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Automation: Goods

Paragraphs have been added by Khurram Mahmood and Issac Hsuing

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.

Two innovations from 1800 to 1950: Replaceable Parts and Multiple Products

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.

Replaceable Parts


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.

Multiple Products


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


Current Advances in Manufacturing Automation

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?

Steps in discrete manufacturing

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.

Design and Analysis

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

  1. FROM MAINFRAME TO PC: The first CAD program was a mechanical drafting program created by GM in the 60s. It cost $1.2M and required a mainframe. As computer power has constantly increased due to Moore's law, CAD/CAE software has gravitated to PCs with a corresponding decrease in cost.
  2. CONSTANTLY IMPROVING GRAPHICS: The improvement in graphics in CAD/CAE parallels the general improvement in computer graphics as computers have become more and more powerful.
  3. Rather than constantly reinvent the wheel, design software is increasingly developing libraries of basic components and operations relevant to the industry.
  4. SOFTWARE INTEGRATION: As business software has progressed from individual software programs to office suites so has CAD/CAE software progressed from individual programs to design suites that integrate:
    1. All aspects of the design process
    2. CAE with CAD
    3. CAD/CAE with manufacturing
  5. COLLABORATIVE SOFTWARE: Lotus Notes was the first software to foster collaborative efforts of business people, engineers are also developing collaborative software to promote collaboration among the design team.
  6. INCREASED SPECIALIZATION: As the market for CAD/CAE software has grown standards are constantly being set and revised. The major players focus on the largest design markets and the niche players focus on provided specialized software for niche markets. Software for CAD/CAE has become much more specialized than just having specialized software for the various engineering disciplines such as architecture, aeronautical, civil, electrical, mechanical, and mining. An engineer designing the electrical circuit for an IC would use different software than an engineer designing the electric system for an automobile.

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.

Parts manufacturing

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.

Flexible Manufacturing Systems:

Some interesting sites to surf for FMS are:

Control of flow of parts and resources

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.

Parts Delivery


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.

Quality Control, QC

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.

Parts Coordination and Quality: Surf the Net

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.

Development of a standardized robot programming language [Paragraph supplied by Issac J. Hsiung: Fall 01]

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 that lend intelligent support [Paragraph supplied by Issac J. Hsiung: Fall 01]

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. 


Robotic Assembly: Surf the Net

Some interesting sites to surf for robotic assembly are:

CIM, computer integrated manufacturing

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:

Factory equipment connectivity to the Internet and the use of XML[Paragraph supplied by Issac J. Hsiung: Fall 01]

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. 

CIM: Surf the Net

Some interesting sites to surf for CIM are listed below. Remember, these are partial and not total solutions to CIM:



 Production for final demand


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.

Scheduling: This material was supplied by Khurram Mahmood

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.

  Exact Solution Methods and Monte Carlo

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.

 Approximate Solution Methods

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.

  Example of Improvement: The job shop scheduling problem

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.


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/

3. Balas, E., 1969, Machine Sequencing via Disjunctive Graphs: An Implicit Enumeration Algorithm, Operations Research,.17 (6), pp 941-957

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)

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

9. Cerny, V., 1985, Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm, J. Optimization Theory and Applications, 45, pp 41-51

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

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)

20. Nowicki, E. and Smutnicki C.,1993, A fast taboo search algorithm for the job shop problem, Management Science

21. Rechenberg, I., 1973, Optimierung technischer Systeme nach Prinzipien der biologishchen Evolution (Problemata: Frommann-Holzboog)

Scheduling: Surf the Net



Human Factors

International Competition

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.

Japanese organizational innovations

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.

US response

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.

Competition: Surf the Net

Some interesting sites to surf for competitiveness are:  


Automation in agriculture and services

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:

Automation in Services and Agriculture: Surf the Net

Some interesting sites to surf for automation in agriculture and the services are:

Latest revision: 16 Jan 06