Diagnosing the delivery reliability of a
make- and engineer-to-order company
Master thesis, MSc Technology and Operations Management
University of Groningen, Faculty of Economics and Business
Author: J.J.H. Severs
Student number: 1399047
Date: 23-08-2013
Supervisor University: dr. G.D. Soepenberg
Co-assessor University: dr. X. Zhu
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Abstract
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Content
1. Introduction ... 3
2. Theoretical background... 4
2.1. ETO characteristics ... 4
2.2. Problems within ETO companies ... 5
2.2.1. Problems caused by dynamics... 5
2.2.2. Problems caused by uncertainty ... 5
2.2.3. Problems caused by complexity ... 6
2.3. General capacity problems... 6
2.4. Conceptual Model ... 7
3. Methodology ... 9
3.1. Case selection ... 9
3.2. Data collection – Sources of evidence ...10
3.2.1. Documentation ...10
3.2.2. Interviews...10
3.2.3. Direct observations ...11
3.3. Data analysis ...11
3.3.1. Determining the relevant problem area ...11
3.3.2. Determining the PPC causes within problem areas ...13
4. Results ...15
4.1. Determining the relevant problem areas ...15
4.2. Determining PPC causes within problem areas ...19
5. Conclusion...20
5.1. Research question ...20
5.2. Methodology review ...21
5.3. Limitations & future research...21
Reference list: ...23
Appendix ...25
Appendix I: Distribution of lateness for different order routings ...25
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1. Introduction
A constant hectic production floor is a nightmare for a company. Especially when a company wishes to meet the agreed delivery date this can often be the case. Over the years, a large number of scientific researchers have troubled themselves on the problems, the underlying causes and searched for possible solutions to improve the delivery reliability of the production stage for make-to-stock (MTS) and make-to-order (MTO) companies (Hopp & Spearman 2001, Bertrand et al. 1998, Land et al, 2009). In comparison to this, the quantity of research on the topic of the pre-production stage (e.g. sales, engineering, procurement and process planning) is quite scarce. While Land et al. (2009) note that in small and medium enterprises (SMEs), it is common to have uncontrolled delays within the pre-production stage which impact the delivery reliability. Especially for engineer-to-order (ETO) companies this is an interesting topic, in which the pre-production stage consumes a large portion of the total lead time.
According to Gosling et al., (2009), there is a need for empirical research that could give a better insight into the general properties of ETO companies. The root cause of the long lead time of an ETO company lies mostly in the design phase (Pandit & Zhu, 2007). The time needed and the impact of the pre-production stage on the overall delivery reliability, should not be underestimated. In a case study at an ETO company, the time spend within the pre-production stage was 89 per cent of the total lead time (Elfving, 2003). For a customer is receiving the products on the agreed delivery date of high value, as the customer plans his future activities on the on time delivery of the product (Kingsman, 1989). Further research on the topic of the delivery reliability within ETO companies in comparison to MTO companies is therefore helpful in practice. In order to improve this insight, this research diagnosis the delivery reliability of a company which produces both MTO and ETO products. The research question which will be answered in this research is: which problems and underlying causes, cause an insufficient delivery reliability of a MTO/ETO company?
To answer the research question, a single case study is performed. The chosen company is a company which produces both MTO and ETO products. During the case study can be determined if there is a difference in the delivery reliability of MTO and ETO products. Subsequently the causes for an insufficient delivery reliability will be determined.
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2. Theoretical background
In this section of the research proposal the theoretical background will be discussed. In section 2.1. the typical properties of both MTO and ETO companies are discussed, subsequently, the tasks of the pre-production stage are discussed. Followed by section 2.2. which discusses problems caused by the dynamics, uncertainty and complexity of ETO companies and its environment. The objective of this research is to find which decisions cause an insufficient delivery reliability within a MTO/ETO company, as there is a gap in literature concerning this topic. In literature there is however empirical research on this topic concerning production companies, such as MTS companies, this subject will be viewed upon in section 2.3.. Possible the same problems occur within the total production process, including the pre-production stage for ETO products and can therefore be helpful to recognize the same problems within the whole realisation process of an ETO product. Lastly, in section 2.4. the conceptual model will be explained.
2.1. ETO company characteristics
In MTO companies, the production of a product is triggered after a customer orders a product, this product is already engineered, for instance a standard car. This differs from an ETO company in which the product is engineered and produced to meet the requirements of the customer, for instance a special one-off production machine. The supply chain of an ETO company is generally regarded as a supply chain where the customer order decoupling point (CODP) is located at the engineer stage, so each customer order penetrates the engineering phase (Konijnendijk, 1994). Whereas the CODP of a MTO company penetrates the fabrication process. The ETO products are regarded as low volume with an high variety.
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2.2. Problems within ETO companies
According to Bertrand et al (1993), the characteristics of an ETO company can be described by three aspects, i.e.: dynamics, uncertainties and complexity. These aspects will be further explained in the next three sections. The possible problems and causes are linked to these aspects.
2.2.1. Problems caused by dynamics
For MTO and ETO companies it is difficult to predict the demand and the product mix in the short and medium term (Kingsman et al., 1989). Furthermore, it is difficult to anticipate to these fluctuations as it is not feasible to hold stock for customer orders, as these orders are low volume and customer specific. Increasing the capacity by outsourcing the engineering activities is not an option for an ETO company as products are complex and sharing knowledge of the technology with other companies is not preferred from a strategic perspective (Konijnendijk, 1994). Another dynamic element for ETO companies is changing customer specifications, this often occurs within ETO companies, since customers are unfamiliar with the product and over time their requirements change (Konijnendijk, 1994), (Gosling & Naim, 2009). Re-work is therefore an often occurring event within the engineering department of an ETO.
2.2.2. Problems caused by uncertainty
Within ETO companies there are uncertainties on several topics. Uncertainty is defined by Galbraith (1973) as: “the difference between the amount of information required to perform the task and the
amount of information already possessed by the organization”. Firstly, there are uncertainties at the
quotation tasks by the sales department, this uncertainty is seen as the main problem within ETO companies (Konijnendijk, 1994). Using lead time estimations of suppliers, previous process routes, estimations of the lead times and workload of the different departments within an ETO, a due date is set and communicated to the customer.
A problem which can arise, are delays caused by late availability of materials. In small to medium MTO companies this is often a reason for a lower delivery reliability (Land et al., 2009). This can stem from the uncertainty on the needed materials at the moment of the quotation or insufficient insight into the lead times of the suppliers at the purchasing and planning department. In the construction sector, which can be compared is some extent with an ETO company, the integration of the supply chain is hard to achieve, as the relationships with the different suppliers differ (Briscoe & Dainty, 2005). The same can be the case in ETO companies, as there is a high variety in product specifications, the suppliers lead time per raw material can also differ, this uncertainty has a negative effect on the delivery reliability.
6 products (Bertrand, 1993). In the research of Land et al. (2009) a problem at a MTO company was the unrealistic impression of the available capacity. Another common problem is the possibility that the requirements of the customer are technically infeasible. Even though quotation and a due date are communicated to the customer. In such an event, new specifications in cooperation with the customer need to be established.
Furthermore, the needed capacity is uncertain, as the sales department quotes a number of potential orders, it is uncertain if and when these orders will be received (Bertrand, 1993), which increases the variability of the arrival of orders. A success rate of quoted orders which become definite orders of 30 % is not uncommon for ETO companies (Konijnendijk, 1994).
As mentioned earlier, at the start of the quotation process the actual processing time and the exact resources needed are uncertain. As each product is customer specific, the consequence will be a high variability in the needed capacity per resource (Bertrand et al., 1993). With a constant capacity, an increasing in the variability of the engineering time will result in an increase of the average lead time, due to extra waiting times for products and on the other hand more idle times at the resources (Hopp & Spearman, 2001), hereby decreasing delivery reliability.
2.2.3. Problems caused by complexity
The pre-production stage of an ETO company can be regarded as complex. The engineering activities are creative and difficult to execute in a structured order. Besides, the engineering department is responsible for the quotation of possible future orders and for the engineering of placed orders. During the quotation, the engineer estimates the time needed to engineer the order. The prioritizing decision of these two activities makes the pre-production stage even more complex. Besides, the prioritizing decision of pre-production operation become more complex, as these operations are a relative upstream operation. For these operations it is not obvious which orders have a larger possibility of a late delivery due to bottleneck operations downstream.
2.3. General capacity problems
7 Coordinating the required and the available capacity is therefore important, this coordination has a direct impact on the delivery reliability.
The required capacity is the capacity needed to perform the activities which are needed to produce the products on time. In the ideal situation, when there are no constraints and disruptions e.g.; down times, the required capacity can be equal to the available capacity. The required capacity are the number of orders times the processing times and is calculated in man-hours or machine-hours. An uncertainty which causes the need for extra capacity in a pre-production stage for an ETO company are for example the changing specifications of the customer, as mentioned in section 2.2.1.. The available capacity is the capacity which is available to perform the activities. The available capacity in the pre-production stage is calculated in man-hours per department which are available to perform the activities. In the pre-production stage of an ETO company this is calculated by the number of employees times the average working hours. The skills of the employees influence the available capacity. Furthermore, the downtime or in the case of people the illness time have an influence on the available capacity. Next to these factors there are several other factors which influence the available capacity e.g.; engineering software, etc..
2.4. Conceptual Model
In this section the conceptual model is generated, with the factors which will be further investigated during the research. The delivery reliability of the whole production process is defined as the percentage of orders which has been delivered on time in comparison to the initial promised delivery date. The two elements which are important for calculating the delivery reliability are the initial promised delivery date, which is communicated to the customer and the actual time it takes for the whole production of a product, including both the pre-production and production stage. From order acceptance until the order is ready to be shipped to the customer.
The initial due date of an order is generated by the planning department, based on estimations on the predicted number of orders for each resource of the pre-production and production stage, the estimated time needed to process an order at each resource and the estimation of the available capacity of each resource type. All these figures are uncertain and stem from the dynamics and the uncertainty within ETOs (section 2.2.1. and 2.2.2.).
8 per order in an ETO company differ significantly, therefore the capacity for each resource type is calculated as the available man hours per day to process the orders, this figure will be used to calculate the CT (days). Little’s Law is rewritten as: . From this equation can be concluded that it is important to have enough capacity and that the workload should stay below a certain maximum level so the pre-production stage can comply to the agreed due date as also discussed in section 2.3..
The available capacity and workload can be influenced by unforeseen dynamics. One of these dynamics can be caused by changing specifications of the customer, by which additional work has to be executed (Konijnendijk, 1994), (Gosling & Naim, 2009) as a result the workload will increase. On the other hand the available capacity can change, for instance due to holidays, illness and other unforeseen events, employees will not be able to work all the intended hou rs. This will have a negative effect on the working hours of an employee and therefore a negative effect on the capacity. Besides these unforeseen influences, various other influences can have an effect the workload and capacity.
An uncertainty of production companies is the late delivery of materials. Such an event will have a negative effect on the cycle time and therefore decrease the delivery reliability.
The last property which influences both the cycle time and promised delivery date, is the dispatching rule, the rule in which sequence the orders are produced at each operation. Each dispatching rule has a different influence on the delivery reliability. A rule can have a positive impact on the number of orders produced, for example by batching the same order types, while having a negative influence on the delivery reliability. The cycle time can be decrease by implementing the correct dispatching rule at each resource.
In figure 1 the conceptual model is visualized, with all the connections between the factors which influence the delivery reliability, these factors are discussed in this section.
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3. Methodology
This research is of an exploratory nature with the aim to find problems and underlying causes, which cause an insufficient delivery reliability of a MTO/ETO company. According to Yin (2009) a case study can be used as a research method to answer the research question or as noted by Meredith (1998): “the case method lends itself to early, exploratory investigations where the variables are still
unknown and the phenomenon not at all understood”.
3.1. Case selection
The case study for this research is performed at Alfa Laval in Groningen. This site of Alfa Laval is one of multiple production facilities of Alfa Laval, this production facility produces industrial air cooling systems. At this facility, are next to the production department also the engineering, purchasing and procurement department located. Data will be collected at this facility to answer the research question. Alfa Laval is an international company, therefore it has worldwide multiple sales organisations. For this research the sales department of the Benelux in Breda will be visited to gain knowledge about the functional design of sales organisations. The produced products in Groningen range from standard products (MTO) to products which are engineered to customer specifications (ETO). This research will focus on both MTO and ETO orders. The company currently experience that the delivery reliability of orders is not satisfactory and wishes to improve this property. The delivery reliability is important for Alfa Laval, as customers prefer a reliable supplier and customers will purchase their orders at other companies in the case the reliability is not up to a decent level. According to employees of Alfa Laval Groningen, customers sometimes cancel their order or submit a legal claim within Alfa Laval is unable to deliver in time. Alfa Laval has a corporate aim for the delivery reliability of 96.0 %.
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3.2. Data collection – Sources of evidence
The data will be collected from the different operations departments by the following three sources of evidence; documentation, interviews and to lesser extent direct observations. By using different sources of information the conclusions of a research is likely to be more conv incing and accurate because the conclusions are supported by different sources of evidence . Furthermore, constructing validity is also enabled by using different sources of evidence . (Yin, 2009) So the made assumptions are based on information of different resources. If certain outcomes do not match with outcomes of other sources, it is possible to reinvestigate this further, to be certain that the collected outcomes are reliable and valid.
3.2.1. Documentation
Alfa Laval collects historic data in different databases. First of all the Jeeves ERP system, in which historic data is collected. The sales department collects per order the communication with the customer. Furthermore, the pre-production stage use a planning tool in which all completion dates of the different operations are listed. These different historic data sources can help to identify problems and their causes within each operation.
First of all, to calculate the delivery reliability, data of the promised and realised delivery dates of all orders produced in the past year need to be collected. Furthermore, per order additional information is needed concerning the routing, estimated process time per operation, completion date of each operation. Moreover the available capacity per resource type is of interest. This data will be used for a diagnosis framework, which will be further explained in paragraph 3.3..
3.2.2. Interviews
Interviews with employees of different operations at different levels in hierarchy will first used to gain additional insight in the general characteristics of each department. The pre-production stage departments of Alfa Laval are: sales, sales engineering, engineering, planning, procurement and purchasing. The production stage departments are material picking, shop-floor/production and packaging. The interviews will have a identic semi structured structure. The interviews try to ask the same kind of questions to gain insight in the tasks of each operation, available capacity and the characteristics of an order which influence the workload for each operation. The following kind of topics will be discussed with different employees, as the interviews have a semi structured structure, the questions can differ per interview.
What is your function within Alfa Laval Groningen, which tasks do you perform? What is the available capacity of your department (in man hours per time unit)? Which characteristics of an order influence your workload?
11 What problems do you encounter in your department, which influence the delivery
reliability?
Besides these general questions, other questions are asked to gain extra insight in the reason why certain order types or operations effect the delivery reliability in a certain fashion.
3.2.3. Direct observations
At Alfa Laval Groningen various staff meetings are performed daily. First of all a staff meeting of the pre-production stage, in which new orders, arising problems and the progress of each department is discussed. Furthermore a meeting of the planning, purchasing, procurement and production departments, during this meeting arising problems concerning orders which are about to be produced, are discussed. Besides, the prioritizing of orders are discussed. These meetings are of interest to see which problems are encountered.
3.3. Data analysis
To determine the problem areas and the underlying causes, a structured diagnose will be performed. Therefore the framework for diagnosing delivery reliability performance of make -to-order companies of Soepenberg (2010) will be performed. This framework is divided in two parts. In the first part, four steps are perform in which the relevant problem area is determined which is described in the subparagraph 3.3.1.. The second part determines the causes of the insufficient delivery reliability within the problem areas due to production planning and control (PPC) decisions, this part is described in subparagraph 3.3.2..
3.3.1. Determining the relevant problem area
During the first part of the diagnosis framework, four steps are performed. These steps help to determine the relevant problem area, this problem area will be further investigated in the second part of the diagnosis framework.
12 delivery. The distributions in figure 2 are examples of average lateness (right) and variance of lateness (left).
Figure 2 – Distributions of lateness
The second step helps to determine if certain order subsets are mainly responsible for the insufficient delivery reliability. In the case the focus of the investigation is average lateness based, the different order subsets are looked upon, to determine which subsets are responsible for the high average lateness. In the case certain order subsets have a relative high average lateness, the focus for the next steps will be on these order subsets. When the conclusion of step one was that the focus is oriented to the variance of lateness, the variance of lateness of the different subsets are calculated. In the case the variance of lateness within each subset is relative low, the focus for the next step will be on the subsets with a high average lateness. The focus in the follo wing steps for these subsets will be focused on the average lateness. In the case the variance of lateness within each subset is high, the focus remains variance oriented for all order subsets.
In the third step, the dataset is analysed to determine if the average lateness or variance of lateness is time dependent. If the focus of the diagnosis is average lateness oriented, the orders are divided in time periods, based on their realised delivery date. This diagnosis step can reveal certain time periods in which the average lateness is high, these time periods will be focus on, in the remaining part of the diagnosis. For variance oriented diagnoses, the orders are also divided in time periods. When the average lateness in certain time periods is high, the remaining diagnosis steps will be on these time periods and average oriented. In the case the variance of lateness is time independent, the focus remains on all time periods and variance based.
13 Figure 3 – The basics of throughput diagrams (Soepenberg, 2010)
Figure 4 – A order progress diagram (Soepenberg, 2012)
3.3.2. Determining the PPC causes within problem areas
During the second part, the causes for the insufficient delivery reliability are identified. In the first part of the diagnosis process the relevant problem area is determined.
variance-14 oriented DP diagnosis. The cause for this problem area is that not all characteristics of an order are taken into account during at the moment the promised delivery date is set. For instance number of activities at a department or routing of an order.
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4. Results
Introduction
In chapter four, the steps of the framework for diagnosing delivery reliability performance of make -to-order companies of Soepenberg (2010) will performed and provided with an explanation.
4.1. Determining the relevant problem areas
Diagnosis step 1: Analyse the distribution of latenessThe first step in the diagnosis proses is determine the percentage of orders which are delivered on time. The customers of Alfa Laval Groningen are responsible for shipping the products, therefore a ready for shipment date is agreed with the customer. In this paper, the terms delivery date and ready to ship date are interchangeable, related terms as delivery reliability are linked to the ready to ship date. The used dataset is of orders which are accepted from 01-04-2012 and produced before 01-05-2013, this data set contains 1001 orders. At Alfa Laval the closest date available to the ready to ship date, is the date the order was produced, Alfa Laval assumes a standard throughput time of two days for packaging, these two days are taken into account in the diagnosis.
Figure 5 - Percentage or late orders
Figure 5 shows the percentages of early, on time and late delivered orders. The percentage of orders delivered on time and early is in total 46.5 %, while the corporate aim of Alfa Laval is 96.0 %. A distribution of lateness helps to determine the focus of diagnosis step 2, whether the diagnosis should be average-oriented or variance-oriented.
16 Figure 6 - Distribution of lateness
In figure 6 the distribution of lateness of all orders is shown, with a peak between zero and two working days late. On average the orders are produced 0.36 working days late. Furthermore, the standard deviation is relative low with 9.57 working days. In the case of a normal distri bution, this would mean that 15.9 % of all orders would be delivered too late by 9.93 working days or more. Decreasing the variance of lateness would increase the peak of the distribution of lateness around 0 to 2 days late and therefore increase the percentage of late orders. As all orders are on average delivered too late and the standard deviation is relative low, therefore step 2 of the di agnosis process will be average-oriented.
Diagnosis step 2: Analyse differences among order subsets
To determine if a specific order subset or all order subsets are responsible for the positive average lateness, different order subsets are viewed upon in step 2. The orders of Alfa Laval Groningen can run through three different routings (subsets), as illustrated in figure 7, 8 and 9, the subsets are listed in ascending order in terms of customization level. (1) Make-to-order (MTO), in this case the order has a is already engineered. The order can pass through the different (pre -)production steps without engineering. (2) Engineer-to-order (ETO), the order is not completely engineered and therefore (some) modules need to be engineered, thereafter the order will pass through the sequential (pre -)production steps. (3) Engineer-to-order with approval from the customer (ETOWA). In this case the customer wishes to approve (some of) the modules which are engineered. After the approval of the customer, the engineering department develops the detailed technical drawings. Hereafter, the order will pass through the remaining (pre-)production steps. The different routings and higher level of customization have an influence on the average throughput time, the average throughput time per subset are 27.93, 38.98 and 53.86 working days respectively.
0 2 4 6 8 10 12 14 16 18 -25 -20 -15 -10 -5 0 5 10 15 20 25 P e rc e nt ag e of or de rs
17 Figure 7 - Order routing make-to-order products
Figure 8 - Order routing engineer-to-order products
Figure 9 - Order routing engineer-to-order with customer approval
In table 1 the delivery reliability, average lateness, standard deviation and number of orders per subset are shown. Looking at this table, we can conclude that all three subsets do not reach the corporate aim of Alfa Laval of 96.0 % concerning the delivery reliabil ity. Moreover, all three subsets have a positive lateness which indicates an average late delivery. The MTO orders have a relative positive effect on both the average delivery reliability and average lateness of Alfa Laval Groningen. Both the ETO and ETOWA orders have a lower performance on the delivery reliability in comparison to the average of all orders, respectable 39.1 % and 43.9 %. ETO orders have an average lateness of 0,20 working days and a standard deviation of 9.71 working days. The ETOWA orders have an average lateness of 1.68 working days and a standard deviation which are both lower performances than the averages of all orders. The high value of the standard deviation is supported by the distribution of lateness in appendix I.
18 Routing Delivery reliability Average lateness
in working days Standard deviation in working days Number of orders Make to order 50.9 % 0.21 8.61 582 Engineer to order 39.1 % 0.20 9.71 312 Engineer to order with approval 43.9 % 1.68 13.29 107
Table 1 - Average lateness, standard deviation, delivery reliability and number of orders per routing subset
Diagnosis step 3: Analyse differences over time
Step 3 will determine whether time dependency is of influence on the lateness of orders. According to several employees of the pre-production stage, there is a higher demand for industrial coolers during the spring months, which possibly impacts the delivery reliability. To determine if this is correct, the orders are divided in the following time subsets:
1. All orders completed between 01-05-2012 and 30-06-2012 2. All orders completed between 01-07-2012 and 31-08-2012 3. All orders completed between 01-09-2012 and 31-10-2012 4. All orders completed between 01-11-2012 and 31-12-2012 5. All orders completed between 01-01-2013 and 28-02-2013 6. All orders completed between 01-03-2013 and 30-04-2013
In table 2 the average lateness, standard deviation, delivery reliability and the number of orders per period subset are shown.
Subset Average lateness in working days
Standard deviation in working days
Delivery reliability Number of orders 1 -1.14 14.33 33.9 % 162 2 1.21 8.83 46.9 % 196 3 1.50 10.53 38.4 % 146 4 0.70 8.54 54.2 % 155 5 -0.13 6.87 53.2 % 124 6 -0.01 6.41 51.4 % 218
Table 2 - Average lateness, standard deviation, delivery reliability and number of all orders per period subset
Table 2 reveals a relatively positive average lateness during the periods of subsets 2, 3 and 4. Furthermore, the standard deviation seems to be decrease over time. According to employees, this decrease can stem from new working standards in which employees have daily meetings about , among others arising problems and new orders. As a result from these daily meetings, the planning is updated and (structural) solutions are made. The assumption that demand is higher during the spring months is supported by the number of orders during subsets 1, 2 and 6, whereby it should be noted that the sales figures have increased dramatically during the past two years.
19 causes orders with a later due date to be produced first. This results in a promised output curve lying almost constantly below the order completion curve. The EDD dispatching rule is a rule by which the job closest to its due date is worked on first (Hopp et al, 2001).
As the average lateness of subsets 2, 3 and 4 are positive, the focus of step 4 will be on orders completed between 01-07-2012 and 31-12-2012.
Diagnosis step 4: Analyse DP and RP
A further investigation is needed to determine if the average lateness is caused by the delivery time promising process (DP), the realisation process (RP) or both. Looking at the throughput diagrams of the time period 2, 3 and 4 (see appendix II, figure 14, 15 and 16) in appendix II, we can concluded that the promised output curve lies constantly very close to the order completion curve and sometimes exceeds the order completion curve. Periods when the promised output curve lies above the order completion curve is preceded by a sudden shift in the promised output curve. As can be seen in figure 14 around 08-08-2012, figure 15 around 29-10-2012 and in figure 16, between 28-11-2012 and 7-12-28-11-2012. Not all sudden shifts of the promised output curve cause the curve to exceed the order completion curve, as the realisation process can increase the output by working overtime at the expense of extra labour costs or by ensuring a completed order buffer so the order completion curve stays above the promised output curve. As can be seen in figure 14, around 18-07-2012 and 28-08-2012. According to Soepenberg (2010), the irregularities in the promised output curve indicate a uncontrolled DP process. The second part of the diagnosis framework in subparagraph 4.2., will be average-oriented in the DP.
4.2. Determining PPC causes within problem areas
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5. Conclusion
Introduction
This research is focused on the delivery reliability of Alfa Laval Groningen, a make-to-order (MTO) and engineer-to-order (ETO) company of industrial air coolers. For the diagnosis of the delivery reliability, a dataset is analyzed of all orders accepted from 01-04-2012 and completed before 01-05-2013. The aim of this research project was to determine the causes which have a negative effect the delivery reliability of a MTO and ETO company. In order to determine the causes, the diagnosis framework developed by Soepenberg (2010) is applied. The framework is originally developed for the shop-floor of make-to-order companies. This is the first research in which the framework is used to diagnose both MTO and ETO production processes. The production process includes the pre -production operations. Furthermore, during the execution of the framework, a distinguish is made between the MTO and two different ETO processes. The choice to use the diagnosis framework for the operations in the pre-production process is made, because operations of the pre -production stage can be compared to operations on a shop-floor of a MTO company. The remaining of this chapter will answer the research question and review the usefulness of the framework in a make -to-order and engineer-to--to-order company and lastly discuss the limitations and future research.
5.1. Research question
The framework for diagnosing delivery reliability performance of make -to-order companies of Soepenberg (2010) is performed to answer the following research question: Which problems and underlying causes, cause an insufficient delivery reliability of a MTO/ETO company?
The results from the diagnosis framework suggest an average promised delivery time which is insufficient to complete all operations. Therefore it is important to determine if all pre -production and production operations and their status, available capacity and workload, are included in the process of promising a delivery time.
21 customer does not approve the technical drawings within one week, is added to the approval form since January 2013. Other improvements can be made by improving the workload estimations use d for the calculation of the promised delivery dates.
As noted by Soepenberg (2010), the average throughput times could be perceived as too long, although the order completions curve is stable, which suggests sufficient control over the RP. Possible causes underlying the too long average throughput times are extensive buffering which causes unnecessary waiting times and long average material delivery times. The later cause is a present occurring at Alfa Laval Groningen, late deliveries, insufficient quality control of suppliers and poor inventory control were often a cause for delays in the production process. As these causes are often mentioned during daily staff meetings and interviews with employees from the purchasing and material picking department. Although these causes could not be underpinned by analysing data, as Alfa Laval Groningen does not keep track of late material deliveries per order. Still it is likely that improvements can be made to the delivery reliability by improving these internal and external factors.
Lastly, during the daily meetings and interviews with employees, it was interesting to encounter almost all the possible problems which were found in scientific papers used for the theoretical background regarding delivery reliability problems within ETO companies.
5.2. Methodology review
The diagnosis framework of Soepenberg (2010) is a high quality tool, which can be of managerial use in a MTO/ETO company to determine the causes of a low delivery reliability. With a complete dataset including all completion dates and throughput times of the (pre -)production steps, the clear diagnosis steps help to determine the causes of a insufficient delivery reliability. There are however some drawbacks encountered during the diagnosis steps. One drawback is that throughput diagrams cannot give a good rendering of operations which do not process all orders, due to the routing of the order. For example a MTO product will not pass through the engineering operation, the number of orders of the engineering curve will therefore give an incorrect rendering. A solution is to enter the production date of the previous operation, in return the curve will give an optimistic rendering. Furthermore, some operations are performed simultaneously, an order progress diagram cannot show these operation in a correct fashion, which can cause incorrect conclusions during the diagnosis steps.
5.3. Limitations & future research
22 quantitative data. During the daily staff meetings and interviews, the late material delivery and insufficient inventory control became apparent as one of the causes for the low delivery reliability. Another limitation of this research is the lack of historic data concerning the reason an order was delivered late. Furthermore, it is questionable if the findings can be generalized for other ETO/MTO companies.
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Appendix
Appendix I: Distribution of lateness of different order routings
Figure 10 – Distribution of lateness of MTO orders
Figure 11 – Distribution of lateness of ETO orders
0 5 10 15 20 25 -25 -20 -15 -10 -5 0 5 10 15 20 25 P e rc e nt ag e of or de rs
Lateness in working days
0 5 10 15 20 -25 -20 -15 -10 -5 0 5 10 15 20 25 P e rc e nt ag e of or de rs
26 Figure 12 – Distribution of lateness of ETOWA orders
0 2 4 6 8 10 12 -25 -20 -15 -10 -5 0 5 10 15 20 25 P e rc e nt ag e of or de rs
27
Appendix II: Throughput diagram of different time subsets
Figure 13 – Throughput diagram from 01-05-2012 until 30-06-2012
Figure 14 – Throughput diagram from 01-07-2012 until 31-08-2012
28 Figure 15 – Throughput diagram from 01-09-2012 until 31-10-2012
Figure 16 – Throughput diagram from 01-11-2012 until 31-12-2012
29 Figure 17 – Throughput diagram from 01-01-2013 until 28-02-2013
Figure 18 – Throughput diagram from 01-03-2013 until 31-03-2013
