Configuring the Production System - Principles of Management

The production system of an organization refers to how the flow of work is configured. Production can be configured in a number of different ways, depending on the nature of the product, consumer requirements, and available production technologies. Traditionally production systems have been categorized into one of four main categories: job shop, batch production, assembly line, or continuous flow. We will look at each system, and how the systems differ with regard to productivity and costs, before discussing the rise of new production technologies and their role in facilitating what is known as mass customization.


Job shop systems are used when items are ordered individually and tend to be unique to the requirements of a particular customer. Many small manufacturing operations are organized on a job shop basis—a high-end cabinetmaker, for example, may produce specialized cabinets designed for individual homes. Service businesses can also use job shop systems. For example, each job undertaken by a management consulting firm may differ depending on the needs of the client, requiring the firm to develop a different product for each client. Higher-end restaurants also use job shop systems, producing individual items to order from a menu and customizing the orders depending on customer requests.

Small batch production systems are used when customers order in small batches, but when each order is different. The cabinetmaking firm, for example, may be able to produce insmall batches if it serves builders who construct 100 houses a year using the same floor plan. The cabinetmaker may then make 100 cabinets for a particular type of house before altering the equipment to process a different order.

Assembly-line production systems are used to mass-produce large volumes of a standardized product. First popularized by Henry Ford in the 1920s to produce the Model T Ford, assembly-line technology is widely used today. Assembly lines involve breaking down a production process into discrete steps and assigning employees to different work stations on a continually moving line where they perform specialized tasks.

Automobile manufacturers use assembly-line systems to mass-produce specific car models; Dell uses assembly-line systems to mass-produce personal computers; and food manufacturers use assembly lines to produce packaged food. Service enterprises also use assembly-line processes: McDonald’s restaurants use an assembly-line methodology to make Big Macs, doctors may use an assembly-line methodology to perform certain procedures (laser-based eye surgery, for example), and a bank may use assembly-line systems to process mortgage loan applications in a highly standardized fashion.

Finally, continuous flow production systems continuously produce a standardized output that flows out of the system. Oil refineries are a good example: They take crude oil as an input and refine that oil to produce a continuous output of gasoline and other oil-based liquid products. Many continuous flow systems cannot be easily shut down; they tend to operate around the clock, continuously producing a highly standardized output.


Production systems differ in the degree to which their output is standardized, their flexibility, and their costs. Job shop and small batch systems are more flexible than assembly-line and continuous flow systems; they have to be because their outputs are less standardized. They pay a penalty for this flexibility, however, in the form of higher costs. In general, enterprises that use job shop and small batch systems will charge higher prices to recoup their costs.

In terms of the framework introduced in the last chapter, small batch and job shop systems tend to be used to produce a differentiated product offering that is customized to individual customer requirements. Firms using such systems charge high prices to cover the costs of producing a differentiated output.

Assembly-line and continuous flow systems mass-produce or continuously produce a standardized output, and by doing so they can reduce costs in two ways: economies of scale and learning effects. Economies of scale are the cost advantages derived from large-volume production. One source of scale economies is the ability to spread the fixed costs associated with tooling a factory to make a product over a large volume of production.


Thus although it might cost Intel $5 billion to build a facility to mass-produce microprocessors, that fixed cost can be spread over 100 million microprocessors, driving down the cost of each unit. Similarly, automobile companies spread the fixed costs associated with tooling an assembly line to produce a specific car model over a large output—perhaps as much as a million units over five years—thereby lowering the costs of each car.

Another source of economies of scale is the ability of companies producing in large volumes to achieve a greater division of labor and specialization. Specialization is said to have a favorable impact on productivity, mainly because it enables employees to become skilled at performing a particular task. The classic example of such economies is Ford’s Model T car.

Byintroducing assembly-line mass production techniques, Fordachieved greater division of labor (splitting assembly intosmall, repeatable tasks) and specialization, which boostedemployee productivity. Ford was also able to spread the fixedcosts of developing a car and setting up production machineryover a large volume of output. As a result of these economies,the cost of manufacturing a car at Ford fell from $3,000to less than $900 (in 1958 dollars).

Learning effects are cost savings that come from learning by doing. For example, workers learn by repetition how best to carry out a task. In general, labor productivity increases over time and unit costs fall as individuals learn the most efficient way to perform a particular task. Equally important, managers in new production facilities learnsover time how to run the new operation more efficiently. Hence production costs decline because of increasing laborproductivity and management efficiency due to learning.

Learning effects were f irst documented in the aircraft industry during World War II when military aircraft were first being mass-produced on Boeing’s assembly lines. It was observed that each time cumulative production of a particular aircraft model was doubled, unit costs fell to 80 percent of their previous level (that is, the fourth aircraft cost 80 percent as much as the second to assemble, the eight cost 80 percent as much as the fourth, and so on). Further study found the reason: Over time employees found ways to work smarter, made fewer mistakes, and wasted less time, all of which boosted labor productivity.

Learning effects are just as important in service industries as in manufacturing. One famous study of learning in the context of the health care industry found that more experienced medical providers posted significantly lower mortality rates for a number of common surgical procedures, suggesting that learning effects are at work in surgery. 6 The authors of this study used the evidence to argue for establishing regional referral centers for the provision of highly specialized medical care. These centers would perform many specific surgical procedures (such as heart surgery), replacing local facilities with lower volumes and presumably higher mortality rates.

Another study found strong evidence of learning effects in a financial institution. The study looked at a newly established document processing unit with 100 staff and found that over time documents were processed much more rapidly as the staff learned the process. Overall the study concluded that unit costs fell with every doubling of the cumulative number of documents processed since the unit was established.

The implication is that standardization of work processes associated with assembly-line and continuous flow production leads to higher productivity and lower costs (due to scale and learning effects). Indeed, this was the original impetus behind Henry Ford’s famous application of assembly-line technology to automobile production. By moving to an assembly line Ford helped to lower the cost of making cars by realizing economies of scale and learning effects.

He was then able to lower prices and expand the market, creating the first mass market for an automobile with his Model T. In what is now regarded as a classic statement regarding the connection between mass production and product standardization, Ford said that consumers could have the Model T in any color they wanted, “so long as it was black.”

In sum, assembly-line and continuous flow processes allow enterprises to substantially lower costs and thus prices, but a penalty is paid in the lack of product variety—which reduces differentiation. Thus these systems are most often adopted by organizations that are trying to adopt a low-cost strategy rather than a differentiated position in their industry.

McDonald’s, for example, applied the assembly-line philosophy to the production of fast food, which is highlystandardized. This lowered costs and let McDonald’s lower prices and increase demand for its product in a classic example of the low-cost value circle


The last few decades have seen increasing attention devoted to the development of new production technologies that are designed to break the well-established relationship between product customization and higher costs . Many of these new technologies are rooted in the observation that a major source of increased costs associated with greater customization is the setup costs required to produce small batches of output.

It costs a lot to retool an automobile assembly line to produce a new model, so automobile companies prefer to produce fewer models. But what if the time to retool a factory or to set up machinery could be dramatically reduced? Wouldn’t that make mass production of a customized final product economical?

In the automobile industry this insight was first pursued by a remarkable engineer, OhnoTaiichi, who worked at Toyota. 8Beginning in the mid-1950s Ohno began to think about ways to produce auto body parts in small numbers. At that time it could take a full day to set up the equipment to stamp out a particular body part—say a right door panel. As a consequence, auto companies found it economical to stamp out 20–30 days of inventory at a time and warehouse it until it was needed.

Ohno thought this was wasteful: It costs money to store inventory, including the capital tied up in the warehouses and the inventory itself. If machines could be set up quickly, the company would need to produce enough inventory for only a day or so; inventory holding costs would fall; and the productivity of capital would rise, as would Toyota’s profitability. Moreover,Ohno understood that if equipment setup times could be reduced, Toyota could also produce a greater variety of cars without a cost penalty.

Ohno and his engineers began to experiment with a number of techniques to speed up the time it took to change the dies in stamping equipment. These included using rollers to move dies in and out of position along with a number of simple mechanized adjustment mechanisms to fine-tune the settings. These techniques were relatively easy to master, so Ohnodirected production workers to perform the die changes themselves. This in itself reduced the need for specialists and eliminated the idle time that workers previously had enjoyed while waiting for the dies to be changed, which boosted labor productivity.

Through a process of trial and error, Ohno succeeded in reducing the time required to change dies on stamping equipment from a full day to 15 minutes by 1962 and to as little as three minutes by 1971. This meant that Toyota needed to produce only enough inventories for its immediate needs, and costs fell sharply.

In essence, OhnoTaiichi had invented a flexible production technology. Flexible production technologies are a set of methodologies that allow enterprises to produce a wider range of end products from a given production system without incurring a cost penalty. They run the gamut from computer-controlled machine tools grouped into cells of four to six machines, to lower-tech solutions such as those first developed by Ohno and his team, which were based on pulleys and levers.

The automobile industry is among those now rapidly adopting the latest generation of computer-controlled flexible manufacturing technologies, which make intensive use of robotics. Ford is introducing such equipment into its automotive plants around the world, and it hopes to have 75 percent of its production built on flexible assembly lines by 2010. Ifsuccessful, Ford’s investments in flexible factories couldreduce annual costs by some $2 billion a year.

The costsavings come from two main sources: the reduced downtimeassociated with changing a line to produce a different model,and the lower inventory holding costs when products can be produced in small batches. Ford spent $400 million modernizingan 80-year-old assembly plant in Chicago. Prior to theinvestment, the plant could produce only a single model. Nowit can produce eight models from two different chassis.

Increasingly flexible production technologies are allowing mass customization, which is the ability to customize the final output of a product to individual customer requirements without suffering a cost penalty.

Mass customization can enable an enterprise to

(1) better differentiate its product offering, which pays dividends in the form of higher prices or greater demand, and

(2) garner significant cost savings from reductions in inventory holding costs.

One industry that is starting to see the beginnings of a move toward mass customization is apparel, where both manufacturers and retailers are experimenting with this approach. Years ago almost all clothing was made to individual order by a tailor (a job shop production method). Then along came the 20th century and techniques for mass production, mass marketing, and mass selling. Production in the industry shifted toward larger volume and less variety based on standardized sizes.

The benefits in terms of production cost reductions were enormous, but the customer did not always win. Offsetting lower prices was the difficulty of finding clothes that fit as well as tailored clothes once did. People have an amazing variety of shapes and sizes; but shirts often come in just four sizes: small, medium, large, and extra large! It is estimated that the current sizing categories in clothing fit only about one-third of the population. The rest of us wear clothes with less than ideal fit.

The mass production system has drawbacks for apparel manufacturers and retailers too. Year after year, apparel firms find themselves saddled with billions of dollars in excess inventory that is either thrown away or put on sale because retailers had too many items of the wrong size and color. To try to solve this problem, clothing retailers have been experimenting with mass customization techniques. One success story is Lands’ End.

To purchase customized clothes from Lands’ End, the customer provides information on the Lands’ End Web site by answering a series of 15 questions (for pants) or 25 questions (for shirts) covering everything from waist to inseam. The process takes about 20 minutes the first time through, but once the information is saved by Lands’ End it can be quickly accessed for repeat purchases.

The customer information is then analyzed by an algorithm that pinpoints aperson’s body dimensions by running these data points against a huge database of typical sizes to create a unique, customized pattern. The analysis is done automatically by a computer, which then transmits the order to one of five contract manufacturing plants in the United States and elsewhere that cut and sew the garments and ship the finished products directly to customers.

Today customization is available for most categories of Lands’ End clothing. Some 40 percent of its online shoppers choose a customized garment over the standard-sized equivalent when they have the choice. Even though prices for customized clothes are at least $20 higher and they take about three to four weeks to arrive, customized clothing reportedly accounts for a rapidly growing percentage of Lands’ End’s $500 million online business. Lands’ End states that its profit margins are roughly the same for customized clothes as regular clothes, but the reductions in inventories that come from matching demand to supply account for additional cost savings. Moreover, customers who customize appear to be more loyal, with reordering rates that are 34 percent higher than those of buyers of standard-sized clothing.


One key to improving productivity is to make sure the flow of work within a production system is configured optimally. Process evaluation and reengineering are tools commonly used to achieve this. The goal is to analyze the flow of work in an organization, looking for possible causes of low productivity, and to streamline that flow if possible so work is performed more efficiently, thereby lowering costs.

The technique most enterprises use to analyze their work processes is a model cell, which is a fully functioning microcosm of an entire work process. The technique allows managers to conduct experiments and smooth out kinks while working toward an optimal design of work flow. Only when a design is perfected is the new process rolled out across the entire organization.

Most reengineering projects involve several basic principles. One is physically placing adjacent processes near one another, which can accelerate work flow. A health insurance company found that its ability to process claims was slow because the employees who received the claims were located on a different floor from those who processed the claims, and it could take more than a day for files to shuttle from one floor to another. The company quickened the work flow simply by placing receivers and processors next to each other.

A second principle is to standardize procedures at each step in the work flow, which makes it easier for replacement workers to fill in for an absent individual. A third principle is to eliminating loop backs in which work returns to a previous stage for further processing. Loop backs are a major source of low productivity.

For example, in product development projects loop backs can occur when a product is passed from R&D to manufacturing, only to be sent back to R&D when the manufacturing personnel find out that as designed, the product cannot be manufactured economically. The redesign soaks up time and money. If manufacturing and R&D work together on product design, products can be designed with manufacturing in mind.

A fourth principle is to balance work loads across different stages in a process to make sure there are no bottlenecks and no stage has insufficient work. A fifth principle is to separate nonroutine complex tasks and pass them to specialists so the flow of routine work is not slowed down by the need to deal with a complex transaction. Anybody who has stood in line at a bank while a single teller handles a complex transaction can understand the importance of this principle.

Many banks have improved their speed of serving customers by having a specialist customer service representative, rather than a teller, handle complex tasks. A sixth principle is to share the results of improvements in performance so that all can see the benefits of the reengineered processes.

By adopting such principles many enterprises have been able to dramatically improve their flow of work and thus their productivity and costs. Moreover, by analyzing their work flow, managers have often come up with ways to take entire steps out of their organizations’ work processes. The resulting process innovations have enhanced the efficiency of theseorganizations.

For an example, consider Wal-Mart’s management of inventories. In general, inventories are shipped from suppliers to a Wal-Mart distribution center, where they are stored and shipped when needed to a Wal-Mart store. At the distribution center this process involved six steps: unloading the contents of an incoming truck, scanning bar codes on the inventory with a handheld scanner, storing the inventory inside the distribution center, retrieving the inventory, scanning it again, and reloading it on a departing truck.

In the early 1990s Wal-Mart realized that if it could coordinate the flow of trucks so that incoming trucks from suppliers arrived shortly before trucks were scheduled to depart to stores, it could take one step out of the process by simply moving inventory directly from an incoming truck to an outgoing truck parked in an adjacent dock. The technique Wal-Mart developed to do this is called cross-docking.Cross-docking reduced the need to store and retrieve inventory.


This process improve-ment increased labor productivity and reduced the size required for a distribution center (less inventory needed to be stored), which reduced capital requirements. It also increased the efficiency of inventory management (inventory spent less time sitting in a distribution center—it got to the stores quicker, where it could be sold). Although not all inventory can be cross-docked, over 40 percent of Wal-Mart’s inventories are now handled through cross-docking, substantially reducing Wal-Mart’s cost structure.

In 2005 Wal-Mart started to eliminate two more steps in the process—those involving the scanning of inventory. Wal-Mart required suppliers to place radio frequency identification (RFID) tags on all pallets of inventory. The tags emit a radio signal that is picked up by a receiver connected to Wal-Mart’s information systems. By eliminating the need to manually scan inventory, the RFID tags take two more steps out of the process of inventory sorting, thereby boosting labor productivity and further reducing costs.

Process innovations such as those developed by Wal-Mart push out the efficiency frontier and enable a business to stay ahead of its rivals in the quest to establish a sustainable competitive advantage. Many companies currently recognized as having a competitive advantage in their industry, such as Wal-Mart, Dell, Southwest Airlines, Toyota, and Nucor Steel, got there because they were, and continue to be, process innovators.

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