Thomas Ditlev Brunoe

Kjeld Nielsen

Kaj A Joergensen

Stig B Taps

Metrics for Assessing Product Variety Utilization

Keywords: Mass Customization, Assessment, Variety, KPI, Metrics

In mass customization, offering the right variety is critical and it is therefore proposed to develop an assessment system for the capabilities critical for mass customization success. This paper proposes different metrics and methods for assessing the utilization of a company's product variety. Two different methods are selected for further testing which is done on historical data for three different product families from three different companies. It is concluded that different metrics and methods may be relevant for different products, reflecting variety and complexity. However, in general, monitoring utilization of variety has potential to improve business for mass customization companies.

Introduction

In any company, it is essential to offer products, which match the needs and desires of customers to achieve sales and profit. This is true for mass producers as well as mass customizers; however, in mass customization this issue is somewhat more complex than mass production due to a much higher variety and a more complex product structure. As pointed out by Salvador et al. [START_REF] Salvador | Cracking the Code of Mass Customization[END_REF], mass customizers need three fundamental capabilities to be successful: 1) Solution Space Development -Identifying the attributes along which customer needs diverge, 2) Robust Process Design -Reusing or recombining existing organizational and value chain resources to fulfill a stream of differentiated customer needs and 3) Choice Navigation -Supporting customers in identifying their own solutions while minimizing complexity and the burden of choice [START_REF] Lyons | Mass Customisation: A Strategy for Customer-Centric Enterprises[END_REF], [START_REF] Salvador | Cracking the Code of Mass Customization[END_REF]. In order for companies to be able to establish themselves as mass customizers or for existing mass customizers to improve performance, we proposed that an assessment system for Mass Customization performance is established [START_REF] Nielsen | Mass Customization Measurements Metrics[END_REF], [START_REF] Nielsen | Mass Customisation Assessment and Measurement Framework[END_REF].

An essential element in mass customization is product variety. Variety is what differentiates mass customization from other business strategies, however the variety offered to the customer, the solution space, must be carefully designed, since a too high variety will imply higher costs and a too low variety will imply lost sales, since customers will not be able to buy the product matching their individual requirements [START_REF] Pine | Mass customization: The new frontier in business competition[END_REF]. Hence when developing an assessment system for MC, metrics addressing the size of the solution space and whether this size fits with the demand for variety is essential, since variety is a major driver for both cost and revenue. A number of metrics for this purpose have been proposed all related to solution space development [START_REF] Brunoe | Solution Space Assessment for Mass Customization[END_REF]. These metrics however cover a broad area of capabilities related to solution space development, including the solution space at a certain point in time as well as dynamics of solution space development.

The metric "Used variety" (UV) introduced by Piller [START_REF] Piller | Logistische Kennzahlen Und Einflussgroessen Zur Performance-Bewertung Der Mass-Customization-Systeme Von Selve Und Adidas[END_REF], referenced from [START_REF] Blecker | Key Metrics System for Variety Steering in Mass Customization[END_REF] addresses how well the solution space is utilized by the customers, i.e. how much variety is offered vs. how much does actually make sense compared to the customers' requirements. The metric is calculated as indicated in the formula below:

𝑈𝑉 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑣𝑎𝑟𝑖𝑎𝑛𝑡𝑠 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑣𝑎𝑟𝑖𝑎𝑛𝑡𝑠 (1)
Using this metric may be difficult in practice, since the number of perceived variants is not readily available, as well as the number of all possible variants may be difficult to determine for various reasons, which will be addressed below.

The research question of this paper is: How can the utilization of a mass customizer's solution space be measured? The research question will be addressed with a basis on the "Used Variety" metric presented above, however due to certain practical issues in implementing this metric directly, further considerations need to go into defining the metric more specifically, which is done applying the method outlined below. The reasons for assessing the solution space include enabling mass customizers to optimize their solution space as well as their configuration systems, which both influence the performance in relation to solution space utilization.

Method

To answer the research question, a number of sub questions will need to be answered. In order to do this, the following methodical approach has been applied:

1. Clarify the purpose of the metric and based on this evaluate the feasibility of the originally proposed form of the metric. 2. Identify variables, which should be used to define and calculate the metric. 3. Evaluate the availability of data necessary for calculating a value for the metric. 4. Based on findings above, propose alternative methods to calculate the metric. 5. Verification of feasibility of calculating the metric by applying the proposed methods on different data sets containing actual historic product data. 6. Evaluate results from verification and identify the most suitable method in relation to the purpose identified in step 1.

Purpose and evaluation of existing metric

The basic purpose of the metric UV is to evaluate the utilization of a company's solution space. The reason to do so is that variety comes at a cost, and variety, which is not being sold, does not generate income and may imply economic loss. The reasons for variety not being utilized can be very different but relating to the fundamental capabilities of MC, we expect low utilization to be due to either lack in capability for solution space development or choice navigation. Low utilization due to problems within solution space development will imply that a company has developed variety, which is simply not demanded by customers, i.e. development of the wrong products. On the other hand, utilization issues due to poor choice navigation imply that the company may have exactly the right variety, but is not able to guide the customer towards buying the product that matches the specific requirements. Either way, the result is that companies have made investments in introducing variety, which is not paying off.

Hence the metric should be able to assess to what extent the variety offered to customers is actually being utilized, i.e. sold, in order to enable companies to react to low utilization. The metric should be applicable as a KPI and should thus be able to track utilization over time. In order to track performance over time, the KPI should be calculated on a regular basis and therefore it should be possible to calculate based on readily available data from the company's IT systems and not require expensive market surveys or qualitative assessments from the company.

The way of assessing the utilization of variety proposed by Piller [START_REF] Piller | Logistische Kennzahlen Und Einflussgroessen Zur Performance-Bewertung Der Mass-Customization-Systeme Von Selve Und Adidas[END_REF] using the metric Used Variety implies identifying the number of perceived variants and dividing this by the number of all possible variants. In general, assessing variety based on the measure "number of variants" is intuitively an obvious choice since this is based on a simple count. However, in many cases this will be impractical. One of the reasons for this is that in mass customization, the number of theoretical variants easily becomes astronomical. For example, when configuring a Mini Cooper online the configuration choices presented to the customer will result in a number of possible variants well above a 20-digit figure. Another issue that may arise when determining the number of possible variants is how to count continuous configuration variables, e.g. the height and width of a window, which can be any value within a predefined interval or custom printed Tshirts, which can have any image printed on them. In these cases, it makes little sense to determine the number of possible variants, since this figure is literally infinite. This is also the case for products composed by predefined modules or components, which can be combined in various ways to configure a product, where the number of modules included in the configuration is not bound. In this case, the number of possible variants will also literally be infinite. Hence, when determining the value of the UV metric the value will in many cases approach a zero value, as the denominator will be virtually or literally infinite. Furthermore considering an example where a given solution space is expanded with one extra binary option, this will effectively double the number of possible variants (given this option is offered for all products). This again implies that the value of the UV metric will be halved. Considering again the Mini Cooper example, adding an option to include floor mats will double the number of possible variants, thereby halving the value of the UV metric, even though the actual change in product variety would clearly not imply that the utilization of variety is reduced by 50%.

The numerator of the UV metricnumber of perceived variantsmay be determined in a number of different ways. The term "perceived" implies that the number should rely on the perception of the customer, which naturally is difficult to determine quantitatively. However, a reasonable way of measuring number of perceived variants is to assume this is equal to the number of different products (configurations) that have been sold over a period. If assuming this, it will be feasible in many cases to calculate an actual value for this metric, however as indicated above the value may not be a very good representation of the actual utilization of the solution space.

4

Variables & calculation methods

As described by Brunoe et al. [START_REF] Brunoe | Describing Product Variety using Set Theory[END_REF], the utilization of a product family's solution space can be addressed using set theory. The solution space can be described using three sets; Configurable variety, configured variety and ordered variety. The set configurable variety contains all possible product variants, whereas configured variety contains all variants that have been configured and ordered variety contains all variants that have been configured and subsequently ordered. To assess the utilization of a solution space, it must be addressed what the relationship is between the set "configurable variety" and the set "ordered variety, i.e. a small difference between the two sets implies a good utilization of the solution space. However to assess the difference between the two sets, it must be determined what are the elements of the sets.

One possibility is to define the elements in the sets as product variants, which corresponds to the approach suggested by Piller [START_REF] Piller | Logistische Kennzahlen Und Einflussgroessen Zur Performance-Bewertung Der Mass-Customization-Systeme Von Selve Und Adidas[END_REF] for calculating the "used variety" metric. This is in some cases a simple way of measuring the utilization of the solution space, however as pointed out above, there are certain issues in other cases. Calculation of the solution space utilization (SSU1) can be done by applying the formula below:

𝑆𝑆𝑈 1 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑠𝑡𝑖𝑛𝑐𝑡 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑣𝑎𝑟𝑖𝑎𝑛𝑡𝑠 (2)
This form of the metric differs from the one defined by Piller [START_REF] Piller | Logistische Kennzahlen Und Einflussgroessen Zur Performance-Bewertung Der Mass-Customization-Systeme Von Selve Und Adidas[END_REF], since the numerator simply counts the number of distinct configuration. The data needed to calculate the value of this metric is expected to be readily available, assuming the company has a product configurator, where the product family model defining the solution space provides the information needed for calculating the number of different product variants (simply by calculating the product of number of possible values for each configurable variable).

Another possibility for defining the elements of the sets is to let each outcome of each variable in a product configurator represent an element. E.g. if a configurator allows you to choose a product in three different sizes and three different colors, that would correspond to six elements. We define these elements as "configuration options". I.e. a configuration option corresponds to a specific value for a specific variable. Using the configuration options, the number of elements will not become astronomical when representing the solution space, but product configurators will typically contain configuration options in the order of hundreds or thousands. It is then possible, to calculate the solution space utilization based on the number of configuration options (SSU2):

𝑆𝑆𝑈 2 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 different 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑝𝑡𝑖𝑜𝑛𝑠 𝑐ℎ𝑜𝑠𝑒𝑛 𝑖𝑛 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑝𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑠𝑝𝑎𝑐𝑒 (3)
This metric will also be able to assess the utilization of the solution space, however without the drawback of using the SSU1 metric of e.g. reaching near zero values for larger solution spaces and being sensitive towards introduction of simple variety. On the other hand, if the sales of a product family reaches a certain level, it is expected that the value of the SSU2 metric will approach one, as it is likely every configuration option will be chosen by a customer at least once. This leads to the issue regarding this way of measuring solution space utilization, which is that a configuration, which is chosen only one time by a customer, will count the same as a configuration option, which has been chosen thousands of times by customers. Clearly, a configuration option chosen thousands of times indicates a better utilization than a configuration option chosen only once, which this metric does not take into account.

Another way of assessing the utilization, using the configuration options, but taking the issues described above into account, would be to calculate the frequency by which each configuration variable is chosen by a customer. By doing this, it can be determined for each configuration variable, how well it is utilized. Furthermore, to assess the solution space as a whole, a distribution of the frequencies can be determined and illustrated in e.g. a histogram, which will indicate whether all configuration options are chosen similarly often, or some configuration options are chosen almost in every configuration whereas others are chosen very rarely. To represent this distribution, mean values and standard deviation can also be calculated for the frequencies for which configuration options are chosen. Although this way of assessing utilization of the solution space is not a metric in it's simplest form, i.e. a metric which can be calculated as a single figure, we include it in this paper as it gives a deeper insight than the other simpler metrics. The data used for calculating a value for this metric is identical to the SSU1 metric In this paper, the two methods -SSU1 and determining the frequency and distribution of configuration options being chosen are tested on data from three different products from three different companies all producing mass customized products. The metric SSU2 has not been included in the test because of the arguments presented above and since initial test showed that, the metric for all product families would reach the value 1.

Data

The data used for testing the different metrics and calculation approaches have been obtained from three different companies. The product families represent three entirely different product types in terms of application and complexity. Product family A is a family of mass customized capital goods equipment of high complexity. The product family is close to being Engineer to Order products and have thus a very large product variety, however they are standardized and can thus be considered mass customized products. Product Family B is a family of products, which are used as sub components of larger systems in the process industry. The products have medium variety and are customized to meet individual demands, by combining different components. The complexity of these products is relatively low. Product Family C is a family of products for domestic utilities. The product family has medium variety but are slightly more complex than the products in family B but still much less complex compared to family A. Data obtained for the three product families consists of a product family model defining the variety, i.e. variables and there values, enabling determining the total number of variants and configuration options. Furthermore, historic data describing specific configurations which had been sold over a one to two year period was obtained for determining what variety had actually been configured.

Results

The results of testing the SSU1 metric are shown in table 1. At first glance, it can be seen that all three product families have SSU1 metric values very close to one, although product family A has a significantly lower value for the metric, for which the major reason is that the number of possible variants is significantly higher. Furthermore, it can be seen that the number of distinct configurations sold are not particularly high. This is due to the products being B2B products and thus not being sold in volumes comparable to consumer products. Furthermore, for families B and C it indicates that sales includes much repurchase of previously purchased configurations. I.e. one configuration is sold in larger volumes and in multiple orders. 2 and Figure 1 show the results of analyzing the frequencies of which configuration options are chosen by customers. In figure 1, the frequencies are divided into 20 even intervals. The bars in the histogram indicate the number of configuration options, which have been chosen the number of times, which corresponds to the frequency interval. E.g. the first bar for product family A indicates that 358 configuration options have been chosen by customers between 0 and 22 times. The number below each bar indicates the upper bound of the interval. 2 also reflects an analysis of the frequencies of configuration options being chosen, where the mean frequency and standard deviation are calculated. The mean frequency for e.g product family A indicates that configuration options are in average chosen 77 times by customers. The mean is also related to the number of configurations in row two in the table, where the value 0.231 indicates that the average configuration option is chosen in 23.1 % of the configurations. The table also shows the standard deviation for the frequencies, where it is notable that the standard deviation in all three product families is larger than the mean value indicating very spread frequencies, but also a long tail of configuration options where a few configuration options are chosen very frequently and a high number is chosen very rarely, which is also seen in figure 1.

Conclusion

The basic idea of the Used Variety metric [START_REF] Piller | Logistische Kennzahlen Und Einflussgroessen Zur Performance-Bewertung Der Mass-Customization-Systeme Von Selve Und Adidas[END_REF] is intuitively good, however insufficiently described to operationalize it. Using the SSU1 metric a very similar measure can be calculated, however testing on actual data showed that the method did not provide very useful information on utilization of product variety. The metric may however be useful in cases with low variety or in cases with a lower number of predefined product variants. Another method for assessing variety was tested, where the frequency of choosing a configuration option is counted, resulting in a distribution of frequencies, which indicates if there is a large spread in the utilization of configuration options and if there is a large part of the product variety which is rarely configured and thus potentially unprofitable.

The data used to test the methods is obtained from three different companies, however all operation in a B2B setting, limiting volume. The results may have been somewhat different if data were included for consumer products as well, however in terms of assessing the applicability of the method, we consider the testing done in this work as sufficient to draw the conclusions stated above.

Further consideration needs to go into the practical application of the methods, e.g. over how long time should data be sampled? Using data for 1 month will reveal a significantly different result than data for half a year. Furthermore, not all configuration variables may be relevant to include, since some may be redundant, if the configurator as an example contains variables for both user requirements and product characteristics.

In general, we expect the application of product variety assessment tools as a valuable tool for mass customizers in order to optimize their business in terms of determining the right level of product variety and diagnosing the configuration process.

Fig. 1 .

 1 Fig. 1. Results from analysing frequency of which configuration options are chosen illustrated as distribution histograms. X axis labels indicate the upper bound of the interval.

Table 1 .

 1 Results from applying metric SSU1

	Product Family	A	B	C
	Distinct configuration sold	409	157	26
	Number of possible variants	4,26E+61	1,91E08	9,17E09
	Value of SSU1 metric	9,59E-60	8,19E-07	2,83E-09
	Table

Table 2 .

 2 Results from analysing frequency of which configuration options are chosen

	Product family	A	B	C
	Mean frequency	77,0	52,1	44,6
	Mean frequency relative to no. of configurations 0,231	0,156	0,133
	Std. Dev	109,6	79,28	62,48
	Std. Dev relative to no. of configurations	0,267	0,183	0,345
	Table


Gökan May
email: gokan.may@polimi.it

Omid Maghazei
email: omid.maghazei@polimi.it

Marco Taisch
email: marco.taisch@polimi.it

Andrea Bettoni
email: andrea.bettoni@supsi.ch

Marco Cinus
email: marco.cinus@supsi.ch

Annarita Matarazzo

Toward Human-centric Factories: Requirements and design aspects of a worker-centric job allocator

Keywords: worker-centric job allocator, human-centric factory, sustainable manufacturing, social sustainability, factories of the future

The new point of view in which factory and workers are seen is the person at the centre of the production system, so employees should be involved in job design and task balancing processes. The advantages coming by this paradigm shift, from the task-centric organization to the worker-centric factory is doubtless the high correlation among job and worker in terms of skill, experience, and worker's features. Human-centric system is useful to improve the knowledge and the capabilities of workers regardless of age and role, and in this kind of model the job suits the worker and his needs. In this context, it is of paramount importance to design and develop a worker-centric job allocator tool in which the human dimension is a key factor. This study therefore addresses the requirements and design aspects of a worker-centric job allocator as an enabler for human-centric workplaces of the future.

Introduction

The manufacturing sector needs to react and adapt to the emerging sustainability trend, not only for environmental and economic reasons but also for social ones. In this sense, the new point of view in which factory and workers are seen is the person at the centre of the production system, so employees should be involved in job design and task balancing processes. The advantages coming by this paradigm shift, from the task-centric organization to the worker-centric factory is doubtless the high correlation among job and worker in terms of skill, experience, and worker's features.

The new human-centric factory model in the Figure 1 below, as developed by the authors in [START_REF] May | A new Humancentric Factory Model[END_REF], highlights the needs and important aspects for designing the human-centric workplaces of the future whilst addressing the problem from different perspectives (i.e. worker, factory, and context). Human-centric system is useful to improve the knowledge and the capabilities of workers regardless of age and role, and in this kind of model the job suits the worker and his needs. In this context, it is of paramount importance to design and develop a worker-centric job allocator tool in which the human dimension is a key factor. Thus, in this research we address the research question ""How can the jobs in a workplace be allocated to workers to create human-centric workplaces?", on the way to design a worker-centric job allocator in which worker's skills, capacities and needs are considered. This study therefore aims at analyzing requirements and design aspects of a worker-centric job allocator to be used in human-centric workplaces of the future.

Theoretical Background

The review work in this section aims at describing the state of the art for traditional job allocation algorithms, and to find the bridges and the gaps between them and the relatively new research fields of worker-centric job allocator. Define the overall architecture of the production planning and HR organization platform detailing (SW modules functional specifications, data/information flow and GUI) Use cases will be realized analyzing the interaction between the planningplatform and representative stockholders One of the main optimization problems in operations research is job allocation, and it aims at finding a maximum weight matching based on specific multi objective function or criteria. Thus, the problem of job allocation to tasks and workplaces is characterized by a large number of different possibilities for allocating employees to work functions [START_REF] Burkard | Assignment Problems[END_REF]. There are many models for an optimal personnel assignment, but none of these incorporate the concept of human dimension in a holistic manner. The main aspect of a proper job allocator is the consideration of human factors like abilities, preferences, restrictions, and needs.

Dependent upon the type of personnel assignment problem, various instruments have been developed [START_REF] Zuelch | A simulation approach for planning and re-assigning of personnel in manufacturing[END_REF]. For instance, the ifab-Institute of Human and Industrial Engineering at the University of Karlsruhe develops a personnel-oriented simulation tool that considers the complex, dynamic interactions of personnel resources and machinery equipment and takes advantage of the vast array of opportunities for the planning and reassigning of personnel to work functions.

Zuelch, in his paper [START_REF] Zuelch | A simulation approach for planning and re-assigning of personnel in manufacturing[END_REF], highlights the key concepts in the way to design and conceive a worker-centric job allocator. However, the focus here is only on production planning, and themes like worker's abilities, preferences, skills, needs are only considered to the medium term production planning.

Corominas, in his research work [START_REF] Corominas | A model for the assignment of a set of tasks when work performance depends on experience of all tasks involved[END_REF] describes and models the allocation of a set of tasks to a set of workers, when the worker's performance on a task depends on the experience of the worker of this task and of the other tasks involved. The objective is to minimize the completion time. The model is very interesting because the performance is, at least in part, a consequence of experience. When a set of tasks is being performed, the experience acquired in the first stages can obviously influence the capacity of the worker to do this task and other tasks later. Despite this, the model takes into account only the experience factor and is not part of a worker-centric vision. There are essential elements missing in the model, such as needs, preferences, skills, features, and knowledge of workers.

Besides, Lin [START_REF]Assignment problem for team performance promotion under fuzzy environment[END_REF] constructs a general fuzzy assignment problem (GFAP) based on a real scenario and proposes a solution procedure. The model is based on a project team composed by n workers and a manager. The n workers are responsible for performing n jobs and the manager for restraining the total cost. Using the max-min criterion, a mixed nonlinear programming model of the GFAP is constructed, and an algorithm that combines simplex and trade-off approaches is proposed to solve the problem. In this model, the only objective is the increasing of team performance but main aspects of worker centric model are not considered. Thus, one weakness of the model could be the missing human dimension. However, the algorithm is very interesting and with some modifications could be used for future researches in designing worker-centric job allocators.

Koltai [START_REF] Koltai | Formulation of Multi-Level Workforce Skill Constraints in Assembly Line Balancing Models[END_REF] suggests a balancing model for assembly workstations. The model assigns tasks to workstation taking into account skill requirement of tasks and skill level of workers (i.e. under skilled, average, and specialized). Nonetheless, this type of assignment is not dynamic because skills have been categorized a priori.

In traditional job allocation, algorithms are used in static way. However, it is important to have dynamic allocation of resources considering workers' evolution.

The main gap found in the critical analysis of the literature is that models for job allocator and resource assignment are static. In the human-centric point of view, workers evolve during time, in which they could improve skills thanks to experience and training, change functional or anthropometric characteristics, improve knowledge, reconsider personal needs. So, the structure of job allocator should be dynamic, updating resource allocation based on workers' profile. Hence, this could enable us to redefine tasks, operations, jobs considering evolution of worker, factory, and contextual drivers. So, the worker-centric job allocator described in this work is built to address these emerging gaps.

3

The Worker-centric Job Allocator

Job allocator module is a customer-centric production planning tool meant to dynamically assign the available workers to the different tasks and jobs considering workers' relevant characteristics, skills and availabilities; short-term production plans; time-schedules; job and task description. Different alternative jobs-workers matching are then ranked utilizing the assessment module. Job allocator runs when the Production Manager needs to identify the better performing allocation of resources and to better manage human resources. This software module works on a short term basis and has a dedicated GUI for showing alternative for managing data entry, definition of allocation rules and for visualizing job allocations. Further considerations for this tool might be to have dedicated areas in which manager could find the best allocation by using filtering questions, and to have a customized and modular software tool in order to re-use the same software in different factory areas like technical department, production systems and other units.

Needs

The resource allocation in a company is a complex activity that employs many people in the organization. Big companies have a special department which deals this job. Despite all, for human resource management, it is difficult to take into account all worker's needs. The job allocator in a broad sense allows to assign tasks and jobs to the resources in a different way. First of all, the analysis starts from a deep knowledge of the workers and their needs. This information becomes evaluation parameters to build the worker history and through these data, it is possible to better know the worker and to forecast the future needs, for example trainings. The tasks are then allocated according to the "best available resource" to perform that given asset. In this perspective, the role of training results are needed to improve knowledge and capabilities of each worker.

To derive the basic needs and requirements for a worker-centric job allocator, the process started with the interviews with two case companies from white goods and tooling industries. The companies listed the following functions as important for job allocator to be designed: communicate with work profiler; take into account a defined population; choose the best match between workstation skill requirements and worker profile to minimize the training gap up-front; choose best match for each workstation on a set of workstations, in order to avoid looping; minimize the computational time; management of skill matrixaccording to the critical workstation; identify the person who is the closest match to be trained in addition to that workstation, minimizing the training gap, matching interests and physical worker profile; have dedicated areas in which manager could find the best allocation by using filtering questions; a customized and modular software tool in order to re-use the same software in different factory areas like technical department, production systems and other units.

Besides, outputs required from job allocator tools are listed as for each workstation a list of operators with skills and qualifications, physical characteristics and limitations; and indication of match against workstation requirements and hints for designing workstations.

Despite the fact that defining an objective function is left as a further research stream, it is possible to define it in a generic way. According to the companies interview, it is better to shape a multi-objective function.

A multi-objective problem is defined as max{f 1 (x), f 2 (x), ..., f k (x)} where Rk is the objective space. The multi-objective function for job allocator takes into account different objectives, some of them can be: maximize productivity P; maximize worker's satisfaction S; and minimize workers' related costs C.

Also, each single objective could be further specified into a set of decision variables along with a set of parameters. In this case decision variables can be workstations Wi = 1, 2, .., N in a production system. Furthermore, constraints could be defined in a generic way accordingly, for example: c 1 : Assembly time for each workstation doesn't exceed the total working time T c 2 : Anthropometric factor AFi for each workstation is at least higher than the minimum required AF min

The objective function might be defined as max{P,S,-C}, where c1≤ T, c2 ≥ AF min , and W i ≥ 0

Requirements

Requirements analysis in systems engineering and software engineering, encompasses those tasks that go into determining the needs or conditions to meet for a new or altered product, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating and managing software or system requirements [START_REF] Hay | Requirements Analysis: From Business Views to Architecture[END_REF]. It is possible to identify several types of requirements. In the following subsections will be listed three types of system requirements: functional, non-functional and technological. Requirements listed are MUST requirements, this means that the implementation of those requirements is essential to meet the initial specification to which the project is designed. Functional Requirements. Functional requirements explain what has to be done by identifying the necessary task, action or activity that must be accomplished [START_REF] Laplante | Requirements Engineering for Software and Systems[END_REF]. Functional requirements represent what user can do. Based on this, user can: create, delete, update worker data and its profile; share info with other company location/offices/premises; consult the system in every moment; find automatically the best job allocation; update the objective functions for the resource allocation; evaluate the different options on the basis of the objective functions; put in the system data of worker-factory-context; compare graphically different alternatives; and visualize statistics in different ways. Non-Functional Requirements. Non-functional requirements are requirements that specify criteria that can be used to judge the operation of a system, rather than specific behaviours. Non-functional requirements represent what system does. So, system: shows the workers list; shows measured data from each worker; shows the jobs' list; shows skills required from each job; compares worker skills with skills required for a specific job; communicates with existing production plan's data source (ERP); provides the optimum resource allocation considering worker-centricity; provides statistics; provides different alternatives for resource allocation; evaluates different options for resource allocation; keeps track of all job-worker alternatives; allows the communication among Job Allocator and job assessment modules; stores and retrieves data from knowledge database; allows the communication among Job Allocator and data coming from production plans; and provides a graphic interface in order to compare job-worker alternatives. Technological Requirements. As technical requirement, system should have a GUI, a database, and different modules. The Figure 4 above represents the architecture of the job allocator software module which include the software elements and relationships in between them. Thus, building and documenting the software architecture fosters communication between stakeholders, and speeds up the decision process for the high-level design [START_REF] Bass | Software Architecture In Practice, Third Edition[END_REF].

Use Case Diagram and Scenario

Conclusion

Designing human-centric workplaces of the future gains more and more importance in achieving socially sustainable factories and hence workers' satisfaction, which could lead to improvement of the overall performance in manufacturing plants. One of the main pillars in this direction is to design and assign jobs which better fit to workers' skills, characteristics, and needs. In this study, we analyzed the industrial needs and the requirements for designing such worker-centric job allocator. Hence, the results achieved and insights gained based on this study open the way toward development of software modules for worker-centric job allocation for building human-centric factories of the future. Further research could focus on the development of training needs detector tool in the same way adopted for the job allocator tool.

Fig. 1 .

 1 Fig. 1. A new Human-centric Factory Model [1] (May et al., 2014)

Fig. 2 .

 2 Fig. 2. Use Case DiagramA use case diagram is a representation of a user's interaction with the system and depicting the specifications of a use case. As highlighted in the above use case diagram, the scenario is explained below:  User can visualize suggested allocations, that is a list of jobs with assigned workers to each of it;  User can evaluate job allocation alternatives;  User can visualize job allocation statistics, that are statistics of jobs-workers matching;  User can enter the required relevant data;

Fig. 3 .

 3 Fig. 3. Class Diagram for the worker-centric job allocator During the design phase of a software, it is very important to define the static view of the system, and a class diagram defines the basic elements in this context. The Figure 3 above hence illustrates the class diagram for the worker-centric job allocator.

Fig. 4 .

 4 Fig. 4. Job allocator component diagram

Acknowledgement

This work has been partly funded by the European Commission through Man-Made project (Grant Agreement No: FoF.NMP.2013-3 6090730). The authors wish to acknowledge the Commission for its support. The authors also wish to acknowledge their gratitude and appreciation to all the Man-Made partners for their contribution during the development of various ideas and concepts presented in this work.


Marco Seregni
email: marco.seregni@polimi.it

David Opresnik
email: david.opresnik@polimi.it

Christian Zanetti
email: cristiano.zanetti@polimi.it

Marco Taisch
email: marco.taisch@polimi.it

Fred Voorhorst
email: fred.voorhorst@supsi.ch

Mini Factory: A Successful Model for European Furniture Industry?

Keywords: Mini factory, Distributed mini factories network, transnational strategy, Furniture

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The turnover of the European furniture industry in 2011 has been estimated at 90 billions of euro, with a total of 130.000 companies, consisted almost entirely of local small enterprise [1]. These data demonstrate the great importance of this sector both for the EU economy and its relevance among global furniture industry, where EU has a major role, among others, due to its ability to influence international fashion and design trends [2]. However, domestic demand, particularly in Western Europe, suffered a shock in 2009, due to the global crisis. Meanwhile, the growth of the global competition, particularly in the segments with lower unit value, caused a massive shift of production from EU countries to the Far East to reduce the overall production costs and, especially, labour ones [START_REF]European Sector Monitor of the wood/furniture industry[END_REF].

In order to respond to the abovementioned challenges, European manufacturers have to radically change the production system paradigm. Especially to be able to satisfy the customers' dynamic needs and thus to increase the added value of their products. In this sense, the development of production system based on mini factories could be an effective solution, able to ensure higher performance than centralized mass production. However, due to the variety of models named mini factory, it is completely clear what exactly represents such model. Hence, the aim of this paper is twofold. First, it aims to clarify the term mini factory, while secondly it aims to identify the benefits of such models and to evaluate the possibility of implementation of this innovative production organization that can help European furniture SMEs to face the competition of the big international competitors. In order to fulfil those aims, the mini factory models are first categorized. Thus, as a result of the literature review three classes have been identified: Work organization model, Agile Assembly Architecture and Distributed mini factories network. In a second step, this paper explores potential benefits of the mini factory model proposed by Reichwald, Stotko & Piller [START_REF] Reichwald | Distributed mini-factory networks as a form of real-time enterprise: concept, flexibility potential and case studies[END_REF], belonging to the third of the above mentioned categories. Finally, this model is evaluated for its potential applicability onto the field of European furniture, where customization is growing on importance.

Consequently, this article consists of two sections: the analysis of the literature and the verification of the adaptability and the potential benefits of the model of distributed mini factory networks in the field of furniture in Europe.

Mini factory models

An analysis of the manufacturing models identified as mini factory has been conducted using as keywords "mini factory" and "minifactory" on scientific databases Scopus, Web Science and Google Scholar. The most evident result emerging from this activity was the presence of this term in many areas that do not belong to manufacturing area; this issues was solved by narrowing the spectrum of research fields related to the industrial world. Taking as example Scopus database, only documents belonging to the subject areas "Engineering", "Computer Science", "Business , Management and Accounting", "Economics, Econometrics and Finance" have been selected and analyzed. Furthermore, this literature review showed a substantial lack of a unique and universally recognized definition of mini factory; on the contrary, it highlighted a strong variability in terms of characteristics and peculiarities of the models matched to that term. Due to this result, before proceeding with the study of mini factory characteristics compared with the challenges of European furniture industry, it was necessary to sort and classify the different models and case studies identified in the research. For this reason models presented under the name of mini factory are categorized in three classes: work organization model, Agile Assembly Architecture and Distributed Manufacturing Network.

Mini factory as "work organisation model"

Since the 80's the term mini factory has often been associated with innovative model for the innovation of work organisation. Ickis, Edelberg & Morales in 2000 in the description of the organizational changes implemented by the company TANIC during the second half of the 90s , the Nicaragua subsidiary of British American Tobacco , identified mini factory as "[...] a manufacturing cells with sequential and distribution of compact equipment and personnel" [START_REF] Ickis | Tanic[END_REF]. Gilbert, Raulet Teglborg & Crozet in 2013, describing the change in the organization of FAVI value chain activities, a French company specialized in the production of copper alloys, defined the mini factory as "[...] an island of production located in a particular area of the factory containing all the equipment and processes dedicated to a particular client" [START_REF] Gilbert | Work Organisation and Innovation-Case Study: FAVI, France[END_REF]. These two cases show a commonality in the goals, but a diversity in the organizational dimension. In fact, both cases' primary objective is the increase in the quality of the product, in TANIC as a response to decreasing trend in output quality while in FAVI as response to the high competition of Asian manufacturer, advantage on the cost side and resulting in aggressive pricing policies. Whereas for the organizational prospective the two cases show a substantial difference in the logic with which the mini-factories were created: in the first case mini factories are process-based while in the second they are customer-based.

In conclusion mini factory models included in this class can be defined as production organization with few hierarchical levels organized in teams, based on high job enrichment and enlargement; these units manage all production activities, including support ones needed for the completion of the customer's order.

Mini factory as "Agile Assembly Architecture"

The models of mini factory that fall in this category do not refer to an entire production system, but concentrate on a specific phase of manufacturing production: the assembly. In particular in this area have been included models with both technological and functional features that get them in contact with FAS (Flexible Assembly System), defined as "an integrated, computer-controlled, robotic assembly of machines and automatic and programmable and automatic devices transport and handling, able to mount simultaneously medium volumes of a variety of different assemblies" [START_REF] Garetti | Automatic production systems[END_REF].

Muir, Rizzi & Gowdy defined a system mini factory that are developing as "Potentially large collection of mechanically, computationally and algorithmically robotic modules [...]" [START_REF] Muir | Minifactory: A precision assembly system adaptable to the product life cycle[END_REF]; furthermore they defined "high precision, self calibrating, agent based, distributed assembly system" [START_REF] Kume | A high-performance network infrastructure and protocols for distributed automation[END_REF].

Gaugel , Bengel & Malthan associated Mini Factory with "[...] a marketable, miniaturized highly flexible micro-assembly system capable of reproducing the correct size proportions between a product and its production environment and also able to intelligently integrated which had earlier run separate processes" [START_REF] Gaugel | Building a mini-assembly system from a technology construction kit[END_REF].

The abovementioned models can be seen as FAS with extremely small (a room of an apartment or even a desk) through the use of miniaturized machinery, equipment and components, thus equalizing to concept of Microfactory [START_REF] Okazaki | Microfactory-concept, history, and developments[END_REF]. These solutions could represent a technical instrument for facing assembly challenges related to high demand for customized products; however, as automated systems managed by an artificial control, they have a degree of flexibility that does not allow to work any type of piece. Consequently, they require a prior standardization of components in order to operate effectively in a customized production.

In terms of output these systems are widely used for the realization of products of very small dimensions, which need for high accuracy, in some cases in the vicinity of microns; for instance Printed Circuit Boards (PCB) is a product for which these assembly models are widespread.

2.3

Mini factory as "Distributed mini factories network"

In 2005 Reichwald, Stotko, & Piller defined mini factory as "[...] a designed scalable, modular, geographically distributed unit that is networked with other units of this type" [START_REF] Reichwald | Distributed mini-factory networks as a form of real-time enterprise: concept, flexibility potential and case studies[END_REF]. In their model mini factory should performs all operations needed for the effective completion of the customer's order:

• design with an high involvement of customer and supported by user friendly tools;

• customized manufacturing on the basis of the customer's requests recorded in the previous phase; • delivery, with the aim to guarantee a short delivery time;

• afters sales activities, such as repair, maintenance and other supplementary services.

The authors focus on the importance of networking all mini-factories installed with the aim of sharing various types of information (i.e. customer requirements, solutions to problems in mini factory management), thus giving the opportunity to personnel to access to a huge and global knowledge; moreover, localization near customers supports the growth of repeating purchase, hence increasing the customer retention rate. According to Zaeh and Wagner [START_REF] Zäh | Planning Mini-Factory Structures for the Close-to-Market Manufacture of Individualized Products[END_REF] this distributed instantiation of mini factories can also have economic positive impact by reducing logistic costs and delivery time.

The above described model also includes a central command unit which carries out support to the network of mini factories, such as the sourcing of standard components of the product, operators' training and definition of the basic characteristics of the product. Within mini factory, modularity means that each local unit is composed of modules that can be combined together thanks to their standard interfaces; thus local specificity of each mini-factory is easily ensured by a combination of these standard modules.

Model application onto the European furniture sector

The strong global competition and in particular the Asian player has pushed European manufacturers looking for ways to differentiate their offering in order to justify an higher price; this goal has markedly increased the relevance of the following abilities:

• quick and on demand supply;

• rapid response to changing tastes;

• customer support services.

Another very marked feature in the European furniture country in is the strong diversity in national markets, both in terms of design style, product mix and materials; thus selling into these countries requires a high knowledge of local customers and a large network of contacts. From a structural point of view, this sector is mostly composed, with the exception of a few international players, by SMEs, who are then more difficult to respond to these challenges. The model of distributed mini factories network presented, due to its characteristics, could be helpful for SMEs for meeting the growing need for customization of European furniture sector and eventually interfacing with different national markets.

Customization and transnational strategy in furniture industry

Reichwald, Stotko & Piller suggested their model of mini factory was particularly suitable "for creating customized products and services if a company's situation is characterized by a high degree of variability and specificity". For "specificity" the authors mean that products would withstand an high depreciation if rejected by the specific customer. Clearly this concept is strongly linked to customization, as the customized product means that specific product has a low value for other customers, even at the limit zero. Research conducted by Lihra, Buehlmann & Beauregard in 2008 showed that customized portfolio could be implemented as a successful strategy for US producers in order to face competing with low-cost offshore manufacturers, although with different impacts in different segments" [START_REF] Lihra | Mass customisation of wood furniture as a competitive strategy[END_REF].

Focusing on European furniture market, it presents a deep fragmentation in many segments with different features, linked with different customer's profile, as SERİN & ANDAÇ analysis within Turkish furniture showed [START_REF] Serin | An investigation of the preferences of furniture consumers depending on education and age factors in Turkey[END_REF]. According to a classification based on the intended use of furniture, at a very general level the sector can b partitioned into two segments: office furniture and domestic ones. Surely, customization is a competitive lever of primary importance much more in the second segment as a result of a variety and heterogeneity of characteristics required, ranging from functionality to aesthetics. More specifically, for its characteristics, distributed mini factories network can adapt better to specific groups of domestic furniture consumers [START_REF] Cbi | The domestic furniture market in the EU[END_REF] requiring customization in terms of size (i.e. "middle age singles") or in terms of style (i.e. "young couples").

In addition to customization needs, furniture industry in Europe has a strong heterogeneity among national markets [START_REF]European Sector Monitor of the wood/furniture industry 17[END_REF], as confirmed by Tammela, Cane & Helo, a study on Time-Based Competition strategies of Brazilian and Scandinavian furniture companies that highlighted cultural diversity of customers and business partners was a key issue for strategic decision [17]. Thus a transnational strategy in which a company "[...] strives for global efficiency without losing on the advantages of offering a customized product tailored to local or regional habits" [START_REF] Reichwald | Internationalizing Mass Customization-Minifactories as a transnational solution[END_REF], could be a potentially successful solution. Consequently, distributed mini factories network, according to Reichwald, Stotko and Seifert conclusion [START_REF] Reichwald | Internationalizing Mass Customization-Minifactories as a transnational solution[END_REF], could be an effective way for the implementation of transnational strategy within European sector.

3.2

Potential advantages for furniture SMEs

Due to its characteristics, distributed mini factories network represents an intermediate solution with some aspects in common to large international companies and others with local SMEs; in particular SMEs, those represents more than 95% of the European furniture companies, could achieve competitive advantages [START_REF] Frohlich | A taxonomy of manufacturing strategies revisited[END_REF] by joining a distributed mini factories network:

• centralized management of the support activities could enable SMEs to take cost advantage due to economies of scale; • thanks to the integration between the design and production activities, and the logic of local lean supply chain, several inconsistencies between company's operations can be avoided, thus reducing significantly of the overall delivery time and its variance; this potential advantage has an enormous importance for producers in mature furniture markets in response to competitive pressure from manufacturers in the Far East, such as Mitchell & Watt stressed with reference to the American market [START_REF] Mitchell | Strategies for the New American Furniture Industry[END_REF]; • SMEs can obtain significant benefits in the knowledge management, due to the greater amount of information that can be accessed within the network of mini factories. Indeed, thanks to the network in which it is inserted, SMEs are able to access a variety of resources comparable to that of large firms. At the same time in this solution, single manufacturer is able to interact with the customer while obtaining from him complete information and a valuable feedback [START_REF] Buehlmann | How Small Firms Contrast with Large Firms Regarding Perceptions, Practices, and Needs in the U.S. Secondary Woodworking Industry[END_REF]; • providing to the customer a set of information about the product marked higher than any kind of catalogue, thus responding to increasing furniture customer's need for information due to technological knowledge, social values and socio-economic changing [START_REF] Öztop | Factors Influential in Consumers' Furniture Selection and their Preferences regarding Product Features[END_REF]; • reducing the downstream supply chain levels, thus increasing manufacturers' strength against furniture retailers, which has arisen their role over the past years due to their ability to interact directly with the customer [START_REF] Lihra | Multi-channel communication and consumer choice in the household furniture buying process[END_REF].

Limitations and further research

The main limitation of this article is that it is on a conceptual level. This means that the model has not yet been applied and its success evaluated. However, the validity of the conceptualization has been reinforced by: a) working with widely recognized models, b) by working with reliable characteristics from the furniture industry, c) taking into account the trends in manufacturing as also on the furniture market and d) clearly defining boundaries of the mini factory model. The second limitation is that it does not take into account any more specific part of the furniture sector. However, due to the novelty in this field, the possibility from a general perspective had to be assayed first. Consequently, one of the next steps in terms of further research will be to focus on a specific sub-segment of the furniture industry. Furthermore, further research will be carried out in order to define more in detail the operational characteristics for the instantiation of a furniture mini-factories network, such as the supply chain, production planning logics, basic designs concepts. In particular, as demonstrated by some studies ( [START_REF] Jensen | Willingness to pay for environmentally certified hardwood products by Tennessee consumers[END_REF], [START_REF] Aguilar | Consumer willingness to pay price premiums for environmentally certified wood products in the US[END_REF]), there is a growing attention of the customer with regard to environmental sustainability, which translates into a reward in terms of willingness to pay; thus, future research will explore the implications on mini factory model for ensuring high green performance.

Conclusions

In this article, the main models of the mini factory were identified and scrutinized, from which three main types were more closely depicted. The model of Reichwald, Stotko & Piller [START_REF] Reichwald | Distributed mini-factory networks as a form of real-time enterprise: concept, flexibility potential and case studies[END_REF] has been identified to be the most proper one for further application. Based on the comparison between competitive conditions that were deemed optimal for the mini factory and the characteristics of the sector shows, that the model is applicable to the furniture industry, in particular for SMEs. The results of the analysis showed among others that that the furniture has the market condition suitable for the effective implementation of the distributed mini factory network. This means that the production model in question can increase the competitive advantage of the manufacturing enterprises in the furniture industry, thus indicating that this model is a viable answer for those manufacturers in time of economic crisis, supply surplus and lack of demand.

6

Acknowledgements

The work presented here is part of the project "CTC-Close To Customer"; this project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no FoF.NMP.2013-6 608736 -CTC.


Pavan K Sriram
email: pavan.sriram@ntnu.no

Erlend Alfnes
email: erlend.alfnes@ntnu.no

Taxonomy of engineer-to-order companies

Keywords: Production planning and control, engineer-to-order, empirical study and review

In this paper taxonomy has been developed to classify the different forms of ETO firms to enable a like-with-like comparison, arguing that existing taxonomies within the literature are inadequate for production planning and control research purposes. Successful production planning and control concepts from high volume, low variety producers cannot be directly applied, because of the unique nature of ETO products. Secondly through synthesis of the literature and the analysis of the three case studies we describe and examine the production planning and control activities characteristics of the ETO environment to distinguish the different environment within ETO production.

Introduction

Aspects of globalization have stirred an increase in demand for customized products. While having a customized product the customers also expect the products to be manufactured and delivered in short time, at a nearly same cost as mass production with relatively high quality [START_REF] Stevenson | A review of production planning and control: the applicability of key concepts to the make-to-order industry[END_REF]. This market trend requires manufacturing companies to be able to rapidly and cheaply produce customized products with a required quality level. This requirement is somehow controversial with conventional understanding, i.e. a trade-off between customization and low cost through highly efficient production mode, such as mass production. On the other hand, this market trend also provides the manufacturing companies with new opportunities, particularly for these small or medium sized enterprises (SMEs) in developed countries like Norway. The typical examples are customized thruster manufacturers, heavy-duty lifting and material handling equipment companies, special industrial equipment manufacturers, etc. In these manufacturing businesses, the customers normally require the orders to be specified with their specific requirements and to be filled within a short lead-time [START_REF] Amaro | Competitive advantage, customisation and a new taxonomy for non make-to-stock companies[END_REF]. The high customization and short lead-time drive the customers to place their orders with a local company instead of an overseas competitor. However, the high cost of producing a customized product through a traditional production mode, e.g. job shop, may turn the customers away from these local companies [START_REF] Hicks | A typology of UK engineer-toorder companies[END_REF]. Therefore, to keep the high customization, short lead-time and at meanwhile to effectively reduce the production cost become the high priority strategy for manufacturing companies to effectively compete with overseas large and cheap producers [START_REF] Gosling | Engineer-to-order supply chain management: A literature review and research agenda[END_REF]. This is particularly true for SMEs in the developed countries Norway.

In this paper we focus upon different types of engineer-to-order companies and identify taxonomy that demonstrates the importance of the relationship between the degree of design activity, volume of manufacture, and of the distinction between products that are custom built from options, and those that involve custom design elements. This paper examines how these companies can effectively make use of the taxonomy to support management decision making in order to manage their manufacturing operations more effectively.

Methodology

The primary research methodology is multiple case study research, and by placing emphasis on the production planning and control (PPC) system framework of [START_REF] Vollmann | Manufacturing Planning and Control for Supply Chain Management[END_REF], we present empirical examples based on three companies operating within the heavy lift, crane & barge, and aluminum casting industry in Norway. This is carried out in accordance with a case-based approach [START_REF] Voss | Case Research in Operations Management[END_REF]. The cases are conducted using data collected through semi-structured interviews of several informants in the companies, including the managing director and production manager(s). Production planners were also involved in the discussions. Direct observations and the analysis of secondary sources, such as company documentation and corporate website are used for triangulation, to check the internal consistency of data [START_REF] Scandura | Research methodology in management: Current practices, trends, and implications for future research[END_REF]. The central idea is to constantly compare theory and data, iterating toward a theory which closely fits the data [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF].

3 Theoretical background

Production planning and control

In ETO firms the time needed for engineering may represent a significant part of the total lead time. [START_REF] Little | Integrated planning and scheduling in the engineer-to-order sector[END_REF] developed a novel planning and scheduling model for ETO sectors where the MRPII was not a good fit. [START_REF] Jin | A new framework for MRP systems to be effective in engineered-to-order environments[END_REF], presented a new framework for MRP system to be effective in ETO. The system was developed to allow aggregate production planning coordination between engineering, material acquisition and production activities, as well as the information structures but did not focus on coordination with supply chain. [START_REF] Stevenson | A review of production planning and control: the applicability of key concepts to the make-to-order industry[END_REF], presented that production planning and control are the most critical tools to meet customer demands and most of the existing systems are based on the superficial software's and there is a need for industry specific design. Stevenson's work mainly focused on the applicability of classical approaches to PPC, and highlighted the importance of workload control (WLC) concept to make-to-order (MTO) companies. But the scope is limited to assessing the applicability of various existing approaches to PPC.

More authors looked on development of methods/models on integration of Enterprise resource planning (ERP), Supply chain management (SCM), and innovative process as they considered them to be key elements for a successful planning and control system [START_REF] Mcgovern | Modelling Supply Chain Management Processes in Engineer-to-Order Companies[END_REF][START_REF] Samaranayake | Integration of production planning, project management and logistics systems for supply chain management[END_REF]. But the frameworks don't address the effective planning of design, product configuration, and scheduling at different levels. And also it was focusing only on SCM in ETO and lacked applicability to various other planning and control issues in an ETO environment. [START_REF] Caron | Engineer to order' companies: how to integrate manufacturing and innovative processes[END_REF], presented a concept for integrated manufacturing and innovative process in ETO environment. The work described a project management tool integration production planning. It highlighted the dependencies between logistics management and project management, but do not capture how to tackle planning and control in ETO in various stages of manufacturing. Also there have been both lean and agile strategies proposed in the ETO sector, wherein both paradigms attempt to rationalize tools, techniques, philosophies and approaches to manufacturing planning and control. But there are disagreements as to the boundaries, definitions and applicability of leanness and agility [START_REF] Gosling | Engineer-to-order supply chain management: A literature review and research agenda[END_REF]. Also there are studies carried out by [START_REF] Powell | ERP Systems for Lean Production Control[END_REF] shows a synergistic impact in combining the lean and ERP, but it is still not clear from the extant literature as to how either of these approaches can be successfully applied in ETO companies. [START_REF] Sriram | A Concept for Project Manufacturing Planning and Control for Engineer-to-Order Companies[END_REF], proposed a concept for an integrated planning and control conceptual framework based on 'seiban (meaning a manufacturing number in Japanese, is a technique for managing orders in the supply chain which is alternative to traditional MRP logic).' approach for project specific manufacturing, the main focus of the work was limited to development of a new ERP system. This work represents a fundamental difference in the information system requirements for MTS, ATO and ETO companies. A detailed literature on production planning and control approaches for ETO can be found in the article [START_REF] Sriram | A Concept for Project Manufacturing Planning and Control for Engineer-to-Order Companies[END_REF], in this paper we limit our discussions to the taxonomy. This paper aims to; briefly review the literature on the types of ETO companies based upon an analysis of literature review; distinguish ETO firms that provide a framework for identify similarities and differences between companies. This will be tested through a series of case studies as additional work.

Definitions of ETO

The increased number of academic articles on the topic of ETO is one example of the growing importance of the ETO concept. An overview of the number of the published articles shows that the research interest in the subject has been continually high over the past 5 years (source: EBSCO Business Source Premier Database). Although much has been published about ETO in the academic literature, commonly accepted definitions and frameworks have not yet been established. Many academic definitions exist, all of which differ in one or more aspects [START_REF] Gosling | Engineer-to-order supply chain management: A literature review and research agenda[END_REF]. For this work we aim to develop a common comprehensive definition of the ETO concept, which will be deemed too broad to facilitate a common understanding in the work of the academics and of the industry research group on ETO: "Engineer-to-order (ETO) refers to a customer integrated process of unique products and services, which meet the needs of each individual customer with regard to certain product feature. All the operations are performed within a flexible solution space and high engineering responsive process".

ETO existing typology/taxonomy

There are numerous articles presenting classification work in the field of ETO. To establish a more detailed understanding, a literature review of the existing classifications of ETO was conducted. Articles were first identified by using EBSCO and applying pre-defined search terms, through which we identified six articles presenting a classification of ETO. The details of the same are furnished in table 1.

Authors Overview Research Type

Industry Type

Evaluation Dimension

Gosling and Naim, (2009)

ETO is defined as a standard product range offered with the added availability of modifications and customizations. DTO, instead, is defined as where new product introductions with design, engineering and manufacturing based on new customer orders. The two manufacturing systems are therefore clearly distinguished one from the other. In addition ETO is divided, in turn, in ETO and ETS (engineer-to-stock).

Conceptual

)
ETO environment is divided in two different categories: configure to order and invent to order. CTO consists in making product very similar to the previous ones and first engineering task is tailoring or configuring the product to meet the customer requirements. ITO needs more engineering time instead in inventing and designing the product; in ITO companies' previous products are rather different from the current ones.

Conceptual Buildings construction

Degree of engineering time product customization, and degree of customer involvement

Segerstedt and Olofsson, (2010).

Construction concepts and projects also show different preparations before the customer order arrives and before realization of the project. The first and lowest degree of a defined building system is the well-known engineer-to-order where companies design products in ordinary Conceptual (though empirically validated)

Buildings construction

Degree of product customization, and degree of customer involvement construction projects. The design specification process is mainly based on client requirements, norms and standards. Winch (2003) divided the engineer-toorder further in concept-to-order (CTO) and design-to-order (DTO) reflecting the two main contractual forms between the client and the construction company; design-build and design-bid-build respectively.

Hicks et al., (2001)

Four ideal types of company were developed to explain how production processes are organized within ETO firms.

The variables used to classify companies are the depth of product structure, which indicates product complexity, and the volume of production, which determines whether jobbing, batch or flow processes are employed. So type I is a vertically integrated company. Type II outsources component manufacture, but maintains assembly and construction activities in-house. Type III outsources all physical processes.

Conceptual

ETO can be seen as a special case of MTO. In both cases the production flow is driven by actual customer orders. However, in the ETO case both the design and engineering activities are driven by customer orders, but these activities are not parts of the production flow. By doing this, a production dimension covering the typical CODP PD , is established. On the other hand we get an engineering dimension where the product can be designed and engineered based on customer orders.

Conceptual

Generic industry Degree of customer involvement, and degree of product customization Amaro et al., (1999) Arrives at four basic degree of product customization which also holds classification aspects of different degrees of customer involvement. The result of the ty-Conceptual (though empirically validated) Medical and military equipment, polymer and polymer Degree of customer involvement, degree of product customization, and scope of internal pology comprises of three attributes and eleven possible categories. The empirical validation is carried out through 22 company case. products, magnets, conveyors and industrial automation, mechanical assemblies and switchgears responsibility/activities

4

Towards taxonomy for ETO companies

Case study:

Company A is an engineering company: that does project management (100%), contracts, engineering (90%, some is outsourced), doesn't have manufacturing at all (10%, just follow-up). Installation on site is limited to supervision and control (ca 25%). Mainly outsources low-cost production. Company A differentiates itself in the technology used in casting. Company B is an engineering and service company, and supplier of heavy-duty lifting and material handling equipment in a capacity range of 25 to 1000 tones and more. They develop designs, delivers, tests, commissions and carries out service on heavy lifting and material handling equipment for use in harsh and corrosive marine environments. Company C is a global supplier of marine structures with an innovative engineering-and expertise environment. The products are developed and marketed in collaboration with partner firms. They have established a long term co-operation with partners who are of the world's leading providers of ship design, marine equipment and complete system solutions. From the case studies, the significant differences between the three types of ETO are highlighted and elaborated. The differences found in the study are divided as distinguished features of ETO production environment.

Characteristics

Conclusions

One of the main impediments in the study of ETO has been the absence of constructs and the little distinction that has been made between the ETO product delivery strategy and the managerial and organizational style of the companies. Although many of types of ETO firms may have common characteristics, there are many important differences. Through case study we have tried to highlight the significant differences between three firms. The effectiveness of ETO planning and control unfortunately lags far behind the level of manufacturing planning and control practices in, for example, make-to-stock (MTS) or assemble-to-order (ATO) producers. Successful manufacturing planning and control concepts from high volume, low variety producers cannot be directly applied, because of the unique nature of ETO products. This paper presents a taxonomy of ETO firms that provides a framework for identify similarities and differences between firms. It also provides a framework that will help with the transfer of best manufacturing planning and control practices. Secondly there are numerous areas for research in production planning and control for ETO firms. One of the major weakness of previous research is that little has been done to document present industry practice in case studies or surveys. This lack of practical criteria makes a realistic evaluation of the relative merits of the previous research difficult.

Research on the various aspects of production planning and control in ETO firms needs more attention. A framework for integrated models considering all facets of production planning and control is needed. Future research must be grounded with industrial data to ensure that it is relevant to actual concerns.

Table 1 .

 1 Distinguishing features of the ETO production environment in case companies.

		Company A	Company B	Company C
	Product Design	Custom built using stand-	Usually exclusive to one	Custom built
		ard modules	customer
	Product Size	Small and medium size	Generally big	Small and medium size
	Product Change	Low	High	Medium
	Production Volume	Medium to high volume	One to very low volume	Low to medium volume
	Production planning	Stable and sometimes	Dynamic and sometimes	Dynamic and sometimes
		dynamic	chaotic	stable
	Labour Skills	Little or no specialised	Specialised skills	Some specialised skills
		skills required		required
	Major production activity	Assembly and manufactur-	Assembly	Assembly and manufactur-
		ing		ing

Acknowledgement

This research was made possible by the EFFEKT, LIFT and SUSPRO project as well as SFI Norman. The authors would like to thank the participants of the project for providing the inputs and fruitful discussions.