OPERATION MANAGEMENT
Operations management is an area of management concerned with overseeing, designing, and controlling the
process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring
that business operations are efficient in terms of using as few resources as needed,
and effective in terms of meeting
customer requirements. It is concerned with managing the process that converts
inputs (in the forms of raw materials, labor, and energy) into outputs (in the form of goods and/or services).
The relationship of operations management to senior management in commercial contexts can
be compared to the relationship of line officers to highest-level senior
officers in military science. The highest-level
officers shape the strategy and revise it over time,
while the line officers make tactical decisions in support of
carrying out the strategy. In business as in military affairs, the boundaries
between levels are not always distinct; tactical information dynamically informs strategy, and individual people often
move between roles over time.
Ford Motor car assembly line: the classical
example of a manufacturing production system.
According to the United States Department of Education, operations management is
the field concerned with managing and directing the physical and/or technical
functions of a firm or organization, particularly those relating
to development, production, and manufacturing. Operations management programs
typically include instruction in principles of general management, manufacturing and production systems, factory management, equipment maintenance management, production control,
industrial labor relations and skilled
trades supervision, strategic manufacturing policy, systems analysis, productivity analysis and cost control, and materials planning. Management,
including operations management, is like engineering in that it blends art with applied science. People skills, creativity,
rational analysis, and knowledge of technology are all required for
success.
Production systems
In a job shop machines are grouped by
technological similarities regarding transformation processes, therefore a
single shop can work very different products (in these picture four colors).
Also notice that in this drawing each shop contains a single machine.
Flexible Manufacturing
System: in the middle there are two rails for the shuttle to move pallets between machining centers
(there are also FMS which use AGVs), in front of each
machining center there is a buffer and in left we have a shelf for storing
pallets. Usually in the back there is a similar system for managing the set of tools required for different machining operations.
A production system comprises both the
technological elements (machines and tools) and organizational behavior
(division of labor and information flow). An individual production system is
usually analyzed in the literature referring to a single business, therefore
it's usually improper to include in a given production system the operations
necessary to process goods that are obtained by purchasing or the operations carried
by the customer on the sold products, the
reason being simply that since businesses need to design their own production
systems this then becomes the focus of analysis, modeling and decision making (also called "configuring" a
production system) .
A first possible distinction in production systems
(technological classification) is between process production and part
production.
·
Process
production means that the product undergoes physical -chemical transformations
and lacks assembly operations, therefore the original raw materials can’t
easily be obtained from the final product, example include: paper, cement and nylon.
·
Part
production (ex: cars and ovens) comprises both manufacturing systems and assembly systems. In the first
category we find job shops, manufacturing
cells, flexible manufacturing systems and transfer lines, in the assembly category
we have fixed
position systems, assembly lines and assembly shops (both manual and/or
automated operations).
Delivery lead time is the blue bar,
manufacturing time is the whole bar, the green bar is the difference between
the two.
Another possible classification[13] is one based on Lead Time (manufacturing lead time vs
delivery lead time): Engineer to Order (ETO, Purchase to Order (PTO), Make to Order (MTO), Assemble to Order (ATO) and Make to Stock (MTS). According to this
classification different kinds of systems will have different customer order
decoupling points (CODP), meaning that Work in Progress (WIP) cycle stock levels
are practically nonexistent regarding operations located after the CODP (except
for WIP due to queues). (See Order
fulfillment)
The concept of production systems can be
expanded to the service sector world keeping in mind that
services have some fundamental differences in respect to material goods:
intangibility, client always present during transformation processes, no stocks
for "finished goods". Services can be classified according to a
service process matrix:[14] degree of labor intensity
(volume) vs degree of customization (variety). With a high degree of labor
intensity there are Mass Services (e.g., commercial
banking bill payments and state schools) and Professional Services
(e.g., personal physicians and lawyers), while with a low degree
of labor intensity there are Service Factories (e.g., airlines and hotels) and Service Shops (e.g., hospitals and auto mechanics).
Metrics: efficiency and effectiveness
Operations strategy concerns policies and
plans of use of the firm productive resources with the aim of supporting long
term competitive strategy. Metrics in operations management can be broadly
classified into efficiency metrics and effectiveness metrics. Effectiveness
metrics involve:
1. Price (actually fixed by marketing, but lower
bounded by production cost): purchase price, use costs, maintenance costs,
upgrade costs, disposal costs
A more recent approach, introduced by Terry
Hill,] involves distinguishing
competitive variables in order winner and order qualifiers when defining
operations strategy. Order winners are variables which permit differentiating
the company from competitors, while order qualifiers are prerequisites for
engaging in a transaction. This view can be seen as a unifying approach between
operations management and marketing (see segmentation and positioning).
Productivity is a standard efficiency
metric for evaluation of production systems, broadly speaking a ratio between
outputs and inputs, and can assume many specific forms, for example: machine
productivity, workforce productivity, raw material productivity, warehouse
productivity (=inventory
turnover).
It is also useful to break up productivity in use U (productive percentage of
total time) and yield η (ratio between produced volume and productive time) to
better evaluate production systems performances. Cycle times can be modeled
through manufacturing engineering if the
individual operations are heavily automated, if the manual component is the
prevalent one, methods used ainclude: time
and motion study, predetermined motion time systems and work sampling.
ABC analysis is a method for analyzing
inventory based on Pareto
distribution,
it posits that since revenue from items on inventory will be power law distributed then it makes
sense to manage items differently based on their position on a
revenue-inventory level matrix, 3 classes are constructed (A,B and C) from
cumulative item revenues, so in a matrix each item will have a letter (A,B or
C) assigned for revenue and inventory. This method posits that items away from
the diagonal should be managed differently: items in the upper part are subject
to risk of obsolescence, items in the lower part are subject to risk of stock out.
Throughput is a variable which
quantifies the number of parts produced in the unit of time. Although
estimating throughput for a single process maybe fairly simple, doing so for an
entire production system involves an additional difficulty due to the presence
of queues which can come from: machine breakdowns, processing time
variability, scraps, setups, maintenance time, lack of orders, lack
of materials, strikes, bad coordination between
resources, mix variability, plus all these inefficiencies tend to compound
depending on the nature of the production system. One important example of how
system throughput is tied to system design are bottlenecks: in job shops bottlenecks
are typically dynamic and dependent on scheduling while on transfer lines it
makes sense to speak of "the bottleneck" since it can be univocally
associated with a specific station on the line. This leads to the problem of
how to define capacity measures, that is an
estimation of the maximum output of a given production system, and capacity
utilization.
Overall Equipment Effectiveness (OEE) is defined as the
product between system availability, cycle time efficiency and quality rate.
OEE is typically used as key performance indicator (KPI) in conjunction with
the lean manufacturing approach.
Configuration and management
Classic EOQ model:
trade-off between ordering cost (blue) and holding cost (red). Total cost (green)
admits aglobal
optimum.
A typical MRPII construct:
general planning (top) concerned with forecasts, capacity planning and
inventory levels, programming (middle) concerned with calculation of workloads, rough-cut capacity
planning, MPS, capacity
requirements planning,
traditional MRP planning, control (bottom) concerned with scheduling.
When introducing kanbans in real production
systems attaining unitary lot from the start maybe unfeasible, therefore the
kanban will represent a given lot size defined by management
Value
Stream Mapping, a
representation of materials and information flows inside a company, mainly used
in the lean manufacturing approach. The calculation of the time-line (bottom)
usually involves using Little's Law to derive lead time from
stock levels and cycle times
Designing the configuration of production
systems involves both technological and organizational variables. Choices in
production technology involve: dimensioning capacity, fractioning capacity,
capacity location, outsourcing processes, process
technology, automation of operations, trade-off
between volume and variety (see Hayes-Wheelwright matrix). Choices in the organizational area involve:
defining worker skills and responsibilities, team coordination, worker
incentives and information flow.
Regarding the planning of production there is
a basic distinction between the push approach and the pull approach, with the later
including the singular approach of Just
in Time.
Pull means that the production system authorizes production based on inventory
level, push means that production occurs based on demand (fore-casted or
present, that is purchase orders). It should be noticed
that an individual production system can be both push and pull, for example
activities before the CODP may work under a pull system, while activities after
the CODP may work under a push system.
Regarding the traditional pull approach a
number of techniques have been developed based on the work of Ford W. Harris[5] (1913) which came to be
know as the Economic
order quantity (EOQ), which formed the basis of subsequent
techniques as the Wagner-Within
Procedure,
the News Vendor Model, Base Stock Model and the Fixed Time Period Model. These models usually
involve the calculation of cycle stocks and buffer stocks, the latter usually modeled as a function of
demand variability. The Economic
Production Quantity[17] (EPQ) differs from the EOQ
model only in that it assumes a constant fill rate for the part being produced,
instead of the instantaneous refilling of the EOQ model.
Joseph Orlickly and others developed Material Requirement Planning (MRP) at IBM, essentially a push approach to inventory
control and production planning, which takes as input both the Master
Production Schedule (MPS) and the Bill of Materials (BOM) and gives as output a
schedule for the materials (components) needed in the production process. MRP
therefore is a planning tool to manage purchase orders and production orders (also
called jobs).
The MPS can be seen as a kind of aggregate
planning for production coming in two fundamentally opposing varieties: plans
which try to chase demand and level plans which try to keep uniform capacity
utilization. Many models have been proposed to solve MPS problems:
·
Analytical
models (e.g. Magee Boodman model)
MRP can be briefly described as a 3s
procedure: sum (different orders), split (in lots), shift (in time according to
item lead time). To avoid an "explosion" of data processing in MRP
(number of BOMs required in input) planning bills (such as family bills or
super bills) can be useful since they allow a rationalization of input data
into common codes. MRP had some notorious problems such as infinite capacity and fixed lead times, which influenced
successive modifications of the original software architecture in the form of MRP II and ERP.
In this context problems of scheduling (sequencing of production), loading (tools to use),
part type selection (parts to work on) and applications of operations
research have a significant role to play.
Lean
Manufacturing is an approach to production which arose in Toyota between the end of World War II and the seventies.
It comes mainly from the ideas of Taiichi Ohno and Toyoda Sakichi which are centered on the complementary
notions of Just
in Time and Autonomation (jidoka), all aimed at
reducing waste (usually applied in PDCA style). Some additional elements are also
fundamental:[18] production smoothing (Heijunka),
capacity buffers, setup reduction, cross-training and plant layout.
·
Heijunka: production smoothing
presupposes a level strategy for the MPS and a final
assembly schedule developed from the MPS by smoothing aggregate
production requirements in smaller time buckets and sequencing final assembly
to achieve repetitive manufacturing. If these conditions are met, expected throughput can be equaled to the
inverse oftakt
time.
Besides volume, heijunka also means attaining mixed model production, which however may only be feasible trough
set-up reduction. A standard tool for achieving this is the Heijunka box
·
Capacity
buffers: ideally a JIT system would work with zero breakdowns, this however is
very hard to achieve in practice, and none the less Toyota favors acquiring
extra capacity over extra WIP to deal with starvation.
·
Set-up reduction: typically
necessary to achieve mixed model production, a key distinction can be made
between internal and external setup. Internal setups (ex: removing a die)
refers to tasks when the machine is not working, while external setups can be
completed while the machine is running (ex: transporting dies).
·
Cross
training:
important as an element of Autonomation, Toyota cross trained their employees
through rotation, this served as an element of production flexibility, holistic
thinking and reducing boredom.
·
Layout: U-shaped lines or cells
are common in the lean approach since they allow for minimum walking, greater
worker efficiency and flexible capacity.
A series of tools have been developed mainly
with the objective of replicating Toyota success: a very common implementation
involves small cards known as kanbans, these also come in some
varieties: reorder kanbans, alarm kanbans, triangle kanbans, etc. In the
classic kanban procedure with one card:
·
Parts
are kept in containers with their respective kanbans
·
The
downstream station moves the kanban to the upstream station and starts
producing the part at the downstream station
·
The
upstream operator takes the most urgent kanban from his list (compare to queue
discipline from queue theory) and produces it and attach
its respective kanban
The two-card kanban procedure differs a bit:
·
The
downstream operator takes the production kanban from his list
·
If
required parts are available he removes the move kanban and places them in
another box, otherwise he chooses another production card
·
He
produces the part and attach its respective production kanban
·
Periodically
a mover picks up the move kanbans in upstream stations and search for the
respective parts, when found he exchanges production kanbans for move kanbans
and move the parts to downstream stations
Since the number of kanbans in the production
system is set by managers as a constant number, the kanban procedure works as WIP controlling device, which
for a given arrival rate, per Little's Law, works as lead time
controlling device.
In Toyota the TPS represented more of a
philosophy of production than a set of specific tools, the latter would
include: SMED, Value
Stream Mapping, 5S, poka-yoke, elimination of time
batching, lot-size reduction, Rank
Order Clustering,
single point scheduling, multi-process handling and backflush
accounting.
Seen more broadly JIT can include methods
such as: product standardization and modularity, group technology, total
productive maintenance, job enlargement, job enrichment,flat organization and vendor rating (JIT production is very
sensitive to replenishment conditions).
Safety, Risk and Maintenance
Other important management problems involve maintenance policies (see also reliability
engineering and maintenance
philosophy), safety
management systems (see also safety
engineering and Risk management), facility
management and supply chain integration.
v
OPERATION MANAGEMENT
Operations management is an area of management concerned with overseeing, designing, and controlling the
process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring
that business operations are efficient in terms of using as few resources as needed,
and effective in terms of meeting
customer requirements. It is concerned with managing the process that converts
inputs (in the forms of raw materials, labor, and energy) into outputs (in the form of goods and/or services).
The relationship of operations management to senior management in commercial contexts can
be compared to the relationship of line officers to highest-level senior
officers in military science. The highest-level
officers shape the strategy and revise it over time,
while the line officers make tactical decisions in support of
carrying out the strategy. In business as in military affairs, the boundaries
between levels are not always distinct; tactical information dynamically informs strategy, and individual people often
move between roles over time.
Ford Motor car assembly line: the classical
example of a manufacturing production system.
According to the United States Department of Education, operations management is
the field concerned with managing and directing the physical and/or technical
functions of a firm or organization, particularly those relating
to development, production, and manufacturing. Operations management programs
typically include instruction in principles of general management, manufacturing and production systems, factory management, equipment maintenance management, production control,
industrial labor relations and skilled
trades supervision, strategic manufacturing policy, systems analysis, productivity analysis and cost control, and materials planning. Management,
including operations management, is like engineering in that it blends art with applied science. People skills, creativity,
rational analysis, and knowledge of technology are all required for
success.
Production systems
In a job shop machines are grouped by
technological similarities regarding transformation processes, therefore a
single shop can work very different products (in these picture four colors).
Also notice that in this drawing each shop contains a single machine.
Flexible Manufacturing
System: in the middle there are two rails for the shuttle to move pallets between machining centers
(there are also FMS which use AGVs), in front of each
machining center there is a buffer and in left we have a shelf for storing
pallets. Usually in the back there is a similar system for managing the set of tools required for different machining operations.
A production system comprises both the
technological elements (machines and tools) and organizational behavior
(division of labor and information flow). An individual production system is
usually analyzed in the literature referring to a single business, therefore
it's usually improper to include in a given production system the operations
necessary to process goods that are obtained by purchasing or the operations carried
by the customer on the sold products, the
reason being simply that since businesses need to design their own production
systems this then becomes the focus of analysis, modeling and decision making (also called "configuring" a
production system) .
A first possible distinction in production systems
(technological classification) is between process production and part
production.
·
Process
production means that the product undergoes physical -chemical transformations
and lacks assembly operations, therefore the original raw materials can’t
easily be obtained from the final product, example include: paper, cement and nylon.
·
Part
production (ex: cars and ovens) comprises both manufacturing systems and assembly systems. In the first
category we find job shops, manufacturing
cells, flexible manufacturing systems and transfer lines, in the assembly category
we have fixed
position systems, assembly lines and assembly shops (both manual and/or
automated operations).
Delivery lead time is the blue bar,
manufacturing time is the whole bar, the green bar is the difference between
the two.
Another possible classification[13] is one based on Lead Time (manufacturing lead time vs
delivery lead time): Engineer to Order (ETO, Purchase to Order (PTO), Make to Order (MTO), Assemble to Order (ATO) and Make to Stock (MTS). According to this
classification different kinds of systems will have different customer order
decoupling points (CODP), meaning that Work in Progress (WIP) cycle stock levels
are practically nonexistent regarding operations located after the CODP (except
for WIP due to queues). (See Order
fulfillment)
The concept of production systems can be
expanded to the service sector world keeping in mind that
services have some fundamental differences in respect to material goods:
intangibility, client always present during transformation processes, no stocks
for "finished goods". Services can be classified according to a
service process matrix:[14] degree of labor intensity
(volume) vs degree of customization (variety). With a high degree of labor
intensity there are Mass Services (e.g., commercial
banking bill payments and state schools) and Professional Services
(e.g., personal physicians and lawyers), while with a low degree
of labor intensity there are Service Factories (e.g., airlines and hotels) and Service Shops (e.g., hospitals and auto mechanics).
Metrics: efficiency and effectiveness
Operations strategy concerns policies and
plans of use of the firm productive resources with the aim of supporting long
term competitive strategy. Metrics in operations management can be broadly
classified into efficiency metrics and effectiveness metrics. Effectiveness
metrics involve:
1. Price (actually fixed by marketing, but lower
bounded by production cost): purchase price, use costs, maintenance costs,
upgrade costs, disposal costs
A more recent approach, introduced by Terry
Hill,] involves distinguishing
competitive variables in order winner and order qualifiers when defining
operations strategy. Order winners are variables which permit differentiating
the company from competitors, while order qualifiers are prerequisites for
engaging in a transaction. This view can be seen as a unifying approach between
operations management and marketing (see segmentation and positioning).
Productivity is a standard efficiency
metric for evaluation of production systems, broadly speaking a ratio between
outputs and inputs, and can assume many specific forms, for example: machine
productivity, workforce productivity, raw material productivity, warehouse
productivity (=inventory
turnover).
It is also useful to break up productivity in use U (productive percentage of
total time) and yield η (ratio between produced volume and productive time) to
better evaluate production systems performances. Cycle times can be modeled
through manufacturing engineering if the
individual operations are heavily automated, if the manual component is the
prevalent one, methods used ainclude: time
and motion study, predetermined motion time systems and work sampling.
ABC analysis is a method for analyzing
inventory based on Pareto
distribution,
it posits that since revenue from items on inventory will be power law distributed then it makes
sense to manage items differently based on their position on a
revenue-inventory level matrix, 3 classes are constructed (A,B and C) from
cumulative item revenues, so in a matrix each item will have a letter (A,B or
C) assigned for revenue and inventory. This method posits that items away from
the diagonal should be managed differently: items in the upper part are subject
to risk of obsolescence, items in the lower part are subject to risk of stock out.
Throughput is a variable which
quantifies the number of parts produced in the unit of time. Although
estimating throughput for a single process maybe fairly simple, doing so for an
entire production system involves an additional difficulty due to the presence
of queues which can come from: machine breakdowns, processing time
variability, scraps, setups, maintenance time, lack of orders, lack
of materials, strikes, bad coordination between
resources, mix variability, plus all these inefficiencies tend to compound
depending on the nature of the production system. One important example of how
system throughput is tied to system design are bottlenecks: in job shops bottlenecks
are typically dynamic and dependent on scheduling while on transfer lines it
makes sense to speak of "the bottleneck" since it can be univocally
associated with a specific station on the line. This leads to the problem of
how to define capacity measures, that is an
estimation of the maximum output of a given production system, and capacity
utilization.
Overall Equipment Effectiveness (OEE) is defined as the
product between system availability, cycle time efficiency and quality rate.
OEE is typically used as key performance indicator (KPI) in conjunction with
the lean manufacturing approach.
Configuration and management
Classic EOQ model:
trade-off between ordering cost (blue) and holding cost (red). Total cost (green)
admits aglobal
optimum.
A typical MRPII construct:
general planning (top) concerned with forecasts, capacity planning and
inventory levels, programming (middle) concerned with calculation of workloads, rough-cut capacity
planning, MPS, capacity
requirements planning,
traditional MRP planning, control (bottom) concerned with scheduling.
When introducing kanbans in real production
systems attaining unitary lot from the start maybe unfeasible, therefore the
kanban will represent a given lot size defined by management
Value
Stream Mapping, a
representation of materials and information flows inside a company, mainly used
in the lean manufacturing approach. The calculation of the time-line (bottom)
usually involves using Little's Law to derive lead time from
stock levels and cycle times
Designing the configuration of production
systems involves both technological and organizational variables. Choices in
production technology involve: dimensioning capacity, fractioning capacity,
capacity location, outsourcing processes, process
technology, automation of operations, trade-off
between volume and variety (see Hayes-Wheelwright matrix). Choices in the organizational area involve:
defining worker skills and responsibilities, team coordination, worker
incentives and information flow.
Regarding the planning of production there is
a basic distinction between the push approach and the pull approach, with the later
including the singular approach of Just
in Time.
Pull means that the production system authorizes production based on inventory
level, push means that production occurs based on demand (fore-casted or
present, that is purchase orders). It should be noticed
that an individual production system can be both push and pull, for example
activities before the CODP may work under a pull system, while activities after
the CODP may work under a push system.
Regarding the traditional pull approach a
number of techniques have been developed based on the work of Ford W. Harris[5] (1913) which came to be
know as the Economic
order quantity (EOQ), which formed the basis of subsequent
techniques as the Wagner-Within
Procedure,
the News Vendor Model, Base Stock Model and the Fixed Time Period Model. These models usually
involve the calculation of cycle stocks and buffer stocks, the latter usually modeled as a function of
demand variability. The Economic
Production Quantity[17] (EPQ) differs from the EOQ
model only in that it assumes a constant fill rate for the part being produced,
instead of the instantaneous refilling of the EOQ model.
Joseph Orlickly and others developed Material Requirement Planning (MRP) at IBM, essentially a push approach to inventory
control and production planning, which takes as input both the Master
Production Schedule (MPS) and the Bill of Materials (BOM) and gives as output a
schedule for the materials (components) needed in the production process. MRP
therefore is a planning tool to manage purchase orders and production orders (also
called jobs).
The MPS can be seen as a kind of aggregate
planning for production coming in two fundamentally opposing varieties: plans
which try to chase demand and level plans which try to keep uniform capacity
utilization. Many models have been proposed to solve MPS problems:
·
Analytical
models (e.g. Magee Boodman model)
MRP can be briefly described as a 3s
procedure: sum (different orders), split (in lots), shift (in time according to
item lead time). To avoid an "explosion" of data processing in MRP
(number of BOMs required in input) planning bills (such as family bills or
super bills) can be useful since they allow a rationalization of input data
into common codes. MRP had some notorious problems such as infinite capacity and fixed lead times, which influenced
successive modifications of the original software architecture in the form of MRP II and ERP.
In this context problems of scheduling (sequencing of production), loading (tools to use),
part type selection (parts to work on) and applications of operations
research have a significant role to play.
Lean
Manufacturing is an approach to production which arose in Toyota between the end of World War II and the seventies.
It comes mainly from the ideas of Taiichi Ohno and Toyoda Sakichi which are centered on the complementary
notions of Just
in Time and Autonomation (jidoka), all aimed at
reducing waste (usually applied in PDCA style). Some additional elements are also
fundamental:[18] production smoothing (Heijunka),
capacity buffers, setup reduction, cross-training and plant layout.
·
Heijunka: production smoothing
presupposes a level strategy for the MPS and a final
assembly schedule developed from the MPS by smoothing aggregate
production requirements in smaller time buckets and sequencing final assembly
to achieve repetitive manufacturing. If these conditions are met, expected throughput can be equaled to the
inverse oftakt
time.
Besides volume, heijunka also means attaining mixed model production, which however may only be feasible trough
set-up reduction. A standard tool for achieving this is the Heijunka box
·
Capacity
buffers: ideally a JIT system would work with zero breakdowns, this however is
very hard to achieve in practice, and none the less Toyota favors acquiring
extra capacity over extra WIP to deal with starvation.
·
Set-up reduction: typically
necessary to achieve mixed model production, a key distinction can be made
between internal and external setup. Internal setups (ex: removing a die)
refers to tasks when the machine is not working, while external setups can be
completed while the machine is running (ex: transporting dies).
·
Cross
training:
important as an element of Autonomation, Toyota cross trained their employees
through rotation, this served as an element of production flexibility, holistic
thinking and reducing boredom.
·
Layout: U-shaped lines or cells
are common in the lean approach since they allow for minimum walking, greater
worker efficiency and flexible capacity.
A series of tools have been developed mainly
with the objective of replicating Toyota success: a very common implementation
involves small cards known as kanbans, these also come in some
varieties: reorder kanbans, alarm kanbans, triangle kanbans, etc. In the
classic kanban procedure with one card:
·
Parts
are kept in containers with their respective kanbans
·
The
downstream station moves the kanban to the upstream station and starts
producing the part at the downstream station
·
The
upstream operator takes the most urgent kanban from his list (compare to queue
discipline from queue theory) and produces it and attach
its respective kanban
The two-card kanban procedure differs a bit:
·
The
downstream operator takes the production kanban from his list
·
If
required parts are available he removes the move kanban and places them in
another box, otherwise he chooses another production card
·
He
produces the part and attach its respective production kanban
·
Periodically
a mover picks up the move kanbans in upstream stations and search for the
respective parts, when found he exchanges production kanbans for move kanbans
and move the parts to downstream stations
Since the number of kanbans in the production
system is set by managers as a constant number, the kanban procedure works as WIP controlling device, which
for a given arrival rate, per Little's Law, works as lead time
controlling device.
In Toyota the TPS represented more of a
philosophy of production than a set of specific tools, the latter would
include: SMED, Value
Stream Mapping, 5S, poka-yoke, elimination of time
batching, lot-size reduction, Rank
Order Clustering,
single point scheduling, multi-process handling and backflush
accounting.
Seen more broadly JIT can include methods
such as: product standardization and modularity, group technology, total
productive maintenance, job enlargement, job enrichment,flat organization and vendor rating (JIT production is very
sensitive to replenishment conditions).
Safety, Risk and Maintenance
Other important management problems involve maintenance policies (see also reliability
engineering and maintenance
philosophy), safety
management systems (see also safety
engineering and Risk management), facility
management and supply chain integration.
vv
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