Integration of acquired companies - case sudies to create a framework for future acquisitions in Alfa Laval
Bibliographic record
Abstract
This Master’s thesis is performed on behalf of Alfa Laval, a multinational company providing technological products and services. Alfa Laval has in the past had a lot of successful mergers, but there were also those cases that led to sell off and those that had potential to add more value than was actually realized. In the common case, it has been shown that 60-70 per cent of M&As fall short when it comes to realizing the intended synergies. Facts on international M&As show that shareholder value added increased in less than one of five cases. Mainly, I got directives from Alfa Laval to look into Alfa Laval’s merger ventures in the past. To get a good depth on my findings, interviews were conducted on main players in real cases as a pre-study. I could then see patterns that can be changed and optimized and created an AL-acquirer to use in search for a good merger partner and an AL-in, Plan of Action to follow the integration through to fulfil the purpose of the study. The purpose of this Master’s thesis is twofold – the first part consisting of: • Case studies and mapping of two acquisitions in Alfa Laval This first part aims at identifying crucial factors that can be changed and optimized for superior integration in future M&A and to manage multiple acquisitions in Alfa Laval. This through on site observations, interviewing and analysis of outcomes on synergies realized and value added. The second part has the purpose of: • Developing tools for companies to use in search for the right merger partner and for integration in managing merger ventures On the base of the first part, I chose the input and methods that Alfa Laval can use in M&A. The tools are constructed to be helpful throughout the whole organization and to give guidance on how to face a newly acquired company. The AL-acquirer is mainly of use before an acquisition and the AL-in, Plan of Action shall be guidance during the integration phase. I chose to use the cases and interview as well as literature studies as a pre-study in the creation of my tools. My findings tell me, that after evaluating that the acquisition is in line with Alfa Laval’s strategy to grow, then vALue Added by Acquired Company and Similarity with Parent Company, ALikensess, are the two most important criteria in search for the right merge partner, comprising several different factors, including communication, products, personnel, company culture etcetera. High values on these criteria provide greater possibility to succeed in a merge and gives more room for risk taking. But also those companies that score low in some areas can be a potential partner – presupposing that special caution is needed and that the AL-in, Plan of Action is followed through thoroughly for Operations IS/IT, Communication, Products, Management, and Human Resources. Main observation lies in the final study of economic value and the price paid to count for that the value added is put in perspective of the price paid and future potential in Alfa Laval. While working on this Master’s thesis I found that simple methods for real life methods in the M&A area are not to be found. I have taken the first step towards developing methods that will help those concerned with M&A in Alfa Laval and to provide knowledge that enables transfer of experience on such deals
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".