NEXT GENERATION FACTORY LAYOUTS: RESEARCH CHALLENGES AND RECENT PROGRESS Saifallah Benjafaar Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 554555 Sunderesh S. Heragu Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, NY 12180 Shahrukh A. Irani Department of Industrial and Systems Engineering, Ohio State University, Columbus, OH 43210 December, 2000 Abstract There is an emerging consensus that existing layout configurations do not meet the needs of the multi-product enterprise and that there is a need for a new generation of factory layouts that are more flexible, modular, and more easily reconfigurable. In this article, we offer a review of state of the art in the area of design of factory layouts for dynamic environments. We report on emerging efforts in both academia and industry in developing alternative layout configurations, new performance metrics, and solution methods for designing the “next generation” of factory layouts. In particular, we focus on describing efforts by the Consortium on Next Generation Factory Layouts (NGFL) to address some of these challenges. The consortium, supported by the National Science Foundation, involves multiple universities and several manufacturing companies. The goal of the consortium is to explore alternative layout configurations and alternative performance metrics for designing flexible and reconfigurable factories.
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NEXT GENERATION FACTORY LAYOUTS: RESEARCH CHALLENGES ANDRECENT PROGRESS
Saifallah BenjafaarDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN 554555
Sunderesh S. HeraguDepartment of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute,
Troy, NY 12180
Shahrukh A. IraniDepartment of Industrial and Systems Engineering, Ohio State University, Columbus, OH 43210
December, 2000
Abstract
There is an emerging consensus that existing layout configurations do not meet the needs of themulti-product enterprise and that there is a need for a new generation of factory layouts that aremore flexible, modular, and more easily reconfigurable. In this article, we offer a review of stateof the art in the area of design of factory layouts for dynamic environments. We report onemerging efforts in both academia and industry in developing alternative layout configurations,new performance metrics, and solution methods for designing the “next generation” of factorylayouts. In particular, we focus on describing efforts by the Consortium on Next GenerationFactory Layouts (NGFL) to address some of these challenges. The consortium, supported by theNational Science Foundation, involves multiple universities and several manufacturingcompanies. The goal of the consortium is to explore alternative layout configurations andalternative performance metrics for designing flexible and reconfigurable factories.
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1. Introduction
There is an emerging consensus that existing layout configurations do not meet the needs of the
multi-product enterprise [4, 13, 37, 42, 58, 59, 61, 79, 82] and that there is a need for a new generation of
factory layouts that are more flexible, modular, and more easily reconfigurable. Flexibility, modularity,
and reconfigurability could save factories the need to redesign their layouts each time their production
requirements change. Relayout can be highly expensive and disruptive, especially when the entire factory
has to be shut down and production stopped. For factories that operate in volatile environments, or
produce a high variety of products, shutting down each time demand changes, or a new product is
introduced, is simply not an option. In fact, plant managers may prefer to live with the inefficiencies of
an existing layout rather than suffer through a costly relayout, which in turn could become quickly
obsolete. In our own work with over two dozen companies in the last five years, ranging from big to
small, we have encountered mounting frustration with the existing layout choices. This is particularly
acute in companies that continuously introduce and offer a wide range of products whose demands are
variable and lifecycles short. For these companies, being able to design a layout that can either retain its
usefulness over a wide range of product mixes and volumes or be easily reconfigured is extremely
valuable. Equally important is designing layouts that can support the need for increased customer
responsiveness in the form of shorter lead times, lower inventories, and higher product customization.
The current choices of layouts, such as product, process, and cellular layouts do not adequately
address the above needs because they tend to be designed for a specific product mix and production
volume, both assumed to last for a sufficiently long period (e.g., 3-5 years) [29]. The design criterion
routinely used in most layout design procedures - a measure of long-term material handling efficiency,
fails to capture the priorities of the flexible factory (e.g., scope is more important than scale,
responsiveness is more important than cost, and reconfigurability is more important than efficiency). As a
result, layout performance tends to deteriorate significantly with fluctuation in either product volumes,
mix, or routings [4, 10, 49, 61, 62, 65]. Using a static measure of material handling efficiency also fails to
capture the impact of layout configuration on operational performance, such as work-in-process
accumulation, queue times at processing departments, and throughput rates. Consequently, layouts that
improve material handling often result in inefficiencies elsewhere in the form of long lead times or large
in-process inventories [9].
Hence, there is a need for a new class of layouts that are more flexible and responsive. There is also a
need for alternative evaluation criteria for layout design that explicitly account for flexibility and
responsiveness. More importantly, there is a need for new design models and solution procedures that
account for uncertainty and variability in design parameters such as product mix, production volumes, and
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product lifecycles. In this paper, we outline the needs and challenges in designing factory layouts in
highly volatile environments. We offer a review of state of the art in this area and report on emerging
efforts in both academia and industry in developing (1) alternative layout configurations, (2) new
performance metrics, and (3) solution methods for designing the “next generation” of factory layouts. In
particular, we focus on describing efforts by the Consortium on Next Generation Factory Layouts to
address some of the challenges of layout design in dynamic environments. The consortium, founded by
the co-authors of this article, is supported by a major grant from the National Science Foundation and
involves multiple universities and several manufacturing companies. The goal of the consortium is to
explore alternative layout configurations and alternative performance metrics for designing flexible
factories. In addition to acquainting readers with results from the initial phase of this effort, we hope to
initiate through this article a broader discussion about the physical organization and layout of factories in
the future.
The paper is organized as follows. In section 2, we review current practice in layout design for
factories with multiple products and highlight the limitations of current design methods. In section 3, we
review literature on layout design that is pertinent to the central theme of this paper. In section 4, we
describe research being carried out under the Consortium for Next Generation Factory Layouts. In
particular, we describe results from four streams of research dealing, respectively, with design of (1)
distributed layouts, (2) modular layouts, (3) reconfigurable layouts, and (4) agile layouts. In section 5, we
report on some emerging trends in industry, in both technology and business practices, that could
significantly affect the way factories are organized in the future. In section 6, we offer concluding
comments.
2. Current Practice
It has been conventionally accepted that, when product variety is high and/or production volumes are
small, a functional layout, where all resources of the same type share the same location, offers the greatest
flexibility - see Figure 1(a). However, a functional layout is notorious for its material handling
inefficiency and scheduling complexity [22, 31, 59, 66, 68, 80]. In turn, this often results in long lead
times, poor resource utilization and limited throughput rates. While grouping resources based on their
functionality allows for some economies of scale and simplicity in workload allocation, it makes the
layout vulnerable to changes in the product mix and/or routings. When they occur, these changes often
result in a costly relayout of the plant and/or an expensive redesign of the material handling system [42,
49, 74, 82].
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An alternative to the functional organization of job shops is a cellular configuration, where the factory
is partitioned into cells, as shown in Figure 1(b), each dedicated to a family of products with similar
processing requirements [30, 81]. Although cellular factories can be quite effective in simplifying
workflow and reducing material handling, they can be highly inflexible since they are generally designed
with a fixed set of part families in mind. The demand levels are assumed to be stable and their life cycles
considered sufficiently long. In fact, once a cell is formed, it is usually dedicated to a single part family
with limited allowance for intercell flows. While such organization may be adequate when part families
are clearly identifiable and demand volumes stable, they become inefficient in the presence of significant
fluctuations in the demand of existing products or with the frequent introduction of new ones. A more
detailed discussion of the limitations of cellular manufacturing systems can be found in [1, 4, 11, 40, 59,
70, 77]. These limitations resulted in recent calls for alternative cellular structures, such as overlapping
cells [1, 39], cells with machine sharing [11, 70], and fractal cells [4, 59, 77]. Although an improvement,
these alternatives remain bounded by the underlying cellular structure.
(a) Functional layout (b) Cellular layout
Figure 1 - Functional versus cellular layout
Existing layout design procedures, whether for functional or cellular layouts, have been, for the most
part, based on a deterministic paradigm, where design parameters, such as product mix, product demands,
and product routings, are assumed to be all known with certainty [29, 54, 56, 72]. The design criterion
used in selecting layouts is often a static measure of material handling efficiency (i.e., a total adjacency
score or total material handling distance) which does not capture the need for flexibility and
reconfigurability in a dynamic environment [9, 10, 13, 43]. In fact, the relationship between layout
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flexibility and layout performance remains poorly understood and analytical models for its evaluation are
still lacking. The structural properties of layouts that make them more or less flexible are also not well
understood. Indeed, there exists little consensus as to what makes one layout more flexible than another
or as to how layout flexibility should be measured [14, 27, 29, 67, 76, 78, 80]. In turn, this has led to
difficulty in devising systematic procedures for the design and implementation of flexible layout. Current
design criteria also fail to capture the effect of layout on dynamic performance measures such as
congestion, cycle time, and throughput rate. They also ignore the impact of operational parameters such
as setup, batching, and loading/unloading at the individual work-centers. More importantly, they measure
only average performance and in doing so cannot guarantee effectiveness under all operating scenarios.
There are also limitations underlying many of the tools and methods used to design and evaluate
factory layouts, making them less effective in factories with high product variety or short lifecycles. We
list few of these here based on our own experience with several industry cases [37].
Use of the travel chart as input data: The traditional input data for layout design has been the Travel
Chart [73]. However, this chart aggregates the routings and production quantities of all the products
produced in a facility. Being a simple graph, it prevents machine duplication analysis. Thereby, it limits
the facilities planner to the design of mostly a single type of layout – the functional layout. An alternative
could be a Multi-Product Process Chart that captures the unique routing of each product. Such a chart
would be essentially a hypergraph representation of the facility that treats each routing as a hyperedge
connecting a sequence of departments in the layout. With routing information embedded in the layout, the
design of layout configurations, other than the functional layout, becomes possible because partitioning
the edge list allows duplication of machines in several locations in the facility [37].
Number of part samples and sampling criteria used to design a layout: A common practice in
industry is to use the 80-20 rule (or ABC Analysis) to select one sample of products in designing the
entire layout [34]. However, a single sample is rarely an accurate representation for a facility with high
product variety or a changing product mix. This problem is compounded by the use of “production
volume” as the criterion in selecting a sample. Although a volume-based criterion tends to minimize
material handling costs by minimizing material travel distances, it ignores important factors such as
revenue generated by each product, frequency of product ordering, and variability in order sizes.
The phase in the life cycle of a facility when most models and methods are used: In industry, the
dominant use of facility layout design methods tends to be in the midlife or later life of a facility [38]. In
other words, facility planners are often engaged in evaluating an existing layout and proposing
improvements to it. Clearly, there is considerable opportunity for application of layout algorithms at the
conceptual design phase of planning the layout of a facility. Since production data for the entire life of a
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layout is not known at the initial design stage, there is a need for layout design methods that can work
with fuzzy or incomplete data on product mix, routings and production quantities.
3. Literature Review and Classification
The facility layout problem has been formally studied as an academic area of research since the early
1950s. Numerous papers on this topic have been surveyed in [6], [48] and [54], among others. In this
section, we focus on papers that are pertinent to design of layouts in dynamic environments. We first
provide a review of this literature and then offer a possible classification scheme.
3.1 Literature Review
Dynamic facility layout: In a 1976 paper, Hicks and Cowan [28] incorporated the costs of relocating
departments in analyzing a single period layout. Rosenblatt [63] developed a model and solution
procedure for determining an optimal layout for each of several pre-specified future planning periods.
This model takes into consideration material handling cost as well as cost of relocating machines from
one period to the next. Improvements to the branch and bound procedure in [63] are provided in a
number of other papers including [5], [8] and [76]. Heuristic procedure for the dynamic layout problem
can be found in a number of papers including [15], [42], [50] and [75], among others. Variations of the
basic dynamic layout problem are studied in [7], [57] and [75]. In [57], it is assumed that a goal layout
for the last of several pre-specified planning periods can be provided by the designer. A model which
uses this goal layout as an input and provides intermediate layouts for the intermediate planning periods is
developed. A limitation of this approach is that the relative positions of departments are fixed over all the
planning periods - only the sizes and shapes are allowed to vary. For a more complete review of papers
on the dynamic layout problem, we refer the reader to [6].
Facility layout in an uncertain production environment: The concept of robustness in analyzing single
period layouts was introduced in [65]. Suppose the designer is able to estimate multiple production
scenarios for a planning period, for example, optimistic, pessimistic and most likely. A layout is
considered to be robust if it performs ‘well’ under all production scenarios. This layout may not be
optimal under any specific scenario, but it is also not too far off from the optimal under all possible
scenarios [64]. Heuristic strategies for developing robust layouts for multiple planning periods are
presented in [47]. Palekar et al. [62] consider uncertainties explicitly in determining plant layout. They
formulate a stochastic dynamic layout problem under the assumption that the following are known a
priori: (i) material flows between departments for each of several pre-specified planning periods, and (ii)
the probability of transitioning from one flow matrix to another. The model is solved via dynamic
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programming for small sized problems and using heuristics for larger ones. An algorithm for the single
and multiple period dynamic layout problem is presented in [46]. Although this method considers
additional factors such as additional buffer space and layout changeover costs, it is computationally
intractable in the multiple period case.
A method for developing “flexible” layouts is presented in [82]. The flexible layout is based on the
notion that layouts neither remain static for multiple production planning periods nor do they change
during every period. Instead, a layout may remain static for a block of periods, at the end of which the
production has changed so much that a new layout is necessary. The question for the designer is not only
how to change the layout, but also when to do so. Assuming the flow matrices as well as their probability
of occurrence is known for multiple planning periods, the block of periods for which a layout is to remain
static is first determined [82]. The layout problem for each block of periods is then solved and results
combined to generate a layout plan for multiple production periods. Assuming that future production
scenarios along with their probability of occurrence are known, a method for developing multiple period
layouts is discussed in [56]. Like the approach in [57], a limitation of this method is that the relative
positions of departments are fixed over all the planning periods - only the sizes and shapes are allowed to
vary.
Distributed Layouts: In order to address the limitations that come from fixed department locations,
several authors have recently proposed that functional departments should be duplicated and strategically
distributed throughout the plant floor [13, 16, 58]. Duplication would not necessarily mean acquiring
additional capacity but could simply be achieved by disaggregating existing departments, which may
consist of several identical machines, into smaller ones. Montreuil et al. [58] has suggested a maximally
distributed, or holographic, layout where functional departments are fully disaggregated into individual
machines which are then placed as far from each other as possible to maximize coverage. Benjaafar [13]
has shown that, while some disaggregation and distribution is desirable, full disaggregation and
distribution is rarely justified. In fact, the benefits of disaggregation and distribution are of the
diminishing type with most of the benefits achieved with having only few duplicates of each department
(see section 4.1). Benjaafar also showed that even in the absence of reliable information about product
volumes and routings, the simple fact of having duplicates placed throughout the plant can significantly
improve layout robustness. Drolet [16] illustrated how distributed layouts can be used to form virtual cells
that are temporarily dedicated to a particular job order.
Reconfigurable layouts: A shortcoming with several of the above approaches is that they assume
production data, including the products to be produced, their routings, type and number of each
production resource are known for future planning periods. Even the papers that associate a probability
of occurrence with each production scenario implicitly assume that the production resources (type and
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quantity) remain fixed. In today’s volatile manufacturing environment, it is common to see drastic
production changes take place very frequently. It is also common to see old production resources being
de-commissioned and new ones being deployed rather regularly. What is challenging for designers is that
very often, the changes that are to take place in a production cycle (whether it is change in products,
routings, production volume or commissioning and de-commissioning of resources) are known only
slightly ahead of the start of the new production cycle. Thus, it seems reasonable for a designer not to
look beyond the next period and instead generate layouts that can be reconfigured quickly and without
much cost to suit the upcoming period’s production requirements. Heragu and Kochhar [31] discuss this
idea and argue that advances taking place in materials and mechanical process engineering, for example,
lighter composite materials, nano-technology and laser cutting, will allow companies to reconfigure
machines rather easily on a frequent basis. Kochhar and Heragu [42] present a genetic algorithm to solve
the associated dynamic layout problem.
3.2. A Classification Scheme
In view of the above discussion, we can broadly classify approaches to design of factory layouts for
dynamic environments into two major categories. Methods belonging to the first category develop layouts
that are robust for multiple production periods or scenarios. Methods belonging to the second develop
layouts that are flexible or modular enough so that they can be reconfigured with minimal effort to meet
changed production requirements. The first approach assumes that either:
(a) the production data for multiple periods is available at the initial design stage itself so that a layout
that is robust (and causes minimal materials handling inefficiency overall) over the multiple periods
can be identified; or
(b) a layout with inherent features (for example, duplication of key resources at strategic locations within
the plant) can be developed so that once again, such a layout would help us carry out the material
handling functions efficiently through the various production periods.
Papers that take the approach outlined in (a) include [47, 61, 62, 65, 69, and 74], among others. A
limitation of this approach is that it requires that production data for multiple periods be available at the
initial design stage. This requirement is increasingly difficult to fulfill in today’s environment, where
factories are plagued by the unavailability of production data for more than one period at a time.
Therefore, it is unlikely that this approach - at least on its own - would be adequate to address the needs of
factories in the future. The approach described in (b) is more promising since it attempts to build inherent
features into the layout that enable it to adapt to changes in the production environment. Papers that take
this approach are relatively few and include [13, 16, 42, 58].
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The second approach takes the view that layouts would have to be reconfigured after each period.
Therefore, the challenge is to design layouts that minimize the reconfiguration cost while guaranteeing
reasonable material flow efficiency in each period. Papers that try to balance reconfiguration costs versus
material flow efficiency include [5, 8, 15, 41, 49, 57, 63, 74 and 82]. In order to carry out this balancing,
this approach requires knowledge of production for each future period. Unfortunately, as previously
discussed, this is difficult to satisfy in a volatile environment. A more promising approach is one that
attempts to pre-design reconfigurable features into the layout so that reconfiguration costs are always
minimal. Very few papers have taken this approach. Some examples include [32], [42] and [43].
Layout design methods for dynamic environment could also be classified based on the design criteria
used to evaluate layout alternatives. Much of the literature, including papers that deal with dynamic
environments, relies on measures of expected material handling efficiency - a weighted sum of travel
distances incurred by the material handling system – in evaluating candidate layouts. Few papers, such as
[47] and [65], use a robustness criteria where instead of mean performance, a layout is evaluated by its
ability to guarantee a certain level of performance for each period or under each scenario. Others have
used a combined mean and variance criterion to minimize the range of fluctuation in performance – see
for example [61]. A limited number of papers have considered operational performance as an evaluation
criterion. This includes a recent paper by Fu and Kaku [23] who argued that the conventional measure of
average travel distances is indeed a good predictor of operational performance, as measured, for example,
by expected work-in-process. As we will argue in the next section, this is not always the case. In fact, we
will show that in many cases layouts that are designed using operational performance as a criterion can be
very different from those that minimize average material handling effort.
4. Next Generation Factory Layouts
In this section, we describe research being carried out by the Research Consortium on Next
Generation Factory Layouts. The consortium is funded by grants from the National Science Foundation
and several industries and involves collaboration between three universities: the University of Minnesota,
Ohio State University, and Rensselaer Polytechnic Institute. The goal of the consortium is to explore
alternative and novel layout configurations for factories that must deal with high product variety or high
volatility in their production requirements. We report on preliminary work undertaken by consortium
members in the last two years. In particular, we focus on four promising approaches to layout design that
address four distinct needs of the flexible factory. The first three approaches present novel layout
configurations, namely distributed, modular and reconfigurable layouts. In the fourth approach, we use
operational performance as a design criterion to generate what we term agile layouts.
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4.1 Distributed layouts
The distributed layout concept is based on the notion that disaggregating large functional departments
into smaller sub-departments and distributing them throughout the plant floor can be a useful strategy in
highly volatile environments. Having duplicates of the same departments, which can be strategically
located in different areas of the factory floor, is desirable in a variable environment since it allows a
facility to hedge against future fluctuations in job flow patterns and volumes. The distribution of similar
departments throughout the factory floor increases the accessibility to these departments from different
regions of the layout. In turn, this improves the material travel distances of a larger number of product
sequences. As a result efficient flows can be more easily found for a larger set of product volumes and
mixes. Examples of departments with varying degrees of department disaggregation and distribution are
shown in Figure 2. Such a procedure is especially appealing in environments where the frequency with
which product demand fluctuation occurs is too high for a relayout of the plant to be feasible after each
change. Thus, a fixed layout that can perform well over the entire set of possible demand scenarios is
desirable.
Disaggregating functional departments and placing the resulting smaller sub-departments in non-
adjoining areas of the layout poses several important design challenges. For example, how should the
sub-departments be created? How many should be created? How much capacity should be assigned to
each sub-department? Where should each sub-department be placed? How should workload be allocated
among similar sub-departments? There are also questions regarding the impact of department
disaggregation and distribution on operational performance. For example, how would material handling
times, work-in-process, and queueing times be affected? How should material flow be managed, now that
there is greater routing flexibility? How should the competing needs for material handling of similar sub-
departments be coordinated? There are also important questions regarding what performance measure is
appropriate when designing distributed layouts. Should we use a measure of expected material handling
cost over the set of possible demand scenarios, or should we use a measure of robustness that guarantees a
minimum level of performance under each scenario. More importantly, how sensitive are the final
layouts to the adopted performance measure?
4.1.1 Motivating Example
Our initial interest in distributed layouts was motivated by work with REI, a leading manufacturer of
water filtration products. Their facility was initially organized into 10 functional areas with 4 to 8
workstations per department. The size of the facility and the high diversity of product routings made the
distances between individual departments fairly significant. Due to the high product variety and demand
volatility, the company found it almost impossible to develop a meaningful layout for its facility.
In this article, we provided an overview of emerging trends in design of next generation factory
layouts. We surveyed existing academic literature dealing with design of layouts in dynamic
environments. We highlighted some of the limitations of current practice in layout design and outlined
some challenges that need to be addressed by both the research community and practitioners. To this
effect, we described research being carried out by a recently formed university consortium on Next
Generation Factory Layouts whose goal is to address some of these challenges. Finally, we reported on
some emerging trends in industry that could affect layout design in the future.
The goals of this article are to stimulate thought and discussion about alternative and novel ways of
organizing factories of the future. We hope we are in some small measure successful in provoking
thought and laying out possibilities for future research directions.
Acknowledgments: This research is, in part, funded by the National Science Foundation under grantsDMII 9908437, 9900039, and 9821033. Additional support has been provided by Polarfab, St JudeMedical, Recovery Engineering, Motorola, Honeywell, HP, and the St Onge Corporation.
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