Investigating Maintenance Strategy Selection and Performance Measurement Readiness - A Case Study of Tetra Pak

Abstract

Title:
Investigating Maintenance Strategy Selection and Performance Measurement Readiness: A Case Study of Tetra Pak

Authors:
Siri Byrenius & Isa Roslin

Supervisor:
Joakim Kembro, Associate Professor at the Division of Engineering Logistics, Faculty of Engineering, Lund University

Examiner:
Ebba Eriksson Ahre, Postdoc at the Division of Engineering Logistics, Faculty of Engineering, Lund University

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 description:
Maintenance has evolved into a range of increasingly elaborate strategies that rely on vast amounts of data. Many models exist to select among the strategies, but their complexity make them difficult for practitioners to use and therefore they are rarely tested in practice. At the same time, companies collect more and more maintenance data but they do not necessarily know how to leverage it, and even with the right measures chosen there might be difficulties in using them if the data is not of sufficient quality. These challenges are prevalent at Tetra Pak, that collects a lot of data but only analyses a small part of it.

Purpose:
To propose a maintenance strategy that aligns with Tetra Pak’s maintenance goals, and to determine the necessary changes in the company’s performance measurement practices in order to improve the maintenance process.

Research objectives:
1. Explore what maintenance strategy Tetra Pak should use in its packaging manufacturing plants for the laminator rewinder.
2. Investigate what performance measurements are required to adopt the selected strategy and whether Tetra Pak is currently collecting that data.
3. Analyse how Tetra Pak can improve its data quality to successfully implement the selected strategy.

Methodology:
An embedded single-case study was conducted with a mixed method approach combining qualitative interviews, observations and archival records with a quantitative survey and archival records. The research followed a two-fold approach where a maintenance strategy was first selected and the relevant performance measurements along with their availability and quality were then analysed.

Findings:
It was concluded that the maintenance strategy most suitable for the laminator rewinder at Tetra Pak is the predictive strategy. The performance measurements relevant to the strategy are maintenance performance indicators, event data and condition monitoring, for which the study found that the availability and quality at Tetra Pak varies greatly. Improvements in data quality were found to be possible through standardisation, more detailed equipment tree classification and language processing of historical data.

Popular Abstract

The path to world-class maintenance - aligning strategy and data

Corrective maintenance, preventive maintenance, predictive maintenance, total productive maintenance... The list of maintenance strategies goes on and on, with solutions ranging from fixing the breakdown once it occurs to involving the entire organisation. So how are companies supposed to know which strategy suits them the best? And with their huge amounts of collected data, how do they know whether their choice of strategy is working or not?

Achieving successful maintenance can save companies a lot of money, but it is easier said than done. There is a plethora of different maintenance strategies out there, and just as many models for choosing the right one. While most models are too complex for firms to use, some can guide the selection based on organisational values in simpler ways. Applying the Analytical Hierarchy Process to strategy selection allows employees to prioritise what matters most, for example, having spare parts easily available or identifying the cause of a breakdown quickly. At Tetra Pak, the process showed that a predictive maintenance strategy suits the researched machine best.

While choosing the right strategy is a step in the right direction, the quest for successful maintenance does not end there. Selecting the right performance measurements is just as important, as they can help detect problems and identify opportunities for improvement. Most companies collect an abundance of data but have no idea what to do with it or if it is even usable. By ensuring that the measurements are connected to the maintenance strategy and that the data is of the required quality, these problems can be addressed.

For all maintenance strategies, there are two types of performance measurements to track. First, to assess how the strategy is working, we have maintenance performance indicators. These indicators can be drawn from improvements the strategy should bring, such as less downtime and longer intervals between failures for a predictive strategy. Most of these indicators are measured automatically at Tetra Pak, so the quality is generally high. Second, we have event data, which is information about breakdowns, such as how and where they occurred, and how they were fixed. Event data is collected manually at Tetra Pak, which makes the quality varied.

There is also a third type of measurement specific to predictive maintenance called condition monitoring. Conditions can be monitored by sensors that track vibration, pressure, temperature, etc., on the machine. Whenever a condition steps outside its normal interval, it tells the operator that something needs to be checked and potentially restored or replaced. Together, these performance measurements paint an exhaustive picture of how the machine is doing and if the strategy is working.

All in all, with the combination of the right strategy and the right measurements to back it up, the path to world-class maintenance is clear.

• author: Roslin, Isa and Byrenius, Siri
• supervisor: Joakim Kembro
• organization: Production Management; Engineering Logistics
• course: MTTM05 20261
• year: 2026
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Maintenance strategy, maintenance strategy selection, performance measurement, predictive maintenance, data quality, condition monitoring
• other publication id: 6061
• language: English
• id: 9238050
• date added to LUP: 2026-06-15 18:31:19
• date last changed: 2026-06-15 18:31:19