Turning Expert Opinions into Action: AHP in Digital Strategy

A case study conducted with Suzhou Suolimen Nano Purification Technology Co., Ltd. (THREEMAN)

Case Study Authors

Dr Xiaochen Feng and Dr Ayse Begüm Kilic-Ararat (University of Bath)

Context

Digital transformation offers significant opportunities for companies looking to improve operations, enhance competitiveness, and stay ahead in their industries. However, selecting the right technologies to invest in and understanding the potential impact of those investments can be complex and uncertain. Organisations often struggle to align internal priorities and make confident decisions without a clear, structured way to assess the outcomes of digital adoption. This challenge is especially common in sectors undergoing rapid technological change, where the stakes for making the right investment decisions are high.

Objective

To address this challenge, we applied a structured decision-making approach using the Analytic Hierarchy Process (AHP). AHP helps companies make better technology adoption decisions by breaking down complex problems into smaller problems using pairwise comparisons. The tool's outputs help companies assess the importance of metrics used in evaluating the impact of their digitalisation initiatives. It allows stakeholders to evaluate different criteria, such as productivity improvement, cost reduction, or customer satisfaction, and assign relative importance to each. This process transforms diverse expert opinions into a set of prioritised, data-driven outcomes, supporting more transparent and confident decisions about where to focus digital investment efforts. The tool consists of 36 metrics presented under three main categories, which are Business Performance, Operational and Human-centric metrics.

Approach

We implemented this AHP-based decision support tool with Suzhou Suolimen, also known as THREEMAN, a professional electrical automation system integrator based in Suzhou, China. The company serves the machinery, HVAC (heating, ventilation, and air conditioning), and shipbuilding industries, offering services ranging from OEM solutions to full system integration.

As part of a new strategy, THREEMAN aimed to become more digitally enabled but faced uncertainty about which technologies to prioritise and what impact those investments would bring. Using our AHP tool, developed through the Made Smarter: People-Led Digitalisation Centre, THREEMAN was able to explore various digitalisation options and evaluate their potential impact across key organisational goals. The tool supported internal discussions by incorporating perspectives from different staff members and turned subjective views into clear, prioritised outcomes.

Insight

The AHP tool supported THREEMAN in identifying which digitalisation outcomes were most valuable to their business. This helped them confidently prioritise technology investments in line with their strategic goals. The tool showed that Business Performance metrics have priority over Operational and Human-centric metrics for the company. Among the Business Performance metrics, employees considered the Sustainability metrics - particularly the energy consumption metric - as the most important.

The AHP tool process proved practical, collaborative, and highly relevant to their decision-making needs.

Artefacts

Kilic-Ararat, A.B., Khoei, A.A., Nwaiwu, F. and Giannikas, V., 2024. Analytical Assessment of Digitalisation Outcome Metrics in the UK Manufacturing SectorProceedings of the 31st European Operations Management Association (EurOMA) Conference, 24-26 June 2024, Barcelona.

Impact

“This tool helped us understand what matters most in our digitalisation process by taking into account different staff’s opinions. It made our decision-making process much clearer.”

Danying Wang, CEO, THREEMAN

For further information on this case study please contact the P-LD at P-LD@bath.ac.uk

Acknowledgement

The work reported in this paper was supported by the Made Smarter Innovation: Centre for People-Led Digitalisation, at the University of Bath, University of Nottingham, and Loughborough University. The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) Grant EP/V062042/1.

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