2.10 ℎ
7290 /

We actually need 3 ice machines of the actual capacity or one of more capacity in addition the existent to avoid ice bags ordering, so we have to look for a machine with a capacity to
11

add the 8000 kg/week needed to avoid ice bags ordering and for increasing the reliability of the system.
We investigated in the market how much a flake ice machine that can produce at least 90 kg/hour can cost, and we found that there are no commercial machines that can produce 90 kg/hour of flaked ice, so we searched for a bigger machine and found a machine that can produce 104 kg/hour which specs are shown in figure n.

Machine Specifications
Max. production/24hrs
2500 kg
Production/81hrs (week)
8437.5 kg
Weight
390 kg
Power Consumption
7.4 KWh/100 kg

Figure #2 – Proposed ice machine
The approximate cost of the machine is $23600.00, based on this we can study the profitability of adding a new ice machine. We need two ice machines to produce 15290 kg/week, but we need to know how profitable it is to add another ice machine instead of ordering 200 ice bags two times per week. If we assume that the cost of operation of the machine is given by its energy consumption, if it produces the needed 8000 kg/week, we have then: = 7.4 ℎ ∗

8000 /
= 592 ℎ
100

By knowing the energy consumption per week we can formulate the “machine cost” as: = ∗ +
Where:
MC: Machine cost
W: Weeks
EC: Energy consumption (EC = 592 * cost per KWh)
Ii: Initial investment ($23600.00)
To start the calculation we can assume the cost of the energy is negligible and make
= Ii, under this assumption the recovery of the initial investment is made in:

MC

12

=

$23600
=
= 15.73
$1500/
$1500

Where:
RT: Investment recovery time in weeks
If we now assume an initial value of W = 15.73 weeks and use a cost per KWh of
0.19217 cents/KWh we can find the investment recovery time by using a numerical solution as shown in the next table:
RT1
W*EC
MC
RT2-RT1
RT2
W*EC
MC
RT3-RT2
RT3
W*EC
MC
RT4-RT3
RT4
W*EC
MC
RT5-RT4
RT5
W*EC
MC
RT6-RT5
RT6

15.73
1789.90
25389.90
1.19
16.93
1925.65
25525.65
0.090500884
17.0171
1935.94
25535.94
0.006863867
17.0240
1936.72
25536.72
0.000520577
17.0245
1936.78
25536.78
3.94822E-05
17.0245

Table #3 - Numerical solution for the investment recovery time
From the table above we see that the initial investment is recovered in almost 4 months and from there on there is a saving of:
Weekly ice bag ordering price = $1500.00
Machine Energy Consumption = $113.76
Saving = $1386.24
If we multiply the obtained value by 12 months, per year there will be a saving of
$16634.88, then we can conclude that adding a new ice machine increase the reliability, capacity and saves the company $16634.88 per year.

13

PROBLEM #2: EXISTING PROBLEM FROM THE COOLING
CHAMBER #2 UNTIL THE SLICING STAGE
HAM BARS SPECIFICATIONS:
The ham bars have weight and length parameters that are controlled in the inlaying stage. In this part of the process every 20 minutes 5 ham bars are sampled to make sure that the inlay machine do not have any deviations and the portion of the ham paste be of 10.5 kg for every
100 cm.

Figure #3: Weight control and size of the ham bars.
.

HAM BARS ROTATION FROM COOLING CHAMBER #2 TO COOLING
CHAMBER #3

DAILY PRODUCTION DEMAND
Reference

Daily use of bars

113.5 grams
227 grams

33 bars
33 bars

Quantity of daily packages 3,000 packages
3,000 packages

454 grams

22 bars

500 packages

Table #4: Daily Production Demand (Hams Bars).

14

Taking into account that the diary demand for the reference of 227 grams is constant, this calculated ham bars that should be transported from the cooling chamber #2 to cooling chamber #3.
In this stage of the process the sequence FCFS (First Come First Serve) will be chosen for the dispatch of the ham bars because they should be rotated in inventories with respect to their dates of entry to the cooling chamber #2.
Example: If we need 3,000 packages of 227 g and each bar weights 10.5 kg, how many theoretical bars are necessary to cover this demand?

(3,000 packages)(0.227kg)/10.5 kg
We Need = 33 Ham bars aprox.

This data is theoretical because in reality there is assigned 33 ± 3 additional ham bars to cover the quantity of the nonconforming product of reference 227 g.

BOTTLENECK PROBLEM IN COOLING CHAMBER #3
The cooling chamber #3 was a new acquisition from the company with the goal of fixing the process in the ham product line. But the proposed solution did not fix the problem at a 100% because the required study for it was not done before it was purchased.
The first ham bars to enter cooling chamber #3 correspond to all the references of 113.5 g, later the reference of 227 g, and finally the reference of 454 g.
Next, we will explain the following problem: To start the production process of the 227 g packages we need 198 bars to cover the production demand. The total capacity of the cooling chamber #3 is 100 bars, so the needed bars exceed the chamber capacity in almost 50%. This capacity is not enough, thus not being able to store in the cooling chamber all bars to fulfill production. 15

The cooling chamber #1 have the function of intensive cooling and is programmed for ranges of temperatures of -15°C to -18°C for storage carts. The cooling chamber #2 have the function of cooled storage and is where the arrival and rotation of the ham bars occur. At last the cooling chamber #3 is the chamber specialized to get the right slicing stage and its range goes from -8°C to -6°C. The cooling chamber #3 is the bottleneck of the process because it’s smaller than the other two cooling chambers this causes a deviation from the process that can turn into defects. We can use the following flowchart to describe the existent cooling process: Cooling Chamber
#1
WIP#1
TR#1
TC#1

Cooling Chamber #2
WIP#1
TR#2
TC#2

Cooling Chamber
#3
WIP#3
TR#3
TC#3

Figure #4 – Ham Cooling System Arrangement
From the information above we can analyze the current problem in the cooling chamber #3.
The cooling chamber #3 can’t handle the same WIP as the other two cooling chambers, this cause some extra time to get in the overall time in the process since the total time for the cooling process to finish is Ttotal = TC#1+TC#2+TC#3. In addition the cooling chamber #3
TR will not be the same from cooling chamber #2 and #1, since it will be lower this will cause the process to lag in this stage and this could cause that some hams with a lower freezing point could get extremely frozen causing defects in the cutting process and in the same way a ham that needs more time in the cooler could be softer than the optimal consistency and cause defects in the cutting process. Since you have a need of capacity and a way to control the ham temperature since not all the hams that are put in the coolers have the same freezing points and will not reach the desired temperature at the same time. A solution for both problems is adding a new cooling chamber that can handle the other part of the production that comes from cooling chamber #1 and #2 and by doing this they will have the flexibility to divide hams by freezing point and they will be more likely to reach the
16

desired temperature at the desired time and reduce any further cutting defects. The new arrangement would be as follows:
Cooling Chamber
#1
WIP#1
TR#1
TC#2
Cooling Chamber #2
WIP#2
TR#2
TC#2

Cooling Chamber #3
WIP#3
TR#3
TC#3

Cooling Chamber #4
WIP#4
TR#4
TC#4

Figure #5 – New Ham Cooling System Arrangement
By doing this upgrades we can reduce the variation in the process since the hams will be frozen in groups by freezing point in cooling chamber #3 or cooling chamber #4 reducing the variation in the desired temperature and reducing the defects in cut and decrease reprocessing. The total time of the process will be reduced too since by making two parallel processes (cooling in chamber #3 and #4) there will be two parallel batches to cool, so when both cooling chambers are done all the production that entered will be coming out of the cooling process hopefully at the desired temperature.

17

STARVING PROBLEM IN THE SLICING PROCESS
In this stage of the process several critical variables such as time, temperature and the machine parameters are handled for each reference. The slicing velocity, number of slices and thickness of the slices are verified in this stage.

Figure #6 – Ham Bars and Slicing Machine.

Slicing Process

Slicing

Involves 8 activities that are:

This process depends on cooling chamber #3, which in many occasions do not poses the capacity to deliver the necessary quantity of ham bars at the right temperature in the required time. The variation in these parameters is not optimal for the slicing process.

1. Start of the slicing process
2. Find and bring bar from cooling chamber 3. Temperature measurement
4. Peel the bar
5. Feed Treif = 3 bars
6. Treif machine
7. Advance and fixing

Given this situation the production staff, due to the internal demand, tries to fulfill their production programing even if the conditions are not optimal.
Results when forcing the process:

8. End of slicing process
 Product losses
 Overweight in final product

18

PRODUCT LOSSES IN THE SLICING PROCESS OF THE HAM
BARS
The operational product losses happen because of carelessness, improper operations, and neglect from the workers. In this type of product loss, the product is damaged, destroyed or decomposed by diverse operational factors related to bad practices or inadequate designs in the process.
To our process, we may define product losses as the total quantity of hams in kilograms obtained daily in one day of work that cannot get to the packaging stage due to nonconformity in the product. This might happen due to a deviation from the process, or a quality aspect, which get us to an economic loss.
Currently reprocess is established due to two aspects:
1. Tip cuts: The machine has a system of fixation for the ham bar by mean of two claw arms, which are in both extremes holding the ham bar. The claws affect these extremes and they get discarded at the end of the slicing stage. When the ham bars are too soft they are cut in half to avoid the rupture, generating two tip cuts.
2. Defective slices: These are defective slices that are due to their form where it appears holes, incomplete slices, etc., which do not comply with the adequate characteristics to be packaged.

CONSIDERATIONS FOR THE SHEWHART CONTROL GRAPHICS
The Shewhart control graph constitutes an important tool in the statistic control of process.
In short, it uses two sets of values for the allowable and actual measures of qualitative characteristics of the process ranges.
1. Specification Limits: These are tolerance limits determined during the design process of a product or a service. These limits are based on the qualitative analysis of the process. 2. Control Limits: These are obtained by doing a study of the historic real data from the process. By analyzing, obtaining these limits can provide a real image of the state of the process and it should cause inferences about the causes that provoke that the results in the process vary.
It is important to know the difference between the specification limits and the control limits.
The first one is chosen if there is a good sequence of production design by the manufacture design team, basing themselves in economical historic studies, in the quality requirement fixed by internal or external adopted practices, in the feedback from the client, in the market orientation the product has, etc. The control limits are obtained mathematically during the study of data of a process. They define the area where they expect that the measurement values are when the process behaves in a normal manner.
19

At this point, one can understand that one of the objectives from a Shewhart control plot is to have a basis that enables the investigation of assignable causes when a process is worsen.
This is based on the study of probability that holds a measure taken from the sampling in falling within an acceptable region (inside the specification limits). When this measure is positioned outside from these limits, it understands that it pertains to the group of unacceptable. This is precisely what is known as acceptance or unacceptance charts, own analysis of the hypothetical test.
In the next graph one can appreciate two acceptance limits, one upper and one lower.

Figure #7: Graph with one acceptance region and two critical regions.
In this graph one can appreciate just one acceptance limit.

Figure #8: Graph with one acceptance region and one critical region.
The difference between these two graphs is the quantity of acceptance limits taken in account.
The choosing of these limits depends on what is going to be evaluated in the process.
In this model in particular, product losses is evaluated. We then interpret that it is absurd to establish a lower specification limit because the fewer the product losses, the best for the process, in other words it is acceptable. Equivalent mathematical graphs of hypothetical proof with the Shewhart controls and in this form we can assume that both can be used to detect deviation of the metric taken into account and it prevents higher costs by showing error tendencies. 20

Figure #9: Shewhart Control Charts
It is done in conjunction with the assertion that the specified limits are independent from the control limits. It serves as the theoretical basis for the justification that for one Shewhart control plot is acceptable and valid to establish and evaluate just one specification limit. And in this case, the upper limit will be taken into account, what operationally is known as product loss goal.

STATISTICAL STUDY OF PRODUCT LOSSES
Below one can appreciate the graph combined with the histogram and the distribution of the product loss in this point of the process. The form that the graph resembles a normal distribution but it is worth noticing that the Shewhart control graph does not need a normal distribution of the samples for it to be effective.

Grafic #2: Decrease in Production Distribution
21

The control limits of the X-bar graph were calculated and gave the limits shown in graphic
#2 that indicate that by simple inspection that all the values obtained of the samples are inside the control limits. But if a control R-bar graph is done we will realize that there is a lot of variation in product losses by the slicing process.

Graphic #3: R-bar chart for losses control

What we can deduce from this process is resumed in the next points:
1. The process has a shift in the measurement due to an assignable cause that provoke huge problems in the production line.
2. This tells us that for this process it is more probable to obtain quantities of product loss greater than the specified calculated by operational agents from the process.
3. The graph shown in the figure n of R-bar indicates us that the variation from the product losses in the process is extremely random and it gets high values. This graph is important to evaluate and compare how it influences in lowering variation.
4. This type of statistical control is very useful in identifying variations in the processes, such as we have identified for the slicing of the ham bars.

22

OVERWEIGHT IN FINAL PRODUCT
The overweight of the final product originates in the slicing stage. The slicer has specifications in the slicing velocity, number of slices, weight per slice for each product and these are directly associated to weight and bad slicing. If the temperature is too high the ham bars are too soft, the operator reduces the velocity of the slicing machine thus having a thicker slice that affects the product specifications. As a consequence, the weight of the final packaged product is being affected by its overweight.

Due to these defects in the slicing, it is important to implement a quality control measure at the final product, this way we can visualize the variations in the process provoked by the early stages. This way it can be reduced and the final product can be unaltered.
Below show the weight table from the final product corresponding to the month of May.

23

MONITORING DATA TABLE OF COOKED HAM WEIGHT IN MAY

GRAMS (g)

NET WEIGHT
PACKING WEIGHT
LABEL WEIGHT
TOTAL WEIGHT

LOWER SPECIFICATION
LIMIT (LSL)

227
2
6
235

234

UPPER SPECIFICATION
LIMIT (USL)

PRODUCT

242

SLICED COOKED HAM BLUE RIBBON - WEIGHT 227 g
Product Final - Weights Monitoring in May - Grams ( g )
DATE \ PKG
5/2/15
5/4/15
5/5/15
5/6/15
5/8/15
5/9/15
5/11/15
5/12/15
5/13/15
5/14/15
5/15/15
5/16/15
5/18/15
5/19/15
5/20/15
5/21/15
5/22/15
5/23/15
5/25/15
5/26/15
5/27/15
5/28/15
5/29/15
5/30/15

SAMPLING 1

SAMPLING 2

PK1

PK2

PK3

PK4

241
238
238
232
238
237
239
236
237
239
239
238
238
240
236
236
238
239
237
235
240
242
236
241

238
239
238
237
237
241
239
235
234
234
234
238
243
240
236
236
237
240
237
235
241
244
239
244

239
238
237
237
239
241
239
240
240
234
234
240
239
240
237
236
238
238
240
235
242
241
238
242

239
238
239
232
239
237
238
235
237
240
234
240
240
240
239
235
239
237
240
236
239
243
236
247

R

PK5

237
237
238
236
237
238
241
235
234
239
239
240
240
238
236
235
235
240
240
238
240
240
237
243

SAMPLING 3

238.8
238.0
238.0
234.8
238.0
238.8
239.2
236.2
236.4
237.2
236.0
239.2
240.0
239.6
236.8
235.6
237.4
238.8
238.8
235.8
240.4
242.0
237.2
243.4

PK1

PK2

PK3

PK4

PK5

4.0
2.0
2.0
5.0
2.0
4.0
3.0
5.0
6.0
6.0
5.0
2.0
5.0
2.0
3.0
1.0
4.0
3.0
3.0
3.0
3.0
4.0
3.0
6.0

238
238
238
231
238
239
238
240
239
239
234
238
241
237
238
238
235
241
237
237
238
239
238
244

238
238
238
233
238
238
239
238
239
234
239
238
239
238
240
236
236
240
240
239
240
242
237
242

238
238
237
235
234
239
239
239
234
239
234
238
239
241
241
237
236
240
240
238
242
243
236
247

239
239
238
236
239
238
239
240
234
237
236
240
240
241
240
240
235
237
241
240
242
238
236
245

238
238
238
233
238
239
239
239
239
239
234
238
240
238
240
237
235
237
240
236
240
240
235
241

SAMPLING 4

R
238.2
238.2
237.8
233.6
237.4
238.6
238.8
239.2
237.0
237.6
235.4
238.4
239.8
239.0
239.8
237.6
235.4
239.0
239.6
238.0
240.4
240.4
236.4
243.8

PK1

PK2

PK3

PK4

PK5

1.0
1.0
1.0
5.0
5.0
1.0
1.0
2.0
5.0
5.0
5.0
2.0
2.0
4.0
3.0
4.0
1.0
4.0
4.0
4.0
4.0
5.0
3.0
6.0

241
239
238
232
238
237
238
238
237
234
236
241
243
241
241
238
241
238
237
237
239
245
238
245

237
241
237
235
233
239
239
238
239
239
232
238
241
237
236
240
238
237
240
240
241
240
237
241

238
239
238
233
238
239
238
238
239
240
235
236
237
239
237
239
236
238
240
238
241
247
237
242

237
237
239
234
239
238
239
238
234
234
235
240
238
241
241
235
240
240
238
235
240
240
239
247

238
237
238
234
238
237
240
239
235
239
235
238
236
238
236
236
237
237
240
237
238
242
236
242

R
238.2
238.6
238.0
233.6
237.2
238.0
238.8
238.2
236.8
237.2
234.6
238.6
239.0
239.2
238.2
237.6
238.4
238.0
239.0
237.4
239.8
242.8
237.4
243.4

PK1

PK2

PK3

PK4

PK5

4.0
4.0
2.0
3.0
6.0
2.0
2.0
1.0
5.0
6.0
4.0
5.0
7.0
4.0
5.0
5.0
5.0
3.0
3.0
5.0
3.0
7.0
3.0
6.0

239
239
233
232
237
235
238
239
237
234
239
238
238
237
235
235
236
237
239
235
242
246
240
240

239
237
238
235
239
237
238
238
240
237
240
240
240
237
235
236
239
238
240
236
240
242
239
242

237
238
238
233
233
241
238
238
241
239
234
243
238
236
241
235
237
236
241
235
241
240
242
240

238
238
239
235
238
237
241
240
237
241
237
238
238
239
238
236
243
239
240
238
241
242
244
246

238
239
239
234
239
239
239
238
234
234
236
238
238
240
235
235
241
240
237
237
239
242
238
245

R
238.2
238.2
237.4
233.8
237.2
237.8
238.8
238.6
237.8
237.0
237.2
239.4
238.4
237.8
236.8
235.4
239.2
238.0
239.4
236.2
240.6
242.4
240.6
242.6

2.0
2.0
6.0
3.0
6.0
6.0
3.0
2.0
7.0
7.0
6.0
5.0
2.0
4.0
6.0
1.0
7.0
4.0
4.0
3.0
3.0
6.0
6.0
6.0

238.4
238.3
237.8
234.0
237.5
238.3
238.9
238.1
237.0
237.3
235.8
238.9
239.3
238.9
237.9
236.6
237.6
238.5
239.2
236.9
240.3
241.9
237.9
243.3

2.8
2.3
2.8
4.0
4.8
3.3
2.3
2.5
5.8
6.0
5.0
3.5
4.0
3.5
4.3
2.8
4.3
3.5
3.5
3.8
3.3
5.5
3.8
6.0

238.3

3.9

Table #5: Product Final Weights - Ham Blue Ribbon – 227 grams..

24

FREQUENCY HISTOGRAM AND PROCESS OVERVIEW OF COOKED
HAM WEIGHTS SAMPLING IN MAY

Graphic #4: Frequency Histogram
Process Overview
120

0.18

LSL

USL
0.16

100

0.14

FREQUENCY

80

0.12

0.1
60

Frequency

0.08

40

Probability

0.06

0.04
20
0.02

0

0

230

231

232

233

234

235

236

237

238

239 240 241
WEIGHT (G)

242

243

244

245

246

247

248

249

250

Graphic #5:– Process Overview based on Frequency Histogram
By plotting the sampling data of cooked ham for the month of May based on table n we can see if the process is inside or outside the specification limits, how large is the area below the
25

curve that define possible defects in the final product (low weight or overweight) and if this is happening, identify what can be happening in the previous stages that causes this defects in the final product.

Graphic #6: X-Bar Chart for cooked ham sampling data in the month of May.
From the information obtained by analyzing the histogram we can say for sure that there are quality controls that not are being followed that are causing defects in the final product and variation in the overall process. By implementing control charts using data from samplings in the final product we can obtain information about the process and see if it is inside the specified control limits. If we analyze the control chart from figure n we can obtain some information like:




The production from the May 6th labeled as the sample #4 is out of lower control limit, telling us that some ham packages from the production could have gone out without the required weight.
The production from May 30th and May 28th labeled as sample #24 and #22 are out of the upper control limit, telling us that some packages from the production could have gone out with overweight which represents looses for the company.

26

With the information obtained from our x-bar chart, the company can see how the process is going and try to correct any problem in a previous stage of the process that is causing variation and defects in the final production.
In addition to the X-bar chart we have to make an R-bar chart as the one shown in figure n:

Graphic #7: R-Bar Chart for cooked ham sampling data in the month of May.
If we analyze the R-bar chart from figure n after analyzing the X-bar chart from figure n we can obtain information about variation in the process. Some of this information is:




If we look for sample #4 in the R-bar chart we can see that the variation in sample #4 is not as great as we could think by analyzing the X-bar chart since the mean range between the larger weight sample and the lower weight sample is at 4 g. We can say that the production of that day had values near the lower control limit and at some point those values were below the lower control limits, this caused a decrease in the mean as we saw in the X-bar chart.
On the other side on samples #22 and #24 the mean range is at 5.5 – 6.0 g on average, what is somewhat closer to the upper control limit. So this value can give us an alarm that probably there were some packages from the production that could have gone out with overweight

27

ALTERNATIVE TO IMPROVE THE COOLING PROCESS
Further we investigate a found an ideal solution that will be to get a cooling spiral system instead of three cooling chambers that have very limited capacity. The cooling spiral will be automated with a moving conveyor belt instead of manually moving the carts containing the ham from one cooling chamber to another one. Thus reducing time within the system. The cooling spiral works as a buffer storage between the oven and the slicing and packaging. The rotation speed of the conveyor belt is kept at constant speed (it can be adjusted) according to the cooling time required in order to keep the product between the oven and slicing as even as possible. Temperature control is controlled through the provided PLC controller. The input of this data is provided through the use of multiple RTD sensors and programmable controller. The RTD sensors or resistance temperature detectors are used to measure the temperature by associating the resistance of the RTD element with temperature. The RTD element is usually made from a pure material such as nickel, copper or platinum and this element has a predictable change in resistance as the temperature varies. This predictable change is used to determine the temperature of the product we are interested in.

Figure #10: Cooling Spiral Machine

28

STAGES OF FLOWCHART WITH IMPROVEMENTS PROPOSALS
AND STATISTICAL CONTROL ANALYSIS

DRY STORAGE
INGREDIENTS

COLD MEAT
STORAGE

DOSAGE

DOSAGE

BRINE
PREPARATION

MILLING

NEW ICE
MACHINE

MIXED

REST OF THE
MIXED

INLAY

THERMAL
PROCESS
(COOKING)

29

COOLING #1

UNMOLDING

COOLING #2

ADITIONAL
COOLING
CHAMBER WITH
MORE CAPACITY

COOLING #3

SLICING
WEIGTH
STATISTICAL
ANALYSIS WITH
LOWER AND
UPPER CONTROL
LIMITS

ADD STATISTICAL
CONTROL OF
PRODUCTION
LOSSES

PACKING

FINISHED PRODUCT
STORAGE

TRANSPORT TO THE
SALE PLACE

30

IMPACTS OF THE IMPROVEMENTS PROPOSED IN THE PROCESS
Acquisition of a new ice machine:
Having an ice machine would represent continuity in the process since they would reduce or eliminate wait times by shortages and would generate significant savings by eliminating the frequent purchase of bags of ice to the supplier.
Acquisition of a new cooling chamber and better controls in Statistical Process:
A remarkable problematic at this production plant is the variation and lack of control in the texture of the ham bars; But if they quantify the losses that causes them to have a reduced capacity in the cooling chamber # 3 and condition the size based on demand, they would achieve:  Decrease in losses at the stage of slicing and the reduction would be almost 50 % of the current, which would help achieve the goal of less rework.
 The need to modify the slicing parameters is reduced, and linked to this, the presence of the line overweight’s hams have significant decreases.
 To acquire and expand the cooling # 3 the bottleneck at this stage and slicing bar stage would be eliminated, thus speeding the process continuity. This bottleneck is also the cause of starving at the stage of slicing and packaging.
 With these improvements would be optimized processing times and might even increase the current manufacturing capacity, obtaining as much final product.
 With statistical controls we recommend are reinforced, it is possible to identify important variations in the process and to seek the causes for their prompt correction.
 Product quality also would benefit being as pack the product to the right temperature range allows the lifetime is reached and market returns would decrease greatly.

31

BIBLIOGRAFY


Moving from Hypothesis Testing to Control Charts – Lecture 11 - Statistical Quality &
Process Control – Available on internet – South Dakota School of Mines and Technology.



Limits on One Sided Specification – Partners Forum – Available on internet http://www.isixsigma.com/topic/limits-on-one-sided-specification/.



Control Chart. Wikipedia, The Free Encyclopedia – Available on Internet – https://en.wikipedia.org/wiki/Control_chart. 32

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...MIAMI DADE COLLEGE SCHOOL OF NURSING HEALTH HISTORY DATE OF HISTORY IDENTIFYING DATA Age Sex Race Place of Birth Significant Other Occupation Religion SOURCE OF REFERRAL SOURCE OF HISTORY/ RELIABILITY CHIEF COMPLAINTS/REASON FOR VISIT PRESENT ILLNESS: Time of onset Type of onset Original Source Severity Radiation Time relationship Duration Course Association Source of relief Source of aggravation Page 1 of 6 PAST MEDICAL HISTORY General State of Health Childhood Illnesses: Adult Illnesses: Psychiatric Illnesses Accidents and Injuries Operations Hospitalizations CURRENT HEALTH STATUS Current Medications Allergies Tobacco Alcohol Drugs Diet Screening Tests Immunizations Sleep Patterns Exercise & Leisure Activities Environmental Hazards Use of safety measures Page 2 of 6 FAMILY HISTORY: Maternal/Paternal Grandparents Parents Aunts/Uncles Siblings Spouse Children REVIEW OF SYSTEMS: GENERAL: SKIN: NEUROLOGIC: EYES: EARS: NOSE and SINUSES: MOUTH and THROAT: NECK: BREAST: RESPIRATORY: Page 3 of 6 CARDIAC: GASTROINTESTINAL: URINARY: GENITAL: Male: Female: PERIPHERAL VASCULAR: MUSCULOSKELETAL: HEMATOLOGIC: ENDOCRINE: PSYCHIATRIC: Page 4 of 6 SOCIOLOGICAL SYSTEM: A. RELATIONSHIPS WITH FAMILY AND SIGNIFICANT OTHERS 1. Client's position with family 2. Persons with whom client lives 3. Persons with whom client relates B...

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Jose

...Life on the Oregon Trail was difficult to all pioneers. It took day’s maybe weeks to get ready for the journey. A family of four would have to have over a thousand of pounds of food to survive the very long journey. To travel this long journey pioneers needed a wagon to travel. They needed a wagon because they would need to carry all of that food. Before starting the trip most pioneers had cholera. A lot of pioneers died having it and some were lucky to get better. When it was time to travel there would be huge traffic jams. Some pioneers were not trained. This means that they couldn’t control their ox’s. So most of the time the wagon will run into a tree or the ox will take the wagon in the wrong direction. Only a couple of miles into the journey pioneers noticed that they packed to much stuff into their wagon. So they decided to throw stuff along the way. Settlers from the jump off towns picked up the stuff. Most of the stuff was baconflour & iron stoves. Crossing rivers was one of the most stressful things in the journey. Hundreds died trying to cross some rivers. Those rivers are called the Kansas, North Platte, and Colombia Rivers. Many pioneers walked the 2,000 mile journey. That’s because they overloaded their wagons. In 1850 37 settlers drowned crossing the Green River. The wagons weren’t 100% safe. It didn’t have no safe features. Therefore if someone fell out the wagon death was instant. A diary quoted “A little boy fell over the front end of the wagon during......

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Guardian

...ACTA DE CONSTITUCIÓN DEL PROYECTO – PROJECT CHARTER | A. INFORMACIÓN GENERAL. | | Nombre del Proyecto: RECONSTRUCCION Y MEJORAMIENTO DE LA INFRAESTRUCTURA Y EQUIPAMIENTO DE LA “INSTITUCION EDUCATIVA Nº 21015 | | Fecha de Preparación: | 03-12-11 | Preparado por: | Eric Escalante , Roger Molero, Luis Galindo y María José Villa | Autorizado Por: | María Jose Villa | | B. JUSTIFICACIÓN DEL PROYECTO. | * Debido al sismo ocurrido en la ciudad de Pisco, la infraestructura física de la IE N°21015 sufrió graves daños estructurales, por tal motivo se hace necesario una rehabilitación y reconstrucción de las estructuras afectadas. | * La IE N°21015 presta servicio a 1350 alumnos en dos turnos a nivel primaria durante todo el año escolar, siendo la principal institución educativa en toda la ciudad de Mala, por tal motivo se hace urgente su reconstrucción en el menor tiempo posible. | C. OBJETIVOS DEL PROYECTO. | * Rehabilitar y Reconstruir la Infraestructura y Equipamiento de la IE 21015. | * Costo: S/. 3 724 762.25. | * Plazo: 180 Días Calendario. | * Calidad: Según las Normas Vigentes. | D. ALCANCE DEL PROYECTO. | Dentro del Alcance.Producto | * Demolición de Infraestructura en mal estado, patios, veredas, tramos de Cerco de Ladrillo (4.00m) según indica los planos respectivos. | * Construcción de 21 Aulas, 01 Sala de computo, 01 Sala de profesores, 01 Sala de administración y 01 biblioteca. | * Rehabilitación de 06......

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Research in Motion Porter's Five Force

...| |[pic] | |MARIA JOSE PAZ CORADO | | |Datos Personales | | | |Nombre: María José Paz Corado | | | | | |No. Identificación: A-1 1204242 | | | | | |Dirección: 43 Calle “A” 6-92 zona 12 Monte María III, | | |Guatemala, C.A. | | | | | |Teléfonos: 58346620 ...

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