RESOURCE ALLOCATION IN AN ASSEMBLY SYSTEM: FROM MANUAL PLANNING TO AUTOMATED DECISION SUPPORT

Abstract

Title: Resource Allocation in an Assembly System: From Manual Planning to Automated Decision Support.

Authors: Malik Jusopov & Kasper Palm.

Supervisors: Dr. Sandeep Jagtap, Division of Engineering Logistics, Faculty of
Engineering, Lund University
Johannes de la Cour, Internal Logistics Manager, Haldex

Contribution: This thesis has been a complete collaboration between the two authors. Each author has been involved in every part of the process and contributed equally.

Problem Statement: Haldex currently relies on manual planning processes when allocating resources to their assembly lines, with no unified approach between planners despite having access to relevant data and tools. This results in time consuming planning that is subject to inefficiencies.

Purpose: The purpose of this thesis is to evaluate and develop a decision support system for resource allocation in Haldex's assembly system that reduces manual planning effort and improves planning performance through automated decision making.

Research Questions:
RQ1: Which logistical parameters and design choices should be considered when developing a decision support system for short-term production planning?
RQ2: What are the challenges that must be addressed when translating a planning problem into a decision support system?
RQ3: To what extent can an automated decision support model improve the
allocation in an assembly system, compared to the current manual planning approach?

Methodology: This thesis primarily follows an abductive research approach, with a research strategy aligned with the DSR framework. Qualitative data was collected through semi-structured interviews with production planners and continuous dialogue with senior-level management. Quantitative data was also collected, mainly from company systems and employees at Haldex.

Conclusion: With parameters such as order quantities, batch sizes, inventory levels, backlog, and preferred line adherence paired with design choices such as an MILP with an Excel interface, the model consistently provided a production plan that reduces both inventory levels and backlog while adhering to line preference when possible. Thus, the model showed it can improve the current planning process and reduce manual planning by providing an optimal solution in seconds. This was achieved despite challenges related to standardizing tacit knowledge, defining necessary assumptions, and adhering to the current ways of working at Haldex.

Popular Abstract

Solving the Daily Puzzle — Smarter Production Planning That Cuts Inventory by up to 60% BY MALIK JUSOPOV & KASPER PALM, DIVISION OF ENGINEERING LOGISTICS (June 2026)

Imagine standing in front of a giant puzzle every morning. The pieces consist of several assembly lines, customer orders, and products that can only be built on certain lines. Now imagine that your pieces change every day. Incoming deliveries may be delayed, or assembly lines may temporarily break down. This is the reality for production planners in many fast-moving manufacturing companies.

At Haldex, a Swedish manufacturer of brake systems and air suspension solutions for heavy vehicles, planners manually create production plans in spreadsheets. Although they have access to data, such as inventory levels and product specifications, planners largely base their decisions on experience and personal judgement. As a result, different planners often approach the same problem in different ways. Additionally, manual planning requires time and effort. Ensuring orders are met on time, while keeping inventory levels and backlog low and adhering to the production process’s limitations, is not an easy task.

To investigate whether this process could be improved, a digital decision support system was developed. Think of it as an automated optimization model that generates a production plan within seconds. Instead of spending hours every week manually deciding how production should be allocated between assembly lines, planners enter relevant data into the system and receive the optimal solution. The system considers factors such as customer orders, inventory levels, line capacities, and preferred production lines. It is also based on input from planners to ensure their experience is not lost in the digital transformation. The system’s objective is to create a plan that minimizes unnecessary inventory while ensuring that customer orders are fulfilled on time. Planners can then use this as a blueprint before any final decisions, making the planning process more standardized and efficient.

One of the most difficult tasks was translating the planners’ experience into rules that a computer could understand. Human planners often rely on tacit knowledge, experience and intuition that are difficult to describe. Capturing this knowledge and integrating it into an optimization model became a central part of the project. This required simplifications and assumptions, such as components always being available.

As manufacturing companies continue their digital transformation, decision support systems may become increasingly important. Rather than spending valuable time creating production plans manually, planners can focus on improving processes, solving unexpected problems, and making strategic decisions. The production plans generated by the system consistently fulfilled orders on time while reducing inventory levels. Although the system simplifies the real planning problem, it provides planners with better tools for solving an increasingly complex puzzle. Similar to how the system allocates orders efficiently, it allows planners to allocate their time more effectively.

• author: Jusopov, Malik and Palm, Kasper
• supervisor: Sandeep Jagtap
• organization: Production Management; Engineering Logistics
• course: MTTM05 20261
• year: 2026
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Decision Support System, Production Planning, Resource Allocation, MILP, Assembly System, Shift-level
• other publication id: 6057
• language: English
• id: 9235005
• date added to LUP: 2026-06-10 16:47:13
• date last changed: 2026-06-10 16:47:13