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Evolving production technologies: implications for inventory ordering formulations.

Introduction

Developments in technologies used in the control of production in the manufacturing sector have always made demands on the means by which affected firms have attempted to carry out the supporting inventory ordering policies. The complexities of effecting successful inventory ordering policies have emerged at a pace that rivals the complexities of the underlying production technologies. There is a rich history of innovative approaches using analytical techniques to deal with inventory ordering problems and to establish operational policies.

Most early inventory ordering analyses were based on what is called the simple lot size formula. This formula was first derived by Ford Harris[1] of the Westinghouse Electric Corporation. It was developed further, apparently independently, by R.H. Wilson as a part of an inventory control procedure he promoted. The "Wilson formula" provided the basis for stochastic models developed later by T.M. Whitin[2], whose book, The Theory of Inventory Management, was an early work dealing specifically with stochastic inventory models.

Although engineer-practitioners were the first to show interest in developing more sophisticated analytical models, economists and mathematicians[3-5] were the first developers to provide rigorous mathematical models reflecting newer inventory ordering philosophies and ideas. The introduction of material requirements planning (MRP) systems -- systems which were time-phased rather than quantity (i.e. reorder point) oriented -- further heightened the interest of both practitioners and model developers. Their efforts produced surrogate ordering models as lot sizing algorithms such as least unit cost, least total cost, part period balancing, etc. These algorithms are relaxed models of the Wagner-Whitin dynamic programming algorithm[6,7].

An excellent historical perspective of past, current and future changes to inventory ordering systems is provided in Jaikumar's[8] study of the Beretta Company. The study spans a period of about 500 years -- from 1492 until 1987 -- and is divided into what Jaikumar terms epochs. Each epoch begins with the adoption of a significant change in production technology. For example, the epoch of the English system of manufacture began around 1800 with the introduction of the metal lathe and engineering drawings. Jaikumar notes that, while the adoption of a new form of production technology does not eliminate completely the old technology -- for example, craft industries, using the oldest technology, continue to exist -- the new technology eventually becomes dominant.

In the early 1950s, the Beretta Company adopted the policies and philosophies of what Jaikumar terms the dynamic epoch -- what the Japanese call just-in-time (JIT)[8] and what Deming[9] advocates in the fifth of his "fourteen points": "Constantly and forever improve the system using statistical process control"[9, p. 23]. Nearly 40 years later, in 1987, the Beretta Company began its transition into the last epoch identified by Jaikumar as the flexible manufacturing epoch. This epoch may be portrayed as a "lightless" factory or untended manufacturing resulting from sophisticated computer-based systems which replace human workers who previously functioned as production monitors.

Jaikumar says that, in a world of untended manufacturing:

...all controllable costs will be sunk [fixed] before the first product comes off the production

line. After this [adoption of lightless factory technology], the unit cost will be the same

whether the firm makes one unit or many[8, p. 80].

Three important implications emanate from the preceding discussion. First, history clearly shows each new dominant technology adopted by factories of the future will be very different from its predecessors. Second, since most manufacturing industries of today are just beginning to commit to the dynamic epoch technology, these industries may be about 40 years behind the evolution already experienced by the Beretta Company. While it is reasonable to expect that these industries will have learned from analysing the experiences of the Beretta Company, successful adoption of lightless factory technology by most industries lies a decade or more in the future. Finally, as these industries adopt new production technologies, it is likely that elements of each new system will be increasingly "front-end loaded". That is, the cost structure of the total system will become increasingly "sunk" (i.e. more fixed and less variable costs).

This last point is particularly significant. According to Schellenberger[10], one primary assumption of the long-used Wilson formula is that costs addressed by the model consist of only the variable portions of inventory ordering costs and inventory holding costs. There is a need to consider how the development and adoption of new production technologies during the coming decades will influence this and related inventory ordering models. Potential model changes should affect:

* the set of variables addressed by the proposed model; and

* the relationships described (i.e. the form of the model).

The purpose here is to consider the impact on inventory ordering models and their formulations used for planning and control resulting from changing production technologies. First, the authors will present an inventory classification schema which classifies inventory items according to their functional roles in the production process. To understand the functional role of inventory is important because to do so provides valuable insights into deciding the appropriateness of the inventory policy models being considered. Second, the authors extend the inventory schema to consider the usefulness of various inventory ordering policies with their corresponding constructs for each functional category of production inventory.

Functional categories of inventory

Classical categorizations of inventory items[11, p. 247] typically include:

* inventory which has not yet entered the conversion process (i.e. raw

material);

* inventory which is partially converted (i.e. work-in-process); and

* inventory on which all conversion work is complete (i.e. finished goods).

Rather than describe the functional role the inventory items serve, these categorizations actually describe the current physical conversion state of the particular inventory items. The authors, however, feel that, for a categorization procedure to be of value, it should reflect the various purposes for which the existence of the inventory item is justified. In addition, the procedure must reconcile the contradiction posed by the coexistence of the JIT production management philosophy and generally accepted financial accounting inventory reporting practices. JIT considers dormant inventory a capital waste (i.e. an asset of questionable value) and advocates pursuit of its elimination. On the other hand, financial accounting practices customarily consider all inventory an asset of determinable value essential to the production process. An operational schema is needed which:

* simplifies analysis of how a given production system uses the applicable

inventory components; and

* clarifies the economic value of each component.

Vollmann et al.[12, pp. 719-20] have provided a schema which partially addresses these needs. Their schema identifies two functional groupings of inventory termed movement inventory and organizational inventory. Brooking et at[13,14] extended the schema to encompass six categories which reflect the functional roles served by inventory units. Table I identifies these six functional inventory categories and describes how each category is used in a manufacturing conversion system. A brief explanation of each category will be presented followed by a discussion of a diagram depicting the typical progression of an inventory item through the various inventory categories.

Table I.
Functional categories
of inventory and their
uses


Functional category    Description of use


Transition             Undergoing change
Buffer                 Uncoupling transition activities
Investment             Earning a return on investment
Maintenance            Supports property, plant and equipment
Supplies               Supports organizational operations
Dead stock             None


Transition inventory

Transition inventory, the first category, is inventory currently undergoing transformation and functions as a vehicle for profit generation. Three types of transition inventory are:

(1) inventory being transformed from one conversion state to another, e.g. reshaped or assembled to other components - inventory in transition from one conversion state to another;

(2) inventory being inspected or undergoing other quality enhancing functions - inventory in transition from a conversion state of unknown quality to a conversion state of known quality; and

(3) inventory being moved - inventory in transition from one physical location to another.

Movement of inventory within a manufacturing facility does not add value. However, movement from the manufacturer's site to the customer's site does generate utility of place. Therefore, from this perspective, the profit generation criteria for this category are satisfied. Finally, it is important to recognize that the length of time required to complete some transition activity may be shortened, which may reduce the transition inventory level. Nevertheless, this inventory category cannot be removed totally from the system.

Buffer inventory

Buffer inventory is inventory waiting to enter a production activity (i.e. waiting, temporarily at least, to become transition inventory). Functionally, buffer inventory supports the existing production system configuration by uncoupling component conversion activities (e.g. metal cutting, forming, finishing, heat treating, inspection, movement, etc.) constrained by different capacity flow rates among these conversion activities. A change in the configuration of conversion activities may alter the buffer inventory requirements for individual activities. For example, changing the facility layout or progression activity sequence also could require a different level of buffer inventory. A corollary of this is that buffer inventory can be eliminated only if the associated production configuration is changed to eliminate the underlying need(s) to buffer inventory.

During the production cycle an inventory item will:

* join many sequential processing queues;

* become buffer inventory;

* use machine capacity;

* be moved and inspected; and

* eventually become finished goods inventory delivered to the customer.

It is neither practical nor useful to separate physically the units of a stockkeeping unit (sku) into these categories. However, it is useful for production management to know the current proportion of total inventory in each category. Buffer inventory needs may be reduced (perhaps eliminated) by improving the physical flow of inventory. The higher the proportion of buffer inventory, the greater is the likelihood that pending changes to the production system can be justified based on buffer inventory reduction.

Investment inventory

A third functional category of inventory may be benign to the production process. This category has been labelled investment inventory by Brooking et al.[13]. Investing in inventory may be conceived as an alternative to other investment opportunities (e.g. marketable securities or, what could be more important, improved production equipment and monitoring and control systems). There are two types of investment inventory: hedge and quantity discount. Hedge inventory is primarily inventory purchased prior to known orders to avoid the deleterious effects of price changes. In rare cases it can also exist as work-in-process or finished goods inventory when it is, for example, produced to avoid layoff-hiring costs in conjunction with temporary capacity contractions. Quantity discount inventory is purchased above immediate stock needs to attain price discounts from vendors. The viability of investment inventory purchases depends on whether and when the forecasted demand for the inventory is realized, given satisfactory compliance with the firm's investment criteria.

Maintenance inventory and supplies

The authors consider maintenance inventory and supplies to be special types of inventories which are not involved directly in the conversion process. Typically, these inventory units are not purchased as raw materials nor are they ever tracked as work-in-process or finished goods inventories. Maintenance inventories often are monitored, ordered and controlled based on policies significantly different from policies which focus on production-oriented inventories. These inventories primarily support operations by helping to manage and maintain the organization's property, plant and equipment. Generally, for purposes of accounting, maintenance materials and supplies are considered expenses incurred for the period in which they were purchased, or are included in overhead charges - an indirect period expense. Both are used to support managerial-type operations (e.g. planning, ordering, scheduling, personnel, accounting, etc.). This study recognizes these functional categories, but considers them outside the focus here because they are not strictly production inventories.

Dead stock

Some individuals might suggest that dead stock could be considered a sixth functional category of inventory. Since these inventory items do not produce production benefits, they should be eliminated or not ordered. Therefore, examining an inventory ordering policy for dead stock is not useful for the present discussion.

Manufacturing inventory flow

Figure 1 includes all the functional categories identified in Table I except dead stock. The Figure shows the flow of inventory material through a typical manufacturing production system. Boxes represent the existence of the functional roles served by inventory during its life cycle. For example, buffer inventory may exist as raw material, work-in-process, finished goods, and pipeline inventories (i.e. finished goods within a product distribution system). Most investment inventory is:

* purchased raw material that has not yet entered the production system; or

* purchased component parts.

However, as noted above, in rare cases hedge inventory may exist as both work-in-process and finished goods inventory. Transition inventory exists as both work-in-process and pipeline inventory. For completeness, Figure 1 also recognizes maintenance inventories and supplies.

Conceptually, Figure 1 portrays the sequential life-cycle stages of, for example, a unit of material that enters and progresses through the manufacturing system as:

* raw material - buffer inventory;

* work-in-process inventory which flows through the various conversion processes, alternately joining associated activity queues - buffer inventory;

* inventory being worked on, moved and inspected - transition inventory;

* finished goods inventory leaving the system (e.g ownership is transferred to the buyer) - buffer inventory; or

* finished goods inventory in a pipeline (e.g the distribution system is owned by the producer firm) and consequently, depending on whether the inventory is being moved or stored, is alternatively investment inventory or buffer inventory.

Note that Figure 1 accounts for all types of inventory, identifies where the various functional types of inventory exist, and correlates the various inventory types and functions. The authors submit that these functional categories are both mutually exclusive and collectively exhaustive. A given stock-keeping unit of inventory can be assigned to one and only one functional category at any given point in time.

Ordering policies

The premise is that production technology changes will occur over a period of years and will evolve from an existing technology towards a lightless factory form (i.e. towards activities for which costs are increasingly fixed, less variable). Our focus now turns to evolving production technologies and their impact on various inventory ordering policies and order formulations for each functional category of production inventory - buffer, transition and investment.

Ordering buffer inventory

Historically, buffer inventory has been ordered in lot sizes using policies based on the previously discussed Wilson formula, which can be shown as:

(1) [Mathematical Expression Omitted]

where Q is the order quantity (units/sku - order), D is the demand rate (units/time - year)), S is the variable ordering cost ($/sku - order), I is the variable holding cost ($/$ - year), C is the unit cost of the item ($/unit), and 1/2is a multiplier (sku - order)

Most production operations use one or more forms of buffer inventory. Typically, inventory units enter the production system as lot-sized buffer inventory, are processed in lot quantities and, finally, are shipped in lot sizes. The expectation is that future requirements will call for more frequent deliveries of smaller lot sizes, with an emphasis on economy of scope rather than economy of scale, as production organizations move towards lightless factory environments. If the Wilson formula is to continue as the underlying theoretical basis for ordering lot size inventory, changes in production technologies should influence relevant inventory ordering formulations in ways that reduce lot sizes of inventory.

The Wilson formula is generally insensitive to small changes in its independent variable values. Unfortunately, some practitioners have interpreted this to mean that the formula's independent variable values need not be measured with precision. For example, inventory holding costs in the USA were often assumed to be $0.25 per inventory dollar-year during the decade of the 1950s. Based on the escalating changes in the cost of US capital in recent decades - a major component of variable inventory holding costs - it is unlikely that the inventory holding cost value remains constant. In spite of this, many US practitioners continue to use $0.25 per inventory dollar-year. When inventory ordering policies based on the Wilson formula are to be used for ordering inventory under conditions of evolving technologies, the need intensifies to measure accurately the costs of holding inventory and placing orders for inventory. This is particularly important to efforts to reduce buffer inventory levels.

Although it may not be possible to achieve the JIT goal of significantly reducing all inventory ordering costs, it is feasible that the variable portions of these costs can be reduced to near zero. Recall again that, according to Jaikumar[15, p. 80], as the production system becomes more flexible and automated, greater portions of operating costs (e.g set-up costs) will become increasingly fixed.

The same thing is true for inventory holding costs, with one important exception - the cost of capital invested in inventory. First, the cost of capital invested is a rate defined by capital markets, not the production system. Consequently, capital costs will not be affected directly by changes in technology. Second, because the inventory holding cost is a rate, it can never become zero, although its relative value may diminish as the order cycle is shortened. This is fortunate. If the value of I were to reach zero, the value of Q as defined by the Wilson formula then would be undefined. Finally, since the inventory ordering formulation seeks to balance ordering and holding costs, reducing ordering costs will force reduced order cycles, the desired consequence.

Thus, if appropriate inventory holding and ordering costs are used, ordering policies based on the Wilson formula can be expected to result in progressively decreasing lot sizes as system improvements are made until the lot size approaches zero. Buffer inventory, no longer needed by the system, will cease to be ordered by a Wilson-formula-based inventory ordering policy. At this point, the usefulness of the Wilson-formula-based inventory ordering policy will theoretically end.

Ordering transition inventory

The more pertinent characteristics of systems evolving towards lightless factory environments which often incorporate JIT-based operating policies include the following:

* Some of all products are always produced - production is scheduled in terms of rates, not in terms of lots[12,16,17].

* The production system operates as a pull system - implying that it is a make-to-order, not a make-to-stock system[16, p. 744].

* Production schedules are level although the mix of products can vary[12, p. 243; 16, p. 745].

* Purchases are made from JIT vendors[12, pp. 278-9].

In most existing systems, transition inventory is not ordered; rather, buffer inventory is ordered. However, during the conversion process from time to time, portions of inventory become transition inventory. The idea of ordering transition inventory is analogous to operating an MRP system using lot-for-lot order quantities rather than some fixed lot size decided using an algorithm such as part period balancing. The amount ordered is the amount needed to meet immediate needs (i.e. the amount that coincides with the rate of production). A lot-for-lot ordering policy implies ordering costs are not significant (i.e. ordering costs are approaching zero). Conceptually, although several units may be ordered for a given day's production requirements, ordering to meet variable production needs differs from ordering in some variable lot size. This implies there is no economic benefit from producing two or more of the day's production of a given stock-keeping unit back to back.

As technology improves production systems, it can be expected that buffer inventory will be driven to near zero, and transition inventory will become the dominant inventory type. Order policies for transition inventory increasingly will rely on JIT vendor relationships to acquire inventory at a variable rate - in terms of product units per period - equal to the rate of incoming customer orders. Lot-size orders - in terms of units per order - will become the exception. Material will arrive, not as raw material inventory, but as work-in-process units. Arrival will be in terms of a rate of demand, implying that there is no economic benefit of either producing or ordering parts in lot sizes.

Ordering investment inventory

Investment inventory is ordered to meet forecasted, not current, demand. Quantity discount inventory is purchased before production demand to allow acquisition at a known reduced cost. Hedge inventory typically is acquired before production demand is known at current market prices to avoid anticipated price increases. Neither type affects functionally the production process. That is, production demand is independent of the existence (or nonexistence) of either quantity discount inventory or hedge inventory.

Ordering based on vendor quantity discounts. The accepted method of deciding whether to take a quantity discount requires comparing the total annual cost (TAC) of ordering the economic order quantity (EOQ) with the total annual cost of ordering the minimum amount required to gain the price break; that is, ordering [Q.sub.PB] if [TAC.sub.P]B < [TAC.sub.NO] or Q if [TAC.sub.NO] [less than or equal to] [TAC.sub.PB]. Total annual inventory costs with and without price break gains are shown in equations (2) and (3), respectively:

(2) [Mathematical Expression Omitted]

where TACpB is the total annual cost with the price break with units of $/year, CPB is the unit cost with the price break, QPB is the minimum order quantity needed to acquire the price break, and IPB is the holding cost with the high shrinkage value.

(3) [Mathematical Expression Omitted]

where [TAC.sub.No] is the total annual cost without the price break, and INO is the holding cost with the low shrinkage value.

Current practice uses the same inventory holding cost in both equations. This, however, fails to recognize the potential of greater inventory shrinkage resulting from larger order quantities. Shrinkage costs are a function of the holding period. For example, shrinkage costs are likely to be significantly greater for inventory items held for extended periods than for inventory items typically acquired as cycle stock. Cycle stock is inventory acquired to meet anticipated current demand using economic ordering algorithm-based policies excluding quantity discount considerations.

When the two total annual costs are compared, the values of I (i.e. inventory holding costs) should be different for the price break gain situation from the without price gain situation. The total annual cost with the price break (see equation (2)) should include shrinkage costs in the I value. In addition, the total annual cost for the without price gain situation (see equation (3)) should include no (or definitely less) shrinkage costs other than those expressed in [I.sub.PB].

Ordering based on inventory hedging Purchase of hedge inventory can be justified based on return on investment (ROI) criteria, as shown by:

(4) [Mathematical Expression Omitted]

where [C.sub.H] is the expected future cost of the inventory.

Savings would be the difference between the current cost of the items and the anticipated future cost. The cost of ordering hedge inventory may be found using the following equation:

(5) [Mathematical Expression Omitted]

where [TOC.sub.H] is the total order costs of ordering the hedge inventory ($/sku--order); [C.sub.c] is the current unit cost; t is the portion of the year the inventory is held -- if the holding period exceeds one year, then the holding costs should be discounted for the period greater than one year; [I.sub.PB] is the holding cost with the high shrinkage value.

As previously stated, ordering investment inventory should be contingent on whether and when the return on investment to be achieved will meet the firm's investment criteria. For quantity discount inventory, the required return on investment is included in the holding cost of the testing equations (i.e. equations (2) and (3)). For hedge inventory, the return on investment may be calculated using equation (4). It is important to recognize that ordering investment inventory involves greater risk than ordering either buffer or transition inventories because of the extended period for which investment inventories are held.

Philosophically, strict JIT inventory ordering policies would never order investment inventory units since such units would exceed minimum inventory requirements and, consequently, would be considered as waste. Again, investment inventory purchases should be executed only in situations characterized by acceptable compliance with the firm's investment criteria.

Conclusions

Three primary conclusions result from this research:

(1) Evolving production technologies can be expected to alter significantly

the values of variables addressed in inventory ordering policy models,

principally by reducing them.

(2) If adopted inventory ordering policies are to eliminate buffer inventory

units as their need is eliminated, it will be imperative to measure

accurately the values of the variables represented in the inventory order

model.

(3) The usefulness of some existing inventory order policy models will

diminish as production system improvements eliminate the need for the

specific types of inventory addressed in the models, namely buffer and

investment inventories.

It is important that a firm's inventory be evaluated in terms of all functional roles and sequences prescribed by that firm's inventory ordering policies. A functional role-derived inventory classification schema which is both mutually exclusive and collectively exhaustive can elucidate the current role served by each active inventory item, as well as those roles which precede and follow. Such a schema can provide a common base for building concurrently both operating and associated monitoring structures to enhance the economic value of the firm's products. Consequently, the potential to facilitate flexibility in analyses and to formulate successful cost containment policies will be enhanced significantly.

The capability to determine the cost of an inventory item accurately is crucial when developers of inventory ordering models address the myriad issues encountered in inventory cost containment efforts. Underlying cost sources need to be identified and tracked in the various subsystems that support the firm's production system. Structured cost information is necessary to make decisions resulting in sustainable cost containment trade-offs (e.g. product mix flexibility, raw material substitutions, optimal combinations of fixed cost and variable cost components, etc.) in the deployment of financial capital assigned to the production function.

Recommendations

For the immediate term, continuing efforts to contain inventory shrinkage costs in production systems offer significant potential economic returns for manufacturing firms. Concerted efforts are needed to clarify the impact of shrinkage costs on each alternative inventory ordering policy being considered. Research focus should be on how shrinkage costs vary in association with two inventory constructs:

(1) inventory level -- as a function of the level of buffer inventory in the

system; and

(2) time in the system -- as a function of the period inventory remains in the

production system.

For the longer term, time itself may become the focal point for firm management. As changing production technologies produce disparate operating parameters, such as shifting and pervading product cost structures, greater attention must be devoted to the implications, direct and indirect, anticipated for the periods affected by the proposed inventory ordering policies.

First, whereas adapting to information provided by existing cost-reporting systems which in prior periods was once sufficient (e.g. accurate, precise, timely) to support implemented inventory ordering models and their formulations, now such information may drift towards insufficiency. Matching available information to the needed inventory ordering models will become increasingly difficult, and information will be inadequate in some instances.

Second, extending the prior point further, firm management will have to live with the inevitable mistakes that occur. Firm management has always been evaluated on the basis of the successes and failures of its decision making.

However, now both the duration of the affected periods and the impact of the consequences will be greater and simultaneous.

Third, firm management must address the extent to which it will commit itself to proposed inventory ordering policies needed by evolving production technologies. As the affected duration lengthens, and pay-offs (i.e. positive and negative) increase, the level of decision-making risk will rise. Ultimately, firm management must decide the boundaries within which the risk intensity produced by the inventory ordering policies under consideration will influence the willingness of the current firm management to extend its commitment to the proposed new production technology.

[Figure 1 ILLUSTRATION OMITTED]

References

[1.] Harris, F., Operations and Cost, A.W. Shaw Co., Chicago, IL, 1915. [2.] Whitin, T.M., The Theory of Inventory Management, Princeton University Press, Princeton, NJ, 1953. [3.] Arrow, K.J., Harris, T. and Mauschak, J., "Optimal inventory policy", Econometrica, Vol. XIX, 1951, pp. 250-72. [4.] Arrow, K.J., Karlin, S. and Scarf, H., Studies in the Mathematical Theory of Inventory and Production Stanford University Press, Stanford, CA, 1958. [5.] Dvoretzky, A., Kiefer, J. and Wolfowitz, J., "On the optimal character of the (s,S) policy in inventor theory", Econometrica, Vol. XXI, 1953, pp. 586 96. [6.] Wagner, H.M. and Whitin, T.M., "Dynamic version of the economic lot size model", Management Science, Vol. 5, October 1958, pp. 89-96. [7.] Hadley, G. and Whitin, T.M., Analysis of Inventory Systems, Prentice-Hall, Englewood Cliffs, NJ, 1963. [8.] Jaikumar, R., "An architecture for a process control costing system", in Kaplan, R.S. (Ed.), Measures for Manufacturing Excellence, Harvard Business School Press, Boston, MA, 1990, pp. 193-222. [9.] Deming, W.E., Out of the Crisis, Massachusetts Institute of Technology, Cambridge, MA, 1982. [10.] Schellenberger, R.E. "Criteria for assessing model validity for managerial purposes", Decision Sciences, Vol. 5 No. 4, 1974, pp. 644-53. [11.] Schmenner, R.W., Production/Operations Management, 5th ed., Macmillan, New York, NY, 1993. [12.] Vollmann, T.E., Berry, W.L. and Whybark, D.C., Manufacturing Planning and Control Systems, Richard D. Irwin, Homewood, IL, 1988. [13.] Brooking, S.A., Parker, H.J. and Hailey, W.A., "Inventory -- just what is it?", Proceedings, The International Conference of the Belgium Production and Inventory Control Society and the Institute of External Auditors, Ghent, Belgium, 1989, pp. 49-63. [14.] Brooking, S.A., Parker, H.J. and Hailey, W.A., "Categorization of inventory through functional lead time analysis", Proceedings, 34th International Conference American Production and Inventory Control Society, Seattle, WA, 1991, pp. 332-5. [15.] Jaikumar, R., "From filing and fitting to flexible manufacturing: a study in the evolution of process control", Working Paper, Harvard Business School, Boston, MA, 1988. [16.] Chase, R.B. and Aquilano, NJ., Production and Operations Management: A Life Cycle Approach, Richard D. Irwin, Homewood, IL, 1989. [17.] Schonberger, R.J., World Class Manufacturing: The Lessons of Simplicity Applied, The Free Press, New York, NY, 1986.
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Author:Brooking, Stanley A.; Hailey, William A.; Parker, Hugh J.; Woodruff, Charles K.
Publication:International Journal of Operations & Production Management
Date:Oct 1, 1995
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