§ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 

MARTIN ENGELSEN 
FREDRIK HELLICHIUS 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
DEPARTMENT OF TECHNOLOGY MANAGEMENT AND ECONOMICS 
DIVISION OF SUPPLY & OPERATIONS MANAGEMENT 

CHALMERS UNIVERSITY OF TECHNOLOGY 
Gothenburg, Sweden 2025 

www.chalmers.se 
 

  

ANALYZING THE 
DISTURBANCES IN A 
MANUFACTURING COMPANY 
 

 

 

Analyzing the change in disturbances for the Order to 
Delivery process from pre-pandemic until present year. 
Master’s thesis in Quality & Operations Management 
 

 
 
MARTIN ENGELSEN 
FREDRIK HELLICHIUS 
 
 



 
 
 
 
 
 
 
 
 

 
 

Optimizing the Order to Delivery Process in 
Manufacturing: Identifying Disturbances and 

Optimizing Flow 
 
 

MARTIN ENGELSEN 
FREDRIK HELLICHIUS 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 

 

Department of Technology Management and Economics 
Division of Supply & Operations Management 

CHALMERS UNIVERSITY OF TECHNOLOGY 
Gothenburg, Sweden 2025  



 
 
 
 
 
 
 
 
 
 
 
An analysis on the OTD Process at an established manufacturing company. Focusing on the 
identifying key contributors to an unstable OTD process. 
 
MARTIN ENGELSEN 
FREDRIK HELLICHIUS 
 
 
© MARTIN ENGELSEN, 2025 
© FREDRIK HELLICHIUS, 2025 
 
 
 
Department of Technology Management and Economics 
Chalmers University of Technology 
SE-412 96 Gothenburg 
Sweden 
Telephone + 46 (0)31-772 1000 
 
Gothenburg, Sweden 2025



Optimizing the Order to Delivery Process in Manufacturing: Identifying  
Disturbances and Optimizing Flow. 
 
M. ENGELSEN   
F. HELLICHIUS    
 
Department of Technology Management and Economics 
Chalmers University of Technology 
 
 
 
SUMMARY 
 
The Order to Delivery (OTD) process plays a critical role in manufacturing, directly impacting 
lead times, production efficiency, and customer satisfaction. Since the COVID-19 pandemic, 
the studied company, an established manufacturer, has struggled to restore pre pandemic 
OTD performance levels. The number of products deviating from the Standard Activity 
Process (SAP) has more than doubled since 2019, primarily due to an increase in post-
production adjustments, reworks, and component shortages. 
 
This thesis aims to identify the most frequent and impactful disturbances within the OTD 
process by analyzing both qualitative and quantitative data collected from internal databases 
and interviews with key stakeholders. Structured around the three main OTD phases: Order, 
Industrial, and Market. The study applies a data-driven case study approach supported by 
theoretical frameworks including Lean Manufacturing and Six Sigma’s DMAIC methodology. 
 
The thesis does not seek to determine root causes but instead maps and quantifies the 
disturbances that consistently challenge OTD stability. Additionally, it visualizes what an 
“optimal flow” could look like if the process operated without disruptions. The findings provide 
the company with clear guidance on where to focus future improvement efforts to enhance 
responsiveness, reduce non-value-adding activities, and approach ideal performance 
standards. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Keywords: OTD Process, Manufacturing, LEAN, Order process, Industrial Process, Delivery 
Process, Optimal flow, Production disturbances, Quality Management, Operational 
Management  



  



 
 
 
 
 

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Acknowledgement 
 
This master’s thesis was conducted during the spring of 2025 at the Department of 

Technology Management and Economics, as part of the Master’s Programme in Quality 

and Operations Management at Chalmers University of Technology. 

 

First and foremost, we would like to express my deepest gratitude to our supervisor at 

Chalmers, Dan Anderson, for his invaluable guidance, support, and encouragement 

throughout this thesis. His constructive feedback, broad knowledge, and thoughtful 

reflections have been important in shaping the research and improving the overall 

quality of this work. Thank you for always challenging our thinking and for your 

continuous support during this journey. 

 

We would also like to extend our sincere thanks to our supervisors at the company, 

whose insights and openness made this thesis possible. We truly appreciate the trust 

placed in us, the collaborative spirit, and the willingness to involve us in the company’s 

operations and discussions. Your practical perspective and support have contributed 

significantly to the depth of this research. 

 

Additionally, we want to thank all the employees at the company who took the time to 

participate in interviews, share their knowledge, and provide valuable input. Your 

contributions were essential, and we are genuinely thankful for the warm welcome and 

interest shown in our work. 

 

Lastly, we would like to acknowledge our thesis opponents for their constructive 

feedback and engagement. Your comments and critical perspectives have helped refine 

and strengthen the thesis. Thank you for your valuable time and insights. 

 

 

 
  



 
 
 
 
 

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Table Of Contents: 
Acknowledgement .................................................................................................................................... 1 

1. Introduction ............................................................................................................................................ 6 

1.1 Background .......................................................................................................................................... 6 

1.2 Problem description ......................................................................................................................... 6 

1.3 Purpose .................................................................................................................................................. 7 

1.4 Clarification of key concepts .......................................................................................................... 8 

1.5 Delimitations ..................................................................................................................................... 10 

2. Methodology ......................................................................................................................................... 12 

2.1 Research design and approach ................................................................................................... 12 

2.2 Integration of Six Sigma DMAIC approach ............................................................................. 13 

2.3 Literature review ............................................................................................................................. 15 

2.4 Data collection ................................................................................................................................... 16 

2.4.1 Internal database quantitative data collection ............................................................ 16 

2.4.2 Qualitative data collection ................................................................................................... 17 

2.5 Data analysis ...................................................................................................................................... 18 

2.5.1 Quantitative analysis .............................................................................................................. 18 

2.5.2 Statistical Validity Using Chi-Squared & Cramér’s V test: ........................................ 18 

2.5.3 Qualitative analysis ................................................................................................................. 20 

2.6 Reliability ............................................................................................................................................ 21 

2.7 Validity ................................................................................................................................................. 21 

2.8 Ethical Considerations ................................................................................................................... 22 

3. Theoretical Framework ................................................................................................................... 24 

3.1 Order to Delivery Process ............................................................................................................. 24 

3.2 Theoretical Perspectives on OTD Optimization ............................................................ 25 

3.2.1 Existing Research on OTD Challenges and Post-Production Adjustments ....... 25 

3.2.2 Optimal flow visualization ................................................................................................... 25 

3.2.3 Identified KPI’s for a responsive stable OTD-Process ............................................... 26 

3.2.4 Optimal flow visualization within production processes ........................................ 30 

3.3 OTD Research within other companies ................................................................................... 32 

3.4 Theoretical Tools & Frameworks .............................................................................................. 35 

3.4.1 Lean Manufacturing & Waste Reduction ........................................................................ 35 

3.4.2 Six Sigma Methodology ......................................................................................................... 36 

3.4.3 The Pareto Principle (80/20 Rule) ................................................................................... 37 

3.4.4 The 4 Dimensions of Operations ....................................................................................... 38 



 
 
 
 
 

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3.4.5 The Bullwhip Effect ................................................................................................................. 39 

4. Current State Analysis ...................................................................................................................... 41 

4.1 Company background .................................................................................................................... 41 

4.2 Evaluating the Strategic Position Across Four Dimensions ............................................ 41 

4.3 Factory Context and Production Setup .................................................................................... 43 

4.4 OTD process explanation .............................................................................................................. 44 

4.4.1 Order phase .................................................................................................................................... 45 

4.4.2 Industrial phase ............................................................................................................................ 47 

4.4.2.1 Inhouse manufacturing phase ........................................................................................ 47 

4.4.2.2 Production phase ................................................................................................................. 47 

4.4.2.3 Delivery phase ....................................................................................................................... 49 

4.4.3 Market phase .................................................................................................................................. 50 

5. Results from qualitative findings and data analysis ............................................................. 51 

5.1 Qualitative OTD process findings .............................................................................................. 51 

5.1.1 Order phase: Supplier strategies and the effects of global instability ........ 51 

5.1.2 The industrial phase: Production related challenges and improvements 52 

5.1.3 Production and Logistics Flow- and supplier related challenges effects on 
production ............................................................................................................................................. 55 

5.1.4 Culture ......................................................................................................................................... 56 

5.1.5 Conclusions and takeaways for data analysis .............................................................. 57 

5.2 Performance Insights from Data Analysis of the OTD Process ...................................... 57 

5.2.1 Empirical findings from system data ............................................................................... 58 

5.2.1 Overview of late deliveries when deviations exist ..................................................... 59 

5.2.3 Delivery phase data graphs ................................................................................................. 63 

5.2.4 Production phase data graphs ............................................................................................ 65 

5.2.5 Correlations between production disturbances and late deliveries ................... 68 

5.2.6 Statistical Significance for Multiple Variables .............................................................. 70 

5.3 Open Point Data Analysis .............................................................................................................. 71 

5.4 KPI summary for both factories. ......................................................................................... 74 

6. Optimal Flow Analysis: Expected level & prioritizing factors ........................................... 77 

6.1 Analysis of high-Level Disturbances in the OTD Process ................................................. 77 

6.2 Prioritizing factors for Optimal flow within OTD Process ............................................... 80 

7. Discussion .............................................................................................................................................. 83 

7.1 Organizational & Operational Differences ............................................................................. 83 

7.2 The importance of sticking to the production plan for reaching an optimal flow .. 83 

7.4 Signs of Stabilization and Process Improvement in 2024. ............................................... 84 

7.3 Correlation between key production disturbances and literature review. ............... 84 



 
 
 
 
 

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7.5 A Closer Look at Staff Turnover ................................................................................................. 87 

7.6 Quality of data ................................................................................................................................... 88 

7.7 Bullwhip Effect: Chain Reactions of Disruptions ................................................................. 89 

8. Conclusion ............................................................................................................................................. 91 

Attachments ................................................................................................................................................... 93 

References ...................................................................................................................................................... 95 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



 
 
 
 
 

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1. Introduction 

This chapter introduces the background and problem descriptions with current 

challenges of the OTD process. Also it presents the purpose of the study: to identify the 

most common disturbances in each phase and illustrate what the optimal flow could 

look like. 

 

1.1 Background 
 

During the COVID -19 pandemic the company, like all manufacturing companies in the 

industry, experienced major disruptions. Global supply chain bottlenecks and 

semiconductor shortages led to severe delays in sourcing critical components for the 

production. To handle the disruptions, the company implemented temporary solutions. 

However, this often led to post-production adjustments, reworking the products by 

replacing components, updating software or installing missing parts. This led to the 

significant consequence of a prolonged increase in the total lead time for the Order to 

Delivery process (OTD). Despite a return to more stable conditions in 2024, the system 

has not fully recovered, and lead times remain elevated. This suggests that underlying 

inefficiencies persist beyond the initial pandemic impact. 

 

1.2 Problem description  
 

 

The OTD process today is unstable. The company tracks Key Performance Indicators 

(KPIs) that measure how many items deviate from the Standard Activity Process (SAP). 

Any instance where additional resources are required to complete a product is 

categorized as "not following the Standard Activity Process." This issue has become 

increasingly frequent in recent years. To illustrate the scale of the problem, in 2019, 

25% of items did not follow the SAP, but by 2024, this number had risen to 55%. These 

deviations require post-assembly adjustments, such as manual reworks, additional 

quality inspections and modifications to the finished product. These adjustments are 



 
 
 
 
 

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leading to non-value-adding activities that significantly increase costs, time, and 

resource consumption for the company. The growing need for adjustments suggests 

underlying inefficiencies in the OTD process that prevent a return to pre-pandemic 

(2019) performance levels. It has even gone to the extent that sometimes the items are 

delivered to the customer with a missing part that is not crucial for the function of the 

product or that implies any safety concerns, but it is however something that has 

decreased the quality aspect of their process. 

 

Given this shift, stakeholders are interested in investigating whether this cultural 

transition has become a contributing factor to inefficiencies in the OTD process. 

Understanding how pandemic-era work habits continue to influence production could 

provide valuable insights into post-production adjustments and help shape strategies 

for process improvement. 

 

Another key challenge is that the company wants to make more data-driven decisions 

and use that approach to problem-solving. While managers have hypotheses about why 

the deviations are increasing, these insights are largely based on experience rather than 

factual data. They recognize the importance of making decisions based on evidence 

rather than intuition, yet the company’s current approach can be described as 

"firefighting"-reactively addressing issues rather than eliminating their root causes. As a 

result, problems that were thought to be resolved in the past have resurfaced, indicating 

that previous solutions were not effective in the long term. 

 

The increasing frequency of deviations suggests underlying inefficiencies within the 

OTD process. Without identifying and addressing these deviations, the company risks 

further instability, increased operational costs, and decreased product quality. 

 

1.3 Purpose  
 

As the company seeks to return to pre-pandemic production stability, there is a need to 

understand why post-production adjustments have increased and what factors are 

driving these inefficiencies. The purpose is to find the most common disturbances for 

each phase of the OTD process. Additionally, the purpose is also to show what the 



 
 
 
 
 

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optimal flow can look like for the OTD Process if everything was perfect. By combining 

data analysis and literature research the hope is to provide the stakeholders with 

recommendations on what areas to focus on when improving the process. 

In order to return to pre-pandemic stability, it seems necessary to first identify what 

key factors are continuously causing disturbances to the different phases of the OTD 

process. As per request of the stakeholders, they also want to know what the optimal 

flow would look like and that is done by identifying the excepted level of performance.  

The following research questions will be investigated: 

• What are the key factors continuously causing disturbances in the OTD Process? 

• What can the optimal flow look like in the OTD process?  

 

1.4 Clarification of key concepts 
To ensure a clear understanding of the key concepts within the research questions, they 

will be defined below.  

The OTD process is essentially three phases: Order, Industrial and Market. In terms of 

what makes a disturbance a key factor is based on several criteria. One of them is the 

frequency of the disturbance, how often, when, where etcetera. Secondly, the volume of 

the disturbances. Because of the wide variety of problems that exist some sort of 

prioritization has to be done and therefore volume is an important consideration. 

Thirdly, the magnitude of the disturbance, meaning how critical is this issue.  

Furthermore, clarifying “optimal flow” in the OTD Process. The optimal flow is defined 

as, if everything worked perfectly, no material flow, production, quality issues could 

happen, what would the flow look like? An extended clarification of the optimal flow 

and the OTD process is stated in Current State Analysis. 

Performance in the OTD process can be measured using multiple parameters, 

depending on different operational priorities. To clarify what defines as expected 

performance in this study, a list of the most relevant parameters and Key Performance 

Indicators (KPI) is listed below. Also, other key concepts are listed below that are used 

in this thesis. 

Number of adjustments: The number of adjustments refers to the number of corrections 

and modifications of the products after it has passed the standard production process. 



 
 
 
 
 

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In the adjustment phase, reparation and replacement of different parts that can be 

missing from the assembly line is corrected, to meet the required quality standards and 

customer requirements. The adjustment phase is a non-standard assembly process, and 

a high number of adjustments indicates inefficiencies in the production process. 

Number of missing parts: This describes the number of occurrences when a product is 

missing one or more components. The consequences of having high number of missing 

parts can be extra logistics costs, customer dissatisfaction and extra rework at the 

adjustment phase. However, missing parts may not always impact the safety or 

functionality of the product, it depends on the criticality of the part. 

Production efficiency: Production efficiency evaluates how effectively the different lines 

and stations operate. A key metric for this indicator is the First Time Through (FTT). 

FTT measures the percentage of all the products that pass through the specific line or 

station without requiring any rework or adjustment. A high number of FTT point to a 

high-quality production process. 

Open points: Open points refer to all types of irregularities in the production process 

that is not resolved before the end of production line, hence the word “open”. Some of 

the open points needs to go through the adjustment process to be repaired before being 

cleared as ended of the production phase. Different kinds of open points can be missing 

parts, wrongly assembled or not assembled parts. The open points data also shows on 

what type of product the fault is concerning, problem owner of the fault and reporting 

area.  

Remark: a Remark is comment of an issue that has happened in the production line and 

is solved in the production line. For example, if a screw is loose and an operator finds it, 

he reports it as a remark and tighten the screw. If he would not tighten the screw, it 

would be an open point.  

Problem owner: A problem owner is the station, person, line or place where a problem 

has occurred. It is the problem owner’s responsibility to see through that the root cause 

of the fault will be found and solved.   

Planned production start (PPS)/actual production start (APS): PPS is the date when the 

vehicle is planned to start in the production phase. APS is the actual date. Often the two 



 
 
 
 
 

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are the same date and time, however, it can be some issues that delay the start of the 

production process. 

Planned production end (PPE)/Actual production end (APE): PPE and APE is the 

planned versus actual production end. Just like PPS and APS, PPE and APE can be 

delayed due to issues in the process. 

Planned delivery date (PDD)/Confirmed delivery date (CDD)/ Actual delivery date 

(ADD): PPD is the date the customer receives when he orders the vehicle, and CDD is 

the confirmed date, when the product is ended in the industrial phase and is on the 

transport to the customer. ADD, however, is the actual date the product arrives to the 

customer. 

Campaign: Campaign is when a fault on the product, usually from the production phase, 

is found on a large number of products and needs to be adjusted, either internal if the 

products are located in the factory still, or external if it has reached the customer.   

1.5 Delimitations 
 

The thesis will not include connected processes outside Order to Delivery process. 

The thesis will only include the main assembly plants in Europe and the corresponding 

supply chain. Meaning the thesis will only investigate two factories which are referred 

to as factory 1 and factory 2. 

 

Due to company restrictions the limitation of confidential data and other relevant 

information regarding the company will be restricted. When presenting relevant data 

and information regarding the OTD process, an unknown factor X will be added to the 

data to hide the actual data. Some of the internal data from the company was difficult to 

find due to the complexity of being a large international company and relevant data 

cannot be presented to the authors due to limited access to the internal databases. The 

issue of having two different factories to analyze within the OTD process makes location 

a limitation factor, since one of the factories is in another country and travel restrictions 

at the company make the investigation of the certain factory more difficult. Since the 

OTD process involves two factories, a limitation of different kinds of databases of the 



 
 
 
 
 

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two factories to find relevant data, together with language difficulties make it difficult to 

find relevant data.  

Additionally, because the OTD -process is so comprehensive and due to time constraints 

of the project, the market phase will not be investigated as well as the inhouse 

manufacturing part of the Industrial phase. 

 

 

 

 

 

 

 

 

 

  



 
 
 
 
 

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2.  Methodology 

 

The study will utilize a data-driven approach, incorporating historical production data, 

process mapping, and available research literature and stakeholder interviews to 

identify disturbances. By pinpointing the primary inefficiencies, this research aims to 

provide actionable insights that can help optimize production flow, minimize non-

value-adding activities, and improve overall OTD efficiency at the company. 

 

2.1 Research design and approach  
This study investigates the Order to Delivery (OTD) process at the company through a 

structured research approach with both theoretical and empirical data. A mixed method 

approach is used by applying both quantitative and qualitative data. The study also 

follows a case study approach, described by Yin (2009). The case study approach is 

particularly suitable for examining existing processes within their real-world context 

and when the topic of the thesis is complex and broad (Yin, 2009), in this case the real-

world context is the objective from the company’s OTD process. The research moreover 

touches on a deductive approach, meaning that it studied beforehand in established 

theories within quality and operations management to test different hypotheses with 

the data collected (Yin, 2009). The deductive approach is mainly used when looking at 

the optimal flow. However, the study was mainly based on an inductive approach 

through the qualitative analysis of interview responses, as empirical responses 

emerging patterns and themes. This allows for both exploratory and explanatory 

insights and with case studies as a method it can integrate multiple sources of evidence 

(Yin, 2009). An illustration of the research design is shown in figure 1 below. A 

combination of deductive and inductive approaches furthermore allows both 

hypothesis testing and theory and makes the research findings more robust (Bell & 

Bryman, 2019). 

 

 



 
 
 
 
 

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Figure 1:A tree structure of the Master thesis project 

 

 

2.2 Integration of Six Sigma DMAIC approach 
To analyze and improve the OTD process, this study uses the Six Sigma DMAIC approach 

(Define, Measure, Analyze, Improve, Control). The DMAIC approach ensures that 

inefficiencies are identified, measured and addressed in an evidence-based way (Pyzdek 

& Keller, 2014). While DMAIC wasn’t followed in full as a formal research model in the 

thesis, it was a helpful guide in practical project work that was conducted during the 

study. The framework helped structure the thinking, planning and identifying data, but 

not all steps were applied in a strict or detailed way in the thesis itself. The most focus 

for the thesis lies on the Define, Measure (Data collection) and Analyze phase (Data 

analyze), but the Improve and Control phases are only addressed, as the study is more 

exploratory and analytical. Some examples of when using the DMAIC framework are 

that the scope of the problem was defined with Six Sigma tools such as SIPOC and 

focused on bottlenecks in the production and logistic phase of the OTD process. 

 

 

 



 
 
 
 
 

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Figure 2 DMAIC STRUCTURE 

 

 

 

 

  



 
 
 
 
 

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2.3 Literature review 
A literature study was conducted to establish a theoretical foundation for the study 

within the optimal flow of the OTD process. The literature review should be focused and 

relevant, and aligned with the research questions (Bell & Bryman, 2019). The purpose 

of doing a literature review is to contextualize with existing knowledge of literature 

within the subject (Bell & Bryman, 2019), in this case, the OTD process. According to 

Machi and McEvoy (2016) there are several key points regarding finding sources. The 

first one is what type of sources to use. This study uses mostly primary and secondary 

sources, such as academic journals, industry reports and relevant other books and texts 

that were researched to find the most relevant data of the subject. The next step is to 

evaluate the sources by looking at the author, is the author credible, and is the source 

up to date. As outlined by Machi and McEvoy (2016), perhaps the most relevant key 

point when looking at the sources is the relevance. Does the source directly relate to the 

topic the thesis is about or not. Furthermore, to find relevant sources, academic portals 

such as Google Scholar and Chalmers Library were the main literature collection portals 

for this study. Some literature was also found with the help of the supervisor at 

Chalmers and other relevant researchers at Chalmers. The sources was books, industry 

reports, doctoral thesis and peer-reviewed journal articles. Relevant literature of 

competitors OTD process was particularly difficult to find due to many of the 

companies’ processes are classified. However, some were found although some of the 

articles wasn’t a clear competitor. 

 

To find the right sources, the criteria were that the articles should be from the year 

2000 and forward to ensure relevance and aligning with current technologies and 

methods. But as some Theoretical frameworks were described in earlier papers, some 

articles may be from before the year 2000 as well. Also, the authors should have 

relevant knowledge about the subject, so a search of mostly professors and doctorands 

in the fields of subject was looked at. Some of the articles where from Sweden due to the 

authors of this paper is Swedish, hence easier to find Swedish articles, but the majority 

of the articles where English. Another thing was that the articles needed to be unlocked 

for reading, therefore there were plenty of articles that were removed from the filter 

because they weren’t accessible.  Relevant search words in the academic portals were 



 
 
 
 
 

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OTD process, Optimal flow, Production flexibility, supply chain flexibility, Six Sigma 

methodology, Just in time, 4V, sourcing, Lean manufacturing, Pareto principle, Bullwhip 

effect, production process, Manufacturing, supply chain disruptions, Production 

Bottlenecks, Quality Assurance, automotive, Delivery process, covid-19, Methodology. 

As a lot of articles pop up when searching for only one of the search words, a 

combination of the search words where applied.  A literature is not only a summary but 

more a form of argument, and it can demonstrate how existing literature informs and 

justifies the study (Bell & Bryman, 2019). 

 

2.4 Data collection 
The data collection presents how the data was collected within the company, both for 

the quantitative and qualitative data. The subchapter further presents what types of 

internal data are used and what types of interviews with stakeholders were conducted. 

2.4.1 Internal database quantitative data collection 
Quantitative data was gathered from the company internal database, based on historical 

OTD performance, production and logistics data. Several different databases were used 

to retain the relevant data. By investigating and analyzing differences in the OTD 

process before and after the Covid -19 pandemic, the data gathered were extensive. The 

time frame of the data spans from the year 2018 to 2025. The type of data gathered was 

the number of open points within assembly in the factory 1, where the data could be 

sorted out in different stations of the assembly, and what type of fault the open point 

was about. This data was from 2021 until 2024 and are from a system called Tool A. A 

large number of data similar to the open points was also gathered from factory 2. This 

data was not as detailed, but the data spanned from 2018 until 2024. There was an 

internal database program referred to as Tool B that was interesting to the thesis due to 

it had information about the product from order phase and reached to delivery phase, 

the entire OTD process was managed in Tool B. When an order was started it also 

started in Tool B and followed the product when it entered specific phases in the 

process. In Tool B showed when the product was planned to start, and actual start, and 

actual end. People working in the phases could also delay the product in the program 

and change dates of when estimated delivery should happen. This data was one of the 

most important ones due to the fact that you could see where and for how long it had 

been delayed in the process.  In Tool B the data from all products produced could be 



 
 
 
 
 

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seen, what time the products had entered and finished a phase and also what the 

planned time was. So, in that database you could find in which phase the product was 

delayed, and how many products that were delayed. The data also showed if the 

products had entered the adjustment phase, and if it had missing parts, needed 

workshop or had a campaign. However, it didn’t have much detailed information, only 

on a high level. Other data collected was data from a source where all the people from 

the factories, both blue collar and white collar, had been resigned, either voluntarily or 

not. Other data looked at was from relevant Power-Bi sources within the company’s 

internal portal. All data collected was extensive, and also due to company restrictions it 

needed to be multiplied with an unknown factor to present it in the thesis. 

 

2.4.2 Qualitative data collection 
Semi structured interviews were held with key stakeholders at the company, who 

worked or were involved in the OTD process at the company. This approach allowed for 

both consistency within interviews and flexibility to explore in more depth, depending 

on the stakeholder’s experience (Bell & Bryman, 2019). The plan for the interviews was 

to investigate every phase of the OTD process. To do that at least one person from each 

phase of the OTD process was interviewed. For every phase of the OTD process, 

relevant questions were asked related to the stakeholder’s area of expertise. But all the 

interviews were held to find qualitative insights into challenges, inefficiencies and 

optimization efforts for the OTD process within the company. There were different 

types of roles the stakeholders had, such as Production leaders, Data scientists, 

Operators, Quality engineers, forecast managers, Regional Managers, team leaders, 

logistic engineers and Product Managers. The total number of interviews held was 

seven, and the duration of the interviews was approximately 40-120 minutes, 

depending on the complexity of the role and the interview. The interview protocol was 

designed similarly for each interview, and had the same core theme, such as views on 

process disruptions, bottlenecks and flow optimization. But the questions were also 

tailored around the stakeholders area, so all relevant information should be gathered 

for each phase. According to Bell & Bryman (2019), semi structured interviews are 

particularly suitable when the aim is to understand the stakeholders perspectives while 

also comparing the answers. In addition to the interviews, other relevant qualitative 

data was also gathered at informal discussions and internal meetings with relevant 



 
 
 
 
 

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company representatives. Regarding the information about the company, such as 

background, factory comparison, the company OTD process, it is from interviews, 

regular meetings, emails and discussions with relevant people within the organization. 

The stakeholders remain anonymous throughout the thesis, due to company 

restrictions and agreements. 

2.5 Data analysis 
The data analysis presents both quantitative and qualitative data analysis for the thesis, 

how the analysis is conducted, and furthermore how the statistical validity is analyzed. 

 

2.5.1 Quantitative analysis 
The use of quantitative data allows to analyze the relationships, patterns and variations 

of the data over time (Bell & Bryman, 2019). The data from internal databases were 

analyzed with statistical and data visualization technique programs such as Excel and 

Power Bi. The data was then presented in PowerPoint. The data involved relevant 

information about the different phases in the OTD process, mostly from the production, 

logistics and adjustment phase. Data such as type of remark, problem owner, open 

points, type of product, missing parts, delivery dates and reporting owner, over a period 

from 2019 to 2024 was investigated and analyzed to see trends over time and in 

performance.  Other tools such as different matrixes were also used. To determine 

which types of supply chain flexibility are most critical for OTD performance, we 

applied a structured pairwise comparison method inspired by the Pugh Matrix. Drawing 

from the flexibility dimensions proposed by Merschmann & Thonemann (2011), each 

factor was systematically compared against every other factor. For each comparison, 

the factor perceived to contribute more significantly to delivery performance was 

marked as preferred. The total number of “wins” per factor was then tallied, providing a 

ranked list of flexibility dimensions. The five factors with the highest comparative 

advantage were selected for further analysis. 

 

2.5.2 Statistical Validity Using Chi-Squared & Cramér’s V test: 

To evaluate if there was any statistical significance between different categories related 

to the OTD process, both a Chi-squared test and a Cramér’s V test were performed. More 

specifically the categories compared were the following: 

• Missing Parts 



 
 
 
 
 

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• Adjustments 

• Workshop  

• Campaign 

 

To keep consistency, one category was kept constant, namely adjustments. There were 

several reasons for that. Adjustments are the most relevant to the research questions, 

but also a very frequent occurrence in the OTD process.  

 

The chi-squared test determines whether the observed distribution of values, differs 

significantly from what would be expected if the variables were independent. 

(Greenwood & Nikulin, 1996). Also given the large sample size the test is appropriate. 

 

The chi-squared test was calculated by using the following equations: 

 

Equation 1 Formula for chi-squared test 

𝑋𝑐
2 =  ∑

(𝑂𝑖 − 𝐸𝑖)2

𝐸𝑖
 

 

𝑐 = 𝐷𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝑓𝑟𝑒𝑒𝑑𝑜𝑜𝑚 

𝑂 = 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑉𝑎𝑙𝑢𝑒(𝑠) 

𝐸 = 𝐸𝑝𝑒𝑐𝑡𝑒𝑑 𝑉𝑎𝑙𝑢𝑒(𝑠) 

 

Equation 2 Expected value formula 

𝑊ℎ𝑒𝑟𝑒 𝐸𝑟,𝑐 =
𝑛(𝑟) ⋅ 𝑐(𝑟)

𝑛
  

 

𝑟 = 𝑟𝑜𝑤 𝑖𝑛 𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛 

𝑐 = 𝐶𝑜𝑙𝑢𝑚𝑛 𝑖𝑛 𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛 

𝑛 = 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜𝑡𝑎𝑙 

 

 

 

 



 
 
 
 
 

20 
 

After this process was completed, the Cramer’s V test was performed. 

 

Equation 3 Cramer’s formula 

𝐶𝑟𝑎𝑚é𝑟′𝑠 𝑉 = √
𝑋𝑐

2

𝑛(𝑘 − 1)
  

𝑊ℎ𝑒𝑟𝑒 𝑋𝑐
2 = 𝐶ℎ𝑖-𝑆𝑞𝑢𝑎𝑟𝑒𝑑 𝑉𝑎𝑙𝑢𝑒  

𝑛 = 𝑡𝑜𝑡𝑎𝑙 𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒 

𝑘 𝑖𝑠 𝑡ℎ𝑒 𝑠𝑚𝑎𝑙𝑙𝑒𝑠𝑡 𝑜𝑓 (𝑟𝑜𝑤𝑠, 𝑐𝑜𝑙𝑢𝑚𝑛) 

 

According to Cramér, (1999), Cramér’s V gives you the strength association between  

two chosen variables: 

• 0.1 or less is considered weak association. 

• 0.3-0.5 is considered moderate association. 

• 0.5 or more is considered strong association.  

2.5.3 Qualitative analysis 
The qualitative data gathered from interviews was analyzed using thematic analysis. 

This method is suitable to find recurring themes and to identify patterns or themes to 

structure the qualitative insights (Bell & Bryman, 2019). This approach helps 

researchers to structure and analyze data by organizing insights into recurring 

categories, and enhances the validity, clarity and the depth of the findings according to 

Bell & Bryman (2019). A thematic analysis was chosen due to the effectiveness and 

flexibility of capturing detailed and relevant information from the stakeholders 

interviewed from the different phases of the OTD process. 

 

The data collected from the literature review of the optimal flow within the OTD 

process was then evaluated and compared in a Pugh Matrix. Stuart Pugh developed the 

Pugh matrix, a tool used to evaluate and compare the performance of multiple options 

or alternatives (Quigley, 2023). A Pugh matrix consists of a table with rows 

representing different alternatives and columns representing evaluation criteria or 

performance characteristics. Each cell in the table contains a rating or score that reflects 

the relative performance of the option being evaluated on the corresponding criterion. 

The ratings can be based on objective data or subjective judgment. Once all of the 



 
 
 
 
 

21 
 

options have been rated, the scores are summed up and the option with the highest 

total score is identified as the preferred option. The Pugh matrix can be used to 

compare multiple options in a structured, systematic and to identify the strengths and 

weaknesses of each option. It is often used in conjunction with other tools and 

techniques, such as brainstorming and prototyping, to support the decision-making 

process (Quigley, 2023). 

 

2.6 Reliability  
There are some things that need to take into consideration, Reliability and Validity. 

Reliability is an important consideration in research and refers to how reliable the 

process is and how consistent and repeatable the research findings are (Björklund & 

Paulsson,2003).  

 

There are three main aspects of reliability according to Bell & Bryman (2019), those are 

stability, internal reliability and consistency. Stability is when the measure is stable over 

time. The next aspect is internal reliability, if the items correlate with each other, and 

the last is consistency, when there are more than one observer in observations of the 

data, do the observers agree (Bell & Bryman, 2019). 

 

To ensure a high level of reliability, the study had mixed model methods, through 

quantitative and qualitative research designs. For the qualitative part, the interviews 

were semi structured with core questions similar to each other for each stakeholder 

interview. The responses were transcribed and analyzed manually using a common 

structure to minimize biases. For the quantitative data a standardized database queries 

were used to find relevant information and relationships in the data sources. Some 

filtering criteria such as time frames were applied and used in the data base, that 

reduced the risk of variation in the data handling. Using consistent data extraction 

techniques and documenting them clearly enhances the reliability (Björklund & 

Paulsson, 2003).   

 

2.7 Validity 
Validity refers to the research accurately measures and captures what it is intended to 

be measured and how the findings reflect on other phenomenon being studied (Pyzdek, 



 
 
 
 
 

22 
 

2003). There are different kinds of validity according to Bell & Bryman (2019), and the 

thesis analyzes measurement validity by establishing the study with theories from 

relevant literature, and internal validity by looking at the relationship between 

variables (Bell & Bryman, 2019). A literature review was conducted to ensure that the 

frameworks were aligned with the existing knowledge. By defining key concepts based 

on academic sources, the study ensured that it targeted the right elements it aimed to 

examine. For internal validity, one thing to consider was how the qualitative data from 

interviews were collected and handled. Because a possible threat to validity in 

interviews is the response biases from the stakeholders, where they say desirable 

answers for them or respond in a way that only align within company expectations. To 

reduce this, the anonymity of the stakeholders was applied, and confidentiality was 

assured to all participants interviewed. Another thing was that neutral and not leading 

questions were asked to the stakeholders to minimize bias. To further strengthen the 

validity, a cross verification was made between the interviews and literature.  

 

The selection of who to interview also supported validity. The stakeholders were 

particularly chosen to cover and represent the major phases of the OTD process, such as 

Order, Industrial and Delivery phase. This approach of finding and interviewing 

multiple stakeholders from different phases ensured that multiple perspectives were 

included, and that ensured that the study captured the full complexity of the company’s 

OTD process.  

 

2.8 Ethical Considerations 
 

Several ethical considerations were considered for the thesis. One concern is 

confidential data, how confidential and sensitive data is presented, by not exposing too 

much of the company data, and how it is handled. This was of great importance for the 

thesis and the company, and no exposing of sensitive information was shown. There 

was a balance of how much of the data that was presented in the thesis, and due to the 

confidentiality, all numbers are hidden or multiplied by an unknown number. 

Anonymity was assured to all stakeholders interviewed and personal identifiers were 

removed during transcription. As this is a Master thesis, the work of the thesis is 



 
 
 
 
 

23 
 

concluded solely by the authors, and not by AI. However, since AI is a good assistance 

for translating and find other words, help has been gained by AI.  

 

 

 

  



 
 
 
 
 

24 
 

3.  Theoretical Framework 
This chapter will cover the theoretical framework that will act as the research foundation 

for the thesis project by highlighting and explaining key concepts, theories and 

frameworks that help with analyzing the problem.  

 

3.1 Order to Delivery Process 

 

This chapter will explore the various stages of the Order to Delivery (OTD). It will 

investigate existing research as this involves using different models, established 

theories and methodologies to define these stages and highlight their significance. 

Additionally, it will examine The company’s perspective on the OTD process, identifying 

the key steps the company considers essential and how its approach differs from 

established research. 

 

The order to delivery process is an important aspect in a lot of manufacturing 

companies, particularly in this industry. The goal of the theoretical framework is to 

establish reference framework to continue analyzing causes for postproduction 

adjustments. Why are certain products getting all these additional reworks after being 

finished on the production line. This thesis integrate concepts like lean manufacturing, 

Six Sigma Black Belt theories and other important analysis tools. 

 

According to Viswanadham (2000), Krajewski and Ritzman (2005), and Jonsson and 

Mattson (2016), the OTD process for a product can be divided into the following key 

stages:    

 

1. Order Processing & Scheduling, which refers to the process of a customer placing 

an order and the subsequent scheduling of production. 

2. Finishing of ordered goods, which involves assembling and producing according 

to specifications. 

3. Internal transportation of goods. 

4. Distribution and transportation. 

5. Billing. 



 
 
 
 
 

25 
 

3.2 Theoretical Perspectives on OTD Optimization 

This subchapter presents the existing research of the OTD process base on literature, 
and a visualization based on different categories of factors that are connected to the 
OTD process.  

3.2.1 Existing Research on OTD Challenges and Post-Production Adjustments 

Several studies have examined inefficiencies in OTD processes. For instance, 

Narasimhan (2018) highlights the impact of delays and inefficiencies on production 

quality through Makigami Analysis. Industry reports emphasize that post-production 

adjustments often stem from process synchronization gaps between design, supply 

chain, and assembly operations. Alternative sources discussing process synchronization 

and OTD variability include Smith & Brown (2018) and Johnson (2020). Additionally, 

empirical studies on Lean and Six Sigma applications reinforce the argument that 

structured defect prevention is more cost-effective than corrective actions (Pyzdek & 

Keller, 2014). 

 

3.2.2 Optimal flow visualization 
 

Today, most companies want to have a flexible and quickly responsive supply chain. But 

having a flexible supply chain is expensive (Merschmann & Thonemann, 2011). 

Therefore, the right balance between the supply chains flexibility and the supply chains 

uncertainty needs to be reached to find the optimal flow for the company and OTD 

process (Merschmann & Thonemann, 2011). 

 

Supplier relationships also play an important role in the manufacturing industry, and 

their corresponding production efficiency. Lim et. Al (2014) mentions that a close and 

robust relationship between suppliers and manufacturers is of great importance when a 

change in customer order occurs. According to Liker and Choi (2004), they suggest six 

steps to have and maintain a good relationship with the suppliers after analyzing 

American automotive companies:  

• Understand supplier’s practices 

• Lead suppliers to compete against each other 

• Directly supervise suppliers 

• Enlarge suppliers’ technology 



 
 
 
 
 

26 
 

• Send information to selected suppliers  

• Improve practices with suppliers. 

 

By following these steps the automotive companies can be more robust against quick 

changes, both from the customer but also from the market perspective, when a large 

fluctuation in the market happens (Liker and Choi, 2004).  

 

3.2.3 Identified KPI’s for a responsive stable OTD-Process  
 

To find the optimal flow for the company’s OTD process, the optimal flow factors were 

outlined based on Merschmann & Thonemann (2011) and Rokicki, T., & Szebérenyi, A. 

(2024). The following factors within the supply chain flow are presented in Table 1 

below and explained further below the table:  

 

Table 1: Optimal flow categories and corresponding factors 

Categories Various types of factors  

Production 

Process 

complexity: 

 

Impact of pre 

process output 

on post 

process 

performance  

 

Impact of pre 

process changes on 

post process output  

 

Delivery 

Delays 

 

Increased 

Operational 

Costs 

 

Extent 

of on 

time 

delivery  

 

Product 

variation 

 

Frequency of 

redesigns of 

products  

 

Number of items 

changed per 

redesign  

 

   

Sourcing 

complexity 

 

Frequency of 

delays for 

critical 

material  

 

The quality of 

critical material  

 

Frequency of 

changes for 

critical 

material  

 

  



 
 
 
 
 

27 
 

Product 

complexity: 

 

degree of 

modularization  

 

    

Demand 

variation 

 

Predictability 

of demand 

patterns  

 

Extent of sharing 

demand forecasts 

with 

suppliers/customers  

 

   

 

 

 

Order 

process 

variation 

 

Frequency of 

order content 

changes by 

customers  

 

Frequency of short-

term Adjustments 

 

   

Customer-

oriented 

flexibility 

 

Improvement 

of 

responsiveness 

to changing 

market needs  

 

Improvement of 

level of customer 

service  

 

Improvement 

of delivery 

reliability  

 

  

Internal 

flexibility 

 

Level of 

customization  

 

Ability to adjust 

internal production 

capacity  

 

Prolonged 

Lead Times  

 

  

Supply 

chain 

flexibility 

 

Adjustment of 

worldwide 

delivery 

capacity/ 

capability  

 

Supply Market 

Scope 

 

   

 

 

 

 



 
 
 
 
 

28 
 

Production Process complexity: 

 

This factor is evaluating how connected the production process is across stages. If the 

production process has a high complexity, any change within one stage can have major 

impact on the following stages in the production process. Delivery delays and high 

operational costs often come from poorly synchronized production stages.  

 

Product variation 

 

Product variation shows how frequently a product undergoes design changes and 

modifications. The more changes of the product, product variation requires a more 

flexible supply chain and the supply system and corresponding production needs to be 

highly adaptable to changes. 

 

Sourcing complexity 

 

This factor measures the complexity of sourcing, how to handle critical material. The 

OTD process is very dependent on external suppliers and how they need to deliver at 

the right quality and quantity. The uncertainties within sourcing need to be in control to 

reach an optimal flow, as companies with high sourcing complexity are mor vulnerable.  

 

Product complexity: 

 

A simple product requires less handling of material. More standardization of product, 

and a higher degree of modularization is more optimal both for the supply chain but 

also for the production due to faster assembly and easier quality controls.  

 

Demand variation 

 

A good relationship between the company and the customer is of great importance. The 

more flexible the company is in sharing the demand forecasts for the customers and 

suppliers, the more flexibility the supplier can have, and the company becomes more 



 
 
 
 
 

29 
 

trustworthy, enabling better alignment across the value chain. This transparent 

communication helps all parties to be prepared for fluctuations.  

 

Order process variation 

 

The frequency of order changes at the last minute can have a large impact on the 

production and the corresponding supply chain within the company. It is therefore 

important to have a good relationship with the customer. Furthermore, to have a good 

knowledge of the market and how fast a market can fluctuate.  

 

Customer-oriented flexibility 

 

This is connected to order process variation. How to deal with a changing market 

demand, and how to handle customer service and how to improve the reliability of the 

delivery process. A high responsiveness means being able to change priorities and 

adjust delivery times without impacting on the quality of the product.  

 

Internal flexibility 

 

Internal flexibility is how flexible production can be. The more customization, the more 

variants and material within the factory. This is connected to product development lead 

time, the more customization the more time in product development. Internal flexibility 

can also measure how fast and by how much the factory can adjust its capacity.  

 

Supply chain flexibility 

 

Due to changing markets and worldwide problems, a flexible supply chain is of great 

importance for the company. To have the right basis within the supply chain, and a 

robust network is important when more and more worldwide problems arise and occur 

these days. Having diversified and local suppliers and a flexible logistic capability can 

enhance the supply chain flexibility when changing market and worldwide problems 

occur.  

 



 
 
 
 
 

30 
 

3.2.4 Optimal flow visualization within production processes 
For the production phase, there are two stages where you can divide disturbances 

within the optimal flow. There are input disturbances that occur before or outside the 

production process but can disturb the flow. Input disturbances often originate from 

suppliers or the upstream supply chain. There are also process disturbances, what is 

happening inside the production facility itself and disturbing the flow. Process 

disturbances are often related to machinery, material handling or internal processes. 

According to Golinska et. Al (2011) the most important disturbance for production is 

stated below: 

 

Input disturbances:  
Non-standard delivery lead times: Variations in supplier delivery times can lead to 

material shortages. If material is delivered earlier or later it disrupts the production 

planning and can lead to production stops and pushing delivery dates forward. 

 

Poor quality of supplied components: Defective parts require rework or replacement, 

causing delays, as the product needs to have increased inspection time, adjustment time 

and waiting time, and can cause customer dissatisfaction if not handled in time.  

 

Lack of needed components: Inventory shortages disrupt the production flow and can 

occur when there is supplier failure, inaccurate forecasting and transport issues. When 

the material isn’t in the right place at the right time it can lead to the production line 

stopping.  

 

Invalid Bill of Materials (BOM): As BOM can act as the instruction of the assembly 

process, and can lead to assembly errors and delays when the BOM is Outdated or 

incorrect. Delays can be caused by assembling the wrong part as the BOM is not 

updated, and can cause confusion on the assembly line. Extra work in adjustment phase 

needs to be done (Golinska et. Al, 2011) 

 

Process disturbances: 
 



 
 
 
 
 

31 
 

Machine breakdowns: Equipment failures halt production lines due to lack of 

maintenance, overload of capacity, old machines. This creates bottlenecks in the OTD 

flow and disrupts production planning and may lead to overtime work. 

 

Non-standard material requirements: Customers requires sometimes special requests 

of customizations that require special handling in production as it can complicate 

scheduling and resource allocation, and also disturb regular production line. When 

adding more material, and special variants of something you never or almost never built 

before, it introduces more room for errors in the production. 

 

Non-standard supply lead times: Unpredictable and unsynchronized internal flow 

within production can create longer lead times, leading to uncertainties in downstream 

processes.  

 

Poor quality in processes: Internal defects such as assembly faults, incorrect 

installations or no quality inspections, can lead to increased rework, impacting 

throughput as it increasing lead dime and delivery reliability (Golinska et. Al, 2011). 

 

Key Contributions to Digitalization: 
 

Dranov et. Al (2024) discusses the possibility of digitalization of manufacturing systems. 

According to the article, to achieve the optimal flow of a manufacturing process, three 

contributions can be made. The three contributions within digitalization are listed 

below: 

 

Digital Integration: The toolchain combines design, planning, and production stages 

through digital means, facilitating seamless transitions and reducing manual 

interventions. 

 

Enhanced Flexibility: By leveraging digital tools, manufacturers can quickly adjust to 

new product designs or changes in production volume, addressing the need for 

customization and responsiveness. 

 



 
 
 
 
 

32 
 

Improved Efficiency: The integration of digital processes leads to better resource 

management, minimizing waste and optimizing production schedules. 

This approach aligns with Industry 4.0 principles, emphasizing the importance of 

digitalization in modern manufacturing environments. 

 

Another factor in reaching optimal flow within the OTD process is to look at the 

different markets and see if there is a need to adapt to different markets. Brabazon & 

MacCarthy (2017) discusses market specific OTD configurations and emphasizes that 

different markets have unique customer expectations regarding product customization 

and delivery times. According to the study manufacturers should adapt their OTD 

process to align more with the specific market requirements. The study also stated that 

manufacturers need to decide whether to concentrate on upstream factors (such as 

order processing and production planning) or downstream factors (like logistics and 

distribution) based on the characteristics of each market (Brabazon & MacCarthy 

2017). They mention that a European market might prioritize flexibility and 

customization, which suits better with upstream factors to focus on. While an emerging 

market with fewer options and longer transport distances may need a robust 

distribution network, meaning they need to focus on downstream factors. 

 

3.3 OTD Research within other companies 

 

As part of this thesis, research will be conducted to analyze how competitors approach 

the OTD process and what differentiates their methods.  According to Staeblein, T & 

Aoki, K. (2015) the OTD process for two companies, one German and one Japanese 

company showcased that in the order, planning and scheduling phase were quite 

similar. However, the biggest difference was in the production, manufacturing, and 

product variety.   

 

Moreover, Staebliein & Aoki (2015) also analyzed different customer requirements 

based on geographical markets like the European and American. They concluded that 

the European customers had no problem waiting several weeks or even months for 

their product if they were able to customize their order to their specific needs, whereas 



 
 
 
 
 

33 
 

the US customers generally wanted their order within two weeks.  This highlights the 

need for a different OTD process since the European market is more based on Order-to-

Make whereas the US tends to be more Make-to-stock. Because of the short amount of 

time the American customers expect to receive their product the ability to provide 

highly customizable options diminishes. However, for the European market where the 

customers do not have the same expectations on the short lead time. the ability to offer 

customizable experiences is higher. The downside by offering highly customizable 

products is that it increases the complexity of the production which in the current case 

could be one of the reasons for the number of faults occurring in production.  

 

In the order phase, the company’s customers have the chance to change, remove or hold 

the order specification up to six weeks before planned production start (PPS). A freeze 

point three weeks before PPS is also added, after that point there can be no changes of 

the specification, and the build order are sent to the factories. The customers have some 

flexibility if they want to change their specifications. A comparison can be made with 

another large product manufacturer, Toyota. According to Tomino et. Al (2009), Toyota 

allows the customers to change up to 10 percent of the original specification. This 

system enables Toyota to manage customization demands efficiently without disturbing 

their Just in Time production system (Tomino et. Al, 2009). Renault follows a relatively 

similar approach, as they limit the extent to which customers can change their 

specifications within a certain timeframe before production (Brabazon & MacCarthy, 

2017).   

 

Leading automakers have invested in better risk monitoring and early warning systems. 

For example, Toyota took lessons from the earthquake in 2011 that impacted Japan. By 

mapping out their suppliers and creating a system that flags suppliers or parts that 

were most vulnerable in the early stages of a crisis Matsuo, H. (2015). One significant 

practice identified in strong manufacturer-supplier relationships is collaboration when 

problems occur. Toyota, for instance, approaches supplier challenges as joint issues 

rather than isolated supplier failures. According to Bonini (2015), the president of the 

Toyota Production System Support Center, Toyota’s supplier relationships emphasize 

trust and collaboration rather than penalties: 

 



 
 
 
 
 

34 
 

“Our supplier partners open the door for us and say, ‘Look, we know you’re not going to 

penalize us for some mistake we made, and you’re here to help, you’re part of the team (.’” 

Shih, W. C. 2022) 

 

This statement shows Toyota’s commitment and strategy of building long-term supplier 

relationships which are founded on trust and openness, where issues are openly 

communicated rather than concealed out of fear. Such an approach enables continuous 

improvement and aligns closely with Lean principles, particularly in terms of 

transparency, collaboration, and problem-solving. This type of supplier to manufacturer 

collaboration highlights key attributes for an optimized OTD process, emphasizing 

proactive problem-solving and collaboration between organizations, which can be 

leveraged to address similar supplier challenges within other manufacturing contexts.  

  



 
 
 
 
 

35 
 

 

 

3.4 Theoretical Tools & Frameworks 

This subsection aims to introduce the different frameworks and methods used in the 

thesis. It also aims to explain the different frameworks used and how they are relevant 

for the thesis.  

 

3.4.1 Lean Manufacturing & Waste Reduction 

The Toyota Production System (TPS) emphasizes eliminating inefficiencies through 

principles such as Jidoka, Just-in-Time scheduling, and Kaizen Liker, J. (2021). Jidoka 

ensures that defects are detected in early production stages rather than post-

production, which is not exactly what the company is currently doing. If a product 

requires adjustment it is fixed after the product has gone through the production line 

which again is very different to one of the key principles of TPS “Building quality into 

the process” rather than fixing defect later. Furthermore, stopping the production forces 

the action of finding the root cause because stopping production is very expensive 

which could be a useful lens for the company to look through and to analyze why 

defects are allowed to continue through production without stopping or correcting the 

mistakes.  

 

Just-in-Time (JIT) scheduling minimizes inventory and lead time but increases the need 

for accurate defect Halim, A. H., & Ohta, H. (1994). In the case of the company this makes 

their planning even more crucial and sensitive to changes or delays of critical materials 

or deliveries from suppliers. Within the context of the Order-to-Delivery (OTD) process, 

this means that each production activity, from parts procurement to final assembly, 

should ideally be triggered by actual customer demand rather than forecasts or batch 

scheduling. The JIT principle is effective when things are working as planned but leaves 

very little room for disturbances, especially in a large complex OTD process. For 

example, sensitivity to planning and disruptions. JIT requires near perfect coordination 

across the entire supply chain. Even minor disruptions such as delayed shipment of 

critical parts can halt the production line. In the case of the company such issues often 

require post production adjustments. 



 
 
 
 
 

36 
 

 

Because of the sensitivity in JIT, it also requires strong forecasts and high accuracy. The 

JIT wants to reduce inventory and to avoid buffers or backlog in inventory this puts 

pressure on the sales, order, forecast teams. The JIT lacks traditional safety stock usage 

which again highlights the pressure of accurate forecasts and planning.  As explained, 

the room for error is so small in a JIT system having adjustments after production is 

finished goes fundamentally against the JIT system’s foundations.  

 

When JIT is implemented in an effective manner it also highlights the flaws and gaps 

quite clearly which is crucial for continuous improvement, a cornerstone of both lean 

manufacturing and Six Sigma methodology. This visibility helps with identifying 

problems quick.  

 

Kaizen, which focuses on continuous improvement, helps reduce recurring post-

production adjustments. Lean manufacturing highlights that post-production rework is 

a non-value-adding activity and should be prevented rather than corrected at the end of 

the process. 

 

3.4.2 Six Sigma Methodology  

 

Six Sigma refers to a business philosophy. It is an improvement methodology and 

performance metric. The Six Sigma business philosophy is an initiative that enables 

world-class quality and continuous improvement to achieve the highest level of 

customer satisfaction. The Six Sigma methodology utilizes data and statistical tools 

within a phased approach to improve process performance. The Six Sigma metric 

establishes a solid performance level that helps align an organization’s strategic goals 

and values to its customers’ needs and expectations. Sigma is a measure of the 

variability of a process with respect to specification limits. In the case of the thesis, this 

is a great way of measuring variation and variability in the OTD- process.   

 

Six Sigma, particularly the DMAIC (Define, Measure, Analyze, Improve, Control) 

methodology, provides a structured approach to identifying and reducing process 

variability (Pyzdek & Keller, 2014). For the define phase. The SIPOC framework 



 
 
 
 
 

37 
 

(Suppliers, Inputs, Process, Outputs, Customers) is a high-level process mapping tool 

that is especially helpful at the early stages of a project. It helps to capture the key 

elements of a business process in a structured way and clarify scope, stakeholders, and 

deliverables. After a high level tools has been used it is easier to map out the process 

using a more traditional Value Stream Mapping (VSM) tool or any process mapping 

using flow charts is very common to map out the process more visually rather than 

stating everything in a table like its done in SIPOC. Moreover, the Six Sigma 

methodologies are a great way of following and structuring projects. Sticking to the 

principles of defining the problem in a clear way without jumping to conclusions too 

early or even considering results is important when following the DMAIC structure. This 

will be important for overall structure of the thesis and help the endeavour or finding 

key disturbances in the OTD process while additionally, contributing to finding the 

optimal flow, because the optimal flow is related to a process with little to almost no 

disturbances.  

 

Additional Six Sigma methodologies are process mapping or Value Stream Mapping 

(VSM), excellent tools to map out the process and apply the limitations of the project. 

This way a clear way of mapping inputs and outputs is easier to finish. Combining tools 

like SIPOC and Process mapping is a great way to start and understand the project’s 

boundaries and the goal.  

 

3.4.3 The Pareto Principle (80/20 Rule) 

 

The Pareto Principle, also known as the 80/20 rule, is a widely used concept in quality 

and operations management that suggests a large proportion of outcomes often result 

from a small proportion of causes. In manufacturing contexts, it is common to find that 

around 80% of problems stem from 20% of root causes (George, 2002). This heuristic is 

not a strict statistical rule, but it provides valuable lens for prioritizing improvement 

efforts where they will have the greatest effect. 

 

In Lean and Six Sigma methodologies, Pareto analysis is commonly used to identify the 

“vital few” issues that contribute most significantly to defects, delays, or inefficiencies 



 
 
 
 
 

38 
 

(Brassard & Ritter, 2010). By focusing on these key drivers, organizations can optimize 

their use of resources and achieve meaningful results faster. 

 

In this thesis, which aims to improve the Order to Delivery (OTD) process by identifying 

the reasons for post-production adjustments, the Pareto Principle will be applied to 

categorize and analyze these issues. The goal is to determine whether a limited number 

of recurring problems are responsible for the majority of adjustments. If so, targeted 

interventions-such as adjustments in planning, supplier communication, or quality 

checks-can be prioritized to reduce rework and increase overall process efficiency.  

 

3.4.4 The 4 Dimensions of Operations  

 

The 4V Model, originally developed within service operations, is a conceptual 

framework used to describe the key characteristics of an operation. While it was first 

applied to services, authors (Slack et al., 2004) extended its applications, as it is 

increasingly relevant for manufacturing as well, especially in complex industries such as 

automotive.  

 

The model identifies four dimensions: Volume, Variety, Variation, and Visibility, 

influence how operations are designed and managed. 

 

1. Volume refers to the quantity or scale of production. In manufacturing, high 

volume typically allows for standardized processes, automation, and lower unit 

costs. However, it also requires significant coordination and efficiency to avoid 

disruptions. 

2. Variety describes the range of different products or configurations offered. In 

the manufacturing industry, this includes different models, subproduct types, 

trim levels, and customization options. High variety increases complexity in both 

planning and production. 

3. Variation in demand captures how much and how often customer demand 

changes. For manufacturing, this could mean seasonal fluctuations, sales 

campaigns, or last-minute order changes, each of which affects scheduling and 

resource allocation. 



 
 
 
 
 

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4. Visibility reflects how much of the production process is experienced or 

monitored by the customer. While less visible than in services, manufacturing 

customers may still track order progress, expect transparency, or react to delays, 

making visibility a factor in perceived quality and satisfaction. 

 

The 4V Model helps organizations understand the operational challenges tied to their 

specific context (Slack et al., 2004). In this thesis, the model is used to evaluate how 

characteristics like product variety or demand variation impact production flow and the 

need for post-production adjustments, particularly in relation to the order-to-delivery 

process.  

 

3.4.5 The Bullwhip Effect  
 

The Bullwhip Effect refers to the phenomenon where small fluctuations in customer 

demand cause increasingly larger variations in the supply chain. This effect is 

particularly relevant in complex supply chains. Large OEM’s or any company for that 

matter, when trying to secure their own operations and deliveries to the customers has 

a significant consequence further down the supply chain. As demand information 

travels upstream, from dealerships to manufacturers to suppliers, sub suppliers, each 

party of the supply chain tries to secure their own operations. Leading to overreactions 

in orders from suppliers, or sub suppliers and the “whip” is started. This results to 

issues like excess inventory, less efficient capacity utilization which puts pressure on 

the other parties that together create the supply chain your company is dependent on.  

(Paik & Bagchi, 2007). 

 

In the case of the thesis this shows that the bullwhip effect is important when analyzing 

how inaccurate forecasting, demand uncertainty and long lead times can contribute to 

instability in the OTD process and how much securing your own operations can cause a 

multitude of problems in the future and for other organizations that collaborate. 

Understanding these dynamics helps highlight the importance of better information 

sharing and how factors like inventory planning, safety stocks, and batch size can 

improve. An unstable supply chain like the world has seen over the last 5-7 years with 



 
 
 
 
 

40 
 

the pandemic, Suez canal crisis, semi-conductor shortage showcases the effect of the 

“whip” and can be illustrated as in Figure 3 below.  

 

 

Figure 3 The bullwhip effect, illustrating the variation in demands consequences in the supply chain, F. Broek: Supply 

Chain Junction 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 
 
 
 
 

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4.  Current State Analysis 
This chapter aims to give a brief background to the company, its strategic focus and its 

core operations. The goal is to clearly explain the OTD process so the reader 

understands what the OTD process is, its different phases and what each phase entails 

and is collected from the interviews. Furthermore, the goal is also to explain how the 

OTD process today is different than before the pandemic struck the world.  

 

4.1 Company background  

The company is committed to driving sustainable innovation in the transportation and 

industrial sectors. The company operates manufacturing facilities worldwide, 

implementing advanced production methods aligned with Lean manufacturing 

principles and Industry 4.0. The Company Production System ensures continuous 

improvement, quality assurance, and operational efficiency. Automation, robotics, and 

digital transformation play a key role in optimizing production processes, enhancing 

sustainability, and reducing waste. With its strong heritage, commitment to innovation, 

and focus on sustainability, the Company continues to shape the future within its area. 

 

4.2 Evaluating the Strategic Position Across Four Dimensions 

The framework used is called “The 4V’s” and is explained under the Theoretical 

Framework but the point is to visualize an overview of four important metrics in 

business operations. The analysis shows the difference between where the company 

was prior to the pandemic compared to today. The evaluation is based on the 

information from the interviews but also from the data given.   

 

The key points are the following: 

• Variety has increased 

• Volume has increased 

• Variation in demand has changed quite a lot. 

• Visibility, almost the same. 

 

 



 
 
 
 
 

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What’s most interesting is that variety has increased while at the same time the volume 

has increased. 2023 was a record-breaking year in the volume produced and the 

numbers are shown in Table 3 & Table 4. Pushing high volumes while increasing the 

variety of customization of products is tricky, generally and what lean theories says is 

that it is a larger variation of products leads to a larger variation of problems (George, 

2002).  The variety has increased also because of the demand from the customers, the 

company are well equipped to take on such challenges, but it is highly difficult to do 

when running at almost full production capacity as they did 2022 and 2023.  

 

The variation in demand has increased, which is not surprising given the geopolitical 

instability and global disruptions that have occurred since approximately 2017, which 

also contributed to the difficulty in predicting the demand which in return makes it 

more difficult to plan production and supplier material requests.  

 

As for the visibility, this metric has changed the least out of the four factors. The 

company does not bring the customer through the process more than they did earlier. 

They are, however, a little bit better since they have increased their adaptability and 

ability to change and customize their products more so in that sense it has increased. 

However, the visibility is considered medium to low. For example, something regarded 

as high visibility would be someone like a private chef that takes the customer through 

the entire process of their creations, this company does not do that.  

 



 
 
 
 
 

43 
 

 

 

 

Figure 4 Pre pandemic vs today comparison by using the 4 dimensions of operations performance 

 

4.3 Factory Context and Production Setup 
 

Since the company is global and divided into different divisions, a limitation of which 

factories to further analyze was conducted. The two factories chosen are sister factories, 

where they have production of complete products and belonging supply chain. The 

factories cooperate in several fields including supporting divisions such as quality and 

logistics. Factory 1 have one production line where they produce products to many 

markets, and have many variants and different models produced at the same 

production line. In factory 1 they also have production of knocked down products. They 

produce a part of the product and then “knock it down”, pack the part and ship it to 

another factory overseas. This Knocked down process is a part of in-house 

manufacturing, and the company has several factories around the world that can 

produce these products. Factory 1 has several projects of products running through the 

line and produces several different categories of products that each represent different 

technological at the same factory line. Factory 2 is the larger factory by produced 

products, since it has two production lines. One of the lines only produces standard 



 
 
 
 
 

44 
 

products, and the other line produces different variants and models, including a mixture 

of the categories of products. Since factory 2 has two lines including the faster standard 

line, it produces two thirds of the total production for the two factories, and factory 1 

one third.  

 

While the factories are very similar to each other, there are some factors that differ. For 

example, the cultural differences in the factories. The factories are located in different 

countries, where they have other laws and rules within production. In factory 1 a major 

work within ergonomics in production has taken place during the last five years. For 

factory 2 ergonomics is not a major factor, so the operators in the production can stand 

at the same part of the line for a longer period, when in factory 1 there is more rotation 

of operators.   

 

4.4 OTD process explanation 

This chapter provides an overview of the company’s OTD process and examines how it 

compares to theoretical best practices found in the literature. The purpose is to 

establish a clear understanding of how the process is structured in practice and where 

deviations from existing frameworks occur. To achieve this, the OTD process will be 

described and defined, highlighting key characteristics that shape its execution. 

Additionally, this chapter supports answering the research questions by providing a 

clear definition of the OTD process, which serves as a foundation for analyzing its 

different phases and identifying potential disturbances but also provides a foundation 

for later discussions on the implementation of LEAN principles and the identification of 

non-value-adding activities. 

 

The current OTD process is on a high-level three phases: 

1. Order 

2. Industrial 

3. Market 

Where industrial can be divided into sublevels of Inhouse manufacturing, production 

and delivery phase. The production phase can also be divided into sublevels, as Figure 5 

shows. 



 
 
 
 
 

45 
 

 

 

 

Figure 5: The OTD process breakdown, each phase and its subphases 

 

4.4.1 Order phase 
The order phase begins with a program of demand and supply planning process cycle. 

In the program cycle inputs from all sales markets are collected and looked at with a 

bottom-up demand, how many products the different market needs per month. The 

sales areas take the temperature on the different markets and collect as much 

information as possible so the forecast will be reliable and relatively correct. However, 

due to different circumstances in the world such as pandemics, wars, inflation, political 

elections, tolls etcetera, the cycles can change from month to month.  

 

After the planning program is finished, it is sent out to the factories so they can inspect 

and approve it. The factories look at their time and staff schedule to see if they can meet 

the requirements of the planning program. The planning program is also sent to the 

suppliers, so the suppliers know how much the company will buy from them. In the file 

the suppliers see a proposal for a 12-month timeline. For the nearest time it is more 

exact in the planning day to day with a full order book, but the more months ahead the 

more high-level planning and forecasting.  

 



 
 
 
 
 

46 
 

Since the covid-19 pandemic it has been an increase in number of changes of the 

planning program due to the global disruptions. In 2023 it was a volume record in 

number of products built, and that was an increased need after the pandemic and all the 

other crisis in the world. But then the suppliers had difficulty meeting the demand. The 

company historically had at least 2 different suppliers per article, but after the covid-19 

pandemic it is now more common to have 1 supplier per article due to costs. But the 

supply chain is more sensitive and unsecure now.  

 

Within 3 days of the first program realse the factories, suppliers and purchasing have 

approved the program plan and then the order planners select which products will be 

built at the certain factories. After that, the upper management team needs to approve 

the plan and have the chance to make a priority if they feel that some markets are more 

important than others. It is rare, but it can happen that the management can change the 

plan.  

 

Since factory 1 produces all products in one line, factory 2 has two lines, different 

models and variants go to the different factories. The order-planners look at what types 

of products are ordered and plan, so each factory has the most optimal flow. In total, 

factory 1 produces approximately one third of the total products and factory 2 produces 

two thirds. There is a freeze point about 3 to 6 weeks before production, until then the 

customers can change the specification of the order. This flexibility allows the 

customers to change and adapt to their needs. After the freeze point a building 

frequence are handed out, so the right articles are to the right product in the production 

phase. 

 

After the order phase is completed, the industrial process is initiated and that is the 

largest phase and the most complicated one because of the advanced production but 

also because there are so many different flows that can impact production.  

 

 

 



 
 
 
 
 

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4.4.2 Industrial phase 
The industrial phase is divided into three main phases, the Inhouse manufacturing 

phase, production phase and delivery phase. Industrial phase is the largest phase for the 

OTD process and therefore the most complex. The subphases are explained in the 

following subchapters. 

4.4.2.1 Inhouse manufacturing phase 

 

Inhouse manufacturing is a part of the industrial process and includes manufacturing 

process the company performs within the company that is a sub-production.  

Within in-house manufacturing there are production processes such as engine 

manufacturing, gearbox manufacturing, cab manufacturing etcetera. These processes 

are produced in other factories around the world and then shipped to the complete 

product manufacturing plant. The factories have a close connection to each other and 

need to cooperate in the planning, so the right parts arrive in the right order for the 

correct products.  

 

4.4.2.2 Production phase 

Under production there are the following subprocesses: 

 

Assembly. The largest phase and the most complex one. The assembly phase is 

the heart of the production and where several flows intertwine. For example, 

incoming material from hundreds of different suppliers. The assembly phase 

consists of several stations. First, you have the standard assembly line. Here, the 

product is built in a moving assembly line, and the parts are prepared for each 

product on the side of the lines, in so called kitting stations, where operators 

place out the right part for the right products. In order to make this work, 

instructions for each product and part are shown for the operators. Within the 

lines there are also quality stations, where educated operators do a quality 

check. If the operators find a fault, it is either fixed directly if possible or 

reported in the system to be fixed at the adjustment phase. When the product is 

at the end of the line, there is test stations where they check the durability, 

quality, dynamics, and emissions of the product. The last station is a final 



 
 
 
 
 

48 
 

evaluation of the product, to complete the assembly process. If everything is OK, 

then it can go to the next phase. If it is not, then the product is moved to the 

adjustment area. In the adjustment area, reparation of the product is done, both 

mechanical, electrical and paint repairs. The adjustment process is not calculated 

as a standard process.  

 

The assembly process is very complex and for the assemblers it can consist of 

heavy labor work that is both stressful and physically demanding. Additionally, 

the assemblers need to learn several combinations of the different products, 

since the company has many different variants of the products. Because this is 

such a crucial part of the organization a lot of focus and resources is put here. 

This is also where the real value is created, which is why it is paramount that the 

assembly process is seamless and has a continuous flow. However, that is easier 

said than done. The assembly process is where most errors occur due to the 

complexity of production but also because it is very dependent on so many other 

flows like incoming material but also because it is assembled by humans which 

inevitably causes errors.  

 

Workshop. This phase primarily focuses on customization requests from 

customers, such as paint jobs, as well as repairs for damages incurred during the 

assembly process. The workshop process is divided into two parts, internal and 

external workshops. The internal part of the workshop handles the standard 

customizations, while the external part of the workshop deals with the more 

special customizations and is then shipped to other companies outside the 

factory area. This is included in the OTD process and therefore a standard 

process. However, only a certain amount of time is prepared for this process, and 

if the external or internal workshop is delayed, the OTD process will be delayed.  

 

Audit. The Audit department do different kinds of tests on the products after the 

assembly to see if the products are customer ready. Different kinds of tests are 

performed, such as product audits, where a secret percentage of all products 

built should be checked, but also project audits and sample audits. In the audit 

department conformity of production is performed as well, where the auditors 



 
 
 
 
 

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make sure and make a verification that the products meet regulatory and quality 

standards within production. When an auditor finds a problem in an audit, the 

auditor informs the relevant department within the assembly and depending on 

the degree of severity a number of points is set, and then the affected 

department needs to deal with this problem together with the quality 

department. The Audit process is a standard process in the OTD process and 

therefore not relevant in finding nonstandard activities for this thesis.  

 

Factory Handover. This is when the production is completed, and the operators 

transfer the products from the production area to the distribution yard. When 

the product is parked it is the end of the factory handover and end of production 

phase. If a product is delayed, a correction is added to the production process, 

the customer will be informed on the delay and a new estimated time of arrival 

will be planned. When the factory handover is done the product is clear to enter 

the next phase of the OTD process. 

 

4.4.2.3 Delivery phase 
Within the industrial part of the OTD process, delivery is the final step. Delivery is 

divided into two parts, Delivery stock and Transport. In delivery stock the products are 

often placed at the distribution yard, waiting for the transport to be ready. If the 

product stands at the distribution yard for more than six weeks, it needs to have an 

extra maintenance check and possible repair. In some cases, the products can enter the 

delivery yard without being Green OK (GOK), meaning the products are finished and 

ready to enter the next phase. If there are some material shortages, or other stops 

within production, the products can go through the line and it will then be repaired 

afterwards. An extra activity will then be planned, and the product might be delayed to 

the customers. The next step is the transport of products. It is the last step of industrial, 

and the products are transported to the market, either directly to the customer or 

through a dealer, depending on where the customer is located.  

 

 

 



 
 
 
 
 

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4.4.3 Market phase 
 

The market is the last step of the OTD process. Here, the dealers and market companies 

take action and receive the products, take contact with the customers, do a possible 

extra customer requests on the product and after that, hand them over to the customer. 

The Market phase is out of scope for this thesis and therefore will not be researched on 

more than to this point of view.  

  



 
 
 
 
 

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5.  Results from qualitative findings and data 

analysis 

This chapter presents the key findings from both quantitative and qualitative and 

qualitative phases of the study. The chapter is divided into two main chapters, where 

the first part covers the qualitative findings from the interviews collected, and the 

second part covers the quantitative data findings from the tools investigated. The 

purpose of this chapter is to address the research questions: 

 

• What are the key factors continuously causing disturbances in the OTD Process? 

• What can the optimal flow look like in the OTD process?  

 

5.1 Qualitative OTD process findings 
 

Initially, this part of the chapter presents the most important findings from the 

interviews with key stakeholders in the OTD process. The interviews provide valuable 

qualitative insights into the different phases which are further supported by the data 

analysis which includes data related to OTD process.  

 

The findings will be structured to first present the most important insight from 

interviews. The structure of the interview follows the OTD process (Order, Industrial, 

Market). Then followed by a detailed analysis linking the findings to the research 

questions. 

 

5.1.1 Order phase: Supplier strategies and the effects of global instability 

 

To gain a clearer understanding of the order phase and its impact on various 

departments, an interview was conducted with a Forecasting Manager. The primary 

responsibility of the Forecasting Manager is to distribute the forecasted demand and 

create predictions for the expected production volume for the upcoming year. These 



 
 
 
 
 

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predictions are based on input from the sales department and historical data, which are 

then communicated to a wide range of suppliers who rely on these forecasts to adjust 

their production plans accordingly.  

 

The order phase is a critical component of the overall OTD process. This phase lays the 

foundation for production planning by determining the volume each factory needs to 

produce. As a result, it significantly influences the production speed that factories must 

maintain to meet production targets.  

 

According to the forecasting manager, the company’s strategy has been to focus on 

building long-term relationships with a select group of suppliers, rather than relying on 

a broad network. This approach aims to improve collaboration and reliability. Single 

sourcing offers the advantage of easier utilization of economies of scale (Gupta & 

Sharma, 2017). However, this strategy also makes the company more vulnerable to 

disruptions if a supplier fails to deliver. 5.2 Performance Insights from Data Analysis of 

the OTD Process supports this concern, showing that supplier-related disruptions have 

been a key issue over the past few years. 

 

Furthermore, the order phase includes ongoing supplier communication, where 

suppliers are informed about the estimated quantities of specific materials or parts 

needed. However, since suppliers only receive forecasts rather than confirmed orders, 

they may encounter difficulties meeting demand if there are sudden shifts in 

requirements or challenges in maintaining quality standards. 

 

5.1.2 The industrial phase: Production related challenges and 

improvements 

 

To understand what contributing factors there could be for the increase in products 

needing post-production adjustments and not following the standard activity process, a 

Quality Manager was interviewed.  

 



 
 
 
 
 

53 
 

When asked about their thoughts on the rise of products deviating from the standard 

production process needing adjustments. The Quality Manager explained that there has 

been a high demand for products. Especially in 2023, which was a record breaking year 

for the company, which affected the factories by o