1. Advanced manufacturing technologies
In response to more intense and globalized completion, manufacturers are
incorporating more flexible technologies into their production processes. One
of most outstanding is advanced manufacturing technologies (AMT), a set of
tools intended to automate and integrate the different stages of design,
manufacturing, planning and control of the products. AMT results from the
application of information and manufacturing technologies with the aim of
increasing the response ability of plant and improve the results of the
production process.
Product Costing in Advanced Manufacturing Technologies
In a typical job shop operation, manufacturing costs are broken down into
three categories: direct materials, direct labor, and factory overhead. In
AMTs, this classification scheme is somewhat less useful (Foster and
Horngren, 1988a, 1988b).
Direct materials This category of costs is usually the largest, irrespective of
the manufacturing technology. In AMTs, direct materials used in a job are
tracked by the central computer from the time the materials are released
from the stock room until final assembly. This is made possible by bar codes,
optical characters, or magnetic strips attached to the materials. In effect,
bar coding (and its alternatives) allows firms to integrate their materials
resources planning (MRP) systems with their accounting systems. Given the
production schedule, the MRP system schedules the delivery of parts and
components inventory needed for production from the stock rooms and
warehouses to the production floor or to the automated materials handling
system (AMHS). The primary effect of integrating the MRP system with the
accounting system is to reduce and often eliminate the paperwork associated
with tracking direct materials in a conventional job shop environment. Errors
in tracking direct material costs are also minimized.
Direct labor Direct labor in AMTs is minimal by comparison to conventional
technologies. In a conventional job shop operation, for example, an operator
is typically needed at each machine for loading, unloading, set-ups, machine
control, and so on. In AMTs, these functions are handled by programmable

2. controllers and the central computer in conjunction with the AMHS.
Operators are nevertheless required to load and unload the AMHS.
Troubleshooting operators make sure that production machinery is running
smoothly and service the machines when necessary. Technicians and
computer operators also perform maintenance duties. Tool setters get the
tools ready for the job and set-up operators are used to set-up special dies,
fixtures, and jigs. In most cases, it is next to impossible to trace a specific
operator to a particular job or product. A load operator may load 10 units of
product A, then 5 of B and 7 of C, all within a limited range of time. For all
intents and purposes, most labor in AMTs is fixed factory overhead rather
than variable direct labor. The amount of direct labor may be sufficiently
minimal so that very little costing bias is introduced by treating all labor as
fixed factory overhead.
Factory overhead and service support costs
After direct materials, the next largest category of costs in AMTs is factory
overhead or service support costs. The important question from a
management accounting perspective is how to allocate these overhead costs
to the product so that the resultant product costs truly reflect long-run
resource usage.
Unlike a typical job shop, processors and computers keep accurate data on
machine usage in AMTs. Specifically, the central computer collects
processing times for each job on all numerically controlled machines. This
means it is possible to use each machine or assembly of homogeneous
machines or manufacturing cell as a cost center (machine center) to both
accumulate costs in cost pools and to allocate these costs to products.
Consider the factory overhead item “equipment depreciation expense.” One
potential method of allocating this cost to products is to cumulate the
acquisition cost of machinery in each machine center. The cost of common
equipment like the central computer and the AMHS could be allocated to
each machine center based upon the proportion of each machine center’s
equipment costs to the total equipment costs of all machine centers. The
rationale here is that more costly machine centers are also more likely to
place greater demands on the common equipment resources. The cost of
each machine center, inclusive of allocated common equipment costs, could

3. then be divided by the expected productive capacity of the machine center
(in hours) over its economic lifetime. This calculation yields a depreciation
rate for each machine center based upon hourly usage of that center.
Products could then be costed based on this hourly rate and the actual usage
of the machine center by the product.
Operating expenses could be allocated to each product on the basis of
machine center equipment costs, or machine center physical area, or by
machine power usage, depending on the specific expense. Maintenance
expenses, for example, tend to vary with the complexity, automation, and
speed of the machine center, so that maintenance is probably best allocated
on the basis of machine center equipment costs. Building depreciation,
custodial services, water, etc. should probably be allocated on the basis of
machine center area. Power and electricity charges are best allocated to
machine centers on the basis of the rated capacity of the machine or, if
available, actual power usage of the machine center.
Activity-Based Costing in Advanced Manufacturing Technologies
Activity based costing (ABC) maintains as its primary tenet that product
costs are generated by non-volumetric cost drivers such as product
complexity, set-ups, quality control inspections, and materials handling, to
name only a few, as well as the volume of production (Cooper and Kaplan,
1992). This insight is particularly pertinent to AMTs, where most costs,
except for direct materials, are of an overhead or service support nature and
are usually not driven by the volume of production. Take set-up costs, for
example. It is generally immaterial whether the set-up is for a run of 10,000
units or 10 units, as the set-up costs are likely to be the same. Allocating
set-up costs by volume rather than the number of set-ups (the cost driver)
would lead to overcosting high volume products and under-costing low
volume products. If, in turn, prices are cost determined, as in markup
pricing, this would result in high volume products being overpriced and low
volume products being underpriced.
The nature of cost in AMTs is such that the relevant cost drivers must be
carefully defined. Consider set-up costs again. In AMTs there are two types
of set-ups: initial set-ups and subsequent set-ups. New products have to be
programmed, their production scheduling simulated and tested by trial runs

4. in order that they mesh effortlessly within an automated system. These
initial set-ups are quite costly, involving the time and effort of computer
analysts and process engineers. Essentially, these costs are driven by
demand for the product over its life cycle. Therefore, initial set-up costs
should be capitalized in an ABC analysis and allocated to the product based
on the total number of units to be manufactured over the product’s life
cycle. In other words, initial setup costs are driven by the volume of
production. Subsequent set-up costs, on the other hand, are driven by the
number of set-ups rather than the volume of production, as noted above.
JIT, TQM, and Management Accounting
AMTs, especially FMS plants, often adopt Just in Time (JIT) inventory
systems. In a JIT system, delivery of inventory components and parts takes
place immediately prior to production. Ideally, there is almost no standing
inventory of direct materials or work in process. Therefore, a sine qua non
for JIT is a stable demand for the product family and a commitment to Total
Quality Management (TQM), namely, the production of high quality products
via high quality manufacturing processes (Ittner and Larcker, 1998, 2001).
Firms adopting JIT typically reduce the number of their suppliers
dramatically. Long-term contracts stipulating price and acceptable quality
levels are negotiated with the remaining suppliers. In most cases, suppliers
are required to do in-house quality inspections prior to delivery. Inventory is
normally delivered in shop-ready containers to facilitate materials handling.
There is some evidence that plants adopting AMTs, JIT, and TQM within the
same limited time period are not as successful as plants that adopt JIT and
TQM without further investment in AMTs (Sim, 2001). This phenomenon is
probably due to the inability of management and workers to cope with
excessive change within the same time period.
JIT simplifies the management accounting system in some ways and
increases its complexity in others (Foster and Horngren, 1998a). On the one
hand, JIT increases the direct traceabil-ity of costs by reducing joint
overhead costs such as warehousing and materials handling. The reduction
or elimination of these costs also reduces the number of cost pools used to
accumulate costs. As a consequence, JIT changes the basis for allocating
indirect costs to production departments. Instead of using warehouse space

5. as an allocation basis for purchases and material handling costs, the dollar
value of materials or the number of deliveries are used. JIT also simplifies
the internal accounting system by reducing the frequency and detail of
purchase deliveries. Since JIT induces constant flow manufacturing and
minimizes spoilage and reworked units, firms adopting JIT often change from
more complex job costing systems to simpler process costing or even to
back flush costing systems. On the other hand, the performance
measurement requirements of a JIT environment add complexity to the
management accounting system because the focus changes from traditional
financial performance measures, such as cost variances and profit data, that
are weak indicators of plant operational performance, to the incorporation of
non-financial performance measures that are more directly related to the
underlying activities of the plant manufacturing process (Fullerton and
McWatters, 2002; Callen, Morel, and Fader, forthcoming). In particular,
purchase price variances are deem-phasized, as are labor variances,
because of the team effort required to successfully implement and maintain
a JIT philosophy.
Measuring Performance in Advanced Manufacturing Technologies
Computer integration in AMTs has given manufacturing firms the capability
of developing physical and financial performance measures at a fairly
disaggregated level (Maskell, 2000). Such performance measures include (1)
partial productivity measures (e.g., output per employee) and indices of
total factor productivity; (2) partial quality measures (e.g., product quality)
and indices of total quality; (3) partial flexibility measures (e.g., process
flexibility) and indices of total flexibility in FMS; and (4) disaggregated
inventory turnover ratios for all types of inventories and for each product
line in JIT plants. Other performance measures commonly used in AMT
plants include manufacturing cycle time, idle time, scrap and rework costs,
material cost variances, percent of on-time delivery, percent of orders filled,
and so on. There have also been attempts to integrate a number of these
performance measures (e.g., quality, flexibility, and productivity) into one
integrated performance measure. In particular, management accounting has
emphasized the balanced score-card as a desirable comprehensive
integrated performance measure that provides strategic guidance for
achieving long-term firm objectives in a timely fashion. The balanced

6. scorecard integrates four categories of performance measures: financial
(e.g., return on assets), customer oriented (e.g., customer satisfaction),
internal business process (e.g., product quality), and learning growth (e.g.,
hours of employee training). The paucity of evidence currently available
regarding the efficacy of balanced scorecard implementation does not permit
generalization. Nevertheless, intuition suggests that more complex
technological environments are more likely to benefit from an integrated
performance medium that informs management and workers about the
firm’s goals. The only empirical result to date regarding the relationship
between technology and adoption of the balanced scorecard is that firms
that have a higher proportion of new products have a tendency to emphasize
measures in the scorecard that relate to new products.
Since a large portion of costs in AMTs involves fixed resources, an evaluation
of unused capacities is essential for cost management. One approach to
measuring unused capacity and unused capacity cost for each fixed resource
is to determine its utilization possibility. Engineering specifications can be
used to estimate maximum possible utilization (in terms of output or
machine hours or other appropriate cost drivers), which is then reduced for
expected repair and/or maintenance downtime. If available, the utilization
level of competitors may be used as a gauge. This maximum capacity level
is then used to compute unused capacity for each resource in the firm.
Dividing the fixed resource cost by this maximum capacity yields a cost
application rate suitable for reasonably accurate product cost determination.
Multiplying this application rate by unused capacity measured in terms of the
cost driver yields, in turn, an estimate of the unused capacity cost. The
unused capacity costs for each resource is a powerful aid for appropriate
cost management through continuous improvement, outsourcing unused
capacities, and so on. In addition, this approach to product cost
determination can also help in assessing the best competitive market price
for the product.
Investment and the Profitability of Advanced Manufacturing Technologies
Accounting information is a potentially important input into the decision to
invest in AMTs. The problem is that many of the benefits and costs of AMTs,
such as the benefits and costs of productivity, quality, and flexibility, are

7. difficult to quantify. A related problem is that manufacturing systems tend to
be too dynamic and too complex to describe adequately in mathematical
terms. Therefore, although a discounted (after tax) cash flow (DCF) model is
appropriate for AMTs as well as conventional technologies, it is far more
difficult to estimate the cash flows of the former. Research in this area has
concentrated on integrating simulation models of the manufacturing firm
with DCF models to evaluate investment in AMTs versus conventional
technologies. Unfortunately, these models are highly sensitive to the
discount rate and it is not at all clear what discount rate is appropriate,
especially since the risk structure of AMTs is very different from conventional
technologies. The decision to invest in flexible manufacturing is a case in
point. If the FMS and the conventional plant have similar cost structures, the
FMS likely dominates the conventional technology (Callen and Sarath,
1995). FMS plants, however, require extremely large initial capital outlays
by comparison to conventional technologies. Moreover, FMS plants are more
susceptible to swings in the business cycle (which may affect the demand for
the entire product family) yet less susceptible to unpredictable changes in
demand for any given product within a product family. The upshot is that in
many cases investments in AMTs are never fully rationalized and the
decisions to invest in AMTs are often made on the basis of “feel” rather than
the underlying economics. In point of fact, accounting has had far less of an
impact on the decision to adopt AMTs than is prudent. Indeed, recent
archival research studies find mixed results regarding the profitability of
adopting AMTs. Many of these studies are problematic, however, because
their analysis is based on firm level data, whereas AMTs are often adopted at
specific plants only. The few plant level studies to date do provide fairly
convincing evidence that AMTs are more profitable than conventional plants
(Flynn, Sakakibara, and Schroeder, 1995; Callen, Fader, and Krinsky, 2000).
Although the excess profitability of AMT plants over conventional may simply
reflect risk-return tradeoffs – AMT plants may be more risky, for example,
because of minimal buffer stocks – in fact, the empirical evidence suggests
that plants that adopt AMTs are more profitable than conventional plants,
even after controlling for the additional risks (Callen, Morel, and Fader,
2003).