Defining one product data for a product

Silvola, Risto; Tolonen, Arto; Härkönen, Janne; Haapasalo, Harri; Mannisto, Tarja

doi:10.1504/ijbis.2019.099308

Cited by 11 publications

(29 citation statements)

References 80 publications

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“…According to our case studies infrastructure projects are still surprisingly behind practices of other industries (see, e.g. Silvola et al , 2016Silvola et al , 2019. The achievable benefits are not only cost savings, but increased value provided during the life cycle.…”

Section: Discussionmentioning

confidence: 87%

Managing data flows in infrastructure projects - the lifecycle process model

Halttula¹,

Haapasalo²,

Silvola³

2020

ITcon

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Productivity in the construction industry (both houses and infrastructure) has not been improving as expected, while other industries have been able to improve their productivity significantly. The appropriate use of building information modelling (BIM) technologies brings several benefits and advantages to construction projects. The main challenges of project efficiency emerge in the form of numerous requests for information during the construction project, which are considered to be waste in the processes. This highlights the need for a practical process model to plan the information flow for BIM-based projects. The main aim of this study is to propose a model to plan the flow of project information among primary stakeholders especially in infrastructure projects. Our main findings are firstly, the foundation for data management starts from defining unified one data for the product and the for the process. Unified data means one single repository of data – all stakeholders use the same unified data. It is also essential that data responsibilities and ownership are defined. Secondly, we found that the biggest challenges are that the data needs are not planned beforehand, resistance to change, difficulty receiving existing data and data must be modified before use. As a whole, it seems sometimes that the technology on data transfer is more important that what has been transferred and why. Finally our construction, the life cycle model for data flow originates from one data to all stakeholders, single data repository must be updated along the life- cycle of the object covering also the operations and maintenance, where the data has to be updated through the whole life-cycle. This new approach is intended to enable the early involvement of maintenance stakeholders in designing product data for a project lifecycle perspective. The model helps to change the current information flow and gain the benefits that a BIM-based process can offer. This study is based on case studies and is qualitative in nature and naturally needs more validation.

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Section: Discussionmentioning

confidence: 87%

Managing data flows in infrastructure projects - the lifecycle process model

Halttula¹,

Haapasalo²,

Silvola³

2020

ITcon

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“…The productization logic is seen as the necessary prerequisite to bridge the product data, business process, and IT system perspectives along the lifecycle. Being in line with [30], this article takes a step toward practical application. Systematic productization is understood to form an effective frame to product related information and support the increasing data perspective on products along digitalization [42].…”

Section: A Scientific Implicationsmentioning

confidence: 86%

“…Productization may initially involve the technical and commercial modeling of the products by utilizing the product structure concept [27]- [29], whereas in the more operational stage, the focus should be primarily on the commercial side of the product structure once the basic logic exists [27], [28]. Adequate productization logic may also support the necessary product data, business process, and IT system considerations [30]. However, regardless of some initial studies existing, the practical evidence is lacking.…”

Section: Introductionmentioning

confidence: 99%

Commercial and Technical Productization for Fact-Based Product Portfolio Management Over Lifecycle

Lahtinen

Mustonen

Härkönen

2021

IEEE Trans. Eng. Manage.

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Increasing product variety and expanding commercial offering create a challenge for companies in terms of keeping their product portfolio profitable and managing it through the entire lifecycle of products. Effective productization and product portfolio management (PPM) practices, supported by product structure considerations, may hold a key for product profitability over lifecycle. This article examines the current practices and improvement possibilities in productization and PPM, including the targets and key performance indicators (KPI), by considering the product lines of a global engineering company. The analysis utilizes the previous literature, company interviews, and relevant company materials. The article demonstrates company difficulties, which are stemming from inadequate definition of productization and imperfect PPM targets and KPIs over the product lifecycle. An initial framework is formed to support more holistic productization over product lifecycle alongside creating suitable PPM targets and KPIs.

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“…These attributes connect essential business processes through different IT systems and ideally remain unaltered throughout the product life cycle Kropsu-Vehkapera, 2012;Allen and Cervo, 2015;Waddington, 2008;Kumar Das and Mishra, 2011). After NPD, PMD are transferred to other critical business processes through the various IT systems used in sales and marketing, supply chain and service processes (Silvola et al, 2019). The product's DNA is shaped during NPD and is further enriched by other process-related attributes.…”

Section: Sub-assemblymentioning

confidence: 99%

Product-level profitability

Hannila

Koskinen

Härkönen

et al. 2019

JEIM

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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.

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Defining one product data for a product

Cited by 11 publications

References 80 publications

Managing data flows in infrastructure projects - the lifecycle process model

Managing data flows in infrastructure projects - the lifecycle process model

Commercial and Technical Productization for Fact-Based Product Portfolio Management Over Lifecycle

Product-level profitability

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