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.
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
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].
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
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
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
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
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].
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.
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.
Figure 2 - Layouts with varying degrees of distribution
The need for disaggregating and distributing their large functional departments throughout the factory was initially adopted to reduce the large distances that must be traveled by in-process material. However, it was soon discovered, that when coupled with effective workload allocation, this distribution resulted in significantly lower material handling costs and shorter material handling times even when demand variability was high.
4.1.2 A Layout Design Procedure
Some of the above questions are explored in [13, 49]. In particular, we considered situations where the demand for each product is characterized by a finite discrete distribution, represented by a finite number of demand realization scenarios and probabilities of occurrence of each scenario. Demand for each product, characterized by a finite discrete distribution, can be either independent or correlated. The result in both cases is a set of scenarios consisting of different product demand combinations, each with its own probability of occurrence. Characterizing the product demand distributions may be based on historical data and/or forecasts. When the demand distributions are difficult to characterize, equal likelihood can be assigned to the set of possible demand scenarios. Alternatively, the set of scenarios can be aggregated into a smaller subset, which is representative of the range of possible demand realizations. In the case of REI, such an aggregation is possible since orders from different retailers tend to be highly correlated, with order sizes varying over a finite range of discrete order choices.
The basic steps of the procedure can be summarized as follows. From (1) the distribution of demand scenarios, (2) the product routings, and (3) the product unit transfer loads, we determine for each possible demand scenario the amount of material flow due to each product between each pair of departments. This results in a multi-product from-to flow matrix for each demand scenario. The objective is to select a layout that provides efficient flow over the entire set of scenarios. For each scenario, we also need to determine the optimal flow allocation among sub-departments of the same type. Thus, we have a combined layout and flow allocation problem. A model for this layout-flow allocation problem, as well as an effective decomposition solution procedure, are given in [13] and [49].
4.1.3 Some results with a procedure for developing distributed layouts
Preliminary experimentation with distributed layouts, using both randomly generated examples and data collected from REI, indicate that significant benefits can be realized by disaggregating and distributing functional departments (over 40% improvement in most cases) [13]. Although the advantage of distributed layouts is most pronounced when demand variability is high, it is significant even in the absence of variability. This is particularly the case for layouts with large departments or a large number of departments. If the distribution of flow patterns can be categorized a-priori, then including flow
information at the design stage can lead to higher quality layouts. However, material handling costs can be significantly reduced even if no flow information is included (e.g., by a random distribution of sub-departments). In addition the quality of distributed layouts is quite insensitive to inaccuracies in the demand distribution. But most importantly, most of the benefits of department duplication are realized with relatively few replicates. This means that there would be rarely a need to fully disaggregate functional departments.
Having a layout where department replicates are distributed throughout the plant floor can also be effective in handling products with short runs or products with short lifecycles. This can be achieved, for example, by the formation of temporary cells dedicated to a particular product line or customer job order. These cells can be quickly formed, as shown in Figure 3, by finding adjoining replicates that can be temporarily dedicated to a product line or a customer job. This cell is disbanded once the product is phased out or once the customer order is completed. The individual replicates are then free to participate in new cells. An early vision of these virtual cells is also discussed in Drolet [16]. Furthermore, we have found that distributed configurations can be useful in handling growth or contraction in a graceful manner [49]. For example, in many industries product maturity occurs over several periods. Instead of redesigning the facility for each phase of product growth, we found that a distributed layout can significantly minimize rearrangement costs which would be necessary if a functional configuration is adopted. Additional machines are added to the periphery of the existing layout as needed and without necessarily relocating equipment. Growth occurs almost in a concentric fashion that keeps layout space compact and maintains efficient material handling. More importantly, this approach allows adding or subtracting capacity in smaller increments than would be possible otherwise since introducing or removing capacity always takes place at the periphery while maintaining the factory core intact.
Figure 3 – Using distributed layouts to construct Virtual Cells
4.1.3 Research Challenges
Several research challenges remain to be addressed. In our initial effort, we assumed that the number of department copies and the capacity of each copy are known. In practice, these are decisions that facility designers must make before the layout process can be carried out. Our initial models do not account for the cost of disaggregating and distributing departments nor do they capture the economies of scale associated with operating consolidated departments. The infrastructure that is typically shared by a single consolidated department in job shops, such as operators, computer control systems, loading/unloading areas, and waste disposal facilities, must be duplicated in a distributed layout across all department copies. Thus, while there may be material handling benefits to department disaggregation and distribution, these benefits should be carefully traded-off against the advantages of operating consolidated facilities. Therefore, there is a need for an integrated model that combines department duplication and capacity assignment with layout design and flow allocation. In our initial flow allocation model, we assumed full flexibility in assigning workload among duplicates of the same department. In practice, this could result in splitting orders that belong to the same product among several duplicates. This would mean smaller batches and possibly longer and more frequent setups. Order splitting could also cause delays in shipping completed orders due to poor synchronization among individual batches of the same order. Addressing this problem would require either capturing setup minimization in the objective function or placing additional constraints on flow allocation to prevent order splitting.
4.2 Modular Layouts
The focus of this approach is on design of customized layouts for facilities with multiple products. We are considering a novel approach based on the idea that layouts can be constructed as a network of basic modules. Here, we assume that, at least in the short term, the product mix is known and demand is relatively stable. As the product mix evolves and demand changes, certain layout modules will be eliminated and others added. The use of modules is motivated by the fact that none of the prevailing layout configurations (functional, flow line, and cellular) can individually describe the complex material flow network in a multi-product manufacturing facility. Preliminary research on this topic was undertaken and has recently been reported in [35, 37-39]. The research sought to answer the following fundamentally new questions: Could an alternative layout other than the three traditional layouts be a better fit for the material flow network in a multi-product manufacturing facility? And, could this alternative layout be a combination of the three traditional layouts? The proposed concept of designing any facility layout as a network of layout modules provides a meta-structure for the design of multi-product manufacturing facilities. The proposed concept uses the idea of grouping and arranging the machines required for subsets
of operations in different routings into a specific (traditional) layout configuration that minimizes total flow distances or costs.
4.2.1 Motivation
Figure 4 shows an example of a new layout configuration that is being proposed for multi-product manufacturing facilities. It was designed during a study that was done for Motorola Inc. The company wanted to assess the feasibility of changing the functional layout in one of their semiconductor fabs into a cellular layout. The Functional layout in the fab is comprised of seven bays (or process departments) –DIFF, ETCH, FILM, IMPLANT, PHOTO, METROLOGY and BACKEND. Four product routings that were representative of the product flows in the fab were provided for the study. The study found that a cellular layout was not a viable option for the fab. This was because the creation of flowline cells based on grouping one or more routings required significant equipment and process duplication among the cells. However, a visual string matching analysis of the routings revealed that, despite being dissimilar, different pairs of routings had one or more common substrings of operations that were either identical or, at least, had high commonality of operations. Based on this observation, the novel layout shown in Figure 4 was generated. Unlike the functional, flowline or cellular layouts, this layout uses a combination of the three traditional layouts to arrange the equipment in different areas of the facility. In addition, this layout has allowed some machine duplication, as is usually done to design a cellular layout for a multi-product manufacturing facility. In the layout of Figure 4, all pairs of consecutive operations in all the product routings are performed in (a) the same layout module or (b) adjacent layout modules. A layout module is a group of machines in a portion of the overall facility that has a flow pattern characteristic of a traditional layout. Since this work for Motorola Inc., a study of samples of product routings obtained from published data and from industry was conducted. A common observation was made that dissimilar product routings often had common substrings of operations that could be aggregated into layout modules.
4.2.2 Classification of Layout Modules
It appears that the material flow network in any multi-product facility can be decomposed into a network of layout modules, as shown in Figure 4, with each module representing a portion of the entire facility [35, 37]. Each layout module is a group of machines connected by a material flow network with a well-understood flow pattern and method for design of its layout. The initial set of modules we are proposing consists of the three traditional layouts shown in Figure 5. For example, the Flowline and Cell Modules have a part family focus. The Flowline Module is an aggregation of one or more routings that are identical. Whereas, the Cell Module is an aggregation of a family of similar routings based on a
Functional Layout for FILM Department
Figure 4 - Example of a facility layout designed using layout modules
(a) Flowline Module
(c) Cell Module
(b) Branched Flowline Module
(d) Machining Center Module
(e) Functional Layout Module
(f) Patterned Flow Module
Figure 5 - Example layout modules
pre-defined level of commonality of machines used and similarity of sequence in which the machines are used. In contrast, the Functional Layout Module is a group of machines that does not process a family of routings. However, the material flow pattern in its From-To chart could correspond to an acyclic digraph, as in an assembly or disassembly line or, in the worst case, a completely connected digraph.
4.2.4 Solution Approach
The ideal solution would have each product completely processed on a dedicated flowline. Since that would entail significant investment in equipment, a practical approach would be to maximize the number of consecutive operations in a family of routings that are performed in the same module. In order to realize such a structure, we developed a solution approach based on the methods of string matching and clustering used extensively in genetics, molecular chemistry and the biological sciences [35]. At the core of the approach is the concept of a ‘common substring’ and a ‘residual substring’ in a product routing, defined as follows. Common substring is a substring of consecutive operations that is common to two or more operation sequences; Residual substring is the remaining substring(s) of operations in an operation sequence after all common substring(s) are extracted from it. For example, given two operation sequences S a (1→2→3→4→7→8) and S b (1→2→5→6→7→8), the common substrings are 1→2 and 7→8. The residual substrings are 3→4 and 5→6 in sequences S a and S b, respectively. In the current version of the approach [37, 39], given the sample of routings for products produced in the facility, the common substrings between all pairs of routings are first extracted. Next, the frequency of global occurrence of each common substring in the routings of all products produced in the facility is computed. This is followed by an aggregation step where similar substrings are aggregated and each cluster of substrings becomes a layout module. This is followed by a disaggregation step where certain modules are eliminated because they do not fulfill a minimum machine utilization criterion or constraints on machine allocation and duplication among multiple modules. Figure 5 shows the typical result expected from this approach – a facility layout that is a network of dissimilar modules, in this case, a Cell Module (M2), two Patterned Flow Modules (M1, M4) a Flowline Module (M3) and a Functional Layout Module (Machine #2) [37].
4.2.4 Research Challenges
Several important research problems need to be solved: (1) Having identified all common substrings, it will be required to aggregate several of those substrings into a single module to minimize machine duplication costs. A measure of dissimilarity and a threshold value for aggregating similar substrings need to be developed. This is related to the problem of determining the optimal number of modules in the final layout. One idea is to develop measures of connectivity and transitivity of the directed graph
obtained by aggregating a set of common substrings. (2) Feasibility criteria need to be established for allocating machines to several modules subject to machine availability and minimum machine utilization criteria. An iterative loop needs to be incorporated in the design approach to absorb any module that is rejected based on either or both of these criteria. (3) The current approach treats each residual substring as a sequence of operations performed on machines located in process departments. It seems logical to cluster these substrings and aggregate their machines into cell modules based on user-defined thresholds for string clustering. (4) The performance of this new layout will have to be compared with those of Flowline, Cellular and Functional layouts generated for the same facility. An important part of this activity will be the computation of all costs associated with a facility layout, such as WIP, material handling, queuing delays, setups, and processing efficiency.
M1
Inter-module flow or flow between a module and an individual machine
Intra-module flow
Figure 6 - Facility Layout as a Network of Layout Modules
4.3 Reconfigurable Layouts
Our third focus is on design of reconfigurable layouts. Here, we consider the case where resources can be easily moved around so that frequent relocation of departments is feasible. This is motivated by the fact that in many industries (e.g., consumer electronics, home appliances, garment manufacturing, etc), fabrication and assembly workstations are light and can be easily relocated [20, 53]. In fact, even in the metal cutting industry, recent advances in materials and processing technology are making it easier for manufacturing facilities to be configured and reconfigured on a more frequent basis. For example, many discrete manufactured components are made of composite materials that are light in weight and have
much better mechanical properties (e.g., vibration absorption properties). Aluminum composites, for instance, can now replace cast iron parts [24] and, phenolics are replacing aluminum parts, among others [2]. Newer processing technologies such as electron beam hardening, molecular nano-technology and laser cutting is resulting in lighter weight machining equipment [3]. Permanent magnetic chucks that carry their own energy source, that do not obstruct machining, and do not magnetize the cutting tool are also being developed [17]. With these developments in materials and processing technology, we are moving towards processing technologies which employ light weight machine tools and can process light weight parts. It is possible to envision facilities where these light weight equipment are mounted on wheels and are easily moved along suitably designed tracks embedded in the shop-floor [28 and 42]. As a result, it may not be too far fetched to say that the layout will be changed several times a year. In fact, through a workshop and a delphi survey, the committee on Visionary Manufacturing Challenges for 2020 [60] has identified adaptable processes and equipment and reconfiguration of manufacturing operations as two key enabling technologies that will help companies overcome two of the six grand challenges or fundamental goals to remain productive and profitable in the year 2020. These grand challenges are to “achieve concurrency in all operations” and to “reconfigure manufacturing enterprises rapidly in response to changing needs and opportunities”.
When frequent relayout is feasible, the layout design problem can be significantly simplified even when product demand and product mix are highly variable. It becomes possible to focus only on the immediate product mix and the immediate production volumes. However, since we would typically incur (1) some loss in production capacity during the relocation process, and (2) a relocation cost associated with the physical movement of resources (e.g., labor cost, dismantling and reconstruction costs, rewiring costs, and startup/setup costs), we must account for these costs when deciding whether it is beneficial to remove a resource or leave in its current location. A general design and planning framework for carrying out this process is shown in Figure 7. A model and solution approach for this problem is provided in [42]. The objective function of the model consists of two terms: a material handling cost term and a relocation cost term. The magnitude of the relocation costs determine whether a relayout is carried out or not. In the extreme, where relayout costs are insignificant, an entirely new layout can be generated during each period. On the other hand, if relayout costs are prohibitive, the existing layout would be retained. In practice, the two extreme scenarios would be unlikely. Instead it would be desirable to relocate some of the resources during each period. The layout would then evolve gradually over time as flow patterns evolve. The cost of relayout could be reduced if investments in infrastructure that facilitates relayout are made during the initial design of the factory. For example, the facility may be designed so that it has embedded tracks that help decrease the cost of moving equipment. It may also be possible to design all interface devices for control systems so that they are interchangeable and open. In such a case, “plug and
play” features may be implemented at the workstation level. Support services such as compressed gas, water or coolant lines, and waste disposal may have to be suitably designed for the concept.
A primary advantage of reconfiguring a layout when warranted by changes in product mix and volume is that material handling cost can be minimized because equipment can be reconfigured to suit the new production mix and volume. Of course, this cost must more than offset the cost of moving equipment from its current location to a new one. In addition, due to the short term life of a given layout and production data availability for this time period, it is possible to consider optimizing operational performance measures such as minimizing part cycle times, work in process inventory, or throughput.
The potential to frequently alter layouts, therefore, transforms the modern layout problem from a strategic problem in which only long term material handling costs are considered to a tactical problem in which operational performance measures such as reduction of product flow times, work in process inventories, and maximizing throughput rate are considered in addition to material handling and machine relocation costs when changing from one layout configuration to the next. A framework for the reconfigurable layout problem as well as methods for estimating performance measures of such a layout are provided in [29] and [30], respectively. A method for designing layouts with operational performance in mind is given in the next section.
Figure 7 - Reconfigurable Facility Layout Methodology
1）统一原则 在布局设计与改善时，必须将各工序的人、机、料、法四要素有机结合起来并保持充分的平衡。因为，四要素一旦没有统一协调好，作业容易割裂，会延长停滞时间，增加物料搬运的次数。 2）最短距离原则 在布局设计与改善时，必须要遵循移动距离、移动时间最小化，前提是保障合理的作业空间。因为移动距离越短，物料搬运所花费的费用和时间就越小。 3）人流、物流畅通原则 在进行设计与改善时，必须使物流畅通无阻。在设计时应注意：尽量避免倒流和交叉现象，否则会导致一系列意想不到的后果，如品质问题、管理难度问题、生产效率问题、安全问题等。 4）充分利用立体空间原则 随着地价的不断攀升，企业厂房投资成本也水涨船高，因此，如何充分利用立体空间就变得尤其重要，它直接影响到产品直接成本的高低。 5）安全满意原则 在进行设计与改善时，必须确保作业人员的作业既安全又轻松，因为只有这样才能减轻作业疲劳度。切记：过度材料的移动、旋转动作等可能会产生安全事故，每次抬升、卸下货物动作等也可能会产生安全事故。6）灵活机动原则 在进行设计与改善时，应尽可能做到适应变化、随机应变，如面对工序的增减、产能的增减能灵活对应。为了能达成灵活机动原则，在设计时需要将水、电、气集中统一布局，采用自上而下的接入方式，最大限度保障现场整洁，并保障未来现场变化的灵活性。设备尽量不固定基础而采用方便移动的装置。 7）经济产量及生产线平衡原则： 未达到一定的经济产量，布置一条流水线将造成资金浪费。各工序要平衡，按工时和节拍定员分工，达到连续流水作业。 8）舒适原则： 照明、通风、气温应适度，噪音、热气、制造粉尘、震动应隔离。 9）空间优化原则： 库存空间最小化，最大限度减少原材料和成品空间。最大限度地加快作业周转，快速连续移动，制程中仅存放合理数量的在制品。
新工厂的精益布局方式 与传统的“以资产为核心”的布局方式截然相反，精益工厂布局从顾客开始,然后围绕作业员工来设计工序流动。资产为核心的布局方式先从设备、工装开始,最后再考虑工序的流动。 精益布局的目标是为了使作业流程中的浪费和过载最小化，同时增强现场的目视沟通。以下是评估和设计精益工厂布局的常用工具:?价值流图分析–观察整体的材料和信息流动,理解其内在的关联。?办公布局"意大利面图"–画出员工移动的路线来识别无效的移动。 关系矩阵–识别流程和资源之间的内部顾客和供应商关系从而确定适当的位置。 物流评估–理解在不同顾客需求下工厂中的材料和人力的交通繁忙度。 生产准备流程(３P)–重新评估现存的生产设备，或按照精益流程原则设计新的生产线。??新工厂的布局，应该减少或消除产品和物料的大量搬动时间。此外，若把供料(组装)工序,具体放置在生产线的物料被消耗在下游产品上的装配点上,将会减少搬动和等候时间。 应该为新企业进行一种纸上模拟仿真,将工厂布置成精益结构。用创制一张大纸布局的办法,来开始资源的布局,表明了新精益生产线的所占面积。让团队参与时,所用纸张越大越好。标记出所有不能搬动的物体(“界标”)，如电源地沟、房间立柱、排水沟、大型的搬动费用昂贵的设备和其他永久性的建筑结构等。对于每一种确定的资源装备，按照图纸设定比例,准备好同比例尺寸的纸样。 从最靠近顾客的点(通常是发货点)开始向上游工作,把算出的资源装备的纸样,顺着混合产品的流程图进行布局。对可以使搬动量最小，以及使产品从一个工位到另一个工位的流动最佳化的所有想法，要加以检验。对所有用于产品生产的辅助性夹具、支架和小车，都要制作相应比例的纸样,虽然它们本身并不是资源装备。一定要为所需物料的搬动留出容纳的空间，例如自动装卸车的通道和大型的物料集装箱。要把工位设计得紧凑些，但是对操作人员，要尽可能符合人机工程学的要求。 最佳生产线的设计必须不受当前遗留的工作流的限制。头脑里要考虑到“界标”，布局时应该在没有障碍的为理想的生产线准备干净的空地上。最佳的布局可能会建议横穿一条现有的通道。通道并不是一种界标！要牢记OSHA(职业安全与卫生条例）的规定，以及环境与安全性的考虑，最佳精益方案的车间场地布局,应该优先于先前任何主观武断的阻塞性布置。?如果钱不成问题，就可搬动任何东西和每一样东西。作为一个实际问题,有一些物件的搬动，也许是太昂贵而负担不起的。对于某些搬动来说,从投资的回收看来,搬动到新的地方就不能认为是正确的。具体的实例包括：安装在经过特别工程设计的混凝土底座上的重型车床、污秽而危险的工序、需要特殊通风或维护要求的EPＡ（工程实践修正)工序，或吵闹的工序等。应该将实用而价格恰当的办法,推荐给最后的生产线设计，以便使精益生产管理方法的作用达到最大限度,并且显得通情达理。?在结束布局时，所有这些问题都应该考虑到。在完成时,团队的全体成员，都必须同意新的生产线的设计。程序指导委员会还必须对新的精益生产线的布局，给予批准和签字。然后,应该把采用资源装备的纸样所进行的纸张布局,转变为一张正式的企业布置图,这通常要由生产或设备工程师，使用CＡD系统来进行。根据最后的布局,必须制订安装生产线的设备计划。这通常涉及风道的设置和电线的布线、钳工桌的重新搬动，以及和索具公司和其他承包人签合同等。如果新的布局需要生产关停一段时间,也必须对关停和重新布局制订计划。 工场规划布局较为复杂，没有统一的模式，还是要看我们的具体情况，以下思路仅供参考： 1：基本思路（以流水线、或形成一个流生产型态为例子） 1）确定厂房的平面图(尺寸)； 2)产品的工艺流程图、生产线布置图、生产单元布置图、周转库（店铺）布置图、成品库布置图、原料库等（布置图均核算占用面积）； 3)确定车间大物流、分割生产单元位置、确定车间通道及物流方式(笔直流为宜）； 4)按分割好的位置、物流型态，摆放生产单元（一条线一条线的布置）、店铺布置； 5）核算车间能耗(水、电、气、油等),规划设计总桥架,生产线桥架,按桥架要求施工；
工厂布局规划
如何做好工厂及车间的布局规划？ A新建—全新建厂规划 B现成—现成厂房规划 C原地—原地大幅优化 服装企业近几年的趋势是从沿海向内地及国外迁移，原有老厂也按新的生产模式改建。在新建或改建厂房时，如何实现更合理的规划？规划要适应缝制类生产5年~10年的发展，能包含自动流水线、自动设备、物联网等在生产系统的布局，符合工业4.0的扩展架构，能适应未来的柔性快速生产。 一、新工厂规划的方法
二、现有厂房升级的方案
总体规划产能—工厂规划总纲，做什么产品，做多大的量，决定着规划规模，是工厂规划永远的第一手需求资料： 1、产品BOM料表—了解产品的构成，从产成品向前梳理 垂直整合策略—确认零部件自制的程度，对自制零部件生产需作车间规划，对于外购与外协件需确定仓储需求，而往往自制零件的车间规划比总装还要复杂2、订单批量关系—“品-量”与“批-量”的分析，是大批量生产模式还是小批量定制化的生产，生产模式将不同 2、生产工艺流程—确认生产工艺，规划作业单元 3、工艺环境条件—确定特殊区域环境防护 4、生产作业工时—作为资源测算重要参数，需确认 5、物料包装规范—用于制定物料仓库的存储方式 6、成品包装规范—用于制定成品仓库的存储方式 7、辅助配套需求—用于配套设施规划需求收集 8、优化建议汇集—客户自身提出的当前困惑、存在问题、规划建议或在期望在新厂中实现的想法。
三、工厂规划功能区划Step1-整体规划原则Step2-资源配置测算Step3-人车物流动线Step4-厂区模块划分Step5-建筑轮廓设定Step6-精益优化方案Step7-立体物流动线Step8-楼层功能定位
新工厂精益布局规划 Pleasure Group Office【T985AB-B866SYT-B182C-BS682T-STT18】
新工厂的精益布局方式 与传统的“以资产为核心”的布局方式截然相反，精益工厂布局从顾客开始，然后围绕作业员工来设计工序流动。资产为核心的布局方式先从设备、工装开始，最后再考虑工序的流动。精益布局的目标是为了使作业流程中的浪费和过载最小化，同时增强现场的目视沟通。以下是评估和设计精益工厂布局的常用工具: 价值流图分析–观察整体的材料和信息流动，理解其内在的关联。办公布局 "意大利面图"–画出员工移动的路线来识别无效的移动。关系矩阵–识别流程和资源之间的内部顾客和供应商关系从而确定适当的位置。物流评估–理解在不同顾客需求下工厂中的材料和人力的交通繁忙度。生产准备流程(3P)–重新评估现存的生产设备，或按照精益流程原则设计新的生产线。新工厂的布局，应该减少或消除产品和物料的大量搬动时间。此外，若把供料(组装)工序，具体放置在生产线的物料被消耗在下游产品上的装配点上，将会减少搬动和等候时间。应该为新企业进行一种纸上模拟仿真，将工厂布置成精益结构。用创制一张大纸布局的办法，来开始资源的布局，表明了新精益生产线的所占面积。让团队参与时，所用纸张越大越好。标记出所有不能搬动的物体(“界标”)，如电源地沟、房间立柱、排水沟、大型的搬动费用昂贵的设备和其他永久性的建筑结构等。对于每一种确定的资源装备，按照图纸设定比例，准备好同比例尺寸的纸样。从最靠近顾客的点(通常是发货点)开始向上游工作，把算出的资源装备的纸样，顺着混合产品的流程图进行布局。对可以使搬动量最小，以及使产品从一个工位到另一个工位的流动最佳化的所有想法，要加以检验。对所有用于产品生产的辅助性夹具、支架和小车，都要制作相应比例的纸样，虽然它们本身并不是资源装备。一定要为所需物料的搬动留出容纳的空间，例如自动装卸车的通道和大型的物料集装箱。要把工位设计得紧凑些，但是对操作人员，要尽可能符合人机工程学的要求。最佳生产线的设计必须不受当前遗留的工作流的限制。头脑里要考虑到“界标”，布局时应该在没有障碍的为理想的生产线准备干净的空地上。最佳的布局可能会建议横穿一条现有的通道。通道并不是一种界标!要牢记OSHA(职业安全与卫生条例)的规定，以及环境与安全性的考虑，最佳精益方案的车间场地布局，应该优先于先前任何主观武断的阻塞性布置。如果钱不成问题，就可搬动任何东西和每一样东西。作为一个实际问题，有一些物件的搬动，也许是太昂贵而负担不起的。对于某些搬动来说，从投资的回收看来，搬动到新的地方就不能认为是正确的。具体的实例包括：安装在经过特别工程设计的混凝土底座上的重型车床、污秽而危险的工序、需要特殊通风或维护要求的EPA(工程实践修正)工序，或吵闹的工序等。应该将实用而价格恰当的办法，推荐给最后的生产线设计，以便使精益生产管理方法的作用达到最大限度，并且显得通情达理。在结束布局时，所有这些问题都应该考虑到。在完成时，团队的全体成员，都必须同意新的生产线的设计。程序指导委员会还必须对新的精益生产线的布局，给予批准和签字。然后，应该把采用资源装备的纸样所进行的纸张布局，转变为一张正式的企业布置图，这通常要由生产或设备工程师，使用CAD 系统来进行。根据最后的布局，必须制订安装生产线的设备计划。这通常涉及风道的设置和电线的布线、钳工桌的重新搬动，以及和索具公司和其他承包人签合同等。如果新的布局需要生产关停一段时间，也必须对关停和重新布局制订计划。 工场规划布局较为复杂，没有统一的模式，还是要看我们的具体情况，以下思路仅供参考： 1：基本思路（以流水线、或形成一个流生产型态为例子） 1）确定厂房的平面图（尺寸）； 2）产品的工艺流程图、生产线布置图、生产单元布置图、周转库（店铺）布置图、成品库布置图、原料库等（布置图均核算占用面积）； 3）确定车间大物流、分割生产单元位置、确定车间通道及物流方式（笔直流为宜）； 4）按分割好的位置、物流型态，摆放生产单元（一条线一条线的布置）、店铺布置； 5）核算车间能耗（水、电、气、油等），规划设计总桥架，生产线桥架，按桥架要求施工； 6）期间还要关注车间排污、排气、车间辅助设施位置等（如计量室、车间现场办公室、设备保全备品室、模具库房、模具保全区域、车间垃圾废料置场等）。
新工厂布局规划之生产平准化的意义优点及实施问题 当一个企业已经做好了一个流的生产，在可能运用单元式生产方式的地方大范围地运用Cell生产线，换模换线已经能在非常短的时间完成，各流程间的生产平稳且平均的时候，企业已经能够应付一般性的多品种、小批量的弹性生产了，或者已经超越大多数的同行了。 但精益生产并不止步于此，它们甚至把降低批量和均衡生产发挥到极致，更高地提出了平准化的生产模式，意即多种产品混合生产，将批量更进一步降低，甚至少到1件。 生产平准化是多品种混合流水生产中的一个概念。平准就是要求生产平稳地、均衡地进行。平准化不仅要达到产量上的均衡，而且还要保证品种、工时和生产负荷的均衡。所以，它实际上是均衡生产的高级阶段。平准化的生产，要尽可能地降低在最终工序中即产品组装生产线上每天的生产量高峰，同时弱化低谷期，使得流程的表面变得和谐。 一、平准化的意义： 1、使最终工序即组装生产线的计划期(例如，一个月)的单位期间(例如一夭)的生产量差异变小。 2、尽可能缩小批量，使各种产品流动生产。 3、最终理想是一个流的生产和搬运。 生产平准化是Kanban生产的前提，不实行平准化生产，kanban生产方式就必 然失败。同样，实施平准化生产，也必须具备一定的前提，否则也注定不能成功和持久。一旦平准化生产得以实施，将给你的生产管理方面带来许多的好处。 二、平准化的前提 1、生产流程整流化 不仅仅要达到一个流的程度，还要尽量保证产品与产品间的投人与产出顺序一致。这样才能保证生产能够按照开始设计的数量和种类一直进行下去而不会混
乱。 2、缩小生产批量 平准化的终极就是要实现混流生产，即在通过各种方式将批量降到很小的情况下，进一步降低批量，甚至几种不同产品同时在一条线生产。所以之前的大幅度降低生产批量的工作，必须完成才能进行到这一步。 3、快速换线实施 混流生产意味着更加频繁地更换产品种类，所以眼前最紧要的就是将换线换模的时间降到最低。 4、质量的保证 中途动不动就因为质量问题而停线或降低生产速度，生产线将无法平稳地组织生产。 5、标准化的作业 标准化作业也是保证稳定化生产的重要前提。 三、平准化的优点 1、可以让零件的使用量安定化 用的物料将按照稳定的速度，稳定的需求比例进行消耗。所以装配线上的零件使用量和从前面流程的零件领取量都将会安定化。 2、可降低制程间在制品和成品库存，体现在三个方面： ①装配需求的安定化，可有效避免制造过剩的浪费。 ②一个流动式的整流生产，可减少产品在生产流程间的滞留。 ③成品及时出货，可减少成品在仓库的滞留。 3、可缩短生产提前期的平均时间，在两个方面： ①因“一个流”或小批量的生产与搬运，每一个或每一批量产品的平均生产提前期可以缩短。 ②对市场小幅的需求变动，其适应能力也会提高。 四、实施平准化的问题点 1、标准化作业及训练的要求 不同产品的作业多少有些不同，很容易有错误的动作和拿错部件的现象。因此，标准化作业很重要。所以严格按照标准化作业是实施平准化的一个基本要求。另外就是事前的训练很重要，新人或不熟练的员工不可以在平准化的线上作业。 2、通用的机器和工具的要求 因为一条线上要做不同的产品，如果机器工具都是专用的，需要换来换去，平准化就无法推动。另外对不同产品作业最好引人应用FMS(柔性生产系统，Flexible Manufacturing System)和GT(成组技术，Group Technology)的生产
一、注意事项 1、合理设计进排风口位置和尺寸 利用室内外空气温度差所形成的热压作用和室外空气流动产生的风压形成自然通风，但风压作用受自然条件限制，具有多变性，无风时即无风压作用，因此不宜作为厂房自然通风的动力考虑。按照有关规定，在热加工厂房自然通风的设计计算中，仅考虑热压作用，风压作用只作为一项补充因素，不参与通风计算。热加工车间在生产过程中，散发大量的余热和灰尘等污浊气体，恶化了厂房内部环境，必须通过有效地组织厂房自然通风，迅速排除余热和污浊气体而改善内环境质量。当厂房高度和生产散热量为一定时，合理协调进、排气口面积，是提高厂房自然通风效果的关键所在。 在厂房自然通风设计中，必须合理协调进、排气口面积，力求进气口面积不小于或大于排气口面积，这应该是提高自然通风效果的极为重要和有效的技术措施。然而。在实际工程设计中，某些热加工主体厂房，由于缺乏精心的合理规划，造成公辅设施建筑和生活福利建筑，把主体厂房围得严严实实、水泄不通，使厂房失去了大片可开设进气口的宝贵位置，而厂房自然通风设计中。又未认真进行研究推敲，只是迁就于既定的建筑设计现状，不管合理与否，消极的拼命加大天窗面积，将天窗高度加大至8m左右，结果导致进气口面积不足排气口面积的1/3，使厂房自然通风模式形成极不合理的状况．虽然为厂房自然通风天窗增加了大量建设投资，却未获取应有的通风效果。 总之，厂房自然通风设计，绝对不能停留在只是根据既定的建筑布局，单纯的通过通风计算来决定天窗开口面积。可以说这只是消极的设计。积极的设计应该是认真地进行分析研究，反复试算、修改。合理协调进、排气口面积，力求以最低的经济投资，获取最佳的通风效果。具体而言，就是在进行厂房自然通风设计时，首先要在满足通风量需要的前提下，力求取得较低的中和面位置，即争取将进风口面积集中开设在下部作业区范围内。也就是说，要尽最大努力将堵靠在厂房侧墙部位的辅助建筑移位，为进风口让出宝贵的下部侧墙面。因某种原因实在搬不走的，则要将其下部架空，为主厂房留取进风口位置。这样做表面上看出也许要多花一些投资，但与提高厂房内环境质量相比是值得的。而且即使在经济上，最终也不见得多花钱。因为如果堵塞了厂房进气口，造成F1和F2的比例失调，中和面位置提高，势必显著降低排气压力而导致天窗面积大增，而抬高天窗所花费的投资，完全有可能高于辅助建筑下部架空的投资。 2、要想法克服进气短流问题 所谓进气短流，系指由进气口进入厂房内的新鲜空气，在未进入作业区范围之前，就已经被加热而上升至天窗排气口排出室外的现象。显而易见，这样的进
如何做好工厂及车间的布局规划？ A新建—全新建厂规划 B现成—现成厂房规划 C原地—原地大幅优化 服装企业近几年的趋势是从沿海向内地及国外迁移，原有老厂也按新的生产模式改建。在新建或改建厂房时，如何实现更合理的规划？规划要适应缝制类生产5年~10年的发展，能包含自动流水线、自动设备、物联网等在生产系统的布局，符合工业4.0的扩展架构，能适应未来的柔性快速生产。 一、新工厂规划的方法
二、现有厂房升级的方案 总体规划产能—工厂规划总纲，做什么产品，做多大的量，决定着规划规模，是工厂规划永远的第一手需求资料： 1、产品BOM料表—了解产品的构成，从产成品向前梳理 垂直整合策略—确认零部件自制的程度，对自制零部件生产需作车间规划，对于外购与外协件需确定仓储需求，而往往自制零件的车间规划比总装还要复杂 2、订单批量关系—“品-量”与“批-量”的分析，是大批量生产模式还是小批量定制化的生产，生产模式将不同 2、生产工艺流程—确认生产工艺，规划作业单元 3、工艺环境条件—确定特殊区域环境防护 4、生产作业工时—作为资源测算重要参数，需确认 5、物料包装规范—用于制定物料仓库的存储方式 6、成品包装规范—用于制定成品仓库的存储方式 7、辅助配套需求—用于配套设施规划需求收集 8、优化建议汇集—客户自身提出的当前困惑、存在问题、规划建议或在期望在新厂中实现的想法。
三、工厂规划功能区划Step1-整体规划原则 Step2-资源配置测算 Step3-人车物流动线 Step4-厂区模块划分 Step5-建筑轮廓设定 Step6-精益优化方案 Step7-立体物流动线 Step8-楼层功能定位 Step9-建筑参数测算 Step10-参观路径规划 Step11-整体区划方案 Step12-建筑设计需求
新工厂启动会议前需要做的准备工作： A.经理 1.工厂定点 2.找好土地租赁方并谈好租金 3.找好厂房设计单位 B.AME 1.了解新工厂所处大致地理位置（具体的省市区）及与客户距离（若有多个客户需综合 评估） 2.根据客户PPAP节点来确定新工厂进度时间节点（包括启动时间、工厂基建平面出图 时间、设计公司完成基建设计时间、土建施工封顶时间、工厂内部装修时间、工艺布局时间、新工厂进驻时间、产品试制及新员工培训时间、客户PPAP时间） 3.了解新工厂投产后的定点项目（包括新项目和搬迁项目）及客户供货要求（不同项目 的产量、生产班次、计划生产节拍及供货形式） 4.考虑新建工厂的供应商供货形式（JIT/库存，是否采用milk round形式）及当地物料 摆放区域（工厂内预留物料区/工厂外租用物料仓库） 5.如工厂在北方，冬天需要考虑采暖（采暖只有舒适性要求，没有工艺性要求），需了 解当地供蒸汽/热水，采暖费用如何计算 新工厂启动会议 （小组成员：新工厂启动经理、总装工程经理、发泡设备经理、基建科、动力科） 1.介绍新工厂情况 2.各小组成员明确各自职责，并广泛提出厂房基建方面的意见和建议 3.注意收集各方面的意见来为平面布局做参考 新工厂启动会议后进行平面布局 （画图注意：应先按直角坐标系画图，待图完成后按实际厂区位置将图转一定角度即可） 1.根据厂区面积确定厂房面积（总装8000－10000m2，发泡3000－4000m2），立柱间距 为横向间距9m，纵向间距21m 2.规划厂区物流方向（进料车从何处进，卸货后从哪个门出/出货车从何处进，装货后从 哪个门出/私车从何处进出，停车区域离办公室远近，停车是否方便），一般工厂正门设在西门（如有需要可以另设出货门），另外需要一个非机动车通行小门，门卫设在进出公司的机动车驾驶座侧，大门宽为12－15m，小门宽为4m（关于门的设置，一般两个门不要太近，否则车辆易形成滞留，设置两个门的效果不明显；门卫一般不设置在门中间，因为正门一般正对厂房脊柱，门卫设在中间会影响办公楼的视线） 3.此时需考虑如有座椅发运线，其位置的设定对物流方向和厂区大门开设位置的影响 4.如果新工厂在北方，厂房中铲车行进如是直线，热工损耗会很大，且冬天卷帘门对流 使线上工人热量流失情况严重 5.将所有的Dock位置确定，物料卷帘门应设在Dock卸货区旁，方便铲车作业（在冬天 积雪严重的北方，为防止Dock内积雪，Dock雨棚应至少设在离Dock5m处，但雨棚过长也会立柱的关系影响物流车倒入Dock的空间） 6.工厂内部布局总装厂和发泡厂各自进行布局，最后汇总 7.道路宽一般为5－6m，厂房雨棚宽一般为11m（雨棚形式从美观上而言，Y形较好， 排水也较方便，飞翼车高度也不会对厂房高度产生影响，但如果厂房在冬天积雪严重的北方，Y形雨棚会对顶棚承载能力要求过高，因此建议在北方使用与屋顶同一坡度
目录 一、设计题目：减速器工厂的规划设计 (1) 1、设计目的 (1) 2、设计内容 (1) 3、设计步骤 (16) 4、设计报告 (16) 二、减速器工厂的规划设计 (18) 1、需要采集的信息 (18) 2、加工设备数量 (26) 3、工厂布局和车间布局 (37) 4、工厂布局 (40) 5、物流分析 (43) 6、工厂每年的运营成本 (45) 三、课程设计心得体会 (46)
一、设计题目：减速器工厂的规划设计 1、设计目的 1）掌握设计制造工厂设施布局的方法。 2）掌握运用设施布局规划设计相关软件进行工厂布局设计的方法。 3）了解工厂搬运设施和设备以及相关参数的选择方法。 2、设计内容 利用后面给出的产品、成本、加工设备尺寸和工艺卡片等数据，设计和评估发动机减速器制造加工厂。设计时，需要考虑所有可能的因素，如布局图、机器数量、工人数量、物料搬运的方法和设备、缓存区大小、运作策略和公司未来增长情况（公司预计在未来 3 年每年增长 8%）。减速器加工厂的项目数据如下。 1）产品 发动机减速器是工业上用于减少电机转速、提高扭矩的设备。减速器通常由轴承、齿轮组和箱体构 成，图 1 和图 2 分别为二级减速器和三级减速器的产品结构图。减速器物料清单如图3 所示。 低速 轴 低 速齿 轮 高 速小齿 轮 第二 中速小齿 轮 第二 中速齿 轮 高速小齿 轮
入 高 速 齿 低 速 轴 输 入 高 速 齿 轮 第 一 中 速 小 齿 轮 第 一 中 速 轴 低 速 齿 轮 第 一 中 速 小 齿 轮 一 中 速 轴 图 1 二级减速器结构图 图 2 三级减速器结构图
本文部分内容来自网络整理，本司不为其真实性负责，如有异议或侵权请及时联系，本司将立即删除！ == 本文为word格式，下载后可方便编辑和修改！ == 新工厂筹建方案 篇一：新工厂筹建过程归纳 新工厂启动会议前需要做的准备工作： A. 经理 1. 工厂定点 2. 找好土地租赁方并谈好租金 3. 找好厂房设计单位 B. AME 1. 了解新工厂所处大致地理位置（具体的省市区）及与客户距离（若有多个客户需综合评估） 2. 根据客户PPAP节点来确定新工厂进度时间节点（包括启动时间、工厂基建平面出图时间、设计公司完成基建设计时间、土建施工封顶时间、工厂内部装修时间、工艺布局时间、新工厂进驻时间、产品试制及新员工培训时间、客户PPAP时间） 3. 了解新工厂投产后的定点项目（包括新项目和搬迁项目）及客户供货要求（不同项目的产量、生产班次、计划生产节拍及供货形式） 4. 考虑新建工厂的供应商供货形式（JIT/库存，是否采用milk round形式）及当地物料摆放区域（工厂内预留物料区/工厂外租用物料仓库） 5. 如工厂在北方，冬天需要考虑采暖（采暖只有舒适性要求，没有工艺性要求），需了解当地供蒸汽/热水，采暖费用如何计算 新工厂启动会议 （小组成员：新工厂启动经理、总装工程经理、发泡设备经理、基建科、动力科）
1. 介绍新工厂情况 2. 各小组成员明确各自职责，并广泛提出厂房基建方面的意见和建议 3. 注意收集各方面的意见来为平面布局做参考 新工厂启动会议后进行平面布局 （画图注意：应先按直角坐标系画图，待图完成后按实际厂区位置将图转一定 角度即可） 1. 根据厂区面积确定厂房面积（总装8000－10000m2，发泡3000－4000m2），立柱间距为横向间距9m，纵向间距21m 2. 规划厂区物流方向（进料车从何处进，卸货后从哪个门出/出货车从何处进，装货后从哪个门出/私车从何处进出，停车区域离办公室远近，停车是否方便），一般工厂正门设在西门（如有需要可以另设出货门），另外需要一个非机动车 通行小门，门卫设在进出公司的机动车驾驶座侧，大门宽为12－15m，小门宽 为4m（关于门的设置，一般两个门不要太近，否则车辆易形成滞留，设置两个 门的效果不明显；门卫一般不设置在门中间，因为正门一般正对厂房脊柱，门 卫设在中间会影响办公楼的视线） 3. 此时需考虑如有座椅发运线，其位置的设定对物流方向和厂区大门开设位置的影响 4. 如果新工厂在北方，厂房中铲车行进如是直线，热工损耗会很大，且冬天卷帘门对流使线上工人热量流失情况严重 5. 将所有的Dock位置确定，物料卷帘门应设在Dock卸货区旁，方便铲车作业（在冬天积雪严重的北方，为防止Dock内积雪，Dock雨棚应至少设在离 Dock5m处，但雨棚过长也会立柱的关系影响物流车倒入Dock的空间） 6. 工厂内部布局总装厂和发泡厂各自进行布局，最后汇总 7. 道路宽一般为5－6m，厂房雨棚宽一般为11m（雨棚形式从美观上而言，Y 形较好，排水也较方便，飞翼车高度也不会对厂房高度产生影响，但如果厂房 在冬天积雪严重的北方，Y形雨棚会对顶棚承载能力要求过高，因此建议在北 方使用与屋顶同一坡度 的雨棚，且为了屋顶除雪排水，屋顶坡度应由南方的1/20加大到北方的1/18 －1/15），由于集卡较多，还应考虑集卡在厂区内转弯时留有足够的转向空间 8. 确定办公室建一层还是二层（办公室一般层高为4m），相应的厂房高度与 办公室高度一致，否则易导致厂房整体不正气。厂房高度一般由发泡工厂高度 确定，铲车作业高度为4m，悬挂链为2m，消防喷淋为1m（檐口高度高于7m时 要两层消防喷淋），按此高度计算，厂房檐口高度最低为7m（目前康花基地檐
新工厂布局规划的十大经济性原则 原则１：相邻原则 物料、在制品、产品和人员流动较多的部门或设备应该相邻，如下图所示。 原则2：充分利用立体空间 一般厂房的高度均有4~6米，空间没有被充分利用的浪费现象很普遍。一般仓库放置物料的堆积高度为1.6～1.8米，对于原材料、零件等物料的放置，可考虑取用货架或是增加夹层（阁楼）来活用立体空间，使用立体空间放物品时，必须要符合易拿易放的原则。采用货架放置物料时，一般要配置有高度升降功能的电动叉车才可以完成。 原则3：统一原则 把4M（人man、机械machine、材料material、作业方法method）有机统一起来，保持4M平衡的基础上，进行生产布局的设计，减少因没有综合考虑4M四要素，造成的局限性（瓶颈）影响生产，延长停滞时间。 统一原则就是要统一布局的理念，就是要激活生产过程的物流，缩短产品的制造周期，降低成本。在布局中理念重于技术，理念必须先行。所以，统一原则是布局最重要的原则之一。 原则4：最短距离原则 加工不同的产品，加工流程也不同。产品在发生产量、品种的变化时，加工流程也会发生变化。这时上下工序或工程之间的衔接要考虑人、机械、材料的移动距离最短。 搬运距离短时，所花费的时间最短，搬运的费用最少，工序或工程之间的信息也能及时反馈。 移动距离过长时，搬运时间长、费用高，工序之间的信息不能及时反馈，会造成中间在
制品过多，容易产生不良品，造成不必要制造成本的浪费。 原则5：物流顺畅原则 为了减少制造成本,要使人、机械、材料的流动更加合理、更加顺畅。要让生产过程没有阻碍，必须减少作业的交叉、逆流或倒流的情况，禁止发生物流停顿。要在布局设计时，就要使物流像流水从高处向低处流去那样顺畅。 原则只表示基本的想法，实际中应仔细考虑生产批量的大小，产品的换型，用地面积的限制等，把直线式、L字形、U字形、S字形、星形、环形等进行比较分析，再做出综合判断来设计生产线的布局。 原则6：减少存货原则 1、减少生产过程中的存货，因为工作单元的建立能够平衡设备之间的产品流量。 2、减少原材料和制成品的存货，因为生产过程中工作的减少使得工作单元材料的运动更加迅速。 3、提高了设备和机器的利用率，因为有更合理的安排和更迅速的材料流动。 4、减少了在设备和机器上的投资，因为有效利用机器减少了需要的机器和设备，也降低了安装费用。 原则7：便于信息的流动原则 沟通对于任何一个公司都是很重要的，所以布局必须有助于沟通的改善。这不仅要求对开放的空间、半高的隔离间和私人办公室做出规定，也要求对距离作出规定。 原则8：安全满意原则 要使作业员在减轻疲劳、轻松作业的同时，还必须考虑噪音、粉尘、烟雾、温度、光照度等方面的安全要求，以及设备和机器周围的空间要求。
新工厂精益布局规划
新工厂的精益布局方式 与传统的“以资产为核心”的布局方式截然相反，精益工厂布局从顾客开始，然后围绕作业员工来设计工序流动。资产为核心的布局方式先从设备、工装开始，最后再考虑工序的流动。 精益布局的目标是为了使作业流程中的浪费和过载最小化，同时增强现场的目视沟通。以下是评估和设计精益工厂布局的常用工具: 价值流图分析–观察整体的材料和信息流动，理解其内在的关联。 办公布局 "意大利面图"–画出员工移动的路线来识别无效的移动。 关系矩阵–识别流程和资源之间的内部顾客和供应商关系从而确定适当的位置。 物流评估–理解在不同顾客需求下工厂中的材料和人力的交通繁忙度。 生产准备流程(3P)–重新评估现存的生产设备，或按照精益流程原则设计新的生产线。 新工厂的布局，应该减少或消除产品和物料的大量搬动时间。此外，若把供料(组装)工序，具体放置在生产线的物料被消耗在下游产品上的装配
点上，将会减少搬动和等候时间。 应该为新企业进行一种纸上模拟仿真，将工厂布置成精益结构。用创制一张大纸布局的办法，来开始资源的布局，表明了新精益生产线的所占面积。让团队参与时，所用纸张越大越好。标记出所有不能搬动的物体(“界标”)，如电源地沟、房间立柱、排水沟、大型的搬动费用昂贵的设备和其他永久性的建筑结构等。对于每一种确定的资源装备，按照图纸设定比例，准备好同比例尺寸的纸样。 从最靠近顾客的点(通常是发货点)开始向上游工作，把算出的资源装备的纸样，顺着混合产品的流程图进行布局。对可以使搬动量最小，以及使产品从一个工位到另一个工位的流动最佳化的所有想法，要加以检验。对所有用于产品生产的辅助性夹具、支架和小车，都要制作相应比例的纸样，虽然它们本身并不是资源装备。一定要为所需物料的搬动留出容纳的空间，例如自动装卸车的通道和大型的物料集装箱。要把工位设计得紧凑些，但是对操作人员，要尽可能符合人机工程学的要求。 最佳生产线的设计必须不受当前遗留的工作流的限制。头脑里要考虑到“界标”，布局时应该在没有障碍的为理想的生产线准备干净的空地上。
新厂筹建项目计划书 一、项目意义 为配合公司业务拓展建设，促进集团公司建设新型现代化标准化厂房及扩大产能，提高集团公司营运效率，改善集团公司经营环境，实现集团公司安全生产，促进集团公司和谐发展，走出集团公司现有厂区各方面约束，全方位提升集团公司品位，加速实现大型集团公司的目标。 二、主要目标 2012至2013年，完成500亩标准厂房建设，其中：2012年完成亩，2013年完成亩，成为产业特色明显、规模效益显著、资源利用高效的新型现代化标准厂房示范单位。 三、基本原则 1、合理规划。新厂房建设项目应符合市政总体规划和土地利用总体规划，布局选址原则上在奉贤经济开发区内，符合环境保护和安全生产要求。新厂房的建设规模在占地亩以上，一次规划建成。 2、集约高效。按照节约、集约用地的要求，建设企业新型现代化标准厂房。 3、产品集聚。亲厂房的建设应与公司主导产品发展相衔接，努力把标准厂房建设成同类产品集聚、向产品自动生产线前后道工序延伸拓展的生产基地，有助于推动产品结构优化升级。 4、功能配套。标准厂区内的道路、电力、通讯、给排水及污水处理等基础配套设施必须满足集团公司生产经营的基本需要，要配套建造集团公司职工生活、文化娱乐等附属设施，为集团公司长远发展创造良好环境。 四、政策措施 1、用地保障。为保证新厂房建设项目用地，集团公司将动用各种资源，确保在奉贤区政府每年的工业用地指标中申请亩工业用地，用于新厂房建设。 2、建设方式。集团公司将以招标形式引进专业合格资质建筑企业建设新厂区。 3、建设管理。集团公司成立新厂区筹建领导小组，任组长，任副组长，筹建领导小组下设筹建办公室，全面负责新厂区筹建的各项具体工作。包括对外与政府相关部门对口业务的申报、与建筑单位的协调，以及对内与集团公司各子公司和职能部门沟通。筹备小组的工作期限可以持续到老厂全部搬迁到新厂，新厂全面正常营运之后。在新厂区建设期间，集团公司各子公司及相关部门对筹建办公室工作应予积极支持和配合。办公室负责人与全体工作人员必须分工明确，各司其职，加强沟通，密切配合，建立一月一考核、半年一督查、年度一小考、三年一大考的工作考核机制，切实推进新厂区建设工作顺利完成。 五、项目概况 1、项目名称：集团新厂筹建项目计划书 2、项目介绍
新工厂精益布局规划方法【详细版】 一、工厂总体布局规划 工厂总体布局规划是制造业工厂设施规划的核心，也是物流系统规划的重要环节。除了传统的经验做法外，主要有摆样法、数学模型法、图解法、系统布置设计SLP法等。 1、摆样法 该方法是最早的布局方法，它利用二维平面比例模拟方法，按一定比例制成的样片在同一比例的平面图上表示设施系统的组成、设施、设备或活动，通过相互关系分析，调整样片位置可得到较好的布置方案。该方法适用于较简单的布局设计，对复杂的系统就不能十分准确，而且花费时间较多。 2、数学模型法 该方法运用运筹学、系统工程中模型优化技术(如线性规划、随机规划、多目标规划、运输问题等)研究最优布局方案，提供系统布置的精确性。但是数学模型的求解困难，但可以利用计算机的强大功能，帮助人们解决设施布置的复杂任务。 3、图解法 该方法产生于20世纪50年代，有螺旋规划、简化布置规划及运输行程图等具体方法。
其优点在于将摆样法和数学模型法结合起来应用，但现在实践中应用得较少。 4、系统化布置设计SLP法 该方法是最具代表性的布局方法，它使工厂布置设计从定性阶段发展到定量阶段，是当前布局设计的主流方法。 二、系统化布置设计SLP法 系统化布置设计(Systematic Layout Planning，SLP)是典型的分析设计法，是美国人R.缪瑟(R.Muther)在总结了大量工厂布置设计经验的基础上，提供的一种以作业单位物流与非物流的相互关系分析为主线的设施规划方法。SLP法的程序布置具体程序如下: 1、准备原始资料 主要是通过充分讨论和研究，对作业单位进行合理的划分。 2、物流分析与作业单位相互关系分析 针对物流中心、配送中心，物流分析是布置设计中最重要的方面。作业单位间的物流分析的结果，可以用物流强度等级及物流相关表来表示;作业单位非物流的相互关系可以用量化的关系密级及相互关系来表示。在需要综合考虑作业单位间物流与非物流的相互关系时，可以采用简单加权的方法将物流相关表及作业单位间相互关系表综合成综合相互关系表。 3、绘制物流—作业单位相互关系图 在分析了物流—活动范围相互关系后，根据物流相关表与作业单位相互关系表，将活动范围和工序展示在线图上，得出各作业单位之间的相对位置关系，也称拓扑关系，称为活动范围关联线图。 4、作业单位占地面积计算 在实际的设施布置设计过程中，受到现有厂房或可利用土地面积与形状的限制等，而不得不把需要的面积与可利用的面积结合起来权衡考虑。