Olov Isaksson

Olov Isaksson

Universitetslektor, docent

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Arbetar vid Företagsekonomiska institutionen
Telefon 08-674 72 07
Besöksadress Kräftriket hus 24
Rum 24:K155b
Postadress Företagsekonomiska institutionen 106 91 Stockholm

Om mig

Olov Isaksson is Associate Professor of Operations Management at Stockholm Business School, Stockholm University. His primary research topics relate to operations management and its intersection with retail, healthcare, marketing, finance and innovation management. With a special interest in data analytics, Olov uses data and empirical approaches to investigate how firms can make better operational decisions.

Olov earned his Ph.D. from École Polytechnique Fédérale de Lausanne, his Diplôme D'Ingénieur in General Engineering from Ecole Centrale Paris and his Master's Degree in Industrial Engineering and Management from Linköping University. Before his academic career he worked in industry in different operations and supply chain management capacities within retail and process industries.

Olov has participated in numerous research projects with major organizations such as H&M, Louis Vuitton, Migros and Region Stockholm. His work has been published in international academic journals such as: Research Policy, International Journal of Production Economics, International Journal of Production Research, IEEE Transactions on Engineering Management and Production Planning and Control as well as more practitioner-oriented outlets.

At Stockholm Business School, Olov is the director of the master program in Operations Management and Control. He is involved in teaching in the courses Advanced Operations Management and Business Analytics: Data, Models and Decisions.


I urval från Stockholms universitets publikationsdatabas
  • 2017. Daniel Seifert, Ralf W. Seifert, Olov H. D. Isaksson. International Journal of Production Research 55 (4), 1117-1128

    Contrary to the long-standing view in the finance literature that firms should maximise payment delays, research in operations management suggests that long payment delays can be suboptimal. In this study, we reconcile these two views by applying a secondary data approach to established operations management theory. Based on a sample of 3383 groups of public US firms from a novel database, we find that our data are consistent with the causal relations and theoretical predictions of the operations management literature. Firm profitability is positively associated with payment delay. Payment delay, in turn, is positively associated with the capital cost difference between buyer and supplier and negatively associated with the price elasticity of demand and the deterioration rate of inventory. However, we do not observe any significant interaction effects between these factors, which raise a number of questions for future research.

  • 2017. Philipp Moser, Olov H. D. Isaksson, Ralf W. Seifert. International Journal of Production Economics 191, 253-266

    Process industry firms have thrived in recent decades, but changes in the markets are currently putting both growth and profitability at risk. In this context, inventory management is increasingly viewed as an essential lever for creating a sustainable competitive advantage. Despite this, many firms struggle to implement best practices because of industry-specific constraints. This research explores how seven fundamental characteristics of process industries drive inventory performance. We empirically investigate four process industries and four peer industries, using financial accounting, credit rating, stock market and trading data and implement a seemingly unrelated regression (SUR) equations model. Our results show that capital intensity, capital costs, transportation costs, delivery time, price volatility, demand uncertainty and gross margin directly affect a company's degree of freedom in terms of inventory management and illustrate that inventory management in process industries follows different dynamics. This study enhances the understanding of inventory drivers and gives practitioners a tool to guide future improvement efforts.

  • 2016. Olov H. D. Isaksson, Markus Simeth, Ralf W. Seifert. Research Policy 45 (3), 699-706

    In addition to internal R&D, external knowledge is widely considered as an essential lever for innovative performance. This paper analyzes knowledge spillovers in supply chain networks. Specifically, we investigate how supplier innovation is impacted by buyer innovation. Financial accounting data is combined with supply chain relationship data and patent data for U.S. firms in high tech industries. Our econometric analysis shows that buyer innovation has a positive and significant impact on supplier innovation. We find that the duration of the buyer-supplier relationship positively moderates this effect, but that the technological proximity between the two firms does not have a significant effect on spillovers.

  • 2016. Olov H. D. Isaksson, Ralf W. Seifert. International Journal of Production Economics 171 (3), 311-320

    The bullwhip effect denotes the phenomenon whereby demand variability is amplified from a downstream site (buyer) to an upstream site (supplier) in the supply chain. This paper contributes to the literature that empirically investigates the bullwhip effect by providing new evidence regarding its prevalence and magnitude. In contrast to previous work, we use a two-echelon approach, which allows us to observe variations at both the upstream and the downstream sites. By drawing on a financial accounting standard regarding information disclosure about major customers, we are able to link 5494 buyers and suppliers in the U.S. between 1976 and 2009. We merge this information with quarterly financial accounting data to form a sample of 14,933 buyer–supplier dyad observations. We correct for sample selection bias using propensity score matching and estimate the average bullwhip effect in our sample to be 1.90 (i.e. 90% demand variability amplification between echelons). A significant bullwhip effect is observed across industries (mining, manufacturing, wholesale and retail) and is supported by several robustness checks. We investigate and discuss how these results can be generalized beyond our sample.

  • 2017. Philip Moser, Olov H.D. Isaksson, Ralf W. Seifert.
  • 2014. Olov H.D. Isaksson, Ralf W. Seifert.
Visa alla publikationer av Olov Isaksson vid Stockholms universitet

Senast uppdaterad: 10 oktober 2020

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