UNIVERSITY OF GRONINGEN
FACULTY OF ECONOMICS & BUSINESS
Master Thesis in Msc International Financial Management
Developing a Prototype Cost Estimation Model – A
Case Study for ContiTech Techno-Chemie GmbH
Author:
Supervisor:
Patrick Kessler
Wim Westerman
Student Number:
s1984357
Co-Assessor:
Hein Vrolijk
Abstract
This study investigates how the supplier for the industrial and commercial vehicle industry, ContiTech can improve its process of cost estimation of prototypes. In doing so it suggests a standardized work procedure for the involved departments and constructs an estimation tool for the costs of prototypes, which aims to improve the overall financial performance of the company. The tool has been created based on concepts like machinery time and auxiliary time cost, build time, activity-based costing and average costing. Crucial contributions to the academic literature are a phased process model
methodology for similar problems and the application of a technological rule, which I call the “common-denominator-approach”. Furthermore the research is an evidence of how to overcome Pettigrew’s Primary Double Hurdle.
1. Introduction
ContiTech Techno-Chemie GmbH is a subsidiary of the multinational corporation Continental AG, located on the outskirts of Berlin. It is active in the “Fluid-Division” of the corporation and its
subordinated Commercial & Industrial Vehicles (CIV) segment, producing hoses, pipes, couplings and other parts related to the transportation of liquids like oil-hoses for cars or multipurpose hoses. The products are being exported worldwide and therefore the company has international operations dispersed on the globe.
Since each customer has very specific product requirements, it is frequently the case that a new prototype has to be developed before the product can manufactured serial. For an employee of the sales department, it is therefore weekly practice to offer prices for prototypes which are inquired. Due to the sheer amount of products being offered and the frequency of new prototypes being developed, the work load to register a new prototype and to retrace its actual costs would be too high. On these grounds, there are usually two different procedures an employee would follow to determine an estimate of the cost of the prototype and thereby derive an offering price.
the cost of input-parts from its suppliers, since those prices might have changed after not ordering those parts over a longer period or have never been requested at all. In addition, the sales
department has to begin communicating details to the product construction department even before sending out an offering price. After choosing a desired contribution margin, the sales price is
determined and offered to the customer.
It turns out that in reality the actual cost of the prototype often seems to be significantly higher than estimated, causing the actual contribution margin to drop, turning the prototype into a loss-maker. Concerning the amount of prototypes offered every month, an underestimation of the actual costs causes tremendous inefficiencies leading to potential losses which should have been profits instead. In the year 2013, ContiTech had a loss of €158,000 stemming from underestimations in prototype costs. This meant that the average contribution margin was -45% instead of the desired 10%. In 2014, there was an improvement in the overall results but the company still incurred losses, which
amounted to €78,000, yielding average contribution margins of -20% instead of the preassigned 23%. Those underestimations stem from operational uncertainties like the lack of knowledge about developments of supplier prices, the development processes themselves etcetera. The fact that ContiTech is operating and sourcing its parts on an international scale even increases the uncertainties and causes a higher fluctuation in the actual cost of prototypes.
An improved overall estimation of actual costs would reduce the effect of operational uncertainties and unleash profit potential which has been unused so far. This would lead to an increase in the overall efficiency of operations and eliminate sources of financial losses stemming from false estimation in the past.
Accordingly, my research question is:
“How can ContiTech Techno-Chemie improve the estimation of the actual costs of prototypes and thereby improve the overall performance of the prototyping department?”
It should be said that the solution which is being developed has to be of a practical nature, since it is aimed to be used on a weekly basis by the employees of the sales department. However, it should be a warning that a too strong simplification of the issue leads to a decreased validity. The compromise would be a financial model, which entails complexity in its prior preparation and analysis and results in a final model which can be simply used on daily base. Keeping the model simple will ensure that the employees make less mistakes and thereby it will reduce a further error source.
The first variable determining the cost of a prototype at ContiTech is material cost for unknown parts, which can be divided into in-house production of parts and externally sourced parts from suppliers. Then there is material cost for known parts, labor cost from production and labor cost from management and administration.
Estimated costs of in-house-produced material components stem from a data analyses of ContiTech SAP enterprise resource planning (ERP) data base and are based on the insights gained from
interviewing a diverse set of involved employees. Furthermore, there is estimated cost of in-house-labor efforts which represents the working effort in terms of in-house-labor hours consumed by the different processes involved in the prototype component production. Those labor hours can be subdivided into auxiliary time and machine time cost, which will be explained as well in a later section. Lastly, there are allocated overhead labor costs, which respectively are the costs allocated for the employees who are not directly involved in the production of a prototype but still belong to the prototyping department. Therefore this overhead allocation represents a managerial and administrative fee to cover all the expenses which were not covered, yet. The estimated cost of externally-produced material components is the outcome of the average cost analyses with a common-denominator-approach as explained throughout the paper.
When a contribution margin is chosen by the employees of the sales department and applied to the estimated cost one can determine a price for a prototype which should not yield negative
contribution margins in reality, thereby turning a potential loss-maker into a profit-maker. This should lead to an improvement overall financial performance at ContiTech.
The remainder of this paper is organized as follows. Section 2 presents the literature review. Section 3 describes the methodology which has been applied to derive the input variables that can
determine the estimated costs by using the common-denominator approach introduced in this paper. It recapitulates all steps taken to create a final tool for the estimation of prototype costs. Section 4 shows the results and discussion, while section 5 will summarize the paper leading to a conclusion and limitations of the research.
2. Literature Review
As explained in the introduction the core problem is that the estimates of actual costs of prototypes tend to be too low, thereby causing the product to become a loss-maker. Those errors in cost estimation can be directly linked to operational uncertainty, which has been identified to have a tremendous negative effects on performance (Field et. al 2006). This is also the case for Conti-Tech’s prototype cost estimation process and therefore it is crucial to improve it.
One type of uncertainty organizations such as ContiTech are confronted with arises from its international supply chain. Simangunsong et al (2012) reviewed the current state of supply-chain uncertainty theory in a literature review. He identified a list of 14 sources of uncertainty and
proposed 10 approaches to cope with them. Accordingly, multinational enterprises have to cope with more uncertainties originating from the internationality of their supply chains as an incorporation of supply chain risk. This highlights the need of an improvement in the estimation process of the prototype cost in an international environment and emphasizes its importance for ContiTech.
By applying multiple regression analyses on a sample of construction companies Kim and Shim (2014) found that the estimation accuracy in the construction sector ranges from -30% to +50% of actual project costs. Also, Moloekken-OEstvold et al. (2004) investigated the accuracy of cost estimation with focus on the Norwegian software industry where they conducted a survey. They found that the majority of projects (60-80%) suffered from an under-estimation of effort and scheduling planned in. It seems as if industries involved in businesses with high amounts of uncertainties, high-technological products and manufacturing all share the problem of estimation errors with regards to their costs.
When estimating costs one can choose between two principal types of method which are either technological or statistical (Kim & Shim, 2014; Louderback, 1982). Niazi et al. (2006) categorized them as qualitative, which can be either intuitive or analogical, or quantitative, being either analytical or parametric techniques. The quantitative statistical method in the form of average cost analyses will rely on historical data to analyze relationships between cost and activities (Lowe et al., 2006; Trost and Oberlender, 2003).
Di Angelo and di Stefano (2010) conducted a parametric cost analysis for web-based e-commerce of layer manufactured objects and found that prototyping services are a diffused methodology which is used to compete in a global market. The same accounts for the business of ContiTech, which needs to cooperate with its international customers and suppliers to provide a prototyping service which is highly competitive in an international environment. It will remain a crucial part in the service
portfolio of the company. Di Angelo et al. (2010) state that there are numerous factors which can be easily deduced that determine the cost of a prototype. I argue that those factors are often firm- and industry-dependent and that those of ContiTech will be deducted by the qualitative and quantitative analysis undertaken in this research paper. In addition, those factors are partly predetermined by the available input factors, which in this case are the information available on the blueprints of the requested prototypes. Furthermore, Di Angelo and Di Stefano (2010) emphasize the concept of build time, which can be deduced as an important factor influencing some components of the prototype’s build cost. Closer attention will be brought to that aspect when analyzing the labor cost efforts which arise in prototype production. To broaden the concept of build cost, Koenigsberger’s concepts of machinery time cost and auxiliary time cost (1964) is used. Accordingly, auxiliary time cost represents the amount of time it takes to prepare a machine for producing a component. It can be seen as a fixed factor and also can be described as the set-up time for the machine. Machinery time cost represents the time it takes for the completion of one unit with the machine. It can be described as a marginal variable, since each further unit of components which is being produced adding one further unit of machinery time cost. Correspondingly, the machining cost per workpiece can be calculated by multiplying the machinery time cost with amount of units to be produced and adding the auxiliary time cost to that.
determining the cost of a prototype, but those factors also need to represent the desired input variables represented in the final tool, which leads to reasonable results in the estimation. This means that all the input variables will be determined by information available on blueprints of the prototypes which are sent to ContiTech’s Sales Department.
Another approach was suggested by Ben-Arieh & Qian (2003), introduced a methodology for using activity-based costing in the determination of the prototype price. Ben-Arieh & Qian’s approach will be used in determining the overhead costs to cover the administrative and managerial expenses of indirectly working employees like the coordinator and the prototype department’s manager.
Altomonte et al. (2015) on the other hand, conducted a survey on a sample of more than 14,000 European companies analyzing the pricing behavior in relation to cost calculations. They apply an average cost approach in determining the costs a firm incurred. This approach will be followed in calculating the cost of externally sourced parts from suppliers and is a quantitative statistical method as previously explained.
Obi (2010) took a comprehensive look at manufacturing product cost estimation from a product proto-typing point of view. Thereby he determined several components he considered crucial for the estimation of product cost, which are material components, labor components, tools and machines components, energy components and overhead of other cost components. Those components combined with the previously identified factors build time will be my initial variables deduced from the literature.
I expect the related information stemming from the databases of ContiTech to be credible, well-documented, accurate and comprehensive and therefore to be in line with the requirements of the “cost estimating and assessment guide of the U.S. Government Accountability Office.
Table 1 summarizes the theories introduced in the literature review. It displays the theoretical concepts crucial to the study in combination with the corresponding authors, the year of publication and the journal the respective article has been published in.
I can summarize that the literature regarding cost estimation has a preference for different
Concept
Author
Year Journal
• Machinery Time and Auxiliary Time Cost
Koenigsberger 1964 “Advances in Machine Tool Design and Research”
• Build Time Di Angelo & Di
Stefano
2010 “International Journal of Production Research”
• Activity-Based Costing Ben-Arieh & Qian 2002 “International Journal of Production
Economics”
• Average Cost Approach Altomonte et al. 2015 “European Economic Review“
"Table 1 - Applied Theoretical Concepts"
3. Methodology
Van Aken (2004), criticized that there a serious utilization problem in the outcome of academic management research. His article argues that this problem of relevance can be mitigated by complementing academic management research with prescription-driven research. He states that with the use of design sciences one can create management theory, which is not only field-tested but creates grounded technological rules as well. Therefore, the purpose of the development of scientific knowledge is to solve not a specific managerial problem but rather a class of managerial problems. In line with this, I intend to create technological rules which can be applied in similar scenarios and which serve the handling of an overall class of managerial and academic problems. Furthermore, Van Aken’s notion justifies design science as an appropriate tool to create prescription-driven tools for managerial problems, which was the aim as explained in the introduction.
Design problems have been described as “wicked” problems, which bring along complex difficulties for which finding appropriate solutions can be very challenging. In addition, each solution to a problem creates new problems (Simon; 1997). Since those characteristics pertain the circumstances given at ContiTech, Simon’s point illustrates the need to engage in a design study approach.
Bayazit (2004) wrote an extensive analyses investigating design research within the last forty years. He explained that the involvement of users in design decisions and the corresponding identification of their objectives were main characteristics, which evolved during the last generation of design methods. This as well emphasizes the strong correlation between users, in this case employees, and the use of design studies. Moreover, user participation is described as a wide and variable concept and hence the designer’s awareness of user values is deemed to be crucial to the success of the participatory design process. According to Bayazit (2004), there have been numerous design researchers and design methodologists, who were developing their design methods in relation to other fields of research by applying the model of system analysis.Systems analysis has been described as a technique utilized when problem solving is the objective. This approach fragments a system into single components to analyze how they interact and create synergies to accomplish a common purpose (Bentley, 2007). Then it is followed by a synthesis which aims to improve the overall efficiency of achieving the common purpose. In the specific ContiTech-case, this would mean to segregate the departments involved in the prototyping process, analyze them and become aware of the how synergies are created between the departments. After that one can think about how to improve the efficiency of achieving the common purpose, which is respectively in this case improving the estimation of costs of prototypes.
As described in Struder’s “Dynamics of Behavior-Contingent Physical Systems” (1970), one has to look for a unit of analyses, comparing dimensions that are both relevant and empirically accessible. Furthermore Struder stated that the designer of a research has to start by analyzing human behavior, which will then serve as a base for deriving quantities, qualities, and relationships. This underlines the need to begin the investigation with interviews with all entities affected by the prototyping process. Also it reflects the fragmentation of a system into its single components as described for system analysis by Bentley (2007).
reductionistic versus too trivial to be much of practical relevance but then lacking sufficient rigorous justification. To overcome this dilemma, Pettigrew specified the so-called primary double hurdle, which has to be overcome by academic management research. Accordingly, management theory should not just meet the criteria of scholarly quality but also result in managerial relevance
(Pettigrew, 1997). Again, this depicts the fusion of an academic objective combined with the practical approach as it was emphasized by ContiTech.
To cover the complexity of this study, I decided to engage in a mixed method study in the form of design research, which entails a qualitative as quantitative dimension as well. Its detailed set-up can be seen as a phased model, which is depicted in Figure 1. The application of this phased model will be explained throughout the remaining part of the method section.
Van Aken (2004) laid out the general approach for this research. Accordingly, in analyzing a specific problem a research has to engage in the problem solving cycle, also called the regulative cycle (Van Strien, 1997). First, one has to define the problem out of a “messy context, this phase has been titled “naming and framing” by Schön (1983). Second the researcher has to plan an intervention, which entails diagnosis, design of alternative solutions and selection. Lastly, one has to follow up by applying the intervention and evaluating the outcome. Following this cycle will lead to a process design which can be seen as a representation of a system or process. Since, I investigate the process of prototype production as an entire entity plus the process of cost accumulation and estimation in the respective department, I once again argue that design research is the appropriate method to engage in.
The three phases are represented in the following subsections that deal with the data collection and data analyses. The approach is cyclic in its nature and therefore, one cannot strictly separate the phases, but rather approach them in a recurrent manner. The phases cannot be seen as separate entities to work on and are all interconnected, providing the follow-up procedures for the preceding steps to be done from analyses of the problem, to finding an intervention, leading to a solution for the problem. The problem solving cycle is in accordance with the concept of problems creating new problems as previously mentioned and hence again it resembles the reason why I chose to engage in utilizing the problem solving cycle in a design science manner.
intend to create is how to employ common denominators as a basis for cost estimation. Since the investigation can be considered a field research project, I considered it crucial to have a qualitative dimension. This should have enabled me to gain a deeper understanding of the subject matter, to portrait the problem from diverse perspectives, and to prevent a tunnel vision solely focusing on the controlling aspects, thereby oversimplifying the complexity of problems. It should be kept in mind that the qualitative dimension still has a supporting role which is in the end subordinated to the quantitative part.
The quantitative dimension was set in place to derive meaningful results which can be implemented in the workplace and facilitate inference for the controlling department. In turn those results were implemented in the tool which will be used by the sales department. The gathered information will cover both primary and secondary data. This can be attributed to the fact that the study consists of two dimensions, as described previously. Since the variables affecting the estimation are of a numerical and categorical nature, I needed to apply a method which considers the deviation of both types of variables. I used a similar approach as applied in Jin et al’s “Improving Accuracy of Early Stage Cost Estimation by Revising Categorical Variables in a Case-Based Reasoning Model” (2014).
Data Collection 1
• First Interview Round • Workflow Analyses
•User Involvement in Tool Design
Data Analyses 1
• Interview Coding & Interpretation • Variable Identification
Data Collection 2
• Collecting Quantitative Data from ERP System (SAP)
Data Analyses 2
• Interpreting the Quantitative Data
• Common-Denominator Approach (Technological Rule) • Extreme Value Assessment & Average Cost Approach
Data Collection & Analyses 3
• Second Interview Round with Management
• Process-based Allocation of Wage-Hours (Machining & Auxiliary Time Cost) •Activity-Based Costing for Administrative & Managerial Overhead Cost
The preceding phases of the methodology are depicted in “Figure 1 – A Phased Process Model of Implementing a Cost Estimation Solution at ContiTech. Future researchers who intend to replicate this study or are coping with problems in similar contexts can make use of this model when analyzing and implementing a solution.
3.1. Data Collection 1 – First Interview Round
As a first step in my field study, I conducted interviews with employees representing each
department that is involved in the workflow of prototype creation. The interviews where hold with open-ended, standardized questions, as done by Hoffmann (2007) and explained in Gubrium’s & Holstein’s “Handbook of Interview Research” (2002). This should ensure consistency among the colloquies, leading to more congruency while analyzing the results. Also it should avoid ambiguity and embrace a cyclic process in the development of the understanding of the subject matter. Furthermore, all interviews were held under identity confidentiality allowing the interviewees to express their feeling and thoughts in a free manner. The fact, that they were interviewed in their usual work-setting, should have also reduced any sentiments of uncomfortableness and insecurity, thereby improving the results of the inquiry. The data which has been collected was recorded in the form of notes, which have been taken during the interview. An excerpt of some interview questions can be found in the appendix.
The initial information collection and interview to understand the overall problem has been
conducted with the responsible controller. By shedding light on the specific numbers and processes, it enabled me to gain a deeper understanding of the necessity of a solution and where there were potential flaws in the value chain. The overall process and its specific flaws is shown in figure 2, under the discussion section 4.1. Since the study takes the perspective of the sales department, which is also the starting point of each prototype order, I chose to take it as a point of origin after
variables would have to be considered in the cost function. Also, it laid further foundation to choose which question would be essentially crucial to the prototype department. The last step in the initial interviewing round was an interview with the head of the prototyping department. He closed the information gaps I needed to fill for a full understanding of the processes and helped me identifying the main drivers of cost in the process. That also contributed to the creation of a process flow, with its respective flaws in section 4.1.
3.2. Data Analyses 1 – Interpreting the Interviews
All interviews did not only serve the function of obtaining understanding of the processes but also helped to determine various scenarios which can occur within the procedure. Hence, the analyses of the first interview round would lead to the creation of a workflow procedure. Furthermore, the analyses entailed exceptional cases and had the purpose of improving my understanding of the quantitative data which was collected after the interviews. But most crucially, it helped me
identifying which information and variables I had to focus on in the subsequent quantitative analysis. This also led me to the creation of the general cost structure of a prototype and enabled me to derive a financial model which determines an appropriate sales price based on the estimated costs.
The identification of the crucial variables for analyses was achieved with the use of coding of the taken notes as suggested by Gubrium and Holstein (2002). To test the reliability of coding I used the test-retest method, which means that the first round of coding is done intuitively without looking at the results. After a time lapse, the second round of coding followed and I recoded the interviews. A consensus of the outcomes indicates a high degree of agreement between the results and should increase their independence, which was the case.
3.3. Data Collection 2 – Collecting quantitative Data
Numerous cost aspects were accessible without any further problems via the ERP system of ContiTech. Those numbers could be immediately implemented in the cost function. The major concern of the quantitative dimension though was set on unknown components, which were not produced in-house and had to be sourced from suppliers. This had been identified as the biggest driver of cost deviation and therefore, the focus of the quantitative analysis was set on that part.
After understanding the processes and variables which were crucial to each department I had gained a better overview of the overall situation. I was able to set out an initial design for the tool which was supposed to be used by the sales department. They design and requirements of the tool which was to be implemented in the sales department was outlaying the information to be collected and also set conditions to which solution approaches I could implement and which not. Detailed information on that matter will follow in the discussion section.
Since it was crucial to develop a tool which could be easily used by every employee of the sales department, I had to identify the intercepting points between easily understandable variables which can be deduced from the blueprints of the prototypes, sufficient technical input not to oversimplify the process and available quantitative data I could access over the SAP system to create solutions in the following quantitative step of the research.
3.4. Data Analyses 2 – Interpreting the Quantitative Data
As it turned out, a major problem in this step of the research was that the quantitative data I had collected from the ERP system was lacking variables, giving me the needed information to connect the collected cost information to the types of material parts sourced from suppliers. As previously explained one of the major requirements to the model was that an employee can derive all input information from a simple blueprint of the prototype. The SAP system contained costs reflecting single parts incorporated into the prototypes, but there was unfortunately not a variable explaining any input information which would have been depicted on the blueprint. Therefore, I had to find a solution that connected the available empirical data from the system with input variables that could be seen on the blueprint. This resembles the previously explained concept by Struder that a
I analyzed the single part accounts and inspected all related variables given by the ERP system. Then in turn, I analyzed the variables, which could be date of shipment, supplier, type of account, etcetera. Basically, this approach represented one of the major contributions this article was supposed to deliver to the academic research arena I call it the “Common-Denominator-Approach”.
After analyzing all the variables connected to the accounts, I decided that my remedy would be connected to the suppliers. Each supplier was specialized in a unique type of material parts and by assigning those material parts to the respective suppliers, I had found a common denominator which would resemble potential input variables from the blueprint with the given costs.
Now I had to find a method to assign appropriate costs to the respective variables, which represented the intervention step within the problem solving cycle. As previously explained, I considered various remedies to create an intervention. I assessed them in terms of technical
feasibility with regards to the available data, scholarly quality and managerial relevance. This reflects the previously explained primary double-hurdle as introduced by Pettigrew (1997).
My most desired approach to implement a solution was a linear regression to estimate costs as done in many cost studies (Lowe et al., 2006; Dodd et al., 2006). I would have assigned dummy variables to the suppliers representing the respective material parts. Since there was a lack of further supportive variables, it turned out that it would not make much sense applying this remedy, since it would have just resembled an average cost calculation, due to the fact that the regression would have merely consisted out of dummy variables.
The lack of empirical accessible data finally led me to the decision to implement a basic average cost analyses. Although, this approach might have given in on scholarly quality, it delivered managerial relevance and as previously explained there was lacking too much data to apply another approach.
receive more in-depth information about it. Therefore, I was able to make quality conscious judgments upon the importance of each value it would have in affecting my statistics. This also happened in accordance with the controller, who in case advised me if an outlier should have been not been taken out. Hence, the analysis of outliers can be described as synthesis of an academic approach combined with a practical approach to come to full circle. This should have enabled me to come to a statistically significant solution which benefited from a deeper understanding of the subject matter.
After the assignment of the material part variables to the suppliers and the analysis of the outliers I proceeded with the next step and calculated average costs as done by Altomonte et al. (2015).
3.5. Data Collection& Analyses 3 – Validating the Collected Data with a Second
Interview Round with the Management & Implementing the Second Cost Driver
“Wage Hours”
After the calculation of the average costs I presented the results to the management of the product construction department and the controlling. Furthermore, I talked the firm’s expert in cost
estimation and based on his long years of experience he was also able to give me insights regarding the authenticity of my results. This was a form of triangulation and led to increase reliability and authenticity of the results.
As identified in the first interview round, labor cost was not considered at all in the estimation. In accordance with the management I decided to implement a process-based allocation of wage-hours to each component, as explained by Koenigsberger (1964).
As mentioned in the theory section, process-based allocation of wage hours can be divided into auxiliary time cost, which represents the time it takes to set up a machine for producing a part, and machining time cost, which constitutes the marginal time effort it takes for every additional part to be produced. This can be expressed in the following formula:
∑ 𝐿𝐻𝑛;𝑑 = ∞
𝑛=1
𝑋𝑀𝑇𝐶𝑑+ ∑ 𝑋𝐴𝑇𝐶𝑛;𝑑 , (𝑛, 𝑑 ∈ ℕ, 4 < 𝑑 < 85)
“n” amount of parts produced for the respective prototype part and with the respective component diameter “d”;
𝑋𝑀𝑇𝐶𝑑; represents the respective machining time cost it would take to set up a machine for the respective prototype part with diameter “d”;
𝑋𝐴𝑇𝐶𝑛;𝑑; represents the auxiliary time cost as explained above containing “n” amounts of parts at given diameter “d”;
The process-based allocation was chosen instead of an overhead-allocation based on customers. I conducted a second interview with the prototyping department, where not just the management was attendant, but also 4 out of 12 employees working in the department. We conducted a panel discussion to determine the average amount of minutes each procedure would take. Thereby I was able to design a table of standard processes and minutes it would take to produce the corresponding parts involved in the production of the majority of components produced in the prototype
department. Each component would usually consist out of two variables, which are the set up time of tools for production, which is a fixed input plus the marginal time of construction it would take for each single part to be constructed. Here we can see the application of Koenigsberger’s auxiliary and machinery time cost concepts (1964).
Based on the created table of auxiliary time cost and machining time cost in conjunction with the process-based wage hour function, I was able to determine the time all in-house produced parts of a prototype would need to be completed. The controlling of ContiTech applies a standard wage rate to the prototyping department, therefore this was the last component to add to the function to
calculate the cost the single parts would create. It can be expressed in the function:
∑ 𝐿𝐶𝑘;𝑑= ∞
𝑘=1
𝑆𝑡𝑑 𝑊𝑅 ∗ 𝑘 ∑ 𝐿𝐻𝑛;𝑑 , (𝑛, 𝑑, 𝑘 ∈ ℕ, 4 < 𝑑 < 85)
where 𝐿𝐶𝑘;𝑑 represents the overall labor cost for a prototype with the diameter “d” and “k” orders, representing the number of prototypes which are being produced;
𝐿𝐻𝑛;𝑑, represents the previously introduced labor hours it would take to complete a prototype part;
Std WR, is resembling the standard wage rate, applied by the controlling department; and “k”, represents the amount of ordered prototypes again.
directly involved in the production of the prototypes, the cost would be obscured by not considering them. As reported in the literature review, using activity-based costing (Ben-Arieh & Qian, 2002) is an appropriate measure in estimating the costs of a prototype. In this case, I did not use it to estimate the entire costs of a given prototype but to allocate the managerial and administrative overhead costs, which constitute a fraction of the overall costs of a prototype. Therefore, I decided in accordance with the controller to include their wages by adding an administrative cost on every prototype order, which was based on an overhead cost. The overhead cost’s base were the overall amount of prototypes produced annually. This base was applied to the overall wages received by the manager and the coordinator.
Lastly, the tool had to be completed by adding the material cost which flows into every component when being produced in-house. Fortunately, this data was already available and I could access it over the SAP ERP-system. To complete the tool the cost of labor and cost of material per component were added up if necessary, which resulted in the total cost of an in-house produced part. It should be mentioned that for the ease of use there were several input variables for in-house produced parts, which did not entail material cost. An example for that would be the procedure of bending pipes, where the employee has to enter the amount of curves in a pipe. This does not add up any material cost but has a tremendous effect on the cost of labor, since it constitutes a highly time consuming process.
4. Discussion
4.1. Interpreting the outcome of the first interview-round
As a main outcome of the first interview round, I was able to create a workflow chart of the current situation, depicting the process of prototype generation at ContiTech. This process is displayed in Fig. 2 “Initial Process Flow of a Prototype Request”. It lays out all entities involved and which actions they usually pursue. The blue boxes represent the general process, while the white ones contain
comments and remarks regarding the single steps in the process.
As previously mentioned, this initial step also served the identification of main-drivers regarding the cost of prototypes and which aspects had not been considered previously, causing the discrepancy between the estimated and actual cost. Furthermore, the diagram laid out which departments had to be interviewed in-depth for each problem which would occur during the study. I saw it as a guide for the study and considered it a base I could come back to if there were any unclear aspects in the analysis.
As one can see in the figure, the usual workflow of a prototype starts with the recipient of a
prototype request, most of the time in the form of a blueprint. The Sales Department hands over the respective blueprint to the Product Construction Department, which creates a material list for the respective prototype parts and returns it to the Sales Department. Based on that an employee in the Sales Department will write an offering to the customer resting upon an experience-based multiplier which is applied to the prices of material parts which are registered in the firms SAP database. After the acceptance of an offering by the customer, the respective production of the given prototype starts.
accelerate the overall time it takes to offer the prototype price to the customer. Therefore it was crucial to find a solution which leapfrogs the involvement of the Product Construction Department before sending an offer to the customer.
An even bigger problem seemed to be the experience-based multiplier markup used by the employees of the sales department. The prices which were used as a base for the cost allocation reflect the cost of serial parts from production. Since the production of a prototype is more costly than the production of a serial part, it becomes necessary to increase those SAP prices, aiming to achieve estimates which are closer to the true cost of a prototype. In the past the prices were increased by applying markups which were experience-based multiplier, meaning that an employee would e.g. multiply the cost of a serial part times two to yield a price which should reflect the actual price more closely. It turned out that most sales representatives had different multipliers resulting in high discrepancies in the determination of a sales price. There was a high need to standardize this process and find a more accurate method of determining the cost and thereby allowing to find a sales price which does not underscore the actual cost of a prototype.
Furthermore, it became obvious that an estimation of labor cost did not play any role in the determination of the estimated cost. The previously mentioned multiplier was supposed to substitute the need to estimate labor costs more accurately and also did not consider externally sourced parts which were used in the production of a prototype.
From this diagram it became obvious that the quantitative data to be collected without further deep considerations had to be material cost for known parts and labor cost from management &
administration. Those fractions of the overall prototype cost were accessible via the company’s ERP system and were representing actual cost which not had to be estimated.
Material cost for unknown parts was the main driver pushing the discrepancy in estimated and actual cost on the material cost aspect. It could be separated in in-house production parts, where the information was accessible via the ERP system as well, and externally sourced parts from suppliers, which was the main concern of finding solution to calculating the cost of a prototype. The approach to estimate externally sourced parts from suppliers will be explained in the preceding section of the discussion and as mentioned before, I consider the solution found to this problem as one of the main contributions of my study to the academic domain.
Manufacturing cost needed a deeper analysis of labor cost for the production of prototypes. It will be divided into machining time cost and auxiliary time cost as done by Koenigsberger (1964).
Based on the cost structure of a prototype, I was able to deduct my proposed financial model, which estimates the cost of a prototype and adds a mark-up, which is the contribution margin to determine the appropriate sales price.
I propose the final financial model:
Sales Price of a Prototype = Applied Contribution Margin * (Estimated Cost of Externally-Produced Material Components + Estimated Cost of In-house-Produced Material Components + Estimated Cost
of In-house-Labor Efforts + Allocated Overhead Labor Costs)
4.2. Interpreting the Quantitative Data
When interpreting the quantitative data I had to find an intersection of the available data and the input requirements of the tool which was to be produced. Once again, it should be emphasized that all the information related to the estimation of costs had to be derivable from the blueprint handed in by the customer.
mentioned before, I call it the “Common-Denominator-Approach”, where I analyzed the single accounts for externally sourced parts with regards to the usability and meaningfulness in terms of implementation in the final tool. As a first step, I analyzed accounts in terms of date of shipment from the supplier to find out if there was a correlation between seasonal-related costs, I had to consider in my further steps of the analyses. As it turned out this was not the case and the date of shipment did not affect the costs, neither positive nor negative correlation has been found. Next, I decided to analyze the different suppliers in-depth, since I considered them the root of the problem with regards to cost estimation. I did so, since I considered them the biggest source of uncertainty, thereby obscuring the authentic estimation of costs. This also can be attributed to the fact that several suppliers are international and therefore the uncertainty with them should increase. Quickly, it became clear that every supplier was specialized on a certain type of parts they would deliver. The suppliers differed in size and therefore in the quantity of parts they deliver. Hence, I decided to create categories of products which would represent the general parts being used in prototype production. Those parts could differ in terms of size or complexity but in general they could be generalized under certain groups. Suppliers which would deliver very similar types of parts were summarized under the same categories. This led to mutually exclusive categories of product parts, suppliers could be assigned to. The six categories which emerged in this process are presented in Table 2, labeled “Categories of Supplier Parts” below.
Title of the Supplier Part
English Translation
Drehteil / Fräßteil Turned / Milled Pars
Rohrbogen Bended Pipe
SAE Flansch SAE Flange
Schlauch Hose
Stanzteil Pressed Part
Andere Teile Other Parts
"Table 2 - Categories of Supplier Parts"
Now, I was able to assign the single part accounts from the collected data to the respective
categories depending on the suppliers they were delivered from. I did not encounter any problems with the assignment of single part accounts, since they all could be allocated either to the five main categories or would fall into the category labeled “other parts”.
After that I examined my dataset for outliers and extreme values. I created descriptive statistics, made use of upper and lower percentiles to identify potential outliers and generated box-plot diagrams. As previously explained, I also benefited from the knowledge gained from the interviews and in conjunction with the controller of the company, I decided upon which extreme values to delete from my dataset. This led to the removal of 8 outliers.
Since the given information was rather limited I decided to utilize the average cost approach as suggested by Altomonte et al. (2015). I calculated the average cost of each category, which would later represent the estimated cost of a given part stemming from this category. The results of this analyses are represented in Table 3, “Average Cost of Supplier Part Categories”, depicted below. For the sake of privacy concern at ContiTech and information closure I did not include the exact costs of the respective prototype part, but I summarized them by giving information on the unit of
measurement.
Category
Unit of Measurement
Drehteil / Fräßteil Quantity
Rohrbogen Quantity
SAE Flansch Quantity
Schlauch Meter
Stanzteil Quantity Andere Teile Quantity
"Table 3 - Average Cost of Supplier Part Categories"
4.3. Validating the Collected Data with a Second Interview Round with the
Management & Implementing the Second Cost Driver “Wage Hours”
As stated in the method section, I continued my research with a validation of the results I had collected so far. I talked to the management, the controller and the previously mentioned expert of cost estimation from the product construction department and assessed the assigned categories in terms of their applicability and meaningfulness. Furthermore, we evaluated the authenticity of the estimated costs for each category.
Since, all the involved parties deemed the results to be realistic, I agreed that the average cost approach would be the remedy to implement. I followed this decision due to the high demand for managerial relevance and practicality of the study.
As a next step, I had to analyze and structure all types of prototype parts which were produced in-house. This step in the process resembles “Material Cost for Unknown Parts – In-House Production of Parts” as depicted in figure 3. Furthermore, it entails “Material Cost for Known Parts”, also shown in figure 3. The cost of a part which is being produced in-house and that is part of a serial production and therefore already known, did not have to be estimated. The respective cost could be found in the database of SAP and I linked the tool I was about to create to this database. Hence, if the scenario occurred that a blueprint entailed a part which was already known, the employee just had to insert the serial number of the respective part in the tool. The tool would then automatically access the database and browse it for the serial number of concern to determine the cost of the component.
While material cost for known parts, could be implemented in an easy and fast manner, the concern in this part of the study was set on material cost for unknown parts. First, I had to find out what would be the cost-drivers of material parts and how they could be structured. As a base, once again I could utilize the knowledge gained from the previously conducted interviews. Furthermore, I strongly benefited from a document I found in ContiTech’s database, which was outlaying the general
structure of material costs. It entailed formulas for calculating respective material costs and presented me an overall structure of determining material costs.
manufacturing department and not in the prototyping department, I did not have to find a remedy for discrepancies in material costs as they could arise if the prototyping department would be completely independent. Accordingly, I could utilize the material costs as found in the SAP system to adjust the formulas.
Once again, I like to emphasize the aspect of finding common denominators, meaning that those categories of material components had to be represented in the blueprints, but also had to be sufficiently meaningful to derive proper costs from them.Consistently, I conducted a feedback-loop, by presenting the categories of material components to representatives of the sales department. This time, I had to evaluate if they were able to deduct the assigned categories from the blueprints. Furthermore, I had to evaluate if they were any crucial types of material components missing, which still had to be included. Since this was not the case and the representative and management of the sales department were pleased with the design, meaningfulness and usability of the categories, I implemented them in the tool.
The structured and adjusted categories, stemming from the document I found in ContiTech’s database, which were to be implemented in the final tool, are represented in figure 4, called “Material Components of a Prototype”, depicted below.
Furthermore, there are distributors which represent a small cost-factor, but still have to be included in the cost structure. Pipes, on the other hand are one of the main components which are being manufactured in the factory. Common procedures applied to them are bending or shortening. Then, there are hoses, which hare the other major branch of components being produced at ContiTech. They can be separated in different categories of hoses, depending on the chemical and component composition of the hoses.
Furthermore, there is another category which relates to hoses but should be seen separately, since it resembles additional parts which can be add to a hose. This could be an insulation or a spiral, which is wrapped around the hose to protect it from external pressures and serves as a general protection. Lastly, as previously mentioned there is polyform which is comprised as an independent category. This category entails polyurethane elastomers and is therefore abbreviated with polyform. According to ContiTech, the company uses polyform, for its extraordinary, multifunctional material
characteristics, which enables the elastomers performance where other material would malfunction or even break down. The independent category has been assigned on the one hand, due to its minor occurrence when compared to the other categories, and on the other hand, due to its highly
independent manufacturing processes are compared to the other categories.
Furthermore, it should be explained that I set all subcategories against a dependent variable, which I identified to be the main-driver behind material costs with respect to each single component. Generally, each pipe, hose or fitting connection can be defined by its diameter, which is measured in inches. This is the most crucial variable in the choosing of a proper prototype, since the desired diameters of the product drive the entire production. Depending on the applied diameter, costs increase due to the increased amount of material being used, increased labor hours due to the prolonged duration of production processes etcetera. The dependent variable of diameter can be separated into the following intervals: 4-10; 12-16; 20-32; >60 and >85 inches.
4.4. Material Cost Implementation
Table 4, labeled “In-House Produced Material Cost Components”, portraits the outcome of the structuring and analyses of the three material categories. It resembles and should cover the majority of the scenarios which can occur when assessing the cost of a prototype. Therefore they are
structured by the previously explained overall categories, which entail subcategories for determining the cost of material.
Label
English Translation
Unit of Meas.
Fitting & Pipe-Related Information
Anzahl Armaturen (Vorfertigungsteil) Amount of fittings (pre-fabricated parts) Quantity
Rohrlänge Gesamt >1m? Total pipe-length>1m Yes / No
Verteiler Distributor Yes / No
Hose-Related Information
Schlauch OL / GC / KUES / KRA / Etc. Hose OL / GC / KUES / KRA / Etc. Meter Schutzschlauch / Wendel / Schrumpfen Insulation-hose / spiral / shrinking Yes / No Polyform-Related Information
Polyform Herstellung Polyform fabrication Yes / No
Polyrohr geformt Poly-pipe formed Yes / No
Polyrohr Gesamt >1m? Total poly-pipe > 1m Yes / No
"Table 4 - In-House Produced Material Cost Components"
metal-plating was a customer’s percentage of last year’s overall metal-plating-expenditures, resulting in metal-plating overhead cost for each respective customer. Then, I created an additional cost estimation field in my tool, which I labeled “Additional Metal-Plating Overhead Cost”. If the ratio of standard galvanization charge to a customer’s overhead cost for galvanization would surpass the amount of prototypes per order, then the additional metal-plating overhead rate would be applied. This would lead to an overall galvanization cost charged on a standard base plus the additional overhead rate.
By adding the cost of a fitting from the SAP system with its galvanic cost per prototype piece, one can determine the cost of a fitting per prototype piece. If the additional overhead rate would be charged, it would not be a direct cost to the fitting, but it would be treated as an additional indirect cost, being added independently as an overhead cost.
“Total pipe-length > 1m”, resembles an additional material cost which arises when the pipe to be worked on is longer than one meter. Since it does not happen that the pipe is longer than two meters there was no need to implement an option for pipes longer than two meters or more. This sub-category has an optional unit of measure, meaning that there is either yes or no as an answer. For the sake of manageability in the tool, this is simply managed by marking the corresponding box with an “X” if the statement is correct and the answer would be a “Yes”. The cost formula for pipes was designed as an if-formula in Excel, which applies the cost per meter if there was not an “X” set in the tool. If the subcategory has be chosen to be accurate and marked with an “X”, the cost per meter is multiplied with two to calculate the cost for pipes longer than one meter.
The distributor does not vary in its cost depending on the diameter applied. Therefore it is measured with an optional unit of measurement as well. If the subcategory is chosen, a cost irrespective of the diameter of the fitting or pipe, is assigned. The information regarding this cost was found in the SAP system of ContiTech.
Hose-related information was the name I gave to the second sub-category in determining material cost. This section contained all information needed to determine material costs for hose
instead is summarized under one overall cost representing those measures which is also taken from ContiTech’s SAP database.
Lastly, in assessing material costs, there was the subcategory I called polyform-related information. Polyform can be seen as branch of special products ContiTech is offering. It is an advanced
production technique for creating stiff hoses, which are highly stable. Polyform still constitutes a percentage of overall production which can be described as rather limited. Still it had to be included in the tool, since it also occurs in the prototype production. There are only two sizes of polyform hoses and both have a standard cost which is listed in the SAP database. This cost is resembled in the field labeled polyform fabrication, by marking the desired size of polyform the respective cost is assigned in turn. Poly-pipe formed constitutes an additional cost which is assigned when the pipes are bended and the cost was determined in consultation with the controlling and the expert in cost estimation of the product construction department. In all other categories, bending a pipe is considered as a labor cost only, since it does not involve any use of further material. In this case, though, the management has decided to summarize material and labor cost as an overall cost. This has been done, because as previously described polyform only constitutes a small percentage of prototypes being produced. Therefore, the management deemed the effort to analyze this product category in-depth unnecessary and demanded a summarized short-cut for estimating the cost of polyform prototypes.
Usually, polyform prototypes are below one meter in length, if they should exceed this length, which is seldom the case, an extra material cost is added. The exact cost in this case was also determined in accordance with the controlling and the cost estimation expert.
4.5. Labor Cost Implementation
The next step was to address the implementation labor cost. As explained, I decided to embrace a process-based allocation. As indicated in the method section, I conducted interviews with the prototyping department to determine the amount of time it would take for the respective prototype parts. The major outcome of the interviews is depicted in table below, called “Table 5 – Time Needed for Prototype Fittings”.
Amount of MTC ATC Minutes Diameter Work Process 6-10 12-16 20-32 40-50 >60 Sawing 𝑋𝑀𝑆𝑎𝑤 𝑋𝐴𝑆𝑎 6−10 𝑋𝐴𝑆𝑎 12−16 𝑋𝐴𝑆𝑎 20−32 𝑋𝐴𝑆𝑎 40−50 𝑋𝐴𝑆𝑎 >60 Purifying 𝑋𝑀𝑃𝑢𝑟𝑖𝑓𝑦 𝑋𝐴𝑃𝑢 6−10 𝑋𝐴𝑃𝑢 12−16 𝑋𝐴𝑃𝑢 20−32 𝑋𝐴𝑃𝑢 40−50 𝑋𝐴𝑃𝑢 >60 Shortening 𝑋𝑀𝑆ℎ𝑜𝑟𝑡 𝑋𝐴𝑆ℎ 6−10 𝑋𝐴𝑆ℎ 12−16 𝑋𝐴𝑆ℎ 20−32 𝑋𝐴𝑆ℎ 40−50 𝑋𝐴𝑆ℎ >60 Purifying 𝑋𝑀𝑃𝑢𝑟𝑖𝑓𝑦 𝑋𝐴𝑃𝑢 6−10 𝑋𝐴𝑃𝑢 12−16 𝑋𝐴𝑃𝑢 20−32 𝑋𝐴𝑃𝑢 40−50 𝑋𝐴𝑃𝑢 >60 Forming 𝑋𝑀𝐹𝑜𝑟𝑚 𝑋𝐴𝐹𝑜 6−10 𝑋𝐴𝐹𝑜 12−16 𝑋𝐴𝐹𝑜 20−32 𝑋𝐴𝐹𝑜 40−50 𝑋𝐴𝐹𝑜 >60 Completing 𝑋𝑀𝐶𝑜𝑚𝑝𝑙𝑒𝑡𝑒 𝑋𝐴𝐶𝑜 6−10 𝑋𝐴𝐶𝑜 12−16 𝑋𝐴𝐶𝑜 20−32 𝑋𝐴𝐶𝑜 40−50 𝑋𝐴𝐶𝑜 >60 Riveting 𝑋𝑀𝑅𝑖𝑣𝑒𝑡𝑒 𝑋𝐴𝑅𝑖 6−10 𝑋𝐴𝑅𝑖 12−16 𝑋𝐴𝑅𝑖 20−32 𝑋𝐴𝑅𝑖 40−50 𝑋𝐴𝑅𝑖 >60 Brazing 𝑋𝑀𝐵𝑟𝑎𝑧𝑒 𝑋𝐴𝐵𝑟 6−10 𝑋𝐴𝐵𝑟 12−16 𝑋𝐴𝐵𝑅 20−32 𝑋𝐴𝐵𝑟 40−50 𝑋𝐴𝐵𝑟 >60 Pressure Testing 𝑋𝑀𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑋𝐴𝑃𝑟 6−10 𝑋𝐴𝑃𝑟 12−16 𝑋𝐴𝑃𝑟 20−32 𝑋𝐴𝑃𝑟 40−50 𝑋𝐴𝑃𝑟 >60 Total XMTC 𝐗𝐀𝐓𝐂𝟔−𝟏𝟎 𝐗𝐀𝐓𝐂𝟏𝟐−𝟏𝟔 𝐗𝐀𝐓𝐂𝟐𝟎−𝟑𝟐 𝐗𝐀𝐓𝐂𝟒𝟎−𝟓𝟎 𝐗𝐀𝐓𝐂>𝟔𝟎
"Table 5 – Time Needed for Prototype Fittings"
minutes it takes to set up the machines for the processes. Furthermore, there is the marginal variable “ATC”, which resembles auxiliary time cost. Auxiliary time cost is the amount of minutes it takes to finish the process for one respective component. Again, there are the variables for each process, but in contrast to the fixed “XM”, there are different ones for each diameter. “XATC” constitutes the total amount of minutes in production for one respective diameter. When applying the formula introduced in the method section, I can determine the overall time it takes to complete a given process for a component with a respective diameter. Multiplying the calculated labor hours with the standard wage rate of the prototype department, which can be found in the SAP system, I am able to compute the labor cost of process with regards to a chosen diameter.
After having the processes in place I had to interlink them with my tool, which meant that I had to assign the processes to the given product categories and their sub-categories. The previously conducted interviews with the prototype department served the purpose of determining which processes belong to which prototype component.
not show in table 4. Therefore, I will briefly mention them now listing all respective labor costs for the single components of the tool.
As a next line in the tool belonging to the subcategory fitting & pipe related information there was “total amount of bends in the pipe”. This process did not add any material cost to the product, since it would merely change the shape of the consisting product. Hence, it is a labor cost process only which is described as first bending followed by an after-check.
If the total pipe-length is bigger than one meter, there are also extra labor costs arising. On both ends of the pipe an employee has to conduct inductive brazing which creates a labor cost
irrespective of the diameter. This cost has been calculated based on the MTC and ATC of the process as well.
Lastly, I enhanced the category of hose-related information an added a further subcategory into it, which consisted of three possible options. A pipe contains hose parts and usually, hoses are not an independently standing entity. Depending on the amount of hose components the time effort
increases needed to integrate them in the pipe. Since I previously explained that the material cost for hoses depends on the type and composition of chemical being used in production, this part of “amount of hose components” is strictly concerned with the labor cost aspect.
In the SAP system they already allocated costs for those scenarios and respectively a pipe with one hose component creates labor cost, while two hoses lead to extra-cost and three implemented hoses would cause labor costs to rise even more.
Concerning polyform production, I just had to implement the labor cost for bending the pipes, which was based on the same cost as bending normal pipes. As previously explained, the management decided to cover the cost of polyform production entirely under the aspect material cost, bends in the pipe constitutes the exception.
4.6. Final Product
Label
English Translation
Unit of Meas.
Fitting & Pipe-Related Information
Anzahl Armaturen (Vorfertigungsteil) Amount of fittings (pre-fabricated parts) Quantity Gesamtanzahl Bögen der Leitung Total amount of bends in the pipe Quantity
Rohrlänge Gesamt >1m? Total pipelength>1m Yes / No
Verteiler Distributor Yes / No
Hose-Related Information
Leitung mit 1 Schlauchanteil Pipe with 1 hose-component Yes / No Leitung mit 2 Schlauchanteilen Pipe with 2 hose-components Yes / No Leitung mit 3 Schlauchanteilen Pipe with 3 hose-components Yes / No Schlauch OL / GC / KUES / KRA / Etc. Hose OL / GC / KUES / KRA / Etc. Meter Schutzschlauch / Wendel / Schrumpfen Insulation-hose / spiral / shrinking Yes / No Polyform-Related Information
Polyform Herstellung Polyform fabrication Yes / No
Polyrohr geformt Poly-pipe formed Yes / No
Gesamtanzahl Bögen der Leitung Total amount of bends in the pipe Quantity
Polyrohr Gesamt >1m? Total poly-pipe > 1m Yes / No
"Table 6 - In-House Produced Material Cost Components"
This, combined with “Table 3 – Average Cost of Supplier Part Categories” constitutes the final tool, which was implemented in ContiTech’s sales department. By making use of it, employees of the sales department are able to estimate the cost of in-house and externally produced prototypes
components, thereby determining the overall cost of requested prototype.
By implementing this tool in combination with a standardized process for estimating prototype costs we shortened the process flow it took for a prototype request from its receiving to its offering to the customer. The new process flow is depicted in “Figure 5 – Flow of a Prototype Request after the Implementation of the Study’s Outcome”.
When we compare figure 5 with figure 1, which has been initially introduced in the beginning to show the initial process flow of a prototype request, we can see that the process cycle is shortened by one step. Now it is not necessary anymore that the sales department contacts the product
Furthermore, there is a standardized procedure in place and old practices like experience-based multipliers can be abandoned in favor of the new estimation procedures. In the new procedure, labor costs have been implemented and externally sourced materials are accounted for as well as the administrative and managerial costs stemming from the prototyping department.
All those process and estimation improvements should lead to an overall improvement of ContiTech’s financial performance with regards the sale of prototypes. If the improvement was actually effective will be assessable after the firm followed the suggested practice for one year or more.
5. Results & Limitations
After the completion of this research the results will be implemented at ContiTech and the ERP-system of SAP will be adjusted to incorporate the financial model. This should automatize the
procedure of estimating actual costs more accurately, relieve employees coping with daily operations from workload, reduce the error source and lead to overall better financial results. The desired outcome is a performance improvement at the international subsidiaries of Continental. The estimation equation will lead by over- and under-estimation to improved results and more accurate actual material cost estimates. This part constitutes the main outcome of the quantitative dimension of the study contributing to ContiTech’s estimation tool.
Generally, the tool consists of three different dimensions. First, there is material cost estimation of in-house produced components which is mainly based on numbers from the SAP system and the outcome of the conducted interviews. Second is labor cost estimation in connection with a
machining time and auxiliary time cost as done by Koenigsberger (1964). As a third and most crucial dimension there is the estimation of externally produced prototype components. Previously, I already described that this resembles the academic main contribution of this paper and I call it the “common-denominator-approach”. This approach resembles a technological rule as described by van Aken (2004), which in this context would be to look for a common-denominator if there is not sufficient data available on the variables one would like to use for ones estimation. By finding representative variables which share similar properties and characteristics as the desired estimation variables and carry sufficient information to predict those, one can circumvent using the desired variables which lack data for estimation by substituting them with the representative variables. In this paper this process was applied by finding intersections between desired input data for the tool and available variables for collecting data. This meant that the desired variable was material parts which can be deducted from a blueprint of a prototype and the supplier was the representative variable which was utilized to group the categories of externally sourced products parts. Thereby it substituted the need for the creation of variables from the blueprints since the supplier categories were resembling the desired variables. By doing so, I outline in this paper how a researcher or practitioner can apply similar approaches when designing tools for a practical environment.
As described, I applied an average cost approach as done by Altomonte et al. (2015) to estimate the costs of externally sourced supplier parts.
The user of the tool should be aware that this is a model which will guarantee no perfection and is rather used as an indicator. It will not provide actual costs of the prototypes but should estimate them in a more accurate manner. A further limitation is that this model will be useful for a limited period of time and that it will have to be rewritten in a couple of years, when prices have changed and factors like inflation have influenced the price. I would call this study a snapshot of the current situation at ContiTech and therefore suggest that after the implementation of this model in the company it will need to be updated on frequent base.
There will be customers at ContiTech which will not be willing to pay that high prices for prototypes. Especially, in a globalized arena ContiTech has to struggle competitively with its high prices due to its production location in Germany. To sustain those customers who will not be willing to pay,
ContiTech’s management will assess strategically how to handle those clients. Several of them will receive the prototypes at prices which score lower than the costs of the respective prototypes.
From an academic point of view this study serves as a layout to how to approach similar problems in business context. It can be seen as a guide to combining practical requirements with academic approaches, thereby finding solutions which represent a synthesis of them. Also the paper represents an approach in analyzing costs portraying them in a field investigation in the form of a design study.
It should be stated that under an academic perspective the study is limited to a context in one industrial sector, therefore I suggest for future researchers to expand similar concepts also into industries like high-tech, service-based or other industries.
A further constraint lies in the simplicity of the solution. It serves the ease of handling for employees but leads to potential disadvantages in the precise estimation of actual costs. To improve this
drawback future researchers in similar studies could engage in fuzzy statistics as done by Chansaad et al. (2014) or make use of other more complex statistical methods.
