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Objective: To demonstrate the manufacturing innovations are frequently processes that take decades to become commonplace. The current trend to automation is not such displacement of labor by machines. It is the shift from production for inventory to production for final demand. Each manufacturing innovation generally requires a complete reorganization of the work to become efficient. The human factors are just as important to success as the technical factors.
We start by considering a 19th century innovation in order to illustrate that major innovations frequently require continual technological advances and may take decades or even a hundred years. The first half of the 20th century manufacturing firms expanded from single to multiple products. Manufacturing innovation in the second half of the 20th century based on the advances in information technology has greatly accelerated the automation of the production of goods and services. In one sense, automation using microelectronics and communications advances is simply the continuation of a trend to increase the amount of capital per worker to obtain higher labor productivity. The current innovations are also changing manufacturing strategy from production for inventory to production for final demand. In making genuine advances the manufacturing process generally has to be completely reorganized and the human factors can not be neglected.
In microeconomic production theory we ask how output and input will adjust to changing input and output prices. We do not ask how firms innovation the production function because such a question is mathematically intractable and economists will only consider those aspects of economics that can be formalized in a mathematical model. We first take a clinical look at one of the most important innovations of the 19th century: replaceable parts and one innovation in the first half of the 20th century: multiple products. It is important to note that while economists take the production function as given, firms do not. They are constantly trying to innovate, that is obtain a better production technology. Also, major innovations can have a slow development over decades, even as in the case of replaceable parts over 100 years.
The economics of interchangeable parts have three factors:
1. Feasibility and cost of the machinery to create parts with the required accuracy.
2. Reduction in assembly costs in the assemblers do not have to be skilled artisans making the parts fit.
3. Reduction in repair costs, because skill required to replace a standardized part is much less than having a skilled artisan create a nonstandard part.
Historically some products have had interchangeable parts since before recorded history. The sinewy string of a prehistoric bow was an interchangeable part in other bows and so were arrows. The progress towards replaceable parts in modern manufacturing of products is directly related to the inventive activity in creating machinery to make parts with greater accuracy and the mass production of such machinery to reduce its cost, see Durfee (1893). With the invention of printing type became interchangeable first within one shop and then between shops. In the 18th century mechanics invented machinery to create geared teeth necessary to create clocks. By the end of the 18th century in England inventors had invented various type of machines to work wood. The idea of interchangeable parts in manufacturing may have originated in the 18th century in France where the military viewed it as a desirable goal in building weapons, but at that time machinery with the required accuracy to create the metal parts did not exist at any cost.
One of the first private successful application of the idea of interchangeable parts in the US was done by Eli Terry starting about 1800 in the production of clocks using wooden parts, see Bourne(1995). Wood working machinery of the time was accurate enough and cheap enough to make serviceable clocks from wood with interchangeable parts. Such clocks could be mass produced at much lower cost that artisan created clocks. In 1814 he was mass producing clocks with brass and steel parts. To obtain machines with the required metal working accuracy he had to employ skilled mechanics to improve the metal working machines.
The idea of interchangeable parts is frequently erroneously attributed to Eli Whitney, who in 1798 obtained a US government contract to build 10,000 muskets, Smith (1981). In 1799 in order to gain more time on his contract he proposed constructing the muskets with interchangeable parts and in Eli Whitney contrived a demonstration in 1801 to show that he had succeeded. By 1808 he had set up a factory in Whitneyville and delivered the muskets whose parts were not completely interchangeable. The problem was the machinery making the parts did not have the required accuracy. Eli Whitney spend considerable effort improving the quality of his metal working machinery.
The realization of interchangeable parts in manufacturing muskets was not realized until 1826 in the Harpers Ferry Armory where Hall succeeded based on his own and Simeon North's extensive improvements in metal working machinery. The first successful application of interchangeable parts to a metal product required over 25 years of inventive activity improving the metal working machinery and organizing the production process.
For the concept of replaceable parts to become common manufacturing practice required continual improvements in the accuracy of metal working machinery and a decline in the cost of such machinery do to mass production. The innovation of replaceable parts slowly spread throughout manufacturing industry during the 19th century and became commonplace by the beginning of the 20th century.
In the first half of the 20 century manufacturing firms shifted from producing single product lines to multiple product lines in several industries.
The economics of producing multiple products has been characterized by Chandler as the economics of scope
1. In their research and development efforts firms have economies in producing a range of related products.
2. Firms have economies in selling a variety of related products.
The corporate merger movement at the end of the 19th century was one of vertical and horizontal mergers. Firms moved backward to obtain secure supplies of raw materials, forward into final consumer products and increased the concentration of firms in manufacturing. At the same time, Dupont became one of the first firms in the 20th century to produce a variety of products. The real expansion in multiproduct firms started in the 1920s. As Chandler (1988) points out the these firms were in electrical, chemical, machinery, metals, and rubber, industries that had extensive research and development based on chemistry and physics. After WW II diversification became commonplace.
1. Bourne, R, 1995, Invention in America, (Fulcrum Publishing: Golden)
2. Chandler, Alfred, 1988, The Structure of American Industry in the Twentieth Century: A Historical Overview, in McCraw, Thomas (ed) The Essential Alfred Chandler: Essays toward a Historical Theory of Big Business (Harvard Business School Press: Cambridge)
3. Durfee, W., 1893, The History and Modern Development of the Art of Interchangeable Construction in Mechanism, Transactions of the American Society of Mechanical Engineers Vol XIV, pp1225-1257
53. Smith,M., 1981, Eli Whitney and the American System of Manufacturing, in Pursell, C. (ed), Technology in America, (The MIT Press, Cambridge), pp 45-61
Diversification created problems that have occupied manufacturing innovators for over 50 years. Producing multiple products in a single factory creates at least two major problems to be overcome:
1. Getting the right input to the right place at the right time.
2. Determining the best production run to match supply with demand.
The input can be a part or work in progress for discrete production or a combination of chemicals in continuous production. The simplest solution to this coordination problem is to stockpile inventory of inputs at each station in the production process to ease the timing and quality control problems. Assemblers can find a correct part in the inventory. This simple solution has a hidden cost in that the inventory or parts and work in progress is a financial investment that garners no rate of return until the firm obtains the payment for the sale of the final product.
The economics of the second problem are determined by the cost and time to changeover from the production of one product to another. The more costly the changeover and the longer the required time, the longer the production run to distribute the fixed costs of changeover. But again, long production runs also have the hidden cost in that the inventory of final products does not garner a rate of return until payment from the sale. Finally, the faster and cheaper the changeover, the greater the variety of products that can be produced at a single factory.
In the second half of the 20th century, manufacturing innovators have used the advances in information technology to increase the automation of production and to greatly increase the flexibility of the manufacturing process so that manufacturing firms could shift their manufacturing strategy from production for inventory to production for final demand.Let us start by considering the status of automation in
manufacturing. Manufactured items are continuous, such as liquids; or
discrete, such as automobiles. The current status of production in
manufacturing is:
a. Continuous process fluids: Chemicals, beer, petrochemicals.
These types of production, whether batch or continuous, are currently
highly automated. So called production workers sit around and watch the
dials.
b. Discrete: In discrete production the size of the production
run determines the efficiency of the process.
(1) Mass: In high volume production it pays to have a specific
machine for each purpose. The precise number that constitutes high
volume depends on the type of product. Automobiles are a good example
because production runs are generally in excess of 200,000 units.
(2) Batch processing: In batch production the production lots
run from 10 to 1000. Examples of batch production are airplanes, large
earth moving equipment, and ships. Batch processes comprise 40%of the
mfg work force. In batch production general purpose machines are used
instead of the specific purpose machines of mass production. The cost
of batch production is 10- 30 times the cost of equivalent mass
production.
(3) Individual production: This type of production exists today
only for artisan items. The cost is 100 times as much as mass
production. For example, compare the cost of auto repairs with the cost
of the original production. How much damage does it take to total a car?
To discuss automation of discrete manufacturing we need to breakdown
production into its components:
a. Design: Buzz words - CAD, computer assisted design; CAE,
computer assisted engineering
b. Parts manufacture: The buzz word here is flexible
manufacturing systems, FMS, which are also called manufacturing
cells. It is very important for the student to realize that a FMS
is really a computer-controlled machine shop and is not a complete
automated manufacturing plant.
c. Parts coordination: To assemble a durable good you must get
the right part to the right place at the right time. When you consider
that auto production involves tens of thousands of parts, this is no
easy matter.
d. Quality control: To reduce waste, inventory of parts and
work-in-progress, it is necessary to greatly improve quality in all
aspects of the manufacturing process. The result is a high quality
product.
e. Assembly: Here we are talking about the assembly line. To
automate assembly requires much more than replacing people with robots.
Efficient use of robots usually requires a complete reorganization of
production.
f. Integration: Buzzword - CIM, computer integrated
manufacturing. Currently automation is proceeding piecemeal in each
area. Advances in computation and communications provide the building
blocks. Integration of the steps into fully automated production will
take time. The various steps have incompatible standards so
communication is difficult.
g. Reorganization: Innovation in manufacturing is much more than
simply substituting machines and software for humans in the production
process. As automation advances, firms must also constantly innovate by
reorganizing their human-machine production process to achieve an edge
in international competition.
Now let us consider each step of discrete manufacturing automation in
detail.
In order that the reader relate the various concepts being developed, we want to discuss the general trends in CAD/CAE with respect to Moore's law and the development trends in software in general.
General trends in CAD/CAE software
Specific Industries: Now let us consider CAD/CAE from the perspective of specific industries. This discussion focuses on examples and definitely does not cover the vast array of software for CAD/CAE.
Innovation: Now let us consider why the software developments in CAD/CAE constitute an innovation.
Most mechanical products are assembled from parts. After designing
the parts, they must be manufactured. Before FMS, parts for batch
production were created in machine shops by skilled machinists, who
would use general purpose machines such as lathes, drills and so on in
sequence to create the parts. To discuss the automation of such a
machine shop (creation of an FMS, which is also known as a
manufacturing cell), we must first consider the functions to be
accomplished at each machine:
a. Move the proper workpiece to the machine
b. Load workpiece onto the machine
c. Select proper tool
d. Establish and set machine speed
e. Control machine motion
f. Sequence different tools
g. Unload part
In mass production, the large volume makes having a special purpose
machine for each operation economical, but in batch production, it
simply is not economical to have special purpose machines for each
operation, because they would sit idle most of the time. The goal in
improving the efficiency of batch parts manufacture has been to create
computer controllers that will make general purpose machines flexible
enough to create multiple parts automatically. Since 1960, considerable
advances in FMS have been made from the numerically controlled machine
to the current flexible manufacturing cell, several general purpose
machines linked and controlled by a computer.
A schematic drawing of an FMS is shown below. Remember each of the
machines shown is a general purpose machine that must be programmed
through the steps a-g listed above.
The current country leading in this area is Japan. Fanuc, Ltd.
created a plant in the 80s that makes robots and CNC machine tools. The
plant is essentially an automated machine shop that produces parts for
these machines. Robots carry the parts from one group of machines to
another. Vehicles automatically store finished parts and retrieve raw
workpieces. There are 19 day shift workers and one night shift worker.
The use of FMS, instead of a general purpose machine shop staffed by
skilled machinists, can reduce the cost of manufacturing parts by a
factor of 5 to 10. With a general purpose machine shop, the machinists
spend a large portion of their time setting up the machines for the
next operation. With a FMS, this set up time is greatly reduced by the
computer which sets up the machines automatically. Because the set up
time is greatly reduced, machine time in creating parts increases from
3- 10% to 50% of the total time. Also, FMS requires from 10 to 30% of
the skilled labor that a general machine shop requires. Besides
reducing labor costs and increasing the output of the machines, a FMS
has two other advantages over a general purpose machine shop. With a
FMS, production can rapidly shift from one part to another. Thus, a FMS
can match supply to demand with very little inventory. In addition, the
use of FMS has led to much greater quality control.
A fundamental issue in the move to production for final demand, not only for a FMS but an entire factory, is how fast a machine can be converted from one job to another. After world war II Japanese manufacturers had powerful incentives to reduce the required land for manufacturing in order to compete internationally because Japan is about the size of California and only about 15% of the land is flat, thus in relationship to other countries land is very expensive in Japan. In the 50s Shigeo Shingo (1985) developed the SMED (single minute change of a die) system to reduce the required time and cost to shift a machine from the production of one product to another in less than 10 minutes. His SMED system consists of carefully observations to streamline and standardize the changeover process. Reducing the fixed cost of changeover makes smaller production runs economic in parts production and reduces the inventory costs of foregone return on investment and the space required to store the inventory. There are consulting firms selling the SMED system worldwide today.
The problems with FMS are the cost of setting up the stations and the
fact that technical expertise is required to set them up and run them.
If a component breaks, the FMS cell shuts down. Despite these
limitations, FMS has moved from the experimental to the rapid growth
stage. The demand by larger firms for higher quality control in order
to install just in time parts management has created incentives for
small firms to install FMS in order to achieve higher quality control.
An example of a small firm that installed an FMS cell is Frost, Inc.
with 1985 sales of $16M. For a $5.1M investment, sales per employee
have climbed from $86,000 to $130,000. Quality control has improved
from 1 reject in 4 to 1 reject in 20. Gross margins have increased to
35% in spite of price decreases of 21% since 1983. With its FMS, Frost
could shift from the production of one item to another in minutes
instead of 12 hours or more. Frost converted to automation at 1/3 the
cost of the expert plans. To capitalize on its experience with FMS,
Frost has set up a consulting company to advise other firms desiring to
follow suit.
The efficient route to automation is not to take several general
purpose machines and replace them with a manufacturing cell. This leads
to the term `islands of automation'. Installing manufacturing cells to
increase efficiency frequently requires reorganizing the entire
production process.
Some interesting sites to surf for FMS are:
The problem is that many plants produce multiple products on the
same assembly line. The right part must be at the right place at the
right time and any program to control this process must run in real
time. In the US, land is inexpensive so traditional US manufacturing
solved this difficult problem by having parts bins at each station so
that the workers could pick out the correct part. A simplified
schematic of this type of assembly line is shown in the diagram below:
This type of assembly line has many serious defects. First, much
inventory is tied up in the form of work in progress, that is the parts
in the bins. The firm does not obtain an economic return on these parts
until the product is sold. In countries where land is expensive this
form of assembly requires extra space for the parts bins. Second,
having parts in bins does not require much emphasis on quality control
since the worker can look through the bin to find a good part. In
traditional Detroit auto production, twenty five percent of the
assembly workforce fixed defects at the end of the assembly line.
After WWII the Japanese made many important innovations in
manufacturing. Since Japan is about the size of California and only
about 15 % is flat suitable for factories, and in Japan because land is
very expensive, manufacturers were encouraged to innovate in
manufacturing organization to save space. The most advanced plan, which
was developed by Toyota, is JIT, just-in-time, where the order for a
final product generates the orders for parts as they are needed (demand
pull). Toyota created this system using order cards without computers.
The ideal of JIT is that there should be no inventory; consequently,
every part must be perfect when it reaches the assembly line. At Toyota
parts are ordered from suppliers only as they are needed. Obviously to
make this work the suppliers must be located adjacent to the Toyota
factory. While some Japanese firms have been able to goad their
suppliers into this level of quality control and obtain instantaneous
coordination with suppliers, few firms outside of Japan have been able
to successfully implement JIT.
For example, in US auto assembly the parts firms are scattered over
several states. The manufacturer must keep a supply of parts on hand in
case transportation is interrupted by, for example, a major blizzard.
Thus, if the parts suppliers are distributed over a wide area it is not
optimal to try to reduce the inventory of parts on hand to one.
The US approach to improve the flow of parts and resources to obtain
substantial savings through reduction of inventory and wastage is the
creation of software inventory control packages. The US contribution is
called manufacturing resource planning, MRP, which schedules
the flow of parts as part of the forecasted production schedule. A more
advanced form of MRP is manufacturing resources planning, MRP II. MRP
II also considers the cash flow required to order the parts and pay
expenses in the forecasted manufacturing plan. It is important to note
that both MRP and MRP II are future oriented plans. US software
approach to lean inventory has advanced to Enterprise
Resource Planning, integration of all corporation information for
analysis and planning, and now Supply
Chain Management, reducing inventory at all levels from input, work
in progress, to output at the factory and in the distribution chain.
These information systems have expanded from just manufacturing
scheduling, to linking manufacturing to all office processes, to
linking the firm to the acquisition of inputs, and the delivery of
outputs.
A common feature of both MRP and JIT is the emphasis on reducing
inventory and making factories more efficient. JIT places more emphasis
on efficiency and quality.
Another aspect of automation in parts manufacturing is how parts are
delivered to their assembly point in a factory. In many older
manufacturing plants, parts are handled as many as 10 to 15 times from
the time they enter the factory until they reach their assembly
station. Obviously, the more a part is handled the greater the chance
for a fiasco. One advance in manufacturing automation is to automate
the delivery of parts to their final destination.
Ideally, parts from outside suppliers are handled twice. Once when they
arrive at the factory and once when they are assembled into the
product. One of the first firms in the US to do this was Apple in the
production of the first Macintosh around 1982. Jobs and the chief
engineer, Irwin, spent two years (probably part time) studying Japanese
production methods. The original Macintosh factory incorporated three
basic concepts:
a. Just in time parts delivery
b. Linear production system
c. Good environment for workers.
The first Mac had about 500 parts. To supply the parts to the linear
assembly line, Apple installed three automatic parts delivery systems
and one manual system:
a. Totes or plastic bins: These stored electronic parts
b. Overhead rail: This delivered bulkier items.
c. Automatically guided vehicles (AGV): Delivered other items
d. Humans delivered screws once a month or so because automating this
delivery would be too expensive.
The trend in automatic delivery of parts is in making advances in AGV
systems and automatic warehouses. To eliminate the possibility of a
Murphy's Law type foul up, parts should be handled twice: when they
arrive and when they are installed on the product. When they arrive,
they would be placed in an automatic warehouse until needed. The
program controlling the assembly process would send parts automatically
to the various assembly sites as needed.
In order to affect Kanban the quality of the parts must be extremely
high because the one part must be correct when delivered. As it has
been pointed out, about 25% of the US auto assembly workforce was
engaged in repairing defects when the autos rolled off the assembly
line before the recent move to better quality control. The old style
quality control, at which the Germans were the masters, was to have
teams of inspectors at various stations and test the products. Because
QC was thought to require additional workers, QC was considered an
added cost. Hence, as you might expect, higher quality products would
cost more.
The new approach to quality control pioneered by the Japanese totally
upended the cost quality relationships. In the 1930s the concepts of
statistical quality control, SQC, were invented in the US. After WW II,
Deming and Juran, SQC experts, could not convince US manufacturers to
adopt SQC. Deming and Juran then went to Japan where they were treated
like heroes as the Japanese manufacturers rapidly adopted SQC, which
enables manufacturing engineers to identify problems in the production
process without inspectors. S.
Taguchi introduced the idea of designing products so that
performance is not affected by minor defects, Ealey (1994).
The Japanese also achieved better quality control by carefully considering the human factors in manufacturing. Shigeo Shingo (1986) created the Poka Yoke system of quality control by systematic observation of the manufacturing process incorporate steps such that quality control become an integral part of the production process. To make continual improvements in quality a permanent part of the factory, Japanese managers have organized workers into quality circles that meet weekly. In these meetings workers propose improvements that engineers and managers review and then implement the best ideas. By eliminating the need for repair workers and inspectors, the new approach saves money and creates satisfied customers. These innovations lead to the Toyota or Japanese system of manufactures. Toyota products have been more reliable than their US counterparts and Toyota has greatly increased its share of the US auto market.
Since the 1980s US and European firms have imitated Japanese quality control concepts. For example, the quality of US automobiles has greatly improved and is slowly closing on the constantly improving Japanese quality standards. Currently, the Europeans have created an international quality standard called ISO9000. Many US firms are becoming certified as having met these standards.
Some interesting sites to surf for parts coordination and quality are:
Many of you have gained your impressions of robots from watching science fiction movies. Before reading the material on robots, you should first view some robotic animations .
From these videos it should be obvious to you that your hand has
much greater dexterity than a robot hand. Also, a great deal of effort
is required to get robots to perform tasks that humans consider very
boring.
Once programmed, however, a robot can perform a task repeatedly without
getting tired or bored.
An industrial robot is generally an arm with a gripper and some
capacity for movement such as straight lines and rotations. Japan has
been successful with robots capable of two straight movements and two
rotations; whereas, the US is going for all 6 degrees of freedom. More
advanced robots have microprocessors as brains. Sequences of motion for
the robot can be programmed into memory by leading the robot through
the desired sequences or programming the robot. Even more advanced
robots have artificial senses such as sight, touch, and force.
A fundamental problem in the assembly of industrial products is fitting
pieces with close tolerances together such as gears or placing a weld
in exactly the correct place. A human is an excellent assembler because
we automatically make minor adjustments in position to fit parts
together correctly. If a robot without sight tries to do the same it
must know exactly where the two parts are in space and the sequence of
motions to fit them together. Lacking the ability to make corrections,
the robot can easily jam or wedge the two parts together. The initial
progress in assembly automation was with robots without senses. These
initial successes required considerable effort to overcome the
orientation problem.
Robots without senses are currently used in painting and welding
automobiles. In painting, the robot is superior to the human because
the robot does not need a fresh air supply and protection from
dangerous chemicals. Moreover, painting is tolerant to deviations in
the positioning of the paint gun with respect to the automobile frame.
In welding auto frames together, the robot is also superior given the
strength required to handle the welders and the adverse conditions
under which the welds must be made. The equipment to have the frames
exactly aligned to make the welds costs much more than the robots.
Also, Kawasaki was able to program a robot without senses to assemble a
motorcycle gearbox by having the robot gripper vibrate slightly to
compensate for inaccurate positioning.
Advances in the use of robots in assembly have required the development
of robots with senses. When a human assembles a product or component of
a product, he or she can usually identify the component parts
instinctively without much thought. To create a program which gives a
robot the capacity to pick up randomly arranged parts is a major
undertaking. To provide a robot with a camera so that it can see is no
problem. What is a problem is providing the robot with the machine
intelligence to interpret the input from the camera. One solution is to
have the parts to be assembled arrive in exactly the right orientation.
This is expensive; thus, while acceptable for mass production, it is
inefficient for batch production. Some success is being achieved at
creating machine intelligence that can recognize parts in an arbitrary
setting. Work is progressing to give robots such senses as sight,
touch, and force. With these senses, a robot can be programmed to make
minor corrections to the sequences of steps it makes.
Much current success in assembly by robots with senses is achieved by
greatly simplifying the task of identifying alternatives. In production
this can be achieved by using bar codes similar to the ones used in
grocery stores. Bradly-Allen has a plant which automatically assembles
many kinds of controllers for electric motors on the same assembly
line. Robots know which sequence of operations to perform on each
product coming down the assembly line by reading the bar code on the
product. Robots with senses are also used for quality control checking
in this plant. A new alternative to reading bar (click on
Alternatives to Barcodes article) codes is to install a chip in each
product with a radio transmitter. The advantage of this technology is
that numerous product identification ICs can be read at the same time.
IBM created a plant here in Austin that employed robots with senses to
assemble laptop computers. The robots were controlled by PC-ATs. This
technology will probably be used to assemble all IBM personal computers
in the near future. Without significant labor costs, IBM can compete
with the clones. Robots, once programmed, put the right chip in the
right slot - something humans do not always do. As the number of
component parts in electronic goods is generally small, robotic
assembly in this area will proceed quickly.
Robots can currently assemble electronic products such as laptop
computers, gear boxes, electric motors, and other components. With each
new plant to assemble a product such as an auto, more and more of the
assembly will be automated. Since robots are not humans, the jobs that
they do best differ from jobs that humans do best. Furthermore, the
best assembly by robots frequently requires a complete redesign of the
product and manufacturing procedure to take advantage of the
capabilities of robots. This product redesign usually involves
simplification and reduction of the number of parts and consequently,
usually results in greater reliability.
In factory automation, software can be thought of as middleware, which facilitates communications between various applications. In the same way, standardization of software allows the various machines and robots involved in the manufacturing process to communicate with each other and with the firm’s central computers. This interconnectivity builds advanced analyses and forecasting abilities into the manufacturing process. RobotScript, a programming language for Robotics, is being developed based on VBScript (Visual Basic Script), which is already present on the Windows Platform. RobotScript enhances the existing VBScript with additional libraries necessary for programming robots and machines. The benefits a standardized programming language are economies of scale as the same code may now be reused for different robots and a decrease in labor costs since most programmers are already familiar with VBScripts intuitive syntax. Integrating RobotScript with VB also means that the Robot can be treated by Visual Basic as a Microsoft ActiveX component, which can then be easily interfaced with other programs on the Windows NT (networking) platform. This versatility allows companies to react rapidly to changes in the production process as well as equipment.
Robots are being built to provide self-diagnosis when they encounter errors or need maintenance. With these advanced intelligent features, companies do not need to hire specially trained/skilled technicians for general maintenance thereby making the process smoother and more efficient. Robots can provide instructions for repair and when more serious maintenance is required they can give detailed information about the errors involved and when they occurred.
Some interesting sites to surf for robotic assembly are:
This is the hard part of automation. Advances are taking place in
each of the steps of automation. Integration of all the steps is
currently impossible because the various types of machines are
incompatible. One step in the advance of automation and the integration
of steps is the creation of standards. Standards in the marketplace are
determined by professional groups or the dominant player. IBM, the
dominant player, set the standards for PCs. Standards have been
established for CAD graphics. GM has devised a language called MAP so
that all machines in manufacturing can talk to each other, and this
protocol has promoted the development of manufacturing communication
standards. Standards ensure compatibility between equipment, and small
players adopt the standards to ensure a market for their products.
Standards allow the small firm to specialize in a niche market knowing
its equipment will be compatible with whatever equipment comes along.
Currently (1995), there are several competing protocols for factory
LANs.
Standards for CAD drawings have been adopted industrywide and now CAD
is being integrated with FMS. In 1992 after a 5 year research program
costing $3.5M, a research group at a Dutch university created a startup
to market their program which would create the software to run a FMS to
create a part designed in a CAD program. Their software can be updated
and extended to accommodate different types of FMSs. This product is at
least 10 times faster than a human planner.
Advances in software to take CAD designs to create the software to run the machines to create the part is directly related to advances in 3D CAD software. A good article that explains this is "The Ultimate DNC; Direct CNC Networking (DCN)" Read Greco Systems's discussion of reusable software is this industry. MDSI has a good discussion of why open software is desirable in this industry. One trend is upgrading older machines that were controlled by paper tapes with computers. Shop floor automations is active in this area.
Complete CIM must solve the data problem. A completely automated plant
from design to final assembly requires a massive data base with all the
designs, the programs to create the parts from the designs, the
programs to route the parts to the assembly line, and the programs to
assemble the final product. Moreover, this database must be integrated
into the office database for sales, accounting and so on. In a
completely automated factory, once the design is complete, a program
would take that design and automatically create all the sets of
instructions for all subsequent steps. The achievement of this goal is
some indefinite time in the future. However, more and more of the
paperwork associated with manufacturing is shifting to electronics.
A recent invention in software tools for manufacturing is the creation of digital factory software to simulate the production process and layout. We use this term generically even though Tecnomatix has a trade mark on the term, digital factory. The use of digital factory software has lead to innovations in manufacturing:
Leaders in the field:
New factory equipment now comes equipped with a built in Internet connection. This means that robots in several factories can be monitored from one central location. Using XML, robots can transfer data and information seamlessly between robots as well as between the robot and the company’s database. XML is particularly useful for storing configuration information such as the facilities in which it is working, input and output equipment, location, etc. This information can be stored and used for quick setup, which is particularly important in a Flexible Manufacturing System. XML can also be used to develop industry specifications for RoboML, a markup language for robotic applications. Read more about this proposal in a thesis by Maxim Makatchev, as well as the website for RoboML at http://www.roboml.org. Entire factories may also be monitored by advanced Neural Networks, which yield startling accuracy with respect to quality control. Managers can drill down and analyze the production process as well as the effectiveness of particular robots, individual processes, or entire plants. This information can be used for tracking inventory, production, and quality control purposes.
Some interesting sites to surf for CIM are listed below. Remember, these are partial and not total solutions to CIM:
In the renaissance in American manufacturing starting in the mid 1980s, US firms imitated the Japanese innovations. For example, Chrysler created Japanese style design teams to greatly accelerate the design of new cars. Firms in the US became converts to quality control as they discovered that consumers preferred quality Japanese products.
The US firms exploited their lead in software to innovate in production in beyond the Japanese approach. Because US suppliers are generally not near the assemblylines US reduction in inventory and work in progress was promoted by software programs starting with material resource planning, MRP and advancing to supply chain management software programs of today. Because US supplier firms are generally not adjacent to the assembly plant it is not possible to reduce inventory to the Japanese levels because of the possibility of transportation mishaps. Firms like Dell have their suppliers keeps supplies adjacent to the Dell assembly area and pay for supplies as they are used, McWilliams(1999). In some cases the supplierÕs 18 wheeler rig is parked at the factory.
Using their lead in software, American firms made innovations culminating the innovation of production for final demand. The application of software to soft as opposed to hard automation increased the trend for rapid changeover from one product to another at lower cost. One example is the use of industrial bar codes to simplify the machine intelligence problem of having each machine perform the correct procedure in assemblylines with multiple products. Another example is the software controlled flexible manufacturing system. Another is factory simulation programs created by Aspen Technology (1999), Dassault Systemes (1999), and Tecnomatix (1999) that allow industrial engineers to simulate the factory organization. Use of factory simulation can greatly reduce changeover times, not just of a single machine but an entire factory. In the auto industry the use of factory simulation programs has the promise of reducing the changeover in product production from 8 weeks to 48 hours, see Ross(1998). With soft automation the cost of batch production is falling to the level of previous mass production so that firms under the aegis of total quality management of pleasing the customer are increasingly producing for niche markets.
The culmination in the production for final demand comes about by the use of advances in communication for sales. Dell is one of the first firms to produce products exclusively for final demand and not inventory. At first customers ordered their build to specifications computers via the telephone. Then with the advance of E-commerce on the internet, Dell began selling through the Internet Their software is developed so that it aids the customer in choosing the most appropriate computer for their needs and allows the customer to follow their order through the production and delivery process. In the 21st century we forecast that production for final demand will become a increasingly common manufacturing practice. Remember some manufacturing may always be more suited to production for inventory than production for final demand.
In the later part of the 20th century the US government has many
steps to promote invention and innovation in manufacturing. One
example, is funding for Sematech (1999), a consortium promoting
integrated circuit production. The Defense Advanced Research Projects
Agency, DARPA (1999), funds numerous inventive and innovative
activities in military production that frequently also have broad
applications in the private sector such as Agile Manufacturing
promoting lean manufacturing and rapid changeover. The Department of
Commerce has initiated the manufacturing extension partnership, MEP
(1999), to promote innovation and imitation in small firm
manufacturing.
The innovations leading to production for final demand in firms
producing multiple products create a difficult scheduling problem of
operating factories shifting from one product to another satisfying
customer demands, minimizing inventory and at the same time fully
utilizing the production resources. In their most general form, a
resource-constrained scheduling problem asks the following question:
given a set of tasks, a set of resources and constraints and a measure
of performance, what is the best way to assign the resources to
the tasks such that the performance is maximized. Formally operation
researchers have devised a variety of formal models of industrial
scheduling problems such as varying in such factors as the types of
machines, their operations, delays, information flows, deterministic or
stochastic elements, for example see Dorn and Froeschl(1993). Planning and scheduling have profound importance for projects
consisting of several tasks and constraints associated with them. On a
factory floor, determining which jobs to execute in what order on which
machines and which employees to assign to a certain job, can mean the
difference between profit and loss. Even for relatively small projects,
however, the number of possible courses of action quickly become so
overwhelming that it becomes almost impossible to achieve an optimum
solution. In general, scheduling problems are NP-hard i.e. no
algorithm exists that can find optimal solutions to these problems in
polynomial time. Heuristics exists for solving exactly some forms of
the problem but typically they become intractable(i.e. take more than
polynomial time) when additional constraints are added or the problem
size grows. As a result, most research has been focussed on either
simplifying the scheduling problem(mostly by making assumptions) to the
point that it is solvable by some algorithm within reasonable time
limits or devising efficient heuristics for finding acceptable (not
necessarily optimal) solutions. In this section, we will briefly discuss the advancements in
scheduling and the resulting performance gains. The point to note here
is that a scheduling problem with reasonable number of tasks is usually
so complex that the most sophisticated scheduling heuristics and
fastest machines do not give us the optimum solutions- they only give
us better solutions. Usually a move from one scheduling paradigm to the
other involves huge investments in time and money and has profound
impact on the functioning of firms and of the economy as a whole. Although the roots of the scheduling problem can be traced back to
prehistoric times, active research in this field began with the
creation of digital computing machines after 1950. Linear Programming
became the first formulation of scheduling problems with the invention
of the Simplex algorithm by Dantzig in 1947 that provided
efficient computation. Other important early Operations Research
methodologies were Monte Carlo simulation techniques, stochastic
optimization, queuing theory, Integer Programming (Gomory, 1958),
Dynamic Programming, Bellman et al.(1982) and a several combinatorial
methods, notably, Balas, (1969) and Branch-and-Bound methods, e.g. Land
and Doig, (1960), Little et al., (1963), Barker and McMahon, (1985). The principles and method of dynamic programming were first
elaborated by Bellman in 1950s. For combinatorial scheduling problems,
dynamic programming algorithms have exponential computational
complexity because in order to calculate the optimal criterion value
for any subset of size p, we have to know the criterion values for each
subset of size k-1. Thus, for a set of n elements, we have to consider 2n
subsets. The branch and bound algorithms divide the problem
into several subproblems and calculates the lower bound for each of the
subproblems. This procedure usually generates a huge tree. The
computational complexity of a branch and bound algorithm is also
exponential. Branch and bound methods are therefore limited to less
than one hundred activities or even fewer in the multi-model cases.
Other enumerative methods also suffer from the exponential
computational complexity for reasonably large problems. Unfortunately most scheduling problems especially the most relevant
ones belong to the class of NP-complete problems which are intractable
since nobody has shown that a polynomial bounded algorithm exists for
these problems. Exact solution methods are thus of limited practical
relevance in obtaining better performance. With the complexity theory developed in computer sciences,Edmonds
(1965), Cook (1971) and Karp, (1972), the focus of research shifted
from exact but intractable methods to approximate but tractable
solutions. Interest in heuristic reasoning within computer sciences was
prompted by outstanding researchers like Herbert A. Simon starting in
the 1960s. Heuristic search tries to enumerate combinatorial
search spaces efficiently by ruling out the areas with low subjective
probability of containing an optimal solution. This application of the
research in neuro-science and human symbol processing to computer
science gave a strong boost to the field of artificial intelligence.
Soon the techniques developed in artificial intelligence were being
used to find better solutions for real world scheduling problems. This class of methods comprises several related algorithms.
Simulated annealing was proposed as a framework for the solution of
combinatorial optimization problems by Kirkpatrick, Gelatt and Vecchi
(1983) and independently by Cerny (1985). As the name suggests, the
inspiration for this method comes from the Physics of cooling solids.
"Simulated annealing allows the allows the algorithms to 'escape' from
bad local optima by performing occasional cost-increasing changes. "
Lewis and Papadimitriou (1998). Simulated annealing gives good
approximations of the optimal in many cases. However the
cost-increasing changes often result in great loss of efficiency. Tabu search, a discrete version of simulated annealing, is a general
framework, which was originally proposed by Glover and subsequently
expanded in a series of papers. One of the central ideas in this
proposal is to guide deterministically the local search process out of
local optima (in contrast with the non-deterministic approach of
simulated annealing). This can be done using different criteria, which
ensure that the loss incurred in the value of the objective function in
such an 'escaping' step (a move) is not too important, or is somehow
compensated for. " Blazewicz, Ecker, Pesch, Schmidt and Weglarz (1996). Genetic algorithms are inspired by the theory of evolution; they
date back to the early work described in Rechenberg (1973). "They have
been designed as general search strategies and optimization methods
working on populations of feasible solutions." Blazewicz et al. (1996)
The traditional genetic algorithms have often not been suitable for
combinatorial optimization problems because of the difficulty in the
representation of the solution. Several improvements have therefore
been proposed. Local search heuristics or genetic enumeration
have compensated for this drawback. Evolutionary techniques like genetic algorithms and neural nets and
other recent techniques like fuzzy logic are mainly being used in
rule-based reasoning and expert systems. Due to the early success of
evolutionary algorithms, researchers strived for a total automation. In
the last few years, however, the focus has shifted to a more realistic
scenario of decision support systems. Decision support systems use
genetic algorithms and neural nets to help managers in decision making.
Due to the massive explosion in the size and diversity of firms, modern
techniques like decision support systems are increasingly becoming
necessities for large firms. We now wish to consider scheduling from the perspective of Simon's
bounded rationality. Because in general scheduling is an NP hard
problem, managers aided with computers can not obtain exact optimal
solutions in polynomial time. Bounded rational behavior is the use of
approximate algorithms. Innovations occur in bounded rational behavior
with the development of better man aided by computer algorithms that
lead to better performance, and in some cases optimal performance. We
wish to demonstrate this with an examination of progress in creating
better algorithms for the job-shop scheduling problem. In general, the work in a job-shop is made-to-order rather than
made-to-stock and thus has crucial customer delivery dates associated
with it. The main challenge of scheduling job-shops is to minimize the
lateness of the jobs and maximize the throughput, both of which can be
measured in different ways. Job-shops minimize inventory while
attempting to meet the due dates within the known capacity constraints.
Job-shop scheduling is the paradigm of choice for companies with
customer customizable products. This problems directly maps to the classic parallel programming
problem which attempts to schedule a certain number of jobs of varying
lengths on a given processors in a given time span. This problem is
formalized as below. Statement of the problem: Number m Î Z+ of
processors, set J of jobs, each j Î J
consisting of an ordered collection of tasks tk[j],1 £ k £ nj,
for each such task t a length l(t) Î Z+0
and a processor p(t) Î 1,2, ,m, where p(tk[j]) &Mac185; p(tk+1[j]) for all j Î J and l £
k £ NJ, and a deadline D Î Z+. Many real world scheduling problems are related to the job-shop
scheduling problem. Most of the scheduling techniques developed in the
last forty years have been used, with varying results, for solving this
problem. Tracing the history of job-shop scheduling problem would thus
give us a general idea of the improvements in scheduling in the last
four decades. The origins of significant interest in the job shop scheduling
problem can be traced to the two well known benchmark problems
consisting of 10 jobs and 10 machines as well as 20 jobs and 5
machines. Both of these problems were introduced by Fisher and Thompson
(1963). While the case of 20 jobs and 5 machines was solved in 10
years, the instance of 10 machines and 10 jobs took 25 years of
extensive research to solve. Better solutions of the later case are
still being explored. Lageweg in 1984 found an optimal schedule of 10
by 10 problem that Carlier and Pinson (1989) proved to be optimal. They
used the branch and bound method to solve the problem. Since then
several other branch and bound algorithms have been applied to find
better solutions to the problem. Carlier and Pinson (1991) showed that
the problem can be solved to optimality within less than 2 minutes of
CPU-time on a small work station. As approximation methods became popular in the early 90s,
researchers applied them to solve the 10 x 10 job shop problem. The
efforts to apply simulated annealing, tabu search, parallel tabu search
and genetic algorithms culminated in the excellent tabu search
implementation of Nowicki and Smutnicki (1993) and Balas and
Vazacopoulos (1995). They achieved greater efficiency. The job shop scheduling problem remains one of the most difficult
combinatorial problems to date and arouses new research interest. The
exact solution methods perform well only for some specific instances of
the problem of the same size. They usually perform poorly for the
instances of different size. Heuristics such as simulated annealing,
tabu search and genetic algorithms are generally more robust under
different problem structures and require only a reasonable amount of
implementation work with relatively little insight into the
combinatorial structure of the problem. Instances of the problem larger
than 10 x 10 proposed among others by Adams, Balas and Zawack (1988)
still remain open. There is thus continued interest in the problem as
more efficient and robust solutions are being explored. The approximation algorithms have not only facilitated the adoption
of new manufacturing paradigms for firms, but have acted as major
catalysts in the conception and implementation of new paradigms.
Advances in the approximate scheduling methods have facilitated the
gradual implementation of production of final demand. Today markets are turbulent and dynamic. Tremendous advance in the
field of information technology, microprocessor technology and
artificial intelligence(application to scheduling and decision support)
in the last decade or so have turned the vision of agile manufacturing
into reality. It is increasingly becoming possible for the firms to
achieve short product development cycle times and respond immediately
to sudden market opportunities (Agility
Forum, 1994). With time we are achieving better solutions but we
are still far from the optimal. It has taken more than 40 years and
billions of dollars in research and development for firms to reach this
stage and there is still a long way to go. The scheduling algorithms
are still far from being optimal in the general case. Much innovation is creating better approximations to difficult
problems. We use scheduling because the problem is clearly defined, but
we strongly assert this applies to a large number of problems, which
may not be clearly defined, that firms face. 3. Balas, E., 1969, Machine Sequencing via Disjunctive Graphs: An
Implicit Enumeration Algorithm, Operations Research,.17 (6), pp
941-957 8. Carlier, J. and Pinson, E, 1994, Adjusting heads and tails fro
the job-shop problem, European Journal of Operations Research,
78, pp 146-161 10. Cook, S., 1971, The Complexity of Theorem Proving Procedures, Proceedings
of the 3rd ACM Symposium on Theory of Computing, pp 151-158 11. Dorn, J. and Froeschl, K (ed), 1993, Scheduling of
Production Processes (Ellis Horwood: New York) 12. Edmonds, J., 1965, Paths, Trees, and Flowers, Canadian
Journal on Mathematics, 17, pp 449-467 20. Nowicki, E. and Smutnicki C.,1993, A fast taboo search algorithm
for the job shop problem, Management ScienceScheduling: This material was supplied by
Khurram Mahmood
Exact Solution Methods and Monte Carlo
Approximate Solution Methods
Example of Improvement: The job shop scheduling problem
References
1. Adams, J., Balas, E. and Zawack, D., 1988, The shifting bottleneck
procedure for job shop scheduling, Management Science, 34, pp
391-401
2. Agility Forum, 1994, http://www.agilityforum.org/
4. Balas, E. and Vazacopoulos, A., 1995, Guided local search with
shifting bottle neck for job shop scheduling, Management Science
Research Report \#MSR R-609, Carnegie-Mellon University, Pittsburgh
5. Barker, J. and McMahon, G., 1985, Scheduling the General Job Shop, Management
Science, 31 (5) pp 594-598
6. Bellman, R., Esogbue, A. and Nabeshima, I., 1982, Mathematical
Aspects of Scheduling and Computations, (Pergamon Press: Oxford)
7. Blazewic, J., Ecker K., Pesch E., Schmidt G. and Weglarz J., 1996, Scheduling
Computer and Manufacturing Processes (Spring: Berlin)
9. Cerny, V., 1985, Thermodynamical approach to the traveling salesman
problem: an efficient simulation algorithm, J. Optimization Theory
and Applications, 45, pp 41-51
13. Fisher, H., and Thompson, G., 1963, Probalistic learning
combinations of local job-shop scheduling rules, in Muth, J., Thompson,
G. (eds), Industrial Scheduling (Prentice Hall: Englewood
Cliffs, NJ)
14. Gomory, R., 1958, Outline of an Algorithm for Integer Solutions to
Linear-Programs, Bullitin of the American Mathematical Society,
64,pp 275-278
15. Karp, R., 1972, Reducibility among Combinatorial Problems, in
Miller, R. and Thatcher, J. (eds) Complexity of Computer Computation
(Plenum Press: New York), pp 85-104
16. Kirkpatrick, S., Gelatt, C. and Vecchi, M., 1983, Optimization by
Simulated Annealing, Science, NO. 220, pp 671-680
17. Land, A. and Doig, A., 1969, An Automatic Method of Solving
Discrete Programming Problems, Econometrica, 28 (3), pp 297-520
18. Little, J. et al., 1963, An Algorithm for the Traveling Salesman
Problem, Operations Research 8 (2), pp 972-989
19. Lewis, R., and Papadimitriou, C., 1998, Elements of the Theory
of Computation, Second Edition, (Prentice-Hall: Upper Saddle River,
NJ)
21. Rechenberg, I., 1973, Optimierung technischer Systeme nach
Prinzipien der biologishchen Evolution (Problemata:
Frommann-Holzboog)
After WW II, US manufacturing managers assumed they were the
greatest and became smugly complacent. Because the Japanese and
European manufacturing plants were destroyed during WW II, the US firms
initially had little competition and US firms could sell all they could
produce. The US made no attempt to innovate new approaches to
manufacturing. In addition, US firms padded the number of levels of
management to justify higher salaries for the top managers and build
bigger empires of flunkies reporting to each manager. They granted
organized labor wage settlements out of line with productivity
advances. Until recently most US business innovations were in the area
of finance with the automation of asset markets, corporate mergers, and
junk bond finance.
While the US manufacturing firms went to sleep, the Japanese built new
plants with an innovative approach to manufacturing. These innovations
in manufacturing should be considered as important as the 19th century
US innovations in assembly line manufacturing and replaceable parts.
The Japanese new production philosophy was built on revolutionary
approach to quality control, flexibility and short product cycles. We
have already discussed the Japanese reorganization to achieve total
quality control in their products.
a. Flexibility: The US standard for manufacturing was to
organize very long production runs to reduce the unit cost of setup.
The Japanese philosophy has been to create plants that could switch
from producing one product to another quickly. This eliminated the need
for production for inventory. (Japan with much higher cost of land than
the US has very high economic incentives to save space.) The economic
value of flexibility is that a firm can produce for final demand and
not for inventory. Better matching of supply and demand results in
better prices and greater profits.
b. Short product cycles: The Japanese firm organizes the design
and development of product in a design team that has representatives
from all functions of the firm. The team leader has the authority to
make decisions. Coordination between the various aspects of the firm is
automatic because they are represented in the design team. A problem in
the previous organization for design was that the design team would
finish the design and present it to the manufacturing engineers to set
up the production process. The manufacturing engineers would take one
look at the design and send it back to the original designers with the
comment we can not make this. The two groups would then redesign a
product which could be manufactured. With design teams the coordination
takes place before the design is released to manufacturing.
In contrast, until recently the US firm with its multilevel hierarchy
had no one in charge in the design process. Conflicts would be resolved
by vice-presidents. Moreover, design was not coordinated with other
aspects of the firm such as manufacturing.
Since the 1980s the US manufacturing firms are playing catchup with
the Japanese. The one area in which we are ahead in software
development in CAD, CAE and CIM. The main US problems are:
a. Defective primary and secondary education system:Since the
National Commission on Excellence in Education published a report Nation at
Risk in 1983 condeming US primary and secondary schools as
the type of schools our worst enemy would create for us, there has been
a movement towards excellence in primary and secondary education. As
discussion of primary and secondary education is frequently heavily
influenced by the political agenda of the speakers, you should be very
wary of taking the various claims at face value. Let us discuss the
issues:
a. Length of the school year: Primarily and secondary students spend on average 178 days in school a year, whereas most European children are in school over 200 days and Asian children over 240 days. But the OECD has produced a table showing that US children are taught more hours of instruction per year than many European countries. US children go to school for longer school day than their European counterparts.
My understanding is that the US summer vacation, based on a long gone agrarian society, is so long that students tend to forget what they learned and must be retaught in the fall. As I see it the problem is creating a longer school year is finance. Teachers would have to be paid more and the public is not willing to fund this move.
b. US pays more to educate students and most of this extra cost is administration. In 1990 the US spent $5521 on pulic school education more that double (corrected for inflation) spent in 1965. The increase went to:
The residual of about 11% may well have gone to increased bureaucracy.
In considering the cost per student, you should keep in mind that in most states there is a tremendous variation in expenditures from the poorest to the richest district. Poor districts would do better with greater resources.
c. US students test at the bottom in math and science. In 4th grade US students test well in science and math, but by the time they are seniors they are at the bottom. But, US students are near the top in reading.
In considering these statistics, you need to remember that other countries tend to have more elitist educational systems where college bound students are selected at an early age and the rest are sent to vocational schools. One issue is whether the comparisons are representative. There is also a question how statistically significant the differences are with Europeans. Japanese study extremely hard in secondary school because the University that they enter determines their career. In the US Universities such a UT definitely pick up the pace from High School.
d. Improvements since 1983.
e. In informational society, if you do not have a good education, you are unlikely to find a well paying job. There is a great deal more that needs to be done to improve primary and secondary education. The conservative agenda is pushing for much more change that the liberal agenda.
In considering increased competition, you should remember that the Japanese primary and secondary education system is public. Research indicates that vouchers are primarily used by better off, better educated parents to take their children out of poorer performing schools. How well vouchers would work on a wide scale is an open question. Many parent may wish to place their children is schools where they are happy rather than obtain the best education.
Certainly we have a long way to go in primary and secondary education.
b. US MBA students are taught finance. The best MBA students
until recently wanted to go to Wall Street and only the rejects went
into manufacturing. Moreover, US managers have a short planning horizon
that precludes making the necessary investment to innovate in
manufacturing.
c. Accounting for Automation: Until recently, US accounting
practice in manufacturing was defective because accountants were
placing a value on automation expenditures only for reduction in direct
labor. They placed no value on increased quality control and greater
flexibility.
d. Poor Management-Labor Relations: Until recently the US
management style was top down in that managers gave orders to workers
and rarely listened to them. Labor unions created rigid work rules that
made reorganizing the workplace very difficult. In addition, executive
privileges angered the workers. For example, Japanese executives listen
to workers suggestions, eat with the workers in their cafeterias, do
not have executive parking lots, and take pay cuts themselves before
asking the workers to take a pay cut.
Since the 80s, surviving US manufacturing managers are making a painful
transition to world class status in manufacturing. Accounting practice
in manufacturing has been upgraded. Manufacturing firms have been
reorganized to imitate the Japanese with design teams to obtain better
products in much shorter time. Business leaders are now painfully aware
that they must work with politicians in order to improve the
educational process. Universities are now emphasizing
manufacturing. We are talking about a decade or two before significant
progress in education reforms will be realized. That is why as a
patriotic citizen, it is my duty to get you students to do some work!
Innovation in automation is a difficult task for a firm because a major
renovation of an old plant is expensive, and creating a new plant is
very expensive. To achieve sufficiently better performance such that
the investment can be considered an innovation, requires much practical
learning through experimentation to achieve the potential of the new
equipment. Because firms need to justify their investments to
stockholders, they need to achieve better performance within the time
span of a year or two. Given the constraints on managers, manufacturing
innovations are generally a sequence of small advances.
GM through its mistakes
illuminated the problems of manufacturing innovations. GM, early in the
80s, set a bold strategy for manufacturing innovation. They were going
to make major steps to automate manufacturing operations to achieve two
objectives. First, they would leapfrog the Japanese, and second, they
would solve their labor problems (rigid work rules and a rigid
seniority system) by eliminating labor as a significant factor. After a
$40B investment the magnitude of GM's mistakes are now apparent. They
tried to advance automation too quickly. They implemented production
technology that was beyond the state of the art. Because the technology
was untried, they had to spend $Bs getting it to work. Instead of
running factories to produce goods to make a profit, they were forced
to run the factories as experiments.
To make matters worse, GM entered an agreement with Toyota to make
Corollas and Prisms (Novas) in an old factory in California. Toyota
supplied the managers and GM supplied the workers. The Toyota managers
modified Japanese style management, which emphasizes teamwork,
flexibility and good upward communication, to achieve Japanese levels
of quality with little automation. Toyota immediately used the acquired
knowledge of North American laborers to set up successful factories in
Kentucky and Ontario. GM finally wised up and used the new management
labor relations in their successful Saturn plant.
Innovations in manufacturing require much more than trying to replace
existing equipment with more automated equipment. A major source of
innovation in manufacturing is better organization and better use of
humans. One example of an organizational innovation is the creation of
decisive design teams with executives from all parts of the firm. This
greatly reduces the design time are results in market-oriented products
which are easier to manufacture. In organizational areas US
manufacturing firms are imitating Japanese firms.
Automation will gradually decrease the cost of batch production to the
level of mass production and create much greater flexibility in
manufacturing. Flexibility is needed to enable suppliers to more
rapidly respond to changes in demand. For example, Chrysler spent $160M
to enable an assembly plant to shift between two types of cars. In the
limit (several hundred years), you will be able to design an object at
home and have the object manufactured automatically at mass production
prices.
Some interesting sites to surf for competitiveness are:
Automation will affect all industries manipulating physical objects.
Consider agriculture first and the harvesting of tomatoes for ketchup
in particular. To build a machine that would mechanically harvest
tomatoes, the first step was to engage the geneticists to create a
tomato vine on which all the tomatoes ripened at the same time. This
enabled the machine to cut the vines off and shake off the tomatoes.
The problem with the first tomato vine with tomatoes that ripened at
the same time was that they tended to split open when they fell from
the conveyor belt into the truck. This necessitated going back to the
geneticist to obtain a tomato vine on which all the tomatoes ripened at
the same time and all the tomatoes had thick skins. The third round was
to obtain a tomato that was square and would not roll off the conveyor
belt. Does the tomato taste good? Well, maybe in the future they will
address that problem. Work is progressing on machines to harvest
oranges and other fruits.
Construction will probably be automated more slowly than manufacturing.
Manufacturing takes place in much more controlled conditions and the
number of contingencies are less than in construction. Construction
will be automated by creating modules in factories to be assembled on
site. The handling of physical objects in the services is also being
automated. Utilities such as electric power generation are similar to
continuous process manufactured items and are highly automated.
Computers have made possible the shipping of sealed standard sized
containers. RR cars are also controlled by computers. Warehouses have
been automated. At Federal Express, the sorting of packages is
automated for overnight delivery.
Since the manufacturing renaissance started in the Reagan administration, manufacturing productivity yearly increases have risen to historic levels. Many factors contribute to productivity improvements:
Some interesting sites to surf for automation in agriculture and the services are:
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