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

CHALMERS UNIVERSITY OF TECHNOLOGY 
Gothenburg, Sweden 2025 

www.chalmers.se 
 

 

Warehouse automation in the 
construction material and lumber 
industry 

A case study of the Swedish company Derome Bygg & 
Industri AB 

Master’s thesis in Supply and Operations Management 

 

 

FRANS BRÄUTIGAM 
ADAM VIKTOR STJERNSTRÖM ERIKSSON 

 

 



 

 

 
 

 

 
 

 



 

 

 
 
 
 
 
 
 
 
 

 
 

Warehouse automation in the construction 
material and lumber industry 

 
A case study of the Swedish company Derome Bygg & 

Industri AB 
 
 

FRANS BRÄUTIGAM 
ADAM VIKTOR STJERNSTRÖM ERIKSSON 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Department of Technology Management and Economics 
Division of Supply and Operations Management 
CHALMERS UNIVERSITY OF TECHNOLOGY 

Gothenburg, Sweden 2025 



 

 

 
 
 
 
 
 
 
 
 
 
 
Warehouse automation in the construction material and lumber industry 
A case study of the Swedish company Derome Bygg & Industri AB 
FRANS BRÄUTIGAM 
ADAM VIKTOR STJERNSTRÖM ERIKSSON 
 
 
© FRANS BRÄUTIGAM, 2025 
© ADAM VIKTOR STJERNSTRÖM ERIKSSON, 2025 
 
 
 
Department of Technology Management and Economics 
Chalmers University of Technology 
SE-412 96 Gothenburg 
Sweden 
Telephone + 46 (0)31-772 1000 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Cover: This thesis presents a framework for selecting suitable automation technologies in 
construction material logistics. The framework is designed to help decision-makers identify 
automation opportunities that match operational characteristics and strategic priorities. Key insights 
and recommendations are presented in Chapter 5 
 
 
 
Gothenburg, Sweden 2025



 

 

Warehouse Automation in the Construction material and lumber Industry 
A case study of the Swedish company Derome Bygg & Industri AB 
 
FRANS BRÄUTIGAM 
ADAM VIKTOR STJERNSTRÖM ERIKSSON 
 
Department of Technology Management and Economics 
Chalmers University of Technology 
 
 
 
SUMMARY  
Suppliers of construction materials face an increasing demand for efficient logistics 
operations, while the use of automation in their supply chains remains limited. A key 
challenge lies in the bulky and heavy nature of construction materials, which complicates 
handling and automation. This study investigates how Derome Bygg & Industri AB (DBI) can 
improve their logistics through implementation of automation in picking, packing, and loading 
operations.  
  
A mixed-methods approach was employed, including interviews with DBI logistic managers 
and operational staff, on-site observations, analysis of historical company sales data, and a 
literature review focusing on warehouse design, automation, and Industry 4.0 technologies. 
Empirical data was also collected from suppliers of automated equipment.   
  
The study examines DBI’s current operations and develops a categorisation of product 
groups suitable for automation. Several technological solutions were found suitable to DBI’s 
context. The analysis indicates that wooden planks and sheet materials offer the 
greatest potential for efficiency gains through automation. The findings provide practical 
implications to support DBI’s future investment decisions and contribute to a broader 
understanding of warehouse automation strategies in the construction material sector.  
 
. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Keywords: Warehouse design, Construction material warehousing, Industry 4.0, Automated 
picking, Picking construction materials, Automated loading, Internal logistics 



 

 

 
 

 
  



 

 

 
 

 

 
 

ACKNOWLEDGEMENTS 

We would like to express our sincere gratitude to our industrial supervisor, Mathias 

Hansson at Derome Bygg & Industri AB (DBI), for his dedicated support and 

valuable guidance throughout this thesis project. His commitment and willingness to 

share insights have been greatly appreciated. Further on, we are also thankful to the 

entire team at DBI for their openness and generosity in sharing their time and 

knowledge with us. Every conversation has contributed meaningfully to our 

understanding and the development of this work. 

We would further like to thank our academic supervisor, Altahir Hassan Irshien Ali, 

at Chalmers for the continuous support, encouragement, and constructive feedback. 

We also extend our thanks to other faculty members and staff at Chalmers who have 

taken the time to assist us during this thesis. Lastly, we would like to thank our 

families and friends for their patience, encouragement, and support throughout this 

intense and rewarding journey. 

 

Frans Bräutigam & Adam Stjernström, Gothenburg, April 2025 

  



 

 

 
 

 

 
 

 

 

  



 

 

 
 

 

 
 

Table of Contents 

List of Figures 

 

List of Tables 

 

1 Introduction ............................................................................................................ 1 

1.1 Background .................................................................................................... 1 

1.2 Problem Statement ......................................................................................... 2 

1.3 Aim & Objectives .......................................................................................... 3 

1.4 Research Questions ........................................................................................ 4 

1.5 Scope & Limitations ...................................................................................... 4 

2 Literature Review................................................................................................... 6 

2.1 Industry 4.0 in Logistics ................................................................................ 6 

2.2 Warehouse Design ......................................................................................... 7 

2.3 Picking ........................................................................................................... 8 

2.4 Loading .......................................................................................................... 9 

2.5 Packing ......................................................................................................... 10 

2.6 Automation .................................................................................................. 10 

2.7 Summary & Knowledge Gap ....................................................................... 11 

3 Methodology ........................................................................................................ 12 

3.1 Research Design........................................................................................... 12 

3.2 Literature Review......................................................................................... 13 

3.3 Empirical data collection ............................................................................. 14 

3.4 Data Analysis ............................................................................................... 17 

3.5 Research Validity & Reliability ................................................................... 20 

4 Results & Analysis ............................................................................................... 21 

4.1 DBI’s current system and strategic goals..................................................... 21 

4.1.1 Current picking operations ................................................................... 23 

4.1.2 DBIs current assortment ...................................................................... 24 

4.2 Sales Quantity .............................................................................................. 29 

4.2.1 Assortment selection ............................................................................ 29 

4.2.2 Baseline Sales Quantity and Number of Order Lines 2024 ................. 33 

4.2.3 Sales Quantity and Number of Order Lines 2029 ................................ 35 

4.2.4 Sub-category sales analysis.................................................................. 36 

4.3 Automation Assessment............................................................................... 44 

4.3.1 Automated picking of sheet materials.................................................. 48 

4.3.2 Automated picking of planed planks ................................................... 53 



 

 

 
 

 

 
 

4.3.3 Automated picking of thin steel profiles .............................................. 57 

4.3.4 Automated packing .............................................................................. 58 

4.3.5 Automated loading ............................................................................... 60 

5 Discussion ............................................................................................................ 64 

5.1 Key Findings ................................................................................................ 64 

5.2 Practical Implications................................................................................... 64 

5.3 Theoretical Contributions ............................................................................ 64 

5.4 Social, Ethical and Ecological considerations ............................................. 65 

5.5 Limitations of the Study............................................................................... 65 

5.6 Future Research Directions .......................................................................... 66 

6 Conclusion ........................................................................................................... 68 

References .................................................................................................................... 70 

 

  



 

 

 
 

 

 
 

List of Figures 

Figure 3.1: Illustrating the framework used for warehouse design. ........................... 13 
Figure 4.1: Step 1 of the framework, understanding the current system & strategic 

goals. ............................................................................................................................ 21 
Figure 4.2: An overview of DBI’s current logistic center. ......................................... 22 
Figure 4.3: Schematic view of the picking process at DBI. ....................................... 24 
Figure 4.4: Step 2 from the framework; define, obtain and analyse the data. ............ 25 
Figure 4.5: Obtaining and analysing the data from the assortment. ........................... 25 
Figure 4.6: Understanding the criteria to select the assortment. ................................. 29 
Figure 4.7: The result of the assortment analysis. ...................................................... 31 
Figure 4.8: Obtain sales data....................................................................................... 33 
Figure 4.9: The first step of the data analysis of the sales. ......................................... 34 
Figure 4.10: The framework shows the steps of future sales analysis. ....................... 36 
Figure 4.11: The fourth step, to establish unit loads. .................................................. 37 
Figure 4.12: The fifth step of the method, determining operating procedures and 

methods. ....................................................................................................................... 45 
Figure 4.13: Illustrating the sixth step of the method in this study. ........................... 50 
Figure 4.14: A high-bay storage and picking solution of sheet materials from 

Systraplan. .................................................................................................................... 51 
Figure 4.15: A solution from Systraplan, where order lines can be consolidated on 

racks before being sent for packaging. ........................................................................ 52 
Figure 4.16: A sheet material storage system, shown by Barbaric. ............................ 52 
Figure 4.17: A multi-levelled solution for storing and picking sheet materials, shown 

by Barbaric. .................................................................................................................. 53 
Figure 4.18: The figure is a modified screenshot of a video from the company 

Barbaric and shows an automated wood handling machine ........................................ 57 
Figure 4.19: Tentoma. RoRo StretchPack® packaging machine. .............................. 59 
Figure 4.20: Tentoma. RoRo StretchPack® packaging machine. .............................. 60 
Figure 4.21: Tentoma. RoRo StretchPack® packaging machine ............................... 60 
Figure 4.22: Illustration of how a conveyor belt can be used for loading. ................. 61 
  



 

 

 
 

 

 
 

List of Tables 

Table 3.1: List of interviews and meetings for the master’s thesis project ................. 16 
Table 4.1: DBIs assortment categories with respective most sold sub categories. IND 

(industry products) is excluded because the sales quantity of IND products is 

negligible...................................................................................................................... 26 
Table 4.2: The table shows examples of DBIs categorization of popular SKUs ........ 26 
Table 4.3: DBIs frequency classes. ............................................................................. 30 
Table 4.4: The table shows what categories and frequency classes of DBIs existing 

assortment which will be included in the planned logistics center. X means that it is 

included and - means that it is excluded. ..................................................................... 31 
Table 4.5: The table shows how the assortment selection in table 4.4 affects how 

many orders can be covered based on historical data. ................................................. 32 
Table 4.6: The table shows the number of SKUs which are planned to be included in 

the logistics centre........................................................................................................ 33 
Table 4.7: The table shows the historical sales quantity for 2024 for the warehouses 

Derome, Trollhättan and Mölndal................................................................................ 35 
Table 4.8: The table shows historical data for the DBIs regional warehouses in 

Mölndal, Derome and Trollhättan combined. This data only considers the assortment 

selection described in Table 4.4. .................................................................................. 35 
Table 4.9: shows the forecast for 2029 based on the historical data. .......................... 36 
Table 4.10: The table shows the five greatest sub-categories sorted by sales quantity.

...................................................................................................................................... 38 
Table 4.11: The table shows the five greatest sub-categories sorted by number of 

sales. ............................................................................................................................. 38 
Table 4.12: The table includes the most sold sub-categories for each assortment 

category, sorted from highest sold quantity. It also includes the most sold product 

category for each sub-category. ................................................................................... 39 
Table 4.13: The table includes the most sold product groups for every sub-category at 

DBI and the sales quantity and number of sales for these product categories. It also 

shows what sub-category and assortment category these product groups belong to. .. 40 
Table 4.14: An evaluation of potential impact from automation of different sub-

categories. .................................................................................................................... 46 
Table 4.15: Only shows sales quantity for significant product groups. ...................... 49 
Table 4.16: Specifying technical specifications of all the product groups in the planed 

pine and planed pine subcategories. ............................................................................. 53 
Table 4.17: The range of technical specifications in the planed pine and planed 

spruce subcategories. An automated picking solution needs to comply with these 

requirements to be able to handle all materials in these subcategories........................ 57 
Table 4.18: Evaluated dimensions from thin steel profiles. ........................................ 58 
Table 4.19: Summarizing the findings of using a conveyor belt for loading (Carlan et 

al., 2023) compared with current state at DBI. ............................................................ 61 
Table 4.20: Analysing how the loading process at DBI can be automated. ............... 63 
 
  



 

 

 
 

 

 
 

 

 



 

 

 
 

 

1 
 

1 Introduction 

This chapter introduces the context and motivation for the study. It begins with a background to the 

construction material industry and the challenges faced in its logistics operations. The chapter then 

presents the problem statement, followed by the aim and objectives of the study. Two research 

questions are defined to guide the investigation. Finally, the scope and limitations are outlined, 

specifying the boundaries within which the research was conducted. 

1.1 Background 

Traditionally, businesses have prioritised cost reductions in manufacturing and other business areas. 

However, logistics has historically not been recognized as a strategic business function on its own 

(Rushton et al., 2022). In recent decades, the strategic importance of supply chain within businesses 

has increased. Industry 4.0 and emerging technology such as Internet of Things (IoT) and big data 

have enabled more comprehensive management of supply chain inputs and outputs (Yavaş & Özkan-

Özen, 2020). Additional enablers of modern, efficient warehouses include technology advancements 

in automation, robotisation and digital communication. The adoption of these innovations enables 

potential to achieve significantly improved supply chain performance. Nevertheless, Patrucco et al., 

(2020) identify several challenges associated with implementing such technologies, particularly for 

the construction materials sector. Despite these challenges, recent technologies have proven essential 

for improved logistics performance and managing complexity in medium and large-sized enterprises. 

  

One of the companies that could benefit from advancing its supply chain in line with Industry 4.0 

principles is Derome Bygg & Industri AB (DBI), a Swedish supplier of building materials and tools, 

mainly serving professional carpenters (M. Hansson, personal communication, January 20, 2025). 

DBI is part of the Derome group and is distinguished from its competitors by its vertically integrated 

supply chain. The company derives its name from its founding village, Derome. It operates its own 

production facilities, warehouses, vehicles and provides support and services to forest owners. 

Distribution is a core component of DBI’s operations. Customers can either load materials into their 

own vehicles at DBI’s warehouses or have the orders delivered by DBI directly to construction sites. 

At the current regional warehouse in Mölndal, customers load their vehicles in the same area where 

DBI prepares orders for delivery. However, the new warehouse (referred to as the logistics centre 

throughout this study) will be closed to customers and dedicated exclusively to distribution using 

DBIs own vehicles.  

  

One distinguishing characteristic of the building materials industry is the combination of short lead 

times, and diverse deliveries in terms of volume and weight (Patrucco et al., 2020). At DBI, 

deliveries are typically fulfilled the day after an order is placed, as demanded by its customers. This 

underlines the critical role of logistics in the company’s total cost structure and highlights the high 

potential return from logistics improvements. According to M. Hansson (personal communication, 

January 20, 2025), DBI recognises that developing a robust and responsive supply chain is essential 

in today’s competitive environment. As key competitors are investing heavily in restructuring their 

logistics operations, DBI seeks to retain their competitiveness. A revised logistics strategy is 

therefore seen as a key enabler for enhancing logistic performance, increasing profitability, and 

supporting long-term growth.  

  

As part of DBI’s logistics strategy, the company plans to establish a structure with central 

warehouses that supply logistics centres (M. Hansson, personal communication, January 20, 2025). 

The preliminary plan also includes connecting each logistics centre to a small number of hubs. These 

logistics centres are expected to support a range of activities, including storage, consolidation, 



 

 

 
 

 

2 
 

packing, and picking. Furthermore, the logistics centres are assumed to accommodate several types 

of building materials, such as those for residential construction, kitchen building, wooden decks, 

hardware store and workwear.  

  

DBI’s vision is to reach state-of-the-art level of operations, which includes implementing recent 

technologies to enable fast and flexible processes (M. Hansson, personal communication, January 

20, 2025). Due to the wide range of stock keeping units (SKUs) in terms of shape and size, the 

operational complexity is considerable. As a result, several systems are expected to be integrated. 

The large number and variety of SKUs also necessitate careful storage planning, with different types 

of storage solutions required to meet diverse material requirements. Furthermore, since various types 

of vehicles enter the logistics centres, the loading operations must be adaptable to ensure efficiency.  

  

The logistics department of DBI is tasked with presenting a new warehousing and logistics strategy, 

which must be approved by the top management, including the CEO and logistics manager (M. 

Hansson, personal communication, January 24, 2025). The company has outlined strategic targets. 

By 2029, DBI aims to increase the sales volume distributed through its delivery service by 50 % 

compared to 2024 and improve its profit margin by reducing costs through more effective logistics 

operations. A key component in achieving these goals is the design and implementation of a new, 

efficient logistics centre. It is assumed that operational costs can be reduced by implementing a 

purposefully designed logistics centre. A key enabler in this effort is the use of automated 

technologies, which also can enhance productivity, revenue, and overall output (Hardy and 

Brougham, 2022). Additional benefits include fewer errors, improved ergonomics, and resilience. 

These benefits align strongly with DBI’s strategic requirements.  

1.2 Problem Statement 

Efficient logistics is crucial to become competitive for construction material suppliers; however, the 

warehouse management remains low in adaptation of recent technology (Demirkesen & Tezel, 

2022). This is also clearly noticed from DBI in practice (M. Hansson, personal communication, 20 

January 2025). For companies, such as DBI, with operations characterised by short lead times and 

high customer requirements, in combination with a varied assortment, inefficient warehouse picking 

and loading are considerable cost drivers. Currently, DBI’s picking and packing operations are 

handled manually, which indicates that the staff must handle heavy and bulky products, such as 

plaster- and wooden boards. This results in a low picking frequency to avoid injuries. Lack of 

performance in this area risks driving costs and decreased service level to customers. In an interview 

with M. Hansson (personal communication, January 24, 2025), it was stated that the current picking 

operations at DBI have reach its full potential within the existing working method, indicating that 

new methods are required.  

  

Furthermore, based on real world observations of loading and unloading operations at DBI’s main 

warehouse in Mölndal, and from the interview with M. Hansson, the logistics responsible of DBI 

(personal communication, January 20, 2025), it becomes apparent that loading construction materials 

is time consuming. This is likely caused by the high complexity and varied goods characteristics. 

DBI has developed their manual processes to support efficient operations and high customer 

satisfaction, however, there are generally drawbacks of manual handling compared to automated 

handling. For example, manual handling can result in high personnel and operations costs, and 

decreased customer satisfaction, if the delivery precision is affected negatively. In addition, delays 

can occur because of inefficient operations. In this context and based on the literature and empirical 

data collected (more details in Results & Analysis chapter 4), picking and loading appears to have 

the largest impact on the overall logistics performance. Thus, the main focus in this study is on the 

picking and loading operations at DBI logistics centre. At DBI, the packing is performed by the 



 

 

 
 

 

3 
 

picker between the picking and loading process. Therefore, packing is also studied to ensure a 

complete process solution from picking to loading and avoiding bottlenecks related to packing.  

 

As previously stated, automation offers many benefits with a strong match to DBIs strategic 

requirements (Hardy and Brougham, 2022). The challenge is that there are also many barriers that 

must be overcome to gain these benefits, otherwise there is a risk of costly errors. The technical 

aspects are one of these barriers (Rathore et al., 2022). The challenge is that automated solutions are 

often designed to handle a specific job and lack flexibility in handling a diverse range of products. 

DBIs assortment vary widely in shape and weight and many different unit loads are used. Therefore, 

to enable automation the assortment was separated in different product categories, and these 

categories were then matched with suitable technology. Since not a single automated solution can be 

used for the whole assortment, an analysis of relative sales quantity corresponding to each 

product category was conducted to ensure automation is applied where it has the highest total 

performance impact.  

 

Automation within warehousing is addressed in previous research, with extensive literature covering 

areas such as picking technologies, storage systems, and overall system integration (Roodbergen & 

Vis, 2009; Boysen et al., 2019; Faccio et al., 2020). However, there is a notable gap in research 

within automation of large and irregular sized construction materials, such as wooden planks and 

sheet materials. Thus, there is a lack of knowledge about automated technology and its effect 

on performance and efficiency within DBI’s future logistics centre. To remain competitive, many 

businesses must face changes in their supply chain, which require decisions regarding investments 

and thorough planning before implementation. Even though there is much research about warehouse 

layout design in the literature, there is limited research within the area of construction materials.  

 

In the article from Demirkesen & Tezel (2022), it is explained that in practice, automation processes 

within construction materials face a distinct set of challenges compared to other industries. 

Therefore, little is known regarding how automation processes and designs could fit into the 

requirements of different material categories in warehousing of the construction industry. Existing 

research within automation in warehouse design focuses on standardised and smaller products. 

Seidlova & Sourek (2010) addresses the challenge of designing warehouses for materials that are 

difficult to handle, like building materials, however it does not address automation. The issue of 

designing warehouses utilizing the technology of Industry 4.0 has also been addressed in previous 

studies (Yavaş & Özkan-Özen, 2020), however only a few studies have focused on the construction 

material industry. One study indicated that automation in the construction materials industry in 

Nigeria is underutilized, which implies increased cost and delays in delivery (Alumbugu et al., 

2022). The underutilization could be caused by a lack of understanding in how to apply technology 

in this area. It can therefore be concluded that there is an apparent lack of knowledge in how to 

implement the automation in the construction material logistics, and to a varied assortment in 

general.  

1.3 Aim & Objectives 

The aim of this study is to develop a better understanding of how automation can be utilised and 

implemented in warehouses in the construction material industry. According to Baker & Canessa 

(2009) and Yavaş & Özkan-Özen (2020), technology is crucial when designing a new warehouse. 

This study aims to contribute new insights in an area where knowledge is currently limited. To 

narrow down the scope, the study focuses on processes where the highest potential performance 

impact from automation could be expected. Previous literature indicates that picking drives most of 

the warehousing cost, and therefore this will be a focus of the study (De Koster et al., 2007).   

  



 

 

 
 

 

4 
 

The study addressed the categorisation of DBI’s assortment to identify the physical characteristics 

and logistical requirements of the products. An investigation and evaluation of feasible automation 

solutions was also conducted for picking, packing, and loading processes corresponding to the 

distinct product categories. Successful implementation of automation may be crucial for DBI, as the 

company are targeting a 50% increase in sales volume through its delivery services, combined with a 

reduction in logistics cost relative to revenue by 2029. By a clearly identifying which product 

categories are suitable for automation, this study aims to provide a decision basis for DBI’s future 

investments. In summary the objectives of this study are to:  

  

• Identify and categorise the SKUs within a construction material warehouse that are 

most feasible for automation.  

• Identify, evaluate, and match suitable automated solutions for picking, packing, and 

loading processes based on the selected SKU categories.  

1.4 Research Questions  

As a logical consequence of the problem statement and aim of the following research questions was 

defined. 
 

RQ1: What SKU categories in construction material warehouses are most suitable for automation, 

and for which of these categories do automation offer the greatest potential improvement in overall 

warehouse performance (productivity, cost-efficiency, and quality)? 
 

From the literature it appears that automation may bring benefits that strongly match the strategic goals 

of DBI (Hardy and Brougham, 2022). However, automated solutions are often highly limited in their 

ability to handle a varied assortment; therefore, the problem becomes deciding what product categories 

are suitable for automation. 
 

RQ2: In what ways can automation be implemented within the picking, packing, and loading processes 

of a construction material warehouse, specifically targeting the SKU categories identified as most 

suitable? 
 

After identifying the product categories most suitable for automation, the subsequent challenge is to 

determine suitable technical solutions that align effectively with these specific categories. 

1.5 Scope & Limitations 

The study focuses on the aspects of warehouse design that have the greatest impact on 

overall logistics performance. The authors initially intended to examine all processes and SKU 

categories at DBI, the scope was later narrowed down to enable a more in-depth analysis of selected, 

relevant aspects of warehouse design. Given the time constraints of the study, the analysis is limited 

to the automation of picking, packing, and loading processes within the logistics centre. Although 

order receiving and returns handling are essential, they are not a within the scope of this study.  

  

The geographical scope of this study was limited to the Gothenburg area; however, the logistics 

centre may potentially be replicated into other regions, if DBI decides to. The preliminary service 

area for the logistics centre is between Strömstad and Halmstad. The optimal placement of the 

logistics centre, transport solutions, financial considerations, and data handling were not within the 

scope of this study, as these aspects would require extensive geographical analyses which is beyond 

the core subject of internal logistics. Furthermore, the influence of the central warehouse and the 

hubs on the logistics centre was not addressed.  

  



 

 

 
 

 

5 
 

The authors aimed to prioritise the investigation of the processes and product groups where 

improvements would lead to the greatest total performance impact. Detailed economic analyses, such 

as return of investments and budgeting, were outside the scope of this study. However, the sales 

quantity analysis and the focus on the most resource-intensive processes aimed to investigate 

automation where it has the highest potential for cost savings and performance improvements, 

ensuring a cost-effective design from the bottom up. Another limitation is that a detailed analysis and 

comparison of specific automation technologies was not conducted due to time constraints and 

limited data availability. While such an investigation would have added value, the study still 

contributes new knowledge to the literature, as the topic has not previously been explored in this 

context.  

  

Considering the logistics centre and the proposed automated solutions, the focus has been on the 

overall concept and the material flows rather than on detailed technical specifications. Due 

to insufficient reliability of the available sources for certain automatic technologies, these have 

deliberately been excluded from the analysis. This decision was made to maintain a high validity and 

reliability of the study’s conclusion and recommendation. Regarding the technologies used for 

picking, loading, and packing, the emphasis was placed on exploring general concepts rather than 

conductin a detailed evaluation or comparison.  

  

The limitations imply that the results and conclusions of the study are specifically tailored for 

DBI’s requirements and assortment and may not be generalisable to companies with different 

product mixes or operational conditions.  

  



 

 

 
 

 

6 
 

2 Literature Review 

The purpose of this literature review is to present a theoretical background to warehouse design by 

explaining how Industry 4.0 can be implemented in a warehouse that is providing construction 

materials, and to identify research gaps connected to picking, packing, and loading of various 

construction materials. Furthermore, literature on the implementation of automation is presented. 

And finally, a summary of the literature review is made together with findings of research gaps. 

2.1 Industry 4.0 in Logistics 

The Fourth Industrial Revolution, Industry 4.0, was introduced in Germany in 2011 to enhance the 

country’s industrial competitiveness through increased digitisation of manufacturing processes 

(Kagermann et al., 2013). While Kagermann et al. (2013) introduces the concept of Industry 4.0, Lu 

(2017) focuses more on the technical integration of physical and digital systems. Industry 4.0 

includes merging physical and digital environments via advanced information technologies, such as 

Internet of Things (IoT), big data, and artificial intelligence (Lu, 2017). This makes it possible to 

connect machinery, people, and systems in real time, and thereby creating more flexible, efficient, 

and data-driven value chains. Further on, Kagermann et al. (2013) explain a key component of 

Industry 4.0, which is cyber-physical systems (CPS), where computer systems interact with physical 

devices in the supply chain, such as warehouses, factories, and vehicles. Physical objects can be 

equipped with sensors and communicate with other devices in a network to provide real-time data. 

Yavaş & Özkan-Özen (2018) in contrast, focus specifically on the role of Industry 4.0 in logistics. It 

is emphasized that the use of CPS and 4.0 technologies in logistics can streamline information and 

resources in the supply chain to enhance efficiency and lower the costs of producing customized 

products. A crucial enabler for industry 4.0 is the real-time data availability. Zhong et al. (2017) 

describe how IoT-based technologies facilitate real-time monitoring and information sharing across 

supply chains, providing greater transparency and operational efficiency. Radio-Frequency 

Identification (RFID) is a core IoT application, enabling accurate tracking and tracing of materials 

throughout the supply chain. Zhong et al. (2017) stress the role of real-time transparency across the 

supply chain, whereas Lee et al. (2017) discuss the design and application of an IoT-based 

warehouse management system for smart logistics and argue that it can increase accuracy and 

efficiency in picking.   

  

Robotics is a key part of modern material management, and through Industry 4.0, Automated Guided 

Vehicles (AGV) has been introduced for internal material handling and is described in Wang 

(2016). AGVs can move material between inbound, storage locations and outbound, without human 

interaction. This can increase both the efficiency and the flexibility in warehouses. Further on, Wang 

et al., (2016) describes industry is heading towards smart material flows, where the products can 

communicate with and guide machines. This indicates that a palletised product with a sensor can 

give a signal to a robot that it is ready to be moved to the next station, decreasing the requirement for 

manual instructions. According to Wang et al. (2016) the result of IoT and robotics in materials 

management include increased efficiency, reduced errors, increased productivity and savings. While 

Wang et al. (2016) examine fully automated systems such as AGVs, Chemweno and Torn (2022) 

highlight the utility of collaboration between humans and robots. Moreover, Chemweno and Torn 

(2022) claims that heavy lifts or monotonous tasks can be reduced through modern collaborative 

robots, or also known as “cobots”, that work together with humans, side-by-side, and thereby 

flexibility can be increased. As a conclusion, robotics enhance efficiency within material handling 

where real-time data and automation cooperate.  

  

In conclusion, Industry 4.0 technologies, including IoT, CPS, robotics, and real-time data integration, 

offer significant potential to transform logistics operations. By adopting these technologies, 



 

 

 
 

 

7 
 

warehouses can achieve higher efficiency, better accuracy, and greater operational flexibility, which 

is critical for industries with complex supply chains, such as the construction material sector. These 

technologies are highly relevant to the development of DBI’s logistics operations.   

2.2 Warehouse Design 

In parallel to the evolution of Industry 4.0, warehouse design principles have also developed to 

address the growing complexity of modern supply chains. In Jones et al. (1997) Lean Logistics is 

described as identifying and eliminating non-value-added activities, while newer paradigms like 

Smart Logistics, explained by Lee et al. (2018), utilize real-time data to optimize inventories, and 

according to Zhong et al. (2017), anticipate demand fluctuations. A foundational approach is 

provided by Baker & Canessa (2009), who propose a structured step-by-step methodology for 

designing warehouses, from determining business requirements to finalizing equipment selections 

and workflows. By integrating lean thinking and advanced digital tools, warehouse design can more 

effectively respond to volatile demand, improve material handling efficiency, and maintain cost-

effective operations in an Industry 4.0 landscape.  

  

In warehouse design, research generally define the central processes as receival, put away, storage, 

picking, packing, and shipping (Tompkins et al., 2010; De Koster et al., 2007), illustrated in Figure 

2.1. Construction materials can be more complex in material handling, since they often do not 

conform to typical unit-load configurations like palletised goods (De Koster et al., 2007; Kasim et 

al., 2013). Furthermore, Kasim et al. (2013) observe that many traditional or paper-based methods 

persist, leading to inefficiencies and difficulties in tracking items. De Koster et al. (2007) highlight 

that order picking implies high costs and labour intensive for warehouse operations. In the 

construction materials context the processes can vary, because of the complexity of large-scale and 

bulky items (e.g., lumber, or heavy building materials). When comparing De Koster et al. (2007) and 

Kasim et al. (2013), both identify inefficiencies in warehousing but Kasim et al (2013) focus on the 

drawbacks of paper-based systems while De Koster et al. (2007) stress the resource intensity of 

picking operations.  

  

  
Figure 2.1: Illustrating the general process flow in warehouse design.  

   Note. Retrieved and adapted from Baker & Canessa (2009)  

  



 

 

 
 

 

8 
 

The framework and tools for the various steps in warehouse design, as described by Baker & 

Canessa (2009), has been foundational for the analysis in this thesis. It was not followed exactly, 

instead it has been adjusted to suit this study and is illustrated in Figure 2.2.  

  

The first step of warehouse design is to define the system requirements (Baker & Canessa ,2009). 

This includes understanding the strategic role of the warehouse. The suggested tools to use for this 

includes using literature on supply chain strategy and scenario planning. Functions like cross-

docking and storage are also considered.   

  

The second step is to define and obtain data and the tools used include gathering relevant spreadsheet 

data from the company database and using checklists to document the current processes (Baker & 

Canessa, 2009). The data to be gathered include product details, order profiles, goods arrival, 

dispatch, and cost. It is suggested to extract database data, consult with different stakeholders in the 

company and then summarize the data in a useful way. The third step includes analysing the data. 

The tools include spreadsheet models and visualizing the flows and processes of the warehouse. 

Specifically, SKU popularity distribution and demand, customer order profiles and inventory 

profiling can be analysed.  

  

The fourth step includes establishing the unit loads to be used and for this simulation or 

mathematical models are commonly used (Baker & Canessa, 2009). It is also suggested to consider 

the full supply chain and unit load constraints from suppliers. After this, the fifth step involves 

determining the operating procedures and methods.   

  

Moving on, the sixth step includes listing equipment types and their characteristics (Baker & 

Canessa, 2009). For this it is common to use spreadsheet models, simulation, or decision trees. 

Equipment can also be evaluated according to the scale of the operation and flexibility requirements. 

The equipment attributes are evaluated to ensure suitability for the application. Full cost analysis is 

also described. Further on, equipment capacity and quantities are calculated in spreadsheets. The 

expected impact on KPIs from implementing distinct types of equipment is analysed. This aims to 

provide a base for equipment selection.  

  

  
Figure 2.2: Step 1-6 of the warehouse design framework.  

   Note. by Baker & Canessa (2009).  

2.3 Picking 

Picking is typically one of the most labour-intensive and costly activities in warehouse operations, 

typically accounting for up to 55% of total operating expenses (De Koster et al., 2017). While 

Industry 4.0 technologies offer significant opportunities for improving warehouse operations it has 

still not been widely adopted in the construction material sector. Alumbugu et al. (2019) studied 



 

 

 
 

 

9 
 

manufacturing warehouses for construction materials in Nigeria and found a reliance on manual 

picking processes, limited barcoding, and minimal real-time data captured, which decreased service-

level reliability. The results suggest that mechanized or partially automated solutions could improve 

the efficiency and reduce long lead times and high labour costs. Further on, automation may enable 

decreased prices on construction material and delivery delays. The lack of automation in 

construction material warehouses emphasized by Alumbugu et al. (2019) might be explained by the 

complexity of this industry as identified by Tompkins et al. (2010) who found that the relative 

complexity is high because of the large, heavy, and non-standardised items such as wooden planks 

and steel profiles. The challenging context identified by Tompkins et al. (2010) may also be a 

contributing reason why previous studies such as Fernandes et al. (2019) have focused on automation 

of smaller goods in a more standardised format. Previous studies have focused on handling small and 

medium sized boxes, however there are no studies found about planks or heavy lumber. Automated 

systems handling heavier and larger items with real-time data integration is not widely documented. 

This gap is particularly relevant for DBI’s operations, where a sizeable portion of the assortment 

consist of large and heavy construction materials. The requirement for improving picking efficiency, 

combined with the specific challenges of non-standardised building materials, highlight the 

importance of exploring new picking solutions. 

2.4 Loading 

Carlan et al. (2023) researched if automated cargo loading and unloading brings benefits when both 

purchase cost and operational cost is considered. The findings indicate that increasing cargo 

volumes, increased labour costs and improved truck fill rate lead to a better Return on Investment 

(RoI) for automated solutions. Using forklifts in loading was compared to using automated loading 

technologies such as loading plate and transport belt. When comparing these technologies, the 

transport belt produced the best economic results when combining project cost and operational cost 

over a 10-year period. This is mainly because it reduces the costs associated with employees and 

energy usage. The other automated technologies however incurred a higher cost after 10 years 

compared to using forklifts.  

  

Carlan et al. (2023) researched the long-term cost implications of different automated loading system 

while Thylén et al. (2025) stress the importance of collaboration within the supply chain for 

successful implementation of automated loading. Thylén et al. (2025) researched the requirements 

for automated loading and unloading specifically for autonomous transport. It is stated that 

automated loading and unloading require close collaboration with the companies operating the 

loading bay and the trucks. Kembro and Norrman (2020) suggest that when implementing automated 

solutions new competencies are required from the employees. Technical skills for handling the 

automated loading and unloading systems become more relevant than manual loading and 

unloading.   

  

When contrasting Thylén et al. (2025) with Agrawal et al. (2023) both studies acknowledge the 

challenges in automated loading, however, Agrawal et al. (2023) provide a more detailed breakdown 

of the barriers to address for successful implementation. Agrawal et al. (2023) focused specifically 

on automation of loading and unloading for autonomous transport. It was indicated that the 

challenges and requirements which must be addressed for a successful implementation of automated 

solutions can be divided into five main categories. These include the physical characteristics of the 

load, the interaction with the information system, the transportation off and onto the trailer, securing 

and arranging the load to the trailer and the legal aspects. When considering the physical 

characteristics of the load the non-uniformity of goods must be managed to prevent damaging the 

goods and ensuring safety. The goods also must be arranged on the trailer in a way that ensures safe 

and legal weight distribution. Additionally, the non-uniformity creates challenges in automatic 



 

 

 
 

 

10 
 

detection of the goods weight, type, and size, which is required for correct loading. The interaction 

with the information system, automatic registering of goods, requires that the identifier tag is placed 

in the right location. Additionally, efficient communication between different information systems is 

required. Another issue is establishing clear boundaries on who is responsible for various stages of 

the loading and unloading. The information system should also be able to plan the optimal loading 

sequence of the goods. Another main area is moving the goods off and onto the trailer (Agrawal et 

al., 2023). An issue with this process is that the environment is constantly changing regarding people 

and goods in the loading area. This must be managed to avoid damage to equipment or people. The 

mix and interaction between automated and manual work also must be managed to ensure safety and 

efficiency. The height difference between the trailer and loading bay also must be addressed. The 

decision-making abilities of the automated system to be able to handle different situations is also 

described. Another area to address is securing and arranging the goods on the trailer. Optimum fill 

rate and goods safety can be challenging with automated solutions like AGVs. Correctly aligning the 

goods must also be addressed. The load must be secured and unsecured through automation. Finally, 

the legal aspects must be considered. One of the aspects is the traceability in various stages which is 

required to detect if the load is correctly secured and to detect when damages to the goods have 

occurred. There are also legal aspects regarding safety for employees in the loading area. Defining 

accountability in various parts of the process is also suggested.  

2.5 Packing 

Efficient packaging and palletising are critical components of effective logistics operations, 

especially when handling large materials such as wooden planks or sheet materials. According to 

Szymonik (2016), well-designed unit loads improve transport, storage, and automation, while 

reducing damages and labour strain. For DBI, applying these principles to their packaging 

operations, could enhance flow efficiency, minimize manual workload, and lower operational costs. 

Szymonik (2016) emphasises that the formation of palletized unit loads should follow predefined 

procedures, considering factors such as the type and arrangement of packaging, pallet structure, 

transportation condition, and logistics labelling.  

2.6 Automation 

Rathore et al. (2022) investigated barriers for implementing disruptive technologies in logistics. One 

of the technologies which was investigated was AI-based robots. The study claims that a diverse 

product range creates challenges for implementing automated AI robots. The varying product 

characteristics creates a technical challenge because the robots are not very versatile. The study 

claims that the Austrian logistics firm KNAPP AG found that the robots can only handle 15 % of the 

product range successfully. It is therefore not feasible to use one automated system to handle all the 

product range. Instead, multiple types of automated solutions adapted for the corresponding product 

types are required. Aside from the technical barrier, the study also found barriers for implementing 

disruptive technologies which include legal and regulatory framework, lack of top management 

support, resistance to change and infrastructure.  

  

While Rathore et al. (2022) focused on barriers for implementing disruptive technologies, Hardy and 

Brougham (2022) address the negative and positive effects of implementing automation. More 

specifically, Hardy and Brougham (2022) researched the impact, barriers, and enablers of automation 

in firms in telecommunications, energy, manufacturing, and media sectors. One of the identified 

benefits of automation includes increased productivity which enables higher revenue and work 

output. The second impact is cost savings. Thirdly, automation enables resiliency and continuity, 

robots can operate 24/7. Finally, another main benefit is improved data accuracy and reduced 

number of errors. One of the possible negative impacts from automation includes automation 

dependency of employees. While depending on the automatic system the employees’ manual skills 



 

 

 
 

 

11 
 

can erode which is problematic for operational adaptability. Secondly, automating bad processes 

creates escalated issues, rework, and inefficiencies. Thus, the findings indicate that there is a risk of 

magnifying underlying flaws through automation. Thirdly, automation can require regular 

maintenance and updates which can be costly if not managed properly  

  

The findings of Hardy and Brougham (2022) indicate that a key enabler of successful automation 

implementation is executive endorsement and approval, which is necessary to ensure prioritization, 

set direction and secure resources for the project. Secondly, IT infrastructure and involvement is 

required. It is foundational for automation projects as automated technology is commonly integrated 

with the IT system. The features of the automated system are limited to the features enabled by the 

IT infrastructure. Thirdly, process knowledge and mastery are emphasized. Knowing what process to 

prioritize and applying the automation correctly is emphasised to avoid costly errors and 

inefficiencies. Finally, resistance is identified as a barrier. This can be addressed by involving and 

engaging employees and front-line users in the project it is possible to build trust, leverage 

operational knowledge and support from the employees which is essential for a successful 

implementation. 

2.7 Summary & Knowledge Gap 

The findings of the literature review suggests that Industry 4.0 is an enabler for a more efficient 

warehouse management and design. However, it is concluded that within warehousing of 

construction materials there are research gaps. For example, He & Turner (2021) suggest that little 

attention has been made within academia of recent technologies applied to warehousing of forestry 

products. Therefore, this study aims to contribute to academia by addressing the issue of how recent 

technologies within construction materials, particularly for forestry products, can be applied to 

warehousing.  

  

Further on, this theoretical review presents recent technologies. Within recent technologies of 

picking and packing within warehousing, little is found about handling construction material. 

Finally, the theoretical review highlights key considerations to be addressed for successful 

implementation of automation. These considerations are crucial when researching a field that is 

poorly studied previously.  



 

 

 
 

 

12 
 

3 Methodology 

The methodology is a mixed methods single-case study. Literature on the topic was systematically 

reviewed to build a theoretical foundation. The empirical data sources include semi-structured 

interviews, company presentations, and historical data analysis (e.g. spreadsheets of historical sales 

and assortment). To improve the reliability of the data, it was validated through reviews by 

stakeholders at DBI. 

3.1 Research Design 

To address the research questions, a mixed-method, case-based approach was applied. This was 

chosen because of the practical, complex, and context-specific nature of the research, focused on the 

logistics operations of Derome Bygg & Industri AB (DBI). Creswell & Clark (2017) suggest using 

mixed methods when a single data type is not sufficient for answering the research questions. This 

justifies the choice of a mixed method for this study as analysing quantitative data is central for 

answering the research questions however qualitative data is required to guide the interpretation of 

this data and to understand the broader context and operations of DBI. This is also in line with 

Creswell & Clark (2017) who argue that mixed methods are recommended when the complexity of a 

real-world problem requires a nuanced understanding. 

 

Yin (2017) provides a framework for case study research. The framework justifies case study 

research when the focus is on a contemporary issue in a bounded system. Our study of DBI focuses 

on the contemporary issue of warehouse automation and conducts and in-depth exploration of DBI 

as a bounded system which justifies the case-based approach of this study. 

  

Cote (2021) distinguish between descriptive, diagnostic, predictive and prescriptive data analysis. 

Descriptive analysis creates the foundation other analysis is built on by visualising and summarising 

what is already known. Diagnostic analysis aims to establish cause and effect by studying what has 

happened. Predictive analytics are used to make predictions about the future based on analysing 

historical data and trends. Prescriptive analysis considers multiple factors to choose between 

different options using a data-driven approach. 

  

For RQ1: What SKU categories in construction material warehouses are most suitable for 

automation, and for which of these categories do automation offer the greatest potential 

improvement in overall warehouse performance (productivity, cost-efficiency, and quality)?  

 

First, a descriptive phase documented the current system using: 

 

• Quantitative data (historical sales data)  

  

• Qualitative data (semi-structured interviews and direct observations). 

 

This aligns with common practice in logistics research where quantitative data is used to analyse 

flows and capacities, while qualitative insights reveal context and operational complexity. Baker & 



 

 

 
 

 

13 
 

Canessa (2009) also suggest analysing SKU popularity distribution as a step-in warehouse design, 

this was conducted by quantitatively analysing the relative sales quantity for every SKU. 

 

Further on, the analysis followed a prescriptive approach for assortment selection using historical 

data. Subsequently, a predictive approach was applied to forecast 2029 material flows. Finally, the 

categorised flows were evaluated for automation suitability using a prescriptive framework. 

 

For RQ2: In what ways can automation be implemented within the picking, packing, and loading 

processes of a construction material warehouse, specifically targeting the SKU categories identified 

as most suitable? 

 

Building on RQ1, the relevant categories were studied using a descriptive approach to evaluate 

product characteristics and applicable technologies. Mixed data about the possible technology was 

then analysed using a descriptive approach. The product categories and their characteristics were 

also analysed using a descriptive approach interpreting mixed data. A prescriptive approach was then 

used to compare the findings about the technology and match this to the product characteristics to 

find the answer for RQ2. 

 

The research is structured according to the warehouse design framework by Baker & Canessa 

(2009), widely cited in logistics and warehouse planning literature, and illustrated in Figure 2.2. This 

framework supports a structured, step-by-step process for warehouse design, and was chosen 

because of its alignment with both academic standards and industry practice. Figure 2.2 summarises 

the research process for the different issues which have been investigated. 

 

Not all steps of Baker & Canessa (2009) were followed; instead, the study focused on steps 1–6, as 

illustrated in Figure 3.1, which emphasise strategic role definition, data collection, analysis, studying 

the product characteristics to establish unit loads, determining operating procedures, and technology 

evaluation. The reason for following these steps is that they encompass the scope of the research 

questions. By following these steps, it was possible to first establish the context needed for adressing 

the research questions. Further on, the method allowed finding the most suitable SKUs and processes 

for automation and pairing these with suitable technology and thus answer the research questions. 

Steps 6-12 of the framework focus on layout design, which is outside the scope of this study.   

 
Figure 3.1: Illustrating the framework used for warehouse design. 

    Note. Adapted from Baker & Canessa (2009), adjusted to suit the context of this study. 

3.2 Literature Review 

The literature review was conducted systematically to provide a comprehensive understanding of 

Industry 4.0 technologies, warehouse design principles, and automation challenges in the sector of 

construction materials. Knowledge of Industry 4.0 was further deepened through targeted research in 



 

 

 
 

 

14 
 

logistics. Insights into warehouse design were gathered through continuous information collection 

focused on the construction sector. The literature search strategy combined keyword-based searches 

and snowballing, following the approach recommended by Webster & Watson (2002).  

  

The keywords were used for searching in academic databases such as Google Scholar and Chalmers 

Library. The keywords contained combinations such as:  

  

• “Warehouse design”  

• “Logistic centre design”  

• “Industry 4.0 in logistics”  

• “Construction material logistics”  

• “Warehouse automation”  

• “Picking automation”  

• “Loading automation”  

• “Challenges in construction supply chain”  

• “Construction material warehousing”  

  

To ensure a balanced searching and finding relevant studies, boolean operators (AND, OR) were 

used for refinements. Studies made after 2010 were prioritised, however a few older yet significant 

and foundational works (e.g. Baker & Canessa, 2009; De Koster et al., 2007) were included.  

  

To find the current research trends within logistic centre design and warehouse automation, focused 

on heavy and large building materials, an iterative searching approach was utilised. After a relevant 

article was found, backward and forward snowballing were used. Backward snowballing, by using 

the reference list to find additional relevant articles. Forward snowballing, by searching for studies 

that have cited that article, to find more recent findings. The literature was collected and evaluated 

and thereafter categorised and prioritised based on its key areas and relevance for this study. In 

addition, relevant academic books were selected to provide a solid theoretical foundation for the 

empirical investigation.  

3.3 Empirical data collection 

To develop a comprehensive understanding of DBI’s current operations, empirical data was gathered 

through a combination of interviews, observations, internal company data analysis, and websites of 

automation technology providers. From this, it was possible to identify feasible opportunities of 

improvements for the new logistics centre. Interviews were conducted with logistics managers and 

operational personnel at DBI to collect information on current challenges and future requirements. 

The interviews have followed a semi-structured approach, which allows flexibility and 

simultaneously covers the main subjects, such as picking routines, loading processes, warehouse 

capacity planning, and automation experiences. The list of interviews and meetings are provided in 

Table 3.1. 

  

Additionally, observations were made during a site visit at DBI’s production facility in Derome and 

regional warehouses in Mölndal, Derome and Landskrona, as well as the central warehouse in 

Landskrona. During these visits picking, packing, and loading operations were primarily observed, 

however bottlenecks, ergonomic challenges, and potential areas of automation were also addressed. 

Company data, such as historical sales volumes, stock levels, and assortment categorisation were 

collected and analysed through spreadsheets and pivot tables. This enabled quantitative analysis of 

sales quantity and frequency categorisation. 
 



 

 

 
 

 

15 
 

When collecting data for answering RQ2, the prioritised sources were academic articles, which 

provided insights about automated packing and loading. However, it was found that little research 

had been conducted about automation of picking in this field. Thus, other sources were needed to be 

able to answer RQ2. Google was used to scan the market for automated technologies in this field. 

The keywords which were used were: 'warehouse automation for construction materials,' 'automated 

picking for heavy items,' 'automated loading,' 'automated picking of sheet materials' and 'automated 

packaging', ‘automated picking of lumber/timber.’ While scientific articles on the topic would have 

been preferred as sources, alternative sources were also used. The authors acknowledge the 

limitations of these sources, however the use of them is justified for illustrating concepts and start 

exploring this under-researched field. Case studies and market examples within warehouse 

automation of construction materials were collected. To develop a better understanding of 

technological options, solutions from Systraplan, Barbaric and Tentoma were investigated to identify 

existing technologies that were suitable for handling bulky and heavy materials. The combination of 

interviews, observations, internal data analysis, and benchmarking provided a triangulation of 

findings, to achieve validity of the results.  

  



 

 

 
 

 

16 
 

Table 3.1: List of interviews and meetings for the master’s thesis project 

# Title / Description Date Participants Purpose / Key Topics 

1 Meeting with logistic 

responsible - supervisor 

2025-01-20 Mathias Hansson Introduction of 

background, purpose and 

aim. 

2 Meeting with logistic 

responsible - supervisor 

2025-01-24 Mathias Hansson Collecting data, 

limitations 

3 Meeting with logistic 

responsible - supervisor 

2025-01-31 Mathias Hansson Strategic goals, overview 

of DBI’s operations 

4 Meeting with Controller 2025-01-31 Johan Lindblom 

– Controller 

Logistics cost structure, 

KPIs, budgeting, flow 

metrics 

5 Meeting with Transport 

Planners and Coordinators 

2025-02-03 Erik Strand and 

other internal 

logistics 

operators 

Transport planning, ERP, 

consolidation, real-time 

issues 

6 Meeting with logistic 

responsible - supervisor 

2025-02-06 Mathias Hansson Technological solutions, 

logistic operations, DBI’s 

organisation 

7 Meeting with Regional 

Warehouse Manager – 

Mölndal 

2025-02-06 Daniel Jakobsson Capacity planning, 

warehouse design, 

staffing, strategy 

8 Meeting with Supply Chain 

/ Procurement 

2025-02-11 Daniel Ahrling Inbound flows, supplier 

deliveries, order 

handling, procurement 

strategy 

9 Meeting with operation 

manager at the regional 

warehouse of Mölndal 

2025-02-11 Jens Baitson Packing routines, 

capacity challenges, 

planning process 



 

 

 
 

 

17 
 

# Title / Description Date Participants Purpose / Key Topics 

10 Study visit - Central- and 

regional warehouse of 

Landskrona 

2025-02-13 Mathias Hansson Studies of operations in 

central warehouse, 

bottlenecks, future plans 

11 Meeting with logistic 

responsible - supervisor 

2025-02-13 Mathias Hansson Bottlenecks in DBI’s 

logistics, logistical costs, 

problem statements, 

sustainable goals, 

economical perspectives, 

available resources, 

storage capacity 

12 Meeting with supervisor 

from Chalmers University 

of Technology and logistic 

responsible - supervisor 

2025-02-19 Altahir Hassan 

Irshien Ali, 

Mathias Hansson 

Requirements of the new 

logistics center, research 

questions 

13 Meeting with logistic 

responsible - supervisor 

2025-03-05 Mathias Hansson Assortment analysis and 

selection 

14 Meeting with Regional 

Warehouse Manager 

(Derome) 

2025-03-12 Roger 

Sandersson 

Warehouse operations, 

staffing, picking/loading 

processes 

15 Interview – Automation 

Expert 

2025-04-07 Per Andersson - 

automation 

expert 

Experiences with 

automation and its effect 

on KPIs and logistics 

3.4 Data Analysis 

The data analysis process in this study followed the structured warehouse design framework by 

Baker & Canessa (2009), focusing on steps 1–6. These include defining system requirements, 

gathering relevant data, analysing product characteristics, establishing unit loads, and evaluating 

operating methods and technology. A more detailed illustration of the method used for the data 

analysis is made in Figure 3.2. The aim was to generate actionable insights for the design of a new 

logistics centre for DBI, with a focus on automation potential in picking, packing, and loading. The 

tools used for analysis was:  

● Defining System Requirements: The first step was to define system requirements as 

suggested by Baker & Canessa (2009). Interviews were held to understand the strategic role 

of the warehouse and were interpreted with strategy literature in mind. Hardy & Brougham 



 

 

 
 

 

18 
 

(2022) emphasize the importance of detailed process knowledge before implementation of 

automation. For this reason, process knowledge was gathered and interpreted using the 

general process flow described in Figure 2.1 (Baker & Canessa, 2022).  

● Spreadsheet Analysis (Excel): Baker & Canessa (2009) suggest gathering spreadsheet data on 

product details, order profiles, SKU demand distribution, etc. It is suggested to use 

spreadsheet calculation models for analysing the data quantitatively. Excel was the primary 

tool for quantitative analysis. Historical sales and stock data from DBI were imported into 

spreadsheets. Pivot tables were used to categorize SKUs by assortment type and frequency 

class. Calculation models were then applied in Excel. This enabled the identification of high-

impact product categories suitable for automation. Figure 3.3 presents an excerpt of the raw 

excel data. For integrity reasons the complete Excel file is not available and most of the 

screenshot is blurred.  

● Assortment Selection: Baker & Canessa (2009) suggest establishing unit loads using 

mathematical models. In this study this involves assortment selection. SKUs were classified 

by order frequency and assortment category, with cutoffs defined through historical data 

(e.g., fast movers: ≥52 orders/year). By using pivot tables and calculation models it was 

possible to find the relative sales quantity and number of order lines for each SKU. Category-

frequency pairs below 1% of total order lines or sales quantity were excluded to streamline 

the logistics design. Using the combination of pivot analysis and stakeholder discussions, the 

number of SKUs in the future logistics centre was reduced while maintaining 99.3% order 

coverage and significantly reducing complexity.  

● Sales quantity calculations: Baker & Canessa (2009) suggest profiling and SKU popularity 

demand using spreadsheet models and calculating total sales quantity. In this thesis, SKU 

popularity distribution was analysed by studying historical demand distribution and 

calculating relative number of order lines for various levels of assortment categorization. 

Quantitative metrics such as sales quantity and number of order lines were calculated for each 

SKU class. Adjustments (e.g., 45% reduction of Derome data for specific categories) ensured 

accuracy in estimating 2024 baseline data. The baseline data was then used to create a 

forecast for 2029.  

● Detailed Category Analysis: By using pivot analysis and spreadsheet calculation models the 

relative sales quantity and number of order lines could be summarized for different detail 

levels of assortment categorisation. SKU data and pictures from DBIs website was 

quantitatively and qualitatively analysed.  

● Qualitative Data Interpretation: Baker & Canessa (2009) suggest using both quantitative data 

and consulting with different stakeholders at the company. Interview notes were coded and 

cross-referenced with observational data to identify bottlenecks and manual inefficiencies in 

current operations. For example, specific challenges in picking and loading heavy items were 

interpreted through both site visits and staff interviews.  

● Automation Assessment: As the fourth step, Baker & Canessa (2009) suggest establishing 

unit loads to be used. As only 5 % of DBIs products are palletised, establishing the unit loads 

required detailed data analysis as there are almost as many different unit loads as there are 

SKUs at DBI. Product characteristics at DBI were mapped by studying the spreadsheet data. 

Individual SKUs were studied in DBIs web shop to determine technical product 

specifications. Empirical data and literature on automation was studied to analyse impact and 



 

 

 
 

 

19 
 

technical specifications of automated equipment. An analysis was then made quantitatively 

and qualitatively to find a match for the DBIs products with automated solutions. This 

matches the steps five and six of the warehouse design approach by Baker & Canessa (2009), 

which include determining operating procedures and listing and evaluating equipment types. 

Automated solutions can commonly only handle a limited part of the assortment (Rathore et 

al., 2022) which further justifies this analysis. 

● Visualisation: Schematic process flows and figures (e.g., current picking flows, layout zones) 

were used to map operations and identify critical inefficiencies as described by Baker & 

Canessa (2009). 

 
Figure 3.2: Summarization of the analysis process for the issues investigated in the study. 

 

Figure 3.3: The figure is a screenshot of the raw spreadsheet data which was used in the quantitative data analysis. 

 



 

 

 
 

 

20 
 

3.5 Research Validity & Reliability 

To ensure reliable results, several types of data were used and compared throughout the study. 

Interviews, internal spreadsheets, company presentations, and literature were all used to cross-check 

findings throughout triangulation, as explained in Yin (2017), this has strengthened the 

conclusions. Assumptions were reviewed by DBI’s logistics management, providing relevant expert 

input. In cases where data was missing or uncertain, conservative assumptions were made and 

clearly explained in the documentation. The entire process was carried out with transparency in 

mind, with clear records of how decisions were made and what sources were used. Since the study 

was conducted within the context of a single organisation, the generalisability of the findings may be 

limited. However, the results are considered robust and well-grounded within the scope of DBI’s 

operations.   



 

 

 
 

 

21 
 

4 Results & Analysis 

This chapter presents the empirical findings from interviews and the data collection, as well as the 

analysis based on the literature related to the case and context. This aligns with the framework of 

Baker & Canessa (2009) that suggest analysing historical data as well as performing interviews with 

relevant company stakeholders and summarizing them in useful ways for warehouse design. The aim 

is to explore how automation can be applied in warehouse design for DBI, and to evaluate which 

product categories and processes are most suitable for improvement.  

4.1 DBI’s current system and strategic goals 

The first step of warehouse design according to Baker & Canessa (2009), as illustrated in Figure 4.1, 

is to understand the current system and strategic goals. Hardy & Brougham (2022) suggests that 

knowledge and mastery of the current process is an enabler of successful automation 

implementation. Otherwise, the risk is to automate an inefficient process and multiply errors. 

Chapter 4.1 focuses on documenting the strategic role of the warehouse as well as documenting the 

current processes as this is crucial for answering the research questions. 

  

Figure 4.1: Step 1 of the framework, understanding the current system & strategic goals. 

    Note. Adapted from Baker & Canessa (2009). 

 

DBI supplies customers with construction material in the southwest part of Sweden. From an 

interview with M. Hansson (personal communication, January 20, 2025), it was stated that they have 

three warehouses, which are in Derome, Landskrona and Mölndal. In 2024, they decided to close the 

warehouse in Trollhättan, because of its excessive costs relative to income. Now, the warehouse in 

Mölndal covers the geographical area Trollhättan previously supported. In 2029, DBI plans to build 

a new logistics centre to cover Derome’s geographical area as well. The logistics centre is planned to 

be state-of-the-art and suit the context of DBI business by being a node in their new efficient supply 

chain. The logistics centre is going to function as storage, consolidation area and cross-docking to be 

able to offer express deliveries (M. Hansson, personal communication, February 6, 2025). Right next 

to the regional warehouse in Derome (a village with the same name as the company) is where 

Derome group’s main production facility, its sawmill, is located. In the same location as the regional 

warehouse and the production facility Derome currently has their central warehouse for wooden 

planks. The production facility and central warehouse will remain after building the logistics centre. 

However, part of the flow handled by the regional warehouse in Derome is planned to be covered by 

the logistics centre. 

 

In the warehouse of Mölndal, south of Gothenburg, DBI store a significant part of their construction 

materials, which are going to be supplied to their customers according to M. Hansson (personal 

communication, January 20, 2025). Based on a visit to the current warehouse in Mölndal, a blueprint 



 

 

 
 

 

22 
 

of the warehouse and an interview with D. Jakobsson (Personal Communication, February 6, 2025), 

the authors developed an understanding of the current warehouse, illustrated in Figure 4.2. The east 

building contains planed planks, wooden boards, plaster boards, and insulation materials etc. Outside 

of the east building there is an open area where the trucks can park to be unloaded or loaded. At the 

west building there is a drive-in area, where customers can drive their own vehicles into a storage 

and pick up the construction material that they require. At the drive-in there is also a section for 

wooden mouldings, which the customer can pick. Next to the drive-in there is a store for hardware 

and tools.  

 

Figure 4.2: An overview of DBI’s current logistic center. 

 

Since DBI has one storage space for only internal use and another storage space shared with 

customers, they are using double the amount of space for many SKUs. This is one of the observed 

issues, stated in the interview with M. Hansson (personal communication, January 20, 2025), at DBI 

in the current situation. This also causes an issue within the interaction of customers in the storage 

area. The customer can accidently move articles from one storage shelf to another, which results in 

errors in the inventory. It can also result in issues for the pickers when an order is received. The 

inventory indicates that there are units left, and the customer that has ordered is promised to get the 

item delivered when it is not available. This is because other customers may physically pick the 

units, creating a mismatch in the inventory data and the real-time inventory. Thereby, the shelf can 

be empty when the picker arrives. The coordinator at DBI will start to search for the missing SKU at 

another regional warehouse or at a closely located DBI store, which is very time consuming and 

could have been avoided if customers were not allowed to pick from the same storage inventory. 
 

Next to the east building there is an office area, which is called the control tower and consists of 

operation manager’s offices, and a space for transport leaders and coordinators. The transport leaders 

plan the routes for the truck drivers, communicate with sellers to fix last minute changes if possible. 

The coordinators are planning in what order the pickers should prioritise to match the reloading of 

Control tower 



 

 

 
 

 

23 
 

the trucks. The coordinators can also plan consolidation of orders. The pickers receive their orders 

from a computer and print their picking list (transport coordinators at DBI, personal communication, 

February 3, 2025). Thereafter they walk to the forklift and follow the picking list to drive to the 

storage location, which is in the picking list for each row, this route is optimised based on the 

picker’s own experience J. Baitson (personal communication, February 11, 2025).  

4.1.1 Current picking operations 

Since picking is commonly the most resource intensive process in warehousing it was a focus area in 

this study. The assortment is highly varied at DBI in weight and size, and therefore the picker is 

required to adapt their methods depending on the SKU to pick, according to J. Baitson (personal 

communication, 28 March, 2025). Some SKUs are time-consuming to handle, while others are fast 

and simple. The picking quantity also affects the efficiency because it directly affects the picking 

time, productivity, and cost. 
 

To further explain the dynamics affecting picking time, a practical example from DBI is provided; 

planed wooden planks are commonly stored in cantilever racks with four levels. Open packages used 

for partial picking are typically placed on level one and two, while full, unopened packages are 

stored on level three and four. In one common figuration, level two holds an open package of one 

SKU, while level four stores a full package of the same SKU. These full packages measure 

approximately one meter in both width and height, and the picking process depends on whether a full 

package or only a limited quantity is required. If the required amount exceeds one full package, the 

picker can use a forklift to retrieve a complete package from the storage area and move it directly to 

the loading zone. If the order requires a smaller quantity, the picker will instead move the package to 

a lower shelf, if no open package is already available, and open it manually. The desired number of 

planks is then picked by hand and placed on the forklift. After picking, the picker drives to the 

packaging area, where the wooden planks are manually wrapped using plastic film and secured with 

straps. The packaged goods are then transported to the consolidation area. All the heavy and/or large 

sized items, such as wooden boards, gypsum boards and thin steel profiles are made in a comparable 

way, and the process flow is illustrated in Figure 4.2.  
 

Other types of goods are picked manually from pallets, such as cement bags. Most of the cement 

bags are made of paper and weigh 20 kg each. While they are most often carried by hand from the 

storage area to the loading zone, they can also be placed on pallets and transported by 

forklift. Hardware and tools are picked by a separate team located in the store area. This team works 

with a dedicated picking list and collects items either from in-store shelves or nearby storage. The 

picked items are then packed and transferred to a designated zone, where the main warehouse 

pickers retrieve and consolidate them with the rest of the order at the loading area. Reinforcement 

mesh is particularly time-consuming to handle because of their size and weight. Upon arrival, it is 

offloaded from the delivery truck and placed in a designated storage area. From there, it is picked 

and loaded directly onto the outgoing truck for delivery.  



 

 

 
 

 

24 
 

 

Figure 4.3: Schematic view of the picking process at DBI. 

 

From an interview with DBI’s controller, J. Lindblom (personal communication, January 31, 2025), 

it was stated that DBI monitors various KPIs, one of them is number of picked order lines per hour. 

However, this metric can be misleading, because of the high variation in item types. Larger and 

heavier items are more time consuming to handle compared to smaller ones. This KPI can be 

compared across different warehouses. For example, in 2024, the number of picked order lines per 

hour in Mölndal was 34% lower than in Derome. The main reason is that Mölndal typically handles 

lower order quantities. This becomes even more clear when studying picked volume per worker, 

which was 17% higher in Mölndal compared to Derome. Additionally, the average quantity per order 

line is higher in Derome than in Mölndal. 
 

These differences highlight the requirement for a more nuanced picking performance measurement 

at DBI. A classification based on material type would improve the accuracy of performance 

comparisons and better reflect the nature of the work. For instance, picking single wooden planks or 

flat steel bars should not be evaluated in the same way as picking small screw packages. To measure 

this, the RFID-technique would be highly supportive. This is also the case within packaging and 

loading.  

4.1.2 DBIs current assortment 

Rathore et al. (2022) found that a barrier for implementing automation is that standardised automated 

solutions cannot handle a varying assortment due to technical constraints. Thus, for being able to 

implement automation, several automated solutions need to be used, adapted to different parts of the 

assortment. Baker & Canessa (2009) emphasises analysing the characteristics of products to be 

handled by the warehouse. For this reason, to further explore the context for answering the research 

question, DBIs assortment was studied. 

The planned logistics center will cover the same area as DBIs current regional warehouses in 

Mölndal, Derome and Trollhättan while Landskrona is outside the geographical scope. The new 

logistics center will not have the same assortment as the regional warehouses but mostly it will have 

the same functions as the current regional warehouses. The Mölndal regional warehouse currently 

has over 7000 SKUs in its assortment.  

Baker & Canessa (2009) suggests analysing historical spreadsheet data about products, dispatch, 

order profiles etc as illustrated in Figure 4.3. This is analysed in depth in chapter 4.2, but Table 4.1 



 

 

 
 

 

25 
 

and Table 4.2 provides an overview.

 

Figure 4.4: Step 2 from the framework; define, obtain and analyse the data. 

    Note. Adapted from Baker & Canessa (2009). 

DBI has organized each SKU in an assortment category. Every assortment category is further 

categorised in subcategories. These are further categorised in product categories and finally 

individual SKUs which is the most detailed level. The analysis follows the framework presented in 

Figure 4.5. Table 4.1 provides an example overview of DBIs assortment categorization, and Table 

4.2 presents examples from DBI’s popular SKUs.  

 

Figure 4.5: Obtaining and analysing the data from the assortment.  



 

 

 
 

 

26 
 

Table 4.1: DBIs assortment categories with respective most sold sub categories. IND (industry products) is excluded because the sales 

quantity of IND products is negligible. 

Assortment category Most sold sub categories 

BM (Building materials) -Sheet materials  

-Roofing and wall material 

-Mortar 

-Rebar and steel 

-Insulation 

JV (Hardware and tools) -Screws 

-Chemicals 

-Cleaning products 

SN (Carpentry materials) -Kitchen interior 

-Tree interior 

-Doors 

TR (Wooden planks) -Planed spruce planks 

-Planed pine planks 

-Wooden goods 

 

Table 4.2: The table shows examples of DBIs categorization of popular SKUs 

Category/Sub category Product group Figure 

BM - Sheet material Plasterboards 

 

BM - Roofing and wall 

materials 

Concrete roof tiles 

 

BM - Mortar Dry mortar 

 



 

 

 
 

 

27 
 

Category/Sub category Product group Figure 

BM - Rebar and steel Thin steel profiles 

 

BM - Insulation Mineral wool 

 

JV - Screws Screws 

 

JV - Chemicals Sealant 

 

SN - Kitchen interior Kitchen carpentry 

 

SN - Wooden interior Wooden moulding 

 



 

 

 
 

 

28 
 

Category/Sub category Product group Figure 

SN - Doors Miscellaneous doors 

 

TR - Plane spruce Plane spruce 

(Construction timber, 

lath, exterior cladding) 

 



 

 

 
 

 

29 
 

4.2 Sales Quantity 

Continuing with the approach for obtaining and analysing the data in the warehouse design by Baker 

and Canessa (2009) the next step, shown in Figure 4.5, was to define future sales quantity 

requirements, by analysing historical sales quantities. 

4.2.1 Assortment selection 

Before calculating the future sales requirements, it was necessary to select the assortment to include 

in the logistics centre. This step is not defined by Baker & Canessa (2009); however, it was 

necessary in this case and is illustrated in Figure 4.7 

 

 

Figure 4.6: Understanding the criteria to select the assortment. 

If the whole assortment would be included in the logistics centre, the size of the logistics centre 

would increase, and it would negatively the operational efficiency. Therefore, not all the SKUs will 

be included in the planned logistic centre.  

Deciding the number of SKUs to include was conducted by analysing historical sales data from the 

Mölndal regional warehouse for February 2024 to February 2025. In the analysed spreadsheet each 

SKU was assigned to a frequency class, fast mover, medium mover, slow mover, and very slow 

mover according to DBIs definition as defined in Table 4.3. Each SKU was also assigned to an 

assortment category explained in Chapter 4.1.3.  

A simplified approach for assortment selection would have been to for cut-off all very slow movers. 

While this would have been simpler, it fails to manage the complexity of DBIs assortment. For 

example, JV SKUs can efficiently be transported from DBIs central warehouse to the logistics 

centre, while other assortment categories are larger and bulkier which justifies including them in the 

logistics centre assortment. The more detailed analysis in this study ensures an assortment selection 

which considers DBIs operational dynamics and allow for better performance compared to choosing 

SKUs purely based on DBIs frequency class.  



 

 

 
 

 

30 
 

By using a pivot table, it was possible to extract how many SKUs belong to each assortment 

category and frequency class. The data also included the number of order lines and the total sales 

quantity for each SKU. Frequency-category pairs were assigned which is presented in Table 4.3. 

Table 4.3: DBIs frequency classes. 

Frequency class Order lines per year 

Very slow movers 0-2 

Slow movers 3-11 

Medium movers 12-51 

Fast movers 52+ 

When selecting the assortment, each assortment category and frequency class pair was evaluated. 

The main criteria for selecting the assortment were that if the category-frequency pair was 

historically included in less than 1 % of order lines or total sold quantity, it was removed from the 

assortment, as it was negligible.  

There is also an exception to the assortment selection when choosing the JV (Hardware and tools) 

assortment. The plan is to supply the JV SKUs from DBIs central warehouse for JV SKUs in 

Landskrona according to M. Hansson (personal communication, January 31, 2025). Truck deliveries 

will arrive from Landskrona to the logistics centre every night which indicates that DBI can fulfil 

their customer promise of delivering the next day without storing the JV articles in the logistics 

centre. The only purpose of including JV SKUs in the logistics centre is for being able to fulfil 

express orders, which is defined as delivering the same day as the customer places and order. For this 

purpose, JV Fast Mover SKUs were included in the assortment. JV medium, slow, and very slow 

movers were all excluded even if these category-frequency pairs were all above 1 % of total order 

lines and sold quantity. The reason for this is that DBI can effectively distribute these from their 

central warehouse which partly eliminates the requirement for including them in the logistics centre 

assortment. JV SKUs are much smaller in size than other parts of SKU’s assortment, and for this 

reason a centralised distribution strategy is used by DBI for these products since it is more effective. 

This is in contrast with the rest of DBIs assortment, which is characterised by large and heavy 

products with short lead times. A highly centralised approach is ineffective for these product 

characteristics caused by higher cost per distance for each shipped SKU. A centralised approach can 

decrease total warehousing cost however also increase the distance the SKUs must be shipped. For 

smaller SKUs which have a lower cost per distance for each shipped SKU, centralisation leads to 

lower total cost, while for larger SKUs where shipping prices per unit is higher, a more decentralized 

approach is preferred.  

This way of choosing the assortment was decided based on data analysis and discussions with M. 

Hansson (personal communication, March 5, 2025). The fast mover IND (Industry materials) and 

fast mover - (unassigned) products are also included in the assortment even though these category-

frequency pairs represent very low sales volumes. The reason for including them is that they are 

expected by customers and required for a complete product offering. This makes these strategically 

important assortment categories for DBI, and they are thus included.  

The result of this assortment selection is that the number of SKUs are reduced from 7573 to 2181 

while still covering 99.3 % order lines when analysing the historical data from the Mölndal regional 

warehouse.   



 

 

 
 

 

31 
 

The included category-frequency pairs and assortment selection is presented in Table 4.4. 

Table 4.4: The table shows what categories and frequency classes of DBIs existing assortment which will be included in the planned 

logistics center. X means that it is included and - means that it is excluded. 

Assortment 

Category and 

Frequency 

Class 

Fast Mover Medium 

Mover 

Slow Mover Very Slow 

Mover 

BM (Building 

materials) 

X X X - 

JV (Hardware 

and tools) 

X - - - 

TR (Wooden 

planks) 

X X X - 

SN (Carpentry 

Materials) 

X X X - 

IND (Industry 

Materials) 

X - - - 

- (Unassigned) X - - - 

The chosen assortment, defined in Table 4.4 as the result from the data analysis of the assortment, 

together with JV SKUs shipped from the Landskrona central warehouse can cover 99.3 % of DBIs 

order lines and 99.7 % delivered quantity fulfilling the customer promise of delivering the next day. 

This number could be even higher assuming SKUs which are not stored in the logistics centre can be 

delivered the next day through a different method of distribution.   

 

Figure 4.7: The result of the assortment analysis. 

 



 

 

 
 

 

32 
 

This choice of assortment can also cover 74 % of express orders measured in complete order lines 

and 96.6 % express orders measured in covered order quantity, as defined in Table 4.5. Express 

orders are defined as delivering the same day as customers place an order which is only possible if 

the SKU is included in the logistics centre assortment. This is not a calculation of the expected 

service level but only how many customer orders are covered by the proposed assortment selection 

logistics. This is also based only on historical data without forecasting; therefore, the number might 

also be lower when considering that the customer demand for different SKUs changes over time. 
 

Table 4.5: The table shows how the assortment selection in table 4.4 affects how many orders can be covered based on historical data. 

Parameter Before After (% covered by new 

assortment + % available 

through different means of 

distribution) 

Number of SKUs 7573 2181 

Covered order lines 100 % 99.3 % + 0.7 % 

Covered order quantity 100 % 99.7 % + 0.3 % 

Covered express order lines 100 % 74 % + 36 % 

Covered express order 

quantity 

100 % 99.6 % + 0.4% 

Customer satisfaction Baseline Maintained 

Cost-efficiency Baseline Improved 

Productivity Baseline Improved 

While the proposed assortment for the planned logistics centre cannot cover all orders compared to 

the current assortment, it is expected to improve productivity and cost efficiency by reducing 

complexity through decreasing the number of SKUs. The SKUs which are missing in the planned 

logistics centre can still be delivered through a different means of distribution, for example through 

shipping from the central warehouse and being consolidated at the logistics centre, to fulfil the 

customer promise. It can also be shipped directly from supplier to customer, or from central 

warehouse to customer. For these reasons, the proposed assortment selection is not expected to 

maintain customer satisfaction and reduce the logistics cost through prioritizing efficient handling of 

the high-runner SKUs. The productivity and cost-efficiency for handling the SKUs not included in 

the assortment might be reduced, however since these only make up a minor part of the throughput, 

the total productivity and cost efficiency is expected to increase, while reducing cost expected 

through a more intelligent use of resources. 

The findings indicate that the same assortment is available in the area covered by DBIs regional 

warehouses in Derome, Mölndal and Trollhättan. The findings also indicate that the Landskrona 

regional warehouse has a partly different assortment, however this is not part of DBIs product range 

for the geographical area the new logistics centre is planned to cover.  

When the assortment category and frequency class pairs had been established the next step was to 

calculate how many SKUs are included in these pairs in the geographical area to be covered by the 

planned logistics centre. This was analysed by studying historical data for the Derome and Mölndal 



 

 

 
 

 

33 
 

regional warehouses. Trollhättan warehouse was not included in this analysis because it was closed 

in 2024 which indicates that th