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Dr. Takao Maruyama

Assistant Professor

Area
School of Management
Faculty of Mgmt, Law & Social Sciences
E-mail
T.Maruyama2@bradford.ac.uk
Dr. Takao Maruyama
2 research projects
10 publications

Biography

Dr Takao Maruyama joined the School of Management in 2019 and is currently Assistant Professor in Business Analytics. He also serves as the Director of Postgraduate Studies.

Before entering academia, Takao accumulated over ten years of industry experience as a Research & Data Analyst within the Higher & Further Education sector. His work involved analysing a wide range of quantitative datasets and communicating key findings to senior management teams and external stakeholders, including UK universities, major media organisations (e.g. Sky, The Guardian), multinational corporations (e.g. GE, Jaguar & Rover, L’Oréal, n power, Pfizer, Siemens plc), and UK Government departments.

Since joining the School of Management, he served as Programme Leader for the MSc Logistics, Data Analytics and Supply Chain Management until 2025. During this time, he developed strong and sustainable connections between students, alumni, and industry professionals, significantly enhancing students’ exposure to real‑world practice. He established annual programme career forums that enabled recent graduates to share their experiences of securing professional roles in the UK, and organised visits to local logistics companies (e.g. Melrose Interiors and GXO), helping students contextualise their classroom learning within real operational settings. He also brought in guest contributors from major organisations such as Morrisons, DHL, and Wincanton.

As a lecturer, Takao has created accessible, practice‑oriented learning materials and study guides using MS Excel, SPSS, SAS, R, and Python. These resources support students on both undergraduate modules such as “Fundamentals of Artificial Intelligence and Data Analytics”, “Big Data Analytics for Business”, “Applied Business Analytics and Simulation” and postgraduate modules including “Applied Machine Learning and Big Data Strategies” and “Business Data Analytics”.  His accessible teaching approach is designed to support students without technical backgrounds, helping them develop a clear understanding of statistics and data analytics, and enabling them to extract meaningful and impactful insights from large datasets.

Takao also took the lead in organising the School of Management’s annual AI Forum, a major event that brings industry and academia together. The most recent 2025 forum gathered academics and local businesses to explore how AI can support sustainability, data privacy, inclusivity, and SME performance. Contributors represented organisations across finance, regional business groups, digital consultancy, AI‑driven technology, and global analytics, reflecting the School’s strong engagement with industry.

In his research, Takao, as a Quantitative Social Scientist, has focused on issues on social inclusion among underrepresented groups such as D/deaf individuals, BAME communities, and women in STEM. Lately, he has been investigating the well‑being and job satisfaction of senior workers, with the aim to inform organisational and governmental policies that support intersectional groups of senior workers in achieving meaningful and sustained employment in an ageing society.

Research

Takao’s research centres on social inclusion and the well‑being of underrepresented groups.  His recent work examines how senior workers navigate employment beyond retirement age and how organisational and policy measures can better support meaningful later‑life work. 

He applies advanced quantitative social science and data analytics techniques, including statistical modelling, predictive analytics, SEM, and machine learning, across areas such as social development, digital public services, and workplace well‑being. 

Takao has also contributed to applied projects such as a Knowledge Transfer Partnership focused on embedding ethical, transparent AI within a UK social housing provider.

Research projects

Date
to
Role
Academic Supervisor/Co-Investigator

The KTP project is proposed to be conducted between Incommunities, a UK social housing provider located in West Yorkshire and the School of Management, University of Bradford. This KTP project endeavours to embed within Incommunities an ethical data-driven business culture adopting an Artificial Intelligence (AI) based innovation strategy, in order to provide more effective support for their tenants who often suffer from problems such as unemployment and lack of income. The innovation within this KTP project is found in the application of AI and machine learning (machine learning being a subset of AI) to management decision making and service provision for its customers. For example, using AI/machine learning, it would become possible for Incommunities to predict potential requirements to repair appliances such as boilers. This would enable Incommunities to become more proactive rather than reactive, enabling them to schedule visits more effectively. Machine learning models would also enable the company to foresee possible non-payment by tenants. This would allow Incommunities to offer any relevant support necessary for those tenants before non-payment takes place. Therefore, the KTP project would make various business decision making processes at Incommunities automated, supported by AI and machine learning models. Simultaneously, the KTP project will ensure that AI and machine learning models are made ethical thereby making them transparent and explainable which can be intervened in order to comply with GDPR (General Data Protection Regulation). We will embed this ethical AI-driven organisational culture through action learning where key stakeholders within and without Incommunities will together explore and identify current issues the organisation faces, benefits and challenges potentially brought by AI-driven business strategy, and short-/long-term business goals supported by AI. In-house staff members within the Business Intelligence Unit will also learn from the Associate how to construct explainable AI and machine learning models through action learning. This way, the KTP project will steadily transform not only existing business intelligence system and business process within Incommunities but also stakeholders' understanding and attitudes toward the automated system.

Date
to
Role
Co-investigator

As part of the Bradford Food Strategy (due to be published Q1/Q2 2022), Sustainable Food Supply Systems is one of four core elements that seeks to create the enabling conditions for businesses to produce local, nutritious, and accessible produce. Promoting local food supply networks is a key element of this work which the City of Bradford Metropolitan and District Council, guided by the Bradford District Sustainable Development Partnership, is looking to explore. We will conduct a Food economy mapping exercise that will inform the development of a Bradford District sustainable food supply system

Teaching

I currently teach the following modules:

  • OIM7502-B Business Data Analytics
  • OIM6014-B Applied Business Analytics and Simulation
  • OIM5015-B Big Data Analytics for Business
On those modules, I also deliver workshops, teaching students data analytics tools such as R, Python, SAS and Tableau. 

Modules

  • Big Data Analytics for Business - OIM5015-B
  • Applied Business Analytics and Simulation - OIM6014-B
  • Big Data Analytics for Business (In Company) - OIM5017-B

Professional activities

  • University of Bradford - PhD
  • University of Bradford - MSc
  • University of East Anglia - PGDip
  • Soka University - BA

  • University of Bradford - Research Analyst (1 September 2015)
  • University of Bradford - HE Data Analyst (2 January 2011)
  • UKRC (Bradford College) - Social Statistics Analyst (2 October 2006)
  • University of Leeds - Research Assistant (28 February 2005)

  • Board member, Transport, Distribution & Logistic (TDL) Board/ Bradford City Council, UK, Bradford: (1 September 2021)

Publications