3 group technology / cellular manufacturing (inselfertigung)

3 Group Technology / Cellular Manufacturing

(Inselfertigung)

Layout & Design Chapter 3 / 2(c) Prof. Richard F. Hartl

Group Technology (GT)

Observation already in 1920ies:product-oriented departments to manufacture standardized products in machine companies lead to reduced transportation

Can be considered the start of Group Technology (GT):Parts with similar features are manufactured together with standardized processes small "focused factories" are created as independent operating units within large facilities.

More generally, GT can be considered a “theory of management” based on the principle "similar things should be done similarly“

"things" .. product design, process planning, fabrication, assembly, and production control (here); but also other activities, including administrative functions.


When to use GT?

See also Chapter 1 (Figure 1.5) Pure item flow lines are possible, if volumes are very

large. If volumes are very small, and parts are very different, a

functional layout (job shop) is usually appropriate In the intermediate case of medium-variety, medium-

volume environments, group configuration is most appropriate


Cellular Manufacturing

Principle of GT: divide the manufacturing facility into small groups or cells of machines cellular manufacturing

Each cell is dedicated to a specified family of part types (or few “similar” families). Preferably, all parts are completed within one cell

Typically, it consists of a small group of machines, tools, and handling equipment


Different Versions of GT

The idea of GT can also be used to build larger groups, such as for instance, a department, possibly composed of several automated cells or several manned machines of various types.

GT flow line classical GT cell GT center


GT flow line

All parts assigned to a group follow the same machine sequence and require relatively proportional time requirements on each machine.

Automated transfer mechanisms may be possible. mixed-model assembly line (Chapter 4)

(Askin & Standridge, 1993, p. 167).

fräsen(aus)bohrendrehenschleifenbohren


classical GT cell

Allows parts to move from any machine to any other machine. Flow is not unidirectional.

Since machines are located in close proximity short and fast transfer is possible.



GT center

Machines located as in a process (job shops) But each machine is dedicated to producing only certain

Part families only the tooling and control advantages of GT; increased material handling is necessary

When large machines have already been located and cannot be moved, or

When product mix and part families are dynamic would require frequent relayout of GT cell



Typical Manufacturing Cell (1)

Often u-shaped for short transport

Even if process layout not possible

Often typical material flow


Typical Manufacturing Cell (2)

Example with 3 workers

Also u-shaped


Advantages of GT Cell

Short transportation and handling (usually within cell) Short setup times because often same tools and fixtures can be

used (products are similar) High flexibility (quick reaction on changes) Investment cost low (no advanced technology necessary) Clear arrangement, few tools/machines easy to control High motivation and satisfaction of workers

(identification with “their" products) Small lot sizes possible short flow times


How to Build Groups/Cells

Basic Idea: Typical Part Families

Items that look alike Items that are made with the same equipment


Items That Look Alike

Most products that look similar are manufactured using similar production techniques (if similar material)

Parts are grouped because they have similar geometry (about the same size and shape) they should represent a part family,

e.g. cog wheels (gear wheels)of similar size and material


Items That Are Made with Same Equipment


How to Build Groups/Cells

Visual inspection “Items that look alike” may use photos or part prints utilizes subjective judgment (experience)

Classification & coding by examination of design & production data (same equipment) most common in industry time consuming & complicated


Codes

The code should be sufficiently flexible to handle future as well as current parts

The scope of part types must be known (e.g. parts rotational, prismatic, sheet metal, etc.?)

The code must discriminate between parts with different values for key attributes (material, tolerances, required machines, etc.)


Codes

Many coding systems have been developed None is universally applicable Most implementations require some customization

Functional classification coding based on part design attributes coding based on part manufacturing attributes coding based on combination of design & manuf. attributes

Structural classification Hierarchical Structure Chain Type Structure Hybrid structure (combination)


Hierarchical Code

Meaning of a digit depends on values of preceding digits. The value of 3 in the third place may indicate

the existence of internal threads in a rotational part: "1232" a smooth internal feature: “2132"

Hierarchical codes are efficient:they only consider relevant information at each digit

But they are difficult to learn and remember because of the large number of conditional inferences.


Chain Code

Each value for each digit of the code has a consistent meaning. The value 3 in the third place has the same meaning for all parts.

Easier to learn but less efficient (longer for same info) Certain digits may be meaningless for some/many parts.


Hybrid Code

Both hierarchical and chain codes have advantages, many commercial codes are hybrid (combination of both)

Some section of the code is a chain code and then several hierarchical digits further detail the specified characteristics.

Several such sections may exist. One example of a hybrid code is Opitz


Optiz Classification System

Three sections

Form Code:5 digitsdescribes the primary design attributes, e.g. shape

Supplementary Code:4 digitsmanuf. attributes. e.g.dimensions, material, accuracy, starting work piece shape

Secondary Code:company specific, e.g. type and sequence of prod. operations

12345 6789 ABCD


Optiz Classification System


Optiz in More Detail2 2 4 0 0


Production Flow Analysis (PFA)

Basic idea: Items that are made with the same processes / the same

equipment These parts are assembled into a part family Can be grouped into a cell to minimize material handling

requirements.


How to Build Groups/Cells using PFA

Many clustering methods have been developed Can be classified into:

Part family grouping: Form part families and then group machines into cells

Machine grouping: Form machine cells based upon similarities in part routing and then allocate parts to cells

Machine-part grouping: Form part families and machine cells simultaneously.


Machine-Part Grouping: Obtain Block Diagonal Structure

Construct matrix of machine usage by parts sort rows (machines) and columns (parts) so that a

block-diagonal shape is obtained

Then it is easy to build groups: Group 1: parts {13, 2, 8, 6, 11 }, machines {B, D} Group 2: parts { 5, 1, 10, 7, 4, 3}, machines {A, H, I, E} Group 3: parts { 15, 9, 12, 14}, machines {C, G, F}


King’s Algorithm (Rank Order Clustering) Binary Ordering

How to obtain block-diagonal shape? Example: 5 machines; 6 parts: Interpret rows and columns as binary numbers

Sort rows w.r.t. decreasing binary numbers

Sort columns w.r.t. decreasing binary numbers

part

machine 1 2 3 4 5 6

A - 1 - 1 - -

B 1 - 1 - 1 1

C - 1 1 1 - 1

D 1 - - - 1 1

E - - - 1 1 -


Binary Ordering

Sort rows w.r.t. decreasing binary numbers

New ordering of machines: B – D – C – A - E

part value

machine 1 2 3 4 5 6

A - 1 - 1 - -

B 1 - 1 - 1 1

C - 1 1 1 - 1

D 1 - - - 1 1

E - - - 1 1 -

25

3224 16

23 8

22 4

21 2

20 1

0101002 = 22 + 24 = 20

20 + 21 + 25 = 35

20 + 21 + 23 + 25 = 43

20 + 22 + 23 + 24 = 29

21 + 22 = 6


Binary Ordering

part

machine 1 2 3 4 5 6 value

B 1 - 1 - 1 1 43

D 1 - - - 1 1 35

C - 1 1 1 - 1 29

A - 1 - 1 - - 20

E - - - 1 1 - 6

value

20 = 1

21 = 2

22 = 4

23 = 8

24 = 1623

+ 2

4 =

24

22 +

24

= 2

0

21 +

22

= 6

20 +23 +

24 =25

20 +21 +

22 =7

22 +23 +

24 =28

Sort columns w.r.t. decreasing binary numbers

New ordering of parts:

6-5-1-3-4-2


Result of Binary Ordering

part

machine 6 5 1 3 4 2

B 1 1 1 1 - -

D 1 1 1 - - -

C 1 - - 1 1 1

A - - - - 1 1

E - 1 - - 1 -

value 28 25 24 20 7 6

2 groups: Group 1: parts {6, 5, 1 },

machines {B, D} Group 2: parts { 3, 4, 2},

machines {C, A, E}

Parts 1, 4, and 2 can be produced in one cell

No complete block-diagonal structure

Remaining items: 6, 5, and 3 produced in both cells Or machines B, C, and E have to be duplicated


Repeated Binary Ordering

Binary Ordering is a simple heuristic no guarantee that „optimal“ ordering is obtained

Sometimes a better better block-diagonal structure is obtained by repeatingthe Binary Ordering until there is no change anymore


Example Binary Ordering

(contd.) After sorting of rows and

columns:

part


B 1 1 1 1 - - 60

D 1 1 1 - - - 56

C 1 - - 1 1 1 39

A - - - - 1 1 3

E - 1 - - 1 - 18

value 28 25 24 20 7 6part


B 1 1 1 1 - - 60

D 1 1 1 - - - 56

C 1 - - 1 1 1 39

E - 1 - - 1 - 3

A - - - - 1 1 18

value 28 26 24 20 7 5

No change of groups in this example


Single-Pass Heuristic Considering Capacities (Askin and Standridge)

extension of simple rule with binary sorting: All parts must be processed in one cell (machines must

be duplicated, if off-diagonal elements in matrix) All machines have capacities (normalized to be 1) Constraints on number of identical machines in a group Constraints on total number of machines in a group


Example Single-Pass Heuristic (Askin and Standridge)

7 parts, 6 machines Given matrix of processing times (incl. set up times) for

typical lot size of parts on machines Entries in matrix not just 0/1 for used/not used) All times as percentage of total machine capacity

At most 4 machines in a group

Not mot than one copy of each machine in each group


Example Single-Pass Heuristic (contd.)

part

machine 1 2 3 4 5 6 7 sum min. # machines

A 0.3 - - - 0.6 - - 0.9

B - 0.3 - 0.3 - - 0.1 0.7

C 0.4 - - 0.5 - 0.3 - 1.2

D 0.2 - 0.4 - 0.3 - 0.5 1.4

E - 0.4 - - - 0.5 - 0.9

F - 0.2 0.3 0.4 - - 0.2 1.1

1

1

2

2

1

2

= 9 machines


Example Single-Pass Heuristic (contd.)

At least 9 machines are needed Not more than 4 machines in a group at least 9/4 = 2,25 groups,

i.e. at least 3 groups

Step 1: acquire block diagonal structure e.g. using binary sorting

Step 2: build groups


Example - Step1: Binary Sorting

For binary sorting treat all entries as 1s. Result is

part

machine 1 5 7 3 4 6 2

D 0.2 0.3 0.5 0.4 - - -

C 0.4 - - - 0.5 0.3 -

A 0.3 0.6 - - - - -

F - - 0.2 0.3 0.4 - 0.2

B - - 0.1 - 0.3 - 0.3

E - - - - - 0.5 0.4solution


Step 2: Build Groups

Assign parts to groups (in sorting order)

Necessary machines are also included in group

Add parts to group until either the capacity of some machine would be exceeded, or the maximum number of machines would be exceeded


Example – Step2

Iterationpart

chosengroup

assigned machines

remaining capacity

1 1

2 5

3 7

4 3

5 4

6 6

7 2

1

1

2

2

2

3

3

D, C, A

C, E, F, B

D, C, A

C, E

D, F, B

D, F, B

D, F, B, C

D (0,8), C (0,6), A (0,7)

D (0,5), C (0,6), A (0,1)

D (0,5), F (0,8), B (0,9)

D (0,1), F (0,5), B (0,9)

D (0,1), F (0,1), B (0,6), C (0,5)

C (0,7), E (0,5)

C (0,7), E (0,1), F (0,8), B (0,7)

table


Results of Example

Machines used: One machine each of types: A, E Two machines of types: B, D, F Three machines of type: C

Single-pass heuristic of Askin und Standridge is a simple heuristic not necessarily optimal solution (min possible number of machines)

Compare result with theoretical min number of machines


Results of Example

part

machine 1 2 3 4 5 6 7 summin.

#heuristic

A 0.3 - - - 0.6 - - 0.9 1 1

B - 0.3 - 0.3 - - 0.1 0.7 1 2

C 0.4 - - 0.5 - 0.3 - 1.2 2 3

D 0.2 - 0.4 - 0.3 - 0.5 1.4 2 2

E - 0.4 - - - 0.5 - 0.9 1 1

F - 0.2 0.3 0.4 - - 0.2 1.1 2 2

Maybe reduction possible?!


LP for min Number of Machines

Minimize total (or weighted) number of machines used when the number of groups is given

Previous example: At least 9 machines necessary Every group has at most M = 4 machines at least 3 groups (try 3)


Given Data

ajk ... capacity of machine k needed for part j

i I ... groups (cells)

j J ... parts

k K ... machines

M ... maximum number of machines per group


Decision Variables

1, if part j is assigned to group i

0, otherwise

1, if machine of type k is assigned to group i

0, otherwise

ijx =

iky =


LP

objective:

constraints:

each part must be assigned to one group

respect capacity of machine k in group i

not more than M machines in group i

binary variables

binary variables

min Kk

ikIi

y

Ii

ijx 1 Jj

Jj

ikijjk yxa KkIi ,

Kk

ik My Ii

1,0ijx JjIi ,

1,0iky KkIi ,


Solution of LP

group parts machines remaining capacity

1 2, 4, 6 B, C, E, F B (0.4), C (0.2), E (0.1), F (0.4)

2 1, 5 A, C, D A (0.1), C (0.6), D (0.5)

3 3, 7 B, D, F B (0.9), D (0.1), F (0.5)

Optimal solution with 10 machines Theoretical minimum number was 9 machines

(not reached because of constraints) Single pass heuristic used 11 machines


Other Approaches for Clustering

Constructive algorithms for sorting: E.g. „direct clustering“ instead of binary sorting

Use similarity coefficients for clustering Askin Standridge § 6.4.4

Group analysis after binary ordering Askin Standridge § 6.4.1


Clustering using Similarity Coefficients

Defineni ... Number of parts visiting machine inij ... Number of parts visiting machines i and j

Similarity coefficient between machines i and j

Proportion of parts visting machine i that also visit machine j

ji

ij

j

ij

i

ijij nn

n

n

n

n

ns

,min,max


Example for Similarity Coefficients

Machine-part matrix


Group analysis after binary ordering


Example

3 group technology / cellular manufacturing (inselfertigung)

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