Product-level profitability

Abstract

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

Productization and product structure enabling BIM implementation in construction

“…, 2017). Companies cannot make full use of their existing systems without consistent PS and supportive data models (Hannila et al. , 2019).…”

“…The key to achieving efficiency in the productization process involves consistent and standardised PS (Harkonen et al, 2017). Companies cannot make full use of their existing systems without consistent PS and supportive data models (Hannila et al, 2019).…”

Digitizing Company Analytics – Digitalization Concept for Valuable Insights

“…Further, digitalization can change the business by applying technology to solve problems and create value. Hence, digitalizing company ecosystems to enable true datadriven, real-time approach to business [12] can reduce the less factual aspects of decision-making. Initial basis for data-driven approach have been described, but examples are scarce, and discussion on viewpoints such as necessary data models, and technologies are lacking.…”

GA-BP neural network modeling for project portfolio risk prediction

“…Project portfolios (PPs) have become crucial managerial modes for organizations facing increasingly competitive circumstances (Biswas and Mukhopadhyay, 2018;Korotkov et al, 2020;Liu and Liu, 2017). Over 60% of enterprises have adopted PPs to strengthen their competitive advantages (Beringer et al, 2013;Hannila et al, 2020;Zwikael and Ahn, 2011). However, a PP typically consists of several projects, programs and subsidiary portfolios according to the Project Management Institute (PMI).…”

“…Over 60% of enterprises have adopted PPs to strengthen their competitive advantages (Beringer et al. , 2013; Hannila et al. , 2020; Zwikael and Ahn, 2011).…”