Manufacturing Flexibility: Dealing with Seasonal Demand Variability

Manufacturing Flexibility: Dealing with Seasonal Demand

Variability

University of Groningen Faculty of Economics and Business

BIRTHE HENNIG Student number: s2000792 b.hennig@student.rug.nl Supervisor: dr. G.D. Soepenberg Co-assessor: M.J. Land June 23, 2014

Acknowledgment. I would like to express my gratitude to my supervisors Eric Soepenberg and Martin Land who were on hand with help and advice throughout the completion of this thesis. Furthermore, special thanks to Arjan Finke and his colleagues, who did not hesitate to

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ONTENT

Abstract ... 1

Introduction ... 4

Theoretical Background ... 6

Seasonal Demand Variability in an ETO Environment ... 6

Manufacturing Flexibility ... 7

Firstorder flexibility types ... 8

Lowerorder flexibility types ... 9

An Integrated Perspective ... 11

Methodology ... 13

Case Selection ... 13

Data Collection ... 13

Introduction to company. ... 14

Key informants interviews and smaller consultations. ... 14

Production and order data ... 15

Observations and fieldwork ... 15

Data Analysis ... 15

Validity and Reliability ... 16

Results ... 17

Alfa Laval ... 17

Production Layout and Processes ... 18

Seasonality Analysis ... 18

Labor Flexibility ... 20

Machine Flexibility ... 24

Routing Flexibility ... 25

Discussion ... 26

Labor Flexibility as Enabler ... 26

Machine and Routing Flexibility as Supporters ... 28

EnablerSupporterModel for Seasonal Changes in Volume ... 29

Conclusion ... 31

Bibliography ... 33

Appendices ... 36

Appendix I: Interview Protocol ... 36

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List of Figures

Figure 1 The Flexibility Funnel (adapted) (Suarez et al., 1996) ... 8

Figure 2 Conceptual model ... 12

Figure 3 Production process ... 18

Figure 4 Seasonality being evident in order intake and output ... 19

Figure 5 Delivery time adherence 2013 ... 21

Figure 6 Labor capacity adjustments to seasonality ... 22

Figure 7 Types of labor agreements ... 23

Figure 8 EnablerSupporterModel for seasonal changes in volume ... 29

-List of Tables

Table 2 Influence factors of lowerorder flexibility types ... 11

Table 3 Research scenario Alfa Laval ... 14

Table 4 Validity and reliability overview ... 16

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I

These days, all kind of companies are facing a fast-moving environment, which is not at least due to shorter product life cycles, increasing customization, and increasing product variety as responses to uncertainty in demand, for instance. To counteract, theory and practice have clarified the importance of exploiting flexibility which can even be seen as a competitive weapon (Vokurka & O’Leary-Kelly, 2000). The multi-dimensional and complex nature of flexibility, however, explains the difficulty of grasping the scope of the concept. By investigating seasonal demand variability and to what extent manufacturing flexibility can offer solutions to deal with it, this research can build up further comprehension of the concept.

So far, the research stream of manufacturing flexibility within the field of operations management is still growing and arousing attention. Different typologies of flexibility arose, as for instance by Slack (1983) or Upton (1994). It is, however, still difficult to rise a common definition, as flexibility itself does not seem tangible and implies multiple dimensions (Beach, Muhlemann, Price, Paterson, & Sharp, 2000a; Jain, Jain, Chan, & Singh, 2013; Upton, 1994). These days, manufacturing flexibility can be seen as a tool in order to be more responsive, for which however difficulties in usage are encountered by managers (Jain et al., 2013). Von Garrel, Seidel and Schenk (2010) found demand variability to be the most important reason for companies to become more flexible, followed by the pricing pressure competitors introduced.

Demand variability is a main driver for manufacturing flexibility, which is often put on a level with uncertainty. Seasonality also introduces demand variability which is rather anticipatory though. Manufacturing flexibility within the scope of seasonality is not yet researched, and does thus, present the gap in research investigated in this study. To fill the gap, a case study approach is chosen to reach an in-depth understanding of how flexibility is used in anticipation of seasonal demand variability. Especially for engineer-to-order (ETO) companies this is a great challenge, since keeping stock is not an option at all due to hardly ever receiving a repetitive order. Severe issues can arise if ETO companies are not flexible enough to deal with seasonal demand due to increasing delivery times, for instance, which might cause dissatisfaction and bad reputation among customers.

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volume flexibility) and lower-order flexibility types (e.g. labor or routing flexibility) are applied, as suggested by Suarez et al. (1996). However, this issue is not only important for practice. Theory also is in need of further investigation to make the topic of manufacturing flexibility more tangible and easier to grasp, which could be done by adding the angle of rather predictable demand variability faced (Vokurka & O’Leary-Kelly, 2000). The research strives to explore the following main question with more specific sub-questions:

How can manufacturing flexibility be used to deal with seasonal demand variability in an ETO setting?

1. How can lower-order flexibility types be planned in order to deal with seasonal changes in mix or volume, respectively?

2. Are some lower-order flexibility types more important in this setting than others?

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T

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This section is aimed at building a theoretical foundation in the field of interest – manufacturing flexibility. Additionally, relevant characteristics of the ETO strategy as well as seasonal demand variability are presented. Thereby, the importance and range of this research can be better grasped and a base is laid for further empirical investigation in the field of manufacturing flexibility and its importance for seasonal demand variability. The section closes with the presentation of a conceptual model.

Seasonal Demand Variability in an ETO Environment

A differentiation between manufacturing companies can be made on the basis of how they meet their demand, either from an inventory point or as completion of a specific customers’ order (Amaro, Hendry, & Kingsman, 1999). This paper is focusing on the latter in terms of the engineer-to-order (ETO) strategy, which is an extreme form of the well-known make-to-order (MTO) strategy. A basic characteristic is the customization of products, indicating that customers can order certain specifications while a basic design is available. Most orders are unique, even if just a small alteration is made. The final product is just produced when an order of a customer is placed (Stevenson, Hendry, & Kingsman, 2005). Moreover, in an ETO environment, engineering has to be executed on which base material is purchased and production is planned. Thus, a stock of finished products is not appropriate. Consequently, forecasting becomes more complex as well as rather unspecific, which adds an additional factor of unpredictability to the ETO planning routine.

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change in the mix of the products composing the total output volume. For managing seasonal demand, far more complex production planning problems arise when compared to stationary demand for instance, since capacity is often simply exceeded and needs to be adapted as for a change in output volume (Metters, 1998). Furthermore, also overcapacity can be a result which needs to be managed properly. To deal with the consequences, a need for flexibility is indicated as to increase or decrease capacity or/and capabilities but without rising additional fixed costs (Salvador, Rungtusanatham, Forza, & Trentin, 2007).

Literature invoked primarily three production strategies which could be used to tackle demand fluctuations, namely chase, level and mixed strategy (e.g. Buxey, 2005; Slack, Chambers, & Johnston, 2010; Vollmann, Berry, Whybark, & Jacobs, 2005). The chase strategy does basically comprise (monthly) adjustments made in capacity to match demand fluctuations in volume as good as possible. Examples for adjusting capacity are over- and idle time, variation in the size of the workforce, introduction of part-time workers, or sub-contracting (Slack, Chambers, Johnston, & Betts, 2009). The level strategy, however, aims at a constant day-to-day production rate with use of stocks, while the mixed strategy includes a mix of both, level and chase strategy (Buxey, 2005). Considering inventory, the latter two strategies do not suit the ETO approach studied in this research and are therefore not applicable.

Manufacturing Flexibility

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can actually be managed to reach a higher level of volume and/or mix flexibility. An often mentioned example is routing flexibility which is not recognized by a customer as such, but while a company has a larger output or offers shorter delivery times, it is expressed through volume or delivery flexibility, respectively (Oke, 2005; Suarez et al., 1996). Figure 1 shows the adapted flexibility funnel, originally presented by Suarez et al. (1996), which represents the flexibility types investigated in this research. Even though the initial approach also included new product flexibility and delivery time flexibility as first-order flexibility types, this paper only focuses on mix and volume flexibility. This is due to the fact that ETO production is addressed which is meant to offer new product flexibility per se. Also seasonal demand variability is addressed for which the aspects of new product flexibility as well as delivery time flexibility are rather not pertinent.

Figure 1 The Flexibility Funnel (adapted) (Suarez et al., 1996)

First-order flexibility types. Oke (2005) describes that first-order types of flexibility

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set-up times are reduced, and thus quicker changes between different products are enabled. Consequently, set-up time reduction entails production time reduction which theoretically frees time for producing a higher output volume at rather constant fixed costs. As shown by the example and visualized in Figure 1, the underlying mechanisms to be mix and/or volume flexible, can be lower-order flexibility types (e.g. Jack & Raturi, 2002; Suarez et al., 1996).

Table 1 Overview of first-order flexibility types

Type Definition Driverc

First

-Order

Flexib

il

ity Typ

es _Volumea the ability to operate profitably at different overall output levels

 Demand variability (degree depends on predictability of demand)

Mixb

the ability to produce different combinations of products effectively given a certain capacity

 Product variety offered  admittance of

alterations/specifications

Sources: a _{= Gupta & Somers (1992);} b _{= Zhang et al. (2003);} c _{= Oke (2005)}

Lower-order flexibility types. Even though first-order flexibility types are the ones

externally tangible, lower-order flexibility types can be recognized as supporters or even enablers of mix and volume flexibility, and are therefore crucial to consider. Consequently, they have an indirect significant impact on customer satisfaction, as found by Zhang et al. (2003). Upton (1995) refers to these types of flexibility as internal, as management can have direct influence on them to shape volume and mix flexibility. This category includes routing and machine flexibility. Slack (1983) describes these as resource flexibility types and adds the important dimension of labor flexibility. Thus, the resources have to be in place and offer a certain degree of flexibility to create first-order flexibility types. Especially for the lower-order flexibility types no clear list of flexibilities is presented for common use. Therefore, this research focuses on the three above mentioned types, namely routing, machine and labor flexibility, as they were extensively discussed in literature, and at the same time are appropriate for an ETO setting dealing with seasonal demand variability. This is due to rather unpredictable nature of orders that come in, which are rarely ever the same.

Routing flexibility can be referred to as the ability to produce a set of parts or products by

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flow time and the bottleneck machine or process may be relieved if not even totally eliminated (Byrne & Chutimab, 1997; Lin & Solberg, 1991). This indicates the possibility of routing flexibility being a source of volume flexibility (Slack, 2005). Due to improved throughput, for instance, a company might have an increased output volume at the end of the day in comparison to the standard process output while incurred costs are basically the same. Moreover, also the change between products (mix flexibility) seems to be made easier by a certain level of routing flexibility (Zhang et al., 2003). By being able to produce one product type with more than one routing possibility, for instance, proper planning may result in producing another additional product type at the same time. A good real-time information system is indispensable to be in place to assess statuses of different machines and parts and consequently, to make adequate decisions (Joseph & Sridharan, 2011).

Gupta and Somers (1992) define machine flexibility as the ability of a machine to vary between different operations without incurring high costs or high amount of time spent. This definition points towards influencing mix flexibility. However, it could also include the flexibility in producing different output volumes, and thus increase volume flexibility. This is due to the fact that operational speed plays an important role in the concept of machine flexibility.

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Whether numerical and functional flexibility are complementary or substituting raises another important issue in the field. While Lepak, Takeuchi, and Snell (2003) found that the forms are complementary in order to ensure proper firm performance, Roca-Puig, Beltrán-Martín, Bou-Llusar and Escrig-Tena (2008) stressed the importance of prioritization of one of the two types. In a descriptive analysis, the latter found that functional flexibility has a greater positive effect on firm performance in an environment characterized by low internal flexibility, and vice versa. Consequently, striving for both seems to be not beneficial.

In Table 2 possible influencing factors of each lower-order flexibility type are presented. They are influencing in the sense that the shorter the set-up time the more flexible a machine is, for instance. The factors should not be seen as a complete listing but rather as possibilities to enhance the single flexibility types. Moreover, some might be influencing the lower-order flexibility in terms of mix, others in terms of volume adjustments, or even both.

Table 2 Influence factors of lower-order flexibility types

Type Influence Factors

L owe r-Orde r Flex ib il ity Typ e

Routing  Amount of route alternatives

 Time and costs associated with changing routes  Quality level adherence

Machine  Utilization rates  Set-up times

 Change-over times (tools)

 Number of different operations a machine can perform  Operational speed

Labor  Number of tasks a worker can perform  Willingness for overtime (e.g.)

 Speed of task fulfillment  Learning ability of the workers

 Quality/efficiency maintenance among different tasks

Sources: Zhang et al. (2003); Sethi & Sethi (1990)

An Integrated Perspective

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Novelty is added by the investigation of anticipation of demand variability presented in repetitive patterns and thus a different angle is applied to the framework of manufacturing flexibility.

First-order flexibility types are seen as actual outcomes of lower-order flexibility types, and are dealt with in terms of responsiveness to seasonal changes in volume (volume flexibility) and seasonal changes in mix (mix flexibility). This is graphically presented in the conceptual model in Figure 2.

Based upon the theory established before, generally all lower-order flexibility types can influence both, mix and volume flexibility. While unpredictability of demand is said to be the main source of environmental uncertainty, this paper mitigates a certain degree of this uncertainty by introducing seasonality in demand. Flexibility does however still seem to be crucial to properly deal with the occurring changes in demand in form of volume or mix.

The next section introduces the procedure to tackle the questions and find appropriate answers by conducting a case study.

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M

The aim of this research is to gain insight into the sources of manufacturing flexibility and their importance when it is dealt with seasonal demand variability, especially in an ETO setting. To tackle this, a case study approach is applied. Especially in the field of this study, operations management, this approach seems most suitable due to its ability to also exploit the contextual richness offered (Sousa & Voss, 2008). As stated before, manufacturing flexibility is rather intangible, thus it is not simply assessed by asking how flexible a company sees itself in the different dimensions. In fact, reviewing production and other order specific data and observing the operations (and its actual flexibility) adds much more value, as flexibility is a complex construct which is not always visible (and exploited) on first sight. Due to time limitations, a single case study was conducted which also implies rather low generalizability (Karlsson, 2009).

Case Selection

To start, an adequate case was found, which was primarily based on two specific criteria:

1. The company applies an ETO strategy and

2. The company is facing at least one kind of seasonal demand variability it is susceptible to (e.g. yearly volume peaks during summer month)

Through the university network, companies were contacted and asked to participate in this study. The company which properly matched the criteria mentioned and was willing to participate in the study was Alfa Laval, an international company engineering and building heat exchangers. The investigated subsidiary was located in Groningen, the Netherlands.

Data Collection

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Table 3 Research scenario - Alfa Laval Research Scenario

1 Introduction to company:

First insights into processes and work at Alfa Laval were gathered (actually part of observations and fieldwork); qualitative data

Observation and fieldwork: Qualitative data gathering through informal talks and observations 2 Key informants interviews:

Gathering qualitative data concerning

 the types of lower-order flexibility used and how they are used

 perceived seasonal demand variability and its impact Afterwards: transcription in note form

3 Production and order data:

Through help of key informants and ERP system quantitative data was gathered

4 Interview and data reviews:

Coding and categorizing interview transcripts and reviewing quantitative data in-depth watching out for discrepancies 5 Smaller consultations with key informants:

Observations in interviews and quantitative data reviews were conferred with key informants; discrepancies were discussed and questions which arose during the reviews were addressed 6 Overall gathered data analysis:

Within case analysis by displaying data to figure out connections between categories

Introduction to company. Two meetings took place with the site and unit manager of Alfa

Laval to get a general overview of their perceived manufacturing flexibility, as well as their seasonal demand variability. Additionally, the production site was visited and first impressions were gathered concerning the processes and production layout. To round it up, documents were reviewed such as other studies on the same case as well as information accessible via the intranet ‘Share’.

Key informants interviews and smaller consultations. Semi-structured interviews were

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leaders, the production planner, the financial manager, and the human resources manager. Each interview took approximately one hour. After the interviews, transcriptions in note form were made, based on the recordings taped during the interviews.

Additionally, as presented in phase four of the research scenario (Table 3), smaller, follow-up consultations took place. Those comprised the findings and questions form interviews which needed confirmation or explanation, and the discrepancies figured out during data review.

Production and order data. Key informants offered access to significant production

data files, including capacity, workload, labor hours (fix and temporary), delivery, backorder, and order intake information. Those files were based on Jeeves, the ERP system of Alfa Laval, which was further used to review single orders. Quantitative data of several years was collected, mostly between 2012 and 2014, sometimes even data from 2010 onwards was available.

Observations and fieldwork. Both were done by having little talks with the workers,

and reviewing the production floor with a team leader, for instance. Moreover, the opportunity was offered to attend the bi-monthly company meeting during which valuable information concerning the company was given and the culture and motivation within the company (among white and blue collars) could be experienced. Field notes were taken while examining the production floor. These notes resulted from informal talks with the employees as well as from the observations made.

Data Analysis

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As already mentioned in the introduction of this section, by applying a single-case study design, as risk of low representativeness is taken and therefore, generalizability is also seen rather low. However, findings are linked to a well-established and researched theory section on which this paper can build up. Moreover, a single-case study has the advantage of gathering in-depth knowledge and thus the ability to change the perspective from observing to experiencing the problem from the roots. In Table 4 an assessment of validity and reliability is presented which further demonstrates a certain degree of generalizability for this research.

Table 4 Validity and reliability overview

Test: Tactics

Assessment

Construct validity:

Reduction of subjectivity by using multiple forms of data gathering: documents, observations, interviews, informal consultations, hard data

+

Internal validity:

Peer debriefing and review of results through supervisors and back up with company mentor

+

External validity:

Only single-case study research design (no embedded replication)

-

Reliability:

Research scenario presented as well as interview protocol (even though semi-structured interviews)

+

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R

This section presents the results of the data collection and analysis within Alfa Laval. After an introduction to the company, the production and processes are shortly described. Afterwards, seasonal demand variability is proven and the lower-order flexibility types, including labor, machine and routing, are analyzed in light of the repetitive demand pattern.

Alfa Laval

Alfa Laval is a company offering highly customized products which can roughly be accounted for 80% as ETO and 20% as MTO. Each order can be assigned to one of four categories from standard to highly specialized respectively. Category 1 can be seen as MTO production while categories 2, 3, and 4 present characteristics of an ETO process. The effort which has to be made for categories 2, 3, and 4 is becoming more and more complex compared to category one. Not only in engineering and preparation, but also the blue collar workers on the shop floor are confronted and need to deal with the special wishes in need of implementation. These categories are applied for all five basic product types Alfa Laval offers. The product types and their differences are presented in Table 5. However, numerous specifications and alterations are possible dependent on the customers’ wishes. Due to those specifications which differ from customer to customer, but also to the durability of the products which is at least 15 years, orders are generally non-repetitive.

Table 5 Overview product types

Product Type Specification

Industrial coolers

TYR Stainless steel tubes THOR Copper tubes

ZT Whole cooler out of stainless steel Commercial coolers LEX

Condensers AB

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place, to cover medium to long-term illnesses and inefficiencies, and to analyze the order intake and its impact on production.

Production Layout and Processes

For each product type, the production stages are arranged in a flow production layout. This means that each of the products flows through different stages within the line while it is built upon at each stage. The process, which is basically the same for all different product lines, is presented in Figure 3 below.

Figure 3 Production process

In general, the production process of Alfa Laval is heavily reliant on the workforce, their experiences and skills. Also the machines, such as the fin presses, are operated and demand expertise in handling. Welding and brazing require highest precision work from the workforce and can be seen as the bottleneck within all lines.

In the remainder of this paper, the main focus is laid on the core production process highlighted by the red box in Figure 3. This is due to the fact that the planning system of the core production is separated from the ones of logistics, the sheet metal production and the header production department. Hence, all three departments are seen as internal suppliers.

Seasonality Analysis

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chosen to display the moving averages due to the fact that numbers among the years are distributed differently and are thus rather volatile, consequently the seasonality does not show off as clearly as it does in the moving averages, which is coincided by the statements of the employees. The single graphs of order intake and output including the curves of each year can be reviewed in Appendix II.

Figure 4 Seasonality being evident in order intake and output

In Figure 4 the seasonality in order intake, which is mainly attributable to the reasons mentioned before, is clearly evident between the weeks 15 and 22. Considering the output curve, the consequences of seasonality in demand are starting to show off around week 23, while a clear-cut ending does not seem evident since no constant high output can be recognized, but rather a generally higher level of peaks and drops. According to the interviewees and slightly represented in Figure 4, the seasonal pressure ceases around week 43. The reason for the ‘gap’ between order intake and actual output is not only attributable to engineering, preparing, planning, and production, but also to the lead times of materials necessary for the first stages in the production process. Another reason for the peak in output coming later and being prolonged are the lead times for the single categories of products which are increasing respectively. This is aggravated by the pressure on capacity which consequently leads to longer lead times. As those are, however, not adapted accordingly during order intake within Alfa Laval, for the simple reason to stay competitive, delivery

0 10 20 30 40 50 60 70 80 90 0 100 200 300 400 500 600 700 800 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 # o f Uni ts H o u rs Weeks

Order Intake (2010-2014) and Output (2012-2014) Moving Average (4 weeks)

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adherence is comparably low among the weeks in which production has to deal with the seasonal demand variability.

Even though, no seasonal – and thus repetitive – pattern in the product variety could be found, the product mix offered by Alfa Laval is challenging the planning for seasonal variability in volume even more. If it could be said that seasonality is due to one specific product type, arrangements with suppliers could be made which could lead to lead time reduction and would thus make on-time delivery more feasible. Since this is not the case, Alfa Laval has to ensure volume but at the same time mix flexibility. As this paper is, however, focusing on seasonal changes, which means repetitive patterns in the mix, the mix flexibility is not a major component of the upcoming analysis and discussion, but rather a needed factor that even hampers dealing with seasonal changes in volume.

Concluding, it can be stated that Alfa Laval faces a seasonal peak in the number of order intakes during spring. The peak is however, not attributable to a specific product type, which in turn aggravates the situation for Alfa Laval. Even though production is often not able to respond accordingly, the company is aware of the issue and tries to plan production anticipatory, which is dealt with in the upcoming paragraphs.

Labor Flexibility

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Figure 5 Delivery time adherence 2013

As mentioned before, Alfa Laval is highly reliant on its workforce, since within the whole internal core production process manpower is needed, while at the assembly places, for instance, they are indispensable. Consequently, the capacity problem mentioned above, as a consequence of seasonality is basically concerning the availability of labor hours. Actions need to be taken to increase labor hours which can be done by scheduling overtime during the beginning of the seasonal peak, and introducing a second shift at a later point in time when a constant high level is foreseeable. To be able to adapt adequately, temporary workers are contracted in cooperation with two agencies. As it can be seen in Figure 6, which presents the labor capacity and the actual output of 2013, temporary manpower is used during the whole year. In general, the ratio of fixed and temporary workers should not drop lower than 70/30, while it is aimed at 80/20 to ensure sufficient skills and experiences in house. During the seasonal peak between week 22 and 38 (Figure 6), the ratio is much lower than intended, which is one reason for the delivery adherence problems presented in Figure 5 beforehand. An analysis has to be made in such a situation and consequently a decision whether and whom to offer a fixed contract, at the expense of flexibility. The partly high fluctuations in the fixed workers line are mainly due to illnesses (medium and long-term) as well as holidays.

85,0% 88,0% 91,0% 94,0% 97,0% 100,0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Weeks

Delivery Adherence 2013

mainly attributable to fixed workers. Generally, this seems to lead to even more severe problems while undercutting the above mentioned ratio. The reasoning is however, not quite correct, since also temporary workers potentially offer specific experiences and skills.

Within Alfa Laval, temps are divided into different contract categories which can also give indication about experiences and skills. In Figure 7 the division is made between directly hired temporary workers and workers hired in cooperation with an agency. The latter, which is most common, offers two kinds of contracts, while ‘Fase A’ temps are the ones hired very flexible without an indication of employment length, temps contracted as ‘Fase B’ receive temporary contract with a minimum employment length determined. Consequently, there are temps within Alfa Laval which support production up to two full years. These temporary workers are very valuable due to their specific experiences gained within Alfa Laval.

This distinction between the temps is of importance experiences and skills are increasing steadily the longer a temp is working for the company. In the course of time, ‘Fase B’ temps offer Alfa Laval planning-wise a great opportunity, as they gain experience, but are at the same time necessarily flexible in terms of capacity due to temporary contracts. This is exactly the flexibility which is lost when temporary workers are offered a fixed contract, as commitment of the company is limited. Additionally, all kinds of temporary workers bring along a certain degree of flexibility per se when they are engaged, especially when they do not have been specialized yet and can be deployed at different stages within different lines.

0 200 400 600 800 1000 1200 1400 1600 1800 2000 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 H o ur s Weeks

Overview labor capacity - output 2013

Temps

Fixed workers Output

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Experience and knowledge are key factors to success while dealing with seasonal increases in volume. This is due to the fact that capacity needs to be fully exploited during the high season, which includes the necessity to properly cope with the adjusted products. This is applicable for fixed workers, but at the same time, also temporary workers need to be prepared. In general, the fixed workforce should cover the low season in the beginning and end of the year. Figure 6 shows, however, the continuous usage of temporary manpower among the whole year. This is due to the fact that training is offered during that time for temporary employees as well as for fixed workers, which is mainly done in anticipation of the high season. Alfa Laval introduced an interesting training concept during the last year, called ‘3=1 and 1=3’, meaning that at least three workers can operate one machine and one machine can be operated by at least three workers. The concept is supported by a tool called ‘knowledge matrix’ which gives an overview of experiences and skills of the fixed workers at the different stages. It is differentiated between basic, good, high or specialist, whilst high means that the worker can operate the machine individually and has experiences working with it; a specialist is able to do small maintenance himself and train others to handle the machine. Consequently, planning can be more efficient and effective, as a machine operator of a fin press cannot only help out at another fin press and thus among different lines, but through 3=1 and 1=3, he is able to help out within the line at different stages, such as the final assembly. This results in less idle time and ensures the important flow. As mentioned, this training is also provided for temporary employees. Their evaluation is however, only done at the end of their active employment period with an endorsement of desirable re-hiring. These training and evaluation processes are important for the low season, but also for the high season, since it is ensured that there are enough employees, both fixed and temporary, available which offer sufficient skills. This leads to flexibility in production processes and thus to flexibility in

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producing a different range of products to actually be prepared to deal with the volume pressure in high season.

The training offered in anticipation of seasonal demand changes and the introduction of overtime as well as engagement of temporary manpower come at a certain cost. Expenses for training are partly taken over by the agencies, since they also have a certain interest in increase skills of their workers. Thus, they train their workers in welding or brazing, for instance, and ensure that they receive a certificate. Still the risk exists for Alfa Laval to invest in temporary workers which might leave the company. Also the costs for employing temps are higher than for fixed worker, due to the degree of flexibility they offer. Accordingly, ‘Fase A’ temps earn 16% and ‘Fase B’ 8% more salary than fixed workers (at basic hourly rate). These costs have to be weighed against the costs occurring for overcapacity, which could be an issue when more fixed workers are employed.

To sum up, in order to deal with seasonal changes in volume, Alfa Laval relies on labor capacity extension through the usage of overtime and the engagement of temporary workers. Moreover, training is offered in anticipation of the high season to ensure flexibility among the production and the ordered adjustments. Both main strategies come at a certain cost which needs to be carefully evaluated in the light of employing additional fixed workers.

Machine Flexibility

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Moreover, as mentioned beforehand, each machine needs to be operated by a worker. A reason is that batch sizes are order-specific and thus very small. Sometimes orders can be combined if they are just planned to be produced within the same days. The same holds for tube cutting for which combination of orders is more common, though.

To sum up, machine flexibility is ensured per se at Alfa Laval, since machines are not the bottleneck anymore as well as the planned utilization is normally 80%. Consequently, high machine flexibility is given which can be exploited during high seasons. It is however, only planned twice-weekly, and is thus not explicitly planned in anticipation of seasonal demand variability.

Routing Flexibility

The main components of the production process within Alfa Laval are the workforce and machines. Consequently, those two can offer a certain potential for routing flexibility. Just when production is planned for machines (twice-weekly) also routing possibilities are considered. Within the core production process, routing is often not used, which is due to the fact that throughput times are not adequate enough to quickly change routes. Moreover, size and weight of the (semi-finished) products, as the coil after assembly, for instance, are unhandy to route through the facility, which would also discriminate the production flow. Thus, routing possibilities are usually not exploited, except for the THOR and TYR line, which are bordering and only distinguish from each other in the material of the tube they utilize. This is important in the beginning of the process when it comes to tube cutting, bending and expanding, as well as in the later stage of welding/brazing, since copper tubes (THOR) are brazed, stainless steel tubes (TYR) however are welded. This is the reason why THOR products can also be produced on the TYR line, as facilities for welding and brazing are available. The other way around it is not possible though.

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D

In their study, Suarez et al. (1996) have introduced the Flexibility Funnel (see Figure 1) which graphically presents the lower-order flexibility types as being the underlying mechanisms to achieve volume flexibility. First-order flexibility types were implemented in this research in terms of seasonal changes in volume or variety (mix). As mentioned in the result section already, there is a need of being mix flexible per se which is not preliminary related to seasonality, because the case company is operating in an ETO environment in which rarely repetitive orders occur. Consequently, rather than considering mix flexibility as only being necessary for seasonality, it is a general hampering factor for adequately dealing with seasonal changes in volume which were discovered.

The suggestion of lower-order flexibility types being underlying mechanisms for first-order flexibility types as presented by Suarez et al. (1996) does also hold for this research. Even though, it was found that a differentiation between the single lower-order flexibility types is necessary when considering flexibility to deal with seasonal demand. The type of flexibility which is anticipatory and actively planned for is labor flexibility. Machine and routing flexibility have to be in place, even though they are not planned directly and in advance for seasonal demand. This is due to the fact that all labor flexibility cannot be exploited if the machines do not offer the capacity and capabilities to leverage that flexibility. Consequently, a new model arises for which the terminologies of supporters and enablers are introduced, which are explicitly discussed below.

Labor Flexibility as Enabler

Already Jack and Raturi (2002) empirically demonstrated that labor flexibility is a major source of volume flexibility. The questions which were raised in the beginning aim at gaining greater insight into the anticipatory dealing with seasonal, repetitive changes of demand in light of the different types of lower-order flexibility and how it is actively planned for. It was found that labor flexibility can be used to be able to respond appropriate in order to not be compelled to increase delivery times. Both types of labor flexibility, numerical and functional, are needed and can be planned for seasonal changes in demand.

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the high season, simply an extension of capacity hours is needed which led the company to hire temporary employees. This decision is done primarily on a medium-term basis, and thus when first signs of demand changes are looming. By introducing more labor hours and consequently overtime or a second shift, also machine capacity increases in terms of available hours to run. The overall capacity is extended.

A very important aspect is ignored so far however, which helps companies chasing demand and being able to respond to seasonal changes, is capability. It is not sufficient to only increase the number of workers if the capabilities are not adequate among the workforce. This might hinder the processes and engender quality issues, delivery delays or other factors causing dissatisfaction of the customer. Especially the ETO environment entails the need of experiences and skills among the workforce as explained in the result section. Salvador et al. (2007) stress the importance of the usage of both, capacity and capability to adjust to changes in demand. Consequently, it can be stated that to chase seasonal demand patterns, volume flexibility is indispensable in both sights, increasing capacity among workers (numerical flexibility) but also increasing skills and knowledge (functional flexibility) to be able to handle the changes. This finding is generally coincided with the findings of Lepak et al. (2003), who researched both types of labor flexibility with regard to the performance of the company.

More specifically, functional flexibility is important to ensure enough capacity and capability to run the machines and take over other tasks especially during high season. This is due to the fact that the seasonal volume changes cannot be attributed to one specific product every year. Therefore, the mix flexibility becomes even more important as, if not dealt with adequately, it may hamper the ability to appropriately deal with seasonal changes in volume. The need for anticipatory planning for functional flexibility during the high season, in form of training for instance, is therefore evident and just as necessary as the numerical flexibility which needs to be ensured.

- 28 - Machine and Routing Flexibility as Supporters

Starting with routing flexibility, it was found that it is used to circumvent, support or eliminate the bottleneck to increase throughput and flow time (Byrne & Chutimab, 1997). Within the studied company, the bottleneck process (welding/brazing) is very labor intensive. Certain experiences and knowledge are needed, as well as the facilities in place, to be able to weld or braze. Consequently, the ability to eliminate a bottleneck, and thus increase routing flexibility, is dependent on functional labor flexibility, meaning that welders may also braze to relieve the bottleneck station. This statement can be generalized, since the same actually holds, if machines would be the bottleneck. To route efficiently, functional labor flexibility is needed to set up machines properly to not loose time and money. This example stresses the importance of being mix flexible again. The necessity is proven that routing flexibility is needed to appropriately deal with seasonal demand variability, is however, not actively and anticipatory planned for, but rather in the short-term scale. Therefore, without potential routing flexibility which helps to establish a certain degree of mix flexibility, dealing with the seasonal changes in volume would be hampered.

Another aspect studied in this research is machine flexibility. Also machines can only be as flexible as their workforce is, in both, hours (numerical) and functional terms. Machine capacity is extended through extension of the labor hours which is only possible due to the general flexibility the machines offer. Moreover, setting up the machines is a process which should not be underestimated, especially in an ETO environment, as not often batches of more than one order can be produced. Therefore, also the functional flexibility of labor is important to ensure machine flexibility.

As described in the paragraphs above, machine and routing flexibility both need to be potentially given to exploit the flexibility that labor (if appropriately planned for) offers during seasonal peaks in demand. Consequently, the new terminology describing those two types is supporter, due to the fact that labor and machine flexibility are actually supporting labor flexibility. Without potential machine and routing flexibility labor flexibility could not be fully exploited, and thus also the impact it may have on seasonal demand is limited.

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production planning with more basic flexibility. This leads to another important aspect which is the fact that with the presence of both types, the hampering effects which come along with the necessity of being mix flexible are eliminated.

Enabler-Supporter-Model for Seasonal Changes in Volume

Based on the discussion beforehand, a model is established, presented in Figure 8, to graphically demonstrate the relationship between the enabler and supporter, and their relation to seasonal changes in volume. Also mix flexibility is shown in the model as being an underlying factor, which is however crucial to consider. Both types of labor flexibility can be seen as enablers of appropriate dealing with seasonal demand variability in volume. Also important – however, not actively and anticipatory planned for as the enablers – are the

supporters, thus machine and routing flexibility. Within the case studied, it was found that

especially potential functional and routing flexibility are essential for mix flexibility in terms of alterations, for instance. This finding is coincided with the findings of Oke (2005).

Figure 8 Enabler-Supporter-Model for seasonal changes in volume

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Consequently, dealing with seasonal demand variability might be affected by the machine flexibility and maybe also routing flexibility, rather than preliminary dependent on functional or numerical labor flexibility. The roles of the types of flexibility might therefore change in case of other production approaches used which though face seasonal demand variability.

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C

Seasonal demand is seen as ‘daily business’ for many companies that offer certain services or produce products which introduce seasonality themselves. The question arose to what extent manufacturing flexibility can help in anticipation of seasonal demand variability. Three lower-order flexibility types were investigated in terms of their influence and possibility to anticipate for seasonal changes in demand volume.

The main finding was that, even though all three types of lower-order flexibility are needed and partly dependent on each other, labor flexibility is the main enabler of manufacturing flexibility to deal with seasonal demand variability. This is mainly due to the fact that it can be appropriately and especially medium-term wise be planned for both, numerical and functional labor flexibility. Overtime and temporary workers can be used to increase capacity which might lead to a second shift, while also cross-functional training was found to enable efficient dealing with seasonal demand challenges. However, also machine and routing flexibility are not to be ignored, as it was figured out that they are supporters of labor flexibility. Those two are not planned anticipatively and actively, especially in the studied company it is done rather short-term, which is due to the fact that potential machine and routing flexibility are already established. The ETO environment investigated introduces another hampering factor which is the overall need of mix flexibility, mainly in terms of product alterations.

For practice, these findings demonstrate a need for anticipatory planning for seasonal demand variability which is mainly done in terms of labor. At the same time, however, potential machine and routing flexibility need to be established to be able to exploit the planned for labor capacity and capabilities. The individual definition and differentiation of supporter and enabler flexibility can be crucial for companies to unhide and be able to exploit the different types of flexibility, especially in anticipation of high seasons.

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Since this research is just based on one case study, it faces certain limitations. The major one is the generalizability, which is not rigorously given, due to the fact that evidence was only found from one case. Moreover, observer bias might also be an issue in just executing one case study. The latter is however diminished by triangulation, which is proven as three different methods of data gathering are used to answer the research question(s). Consequently, reliability of data is enhanced.

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B

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A

Appendix I: Interview Protocol

‘The interview is planned to take no longer than 45 minutes. If you think that certain information is sensitive, please just say so. For proper reconstruction of the interview I would like to record it. Are you fine with that? All findings will solely be used for the completion of my master thesis project.

The goal of this interview is to find out how Alfa Laval is dealing with seasonal variability in demand and/or variety, and thus how you actually plan and exploit flexibility for it. Firstly, I would like to get to know something about how flexible the company is; afterwards we turn to seasonality and the usage of flexibility concerning seasonality.’

Interview information Date: Start time: End time: Tape no.: Information Interviewee Name: Position: Seasonal variability

Seasonality: predictable demand variability which arises in a repetitive manner (either volume or variety/mix wise)

1. Does Alfa Laval face seasonal variability in volume, in your opinion? (e.g. peaks in summer)

2. Does Alfa Laval face seasonal variability in variety/mix, in your opinion? (overall volume stays the same but the mix of products changes in a repetitive manner, e.g. yearly)

3. Does the company plan/prepare for these repetitive changes? To what extent?

Flexibility

How would you describe the flexibility within the production?

Routing flexibility: ability to produce a set of parts or products by using alternate routes through the manufacturing system

1. To what extent do you see the manufacturing system of Alfa Laval as routing flexible? 2. What are the possibilities to change routes within the manufacturing system?

3. What influences the routing flexibility in your opinion? (maybe availability of labor or machine capacity? Changes in variety? Changes in volume?)

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5. Is quality an issue when changing routes? (Concerning labor and/or machines?) 6. Do you use different routes for balancing changes in volume?

7. Do you think routing flexibility could help to chase demand variability in volume and/or mix?

8. You know that there is an annual repetitive peak in demand during summer, is routing flexibility considered when dealing with seasonality? (Do you plan for it?) In short-term or long-short-term (planning)?

Machine Flexibility: ability of a machine to vary between different operations/producing a different volume without incurring high costs or high amount of time spent

1. To what extent are the machines flexible in terms of being able to handle different product types (= product lines) or different volumes (= utilization)?

2. To what extent are machine changes associated with time spent and costs? 3. Is quality an issue when changing machines?

4. Do you use machine flexibility to deal with volume/mix changes?

5. How important is machine flexibility in the light of seasonal demand variability? 6. By adding a second shift, and thus adding machine capacity, can costs be majorly

‘disregarded’?

Labor Flexibility: functional – one person can do more tasks e.g.; numerical – increase number of people

1. Is Alfa Laval flexible in terms of functions of labor? (ability to perform different tasks - %?)

2. Are quality and efficiency consistent among tasks performed by one person? 3. Is Alfa Laval flexible in terms of the amount of employees working? (%base -

%temps)

4. Are quality and efficiency consistent among people?

5. Is labor flexibility used to prepare for seasonal variability in volume and/or mix? (To what extent and how is it planned for?) (for numerical and functional respectively) 6. Are temps ‘coming back’? (cost efficiency – training - %?

7. After reviewing the used capacity numbers, why is the base employee line so fluctuating?

Overall spoken, do you think one type of flexibility presented is more important than another one in planning for seasonal demand?

It seems like labor is the main flexibility needed. Would you say that the other types of flexibility (routing and machine) are important for labor flexibility to be fully exploited?

- 38 - Appendix II: Order intake and output graphs

Remark:

A limitation of the graphs is that data within the years was sometimes recorded irregularly while the average sum is leveling the effects out.