Study
Commercial ProductionRecentStrong effect

Economic-Mathematical Model Optimizes Smartization Projects for Industrial Sustainability

An economic-mathematical model can be used to optimize digital transformation and smartization projects in industrial enterprises by simultaneously minimizing costs, deviations from planned business indicators, and production rhythm disruptions, thereby enhancing sustainability.

Sustainability · 2023

01

Key Findings

  • 01A matrix for selecting indicators to diagnose the results of digital transformation smartization projects was proposed.
  • 02A two-level economic evaluation model was developed to assess the effectiveness of these projects at various stages.
02

Application

Design takeaway

Integrate a multi-objective economic evaluation framework into the design and implementation phases of industrial smartization projects to ensure alignment with cost, performance, and sustainability objectives.

How to apply

When designing or evaluating a smart factory initiative, define clear metrics for cost, productivity deviations, and production flow stability, and use an optimization model to balance these objectives.

Project actions

  • 01When proposing a new product or system, consider developing a simple cost-benefit analysis that includes potential impacts on efficiency and resource use.
  • 02Think about how to measure the success of your design beyond just functionality, including economic and environmental factors.
03

Method & Evidence

AimTo develop and test an economic-mathematical model for evaluating the outcomes of digital transformation smartization projects in industrial enterprises to optimize their implementation for sustainability.
MethodStatistical analysis and economic-mathematical modelling
ProcedureThe study involved developing a matrix for selecting diagnostic indicators, creating a two-level economic evaluation model with three objective functions (cost minimization, deviation from planned indicators, production rhythm disruption), and testing this model at industrial enterprises. Statistical tests for multicollinearity and heteroskedasticity were employed.
ContextIndustrial enterprises undergoing digital transformation and smartization projects.

Variables

IVImplementation of digital transformation smartization projects.
DVEconomic evaluation outcomes (cost, deviation from planned indicators, production rhythm disruption), sustainability.
CVSpecific industrial enterprise context, existing operational processes.
04

Strengths & Limitations

Strengths

  • +Development of a practical, multi-objective evaluation model.
  • +Empirical testing of the model in real industrial settings.

Limitations

The complexity of real-world industrial data and the difficulty in isolating the exact impact of a single smartization project can be challenging to replicate in a simplified study.

Reliability & validity

The study's reliability is supported by statistical tests for multicollinearity and heteroskedasticity. Validity is enhanced by testing the model in actual industrial enterprises, suggesting practical relevance.

Think critically

How might the 'deviation from planned business indicators' and 'production rhythm disruptions' be quantified and measured in a practical design project setting?

05

Design Principles

"Holistic project evaluation should consider economic viability, operational efficiency, and sustainability outcomes concurrently."

Implementing digital transformation and smartization requires significant investment. A robust evaluation model is essential to ensure these projects deliver tangible benefits and contribute to long-term sustainability goals, justifying the investment and guiding future strategic decisions.

06

What This Means for Your Design

This study shows how to create a financial and operational plan for 'smart' factory upgrades that makes sure they save money, improve how things are made, and are good for the environment.

How to use in your project

  • 1.Reference this study when discussing the economic justification or sustainability assessment of a proposed design solution, particularly for industrial or manufacturing contexts.
07

Add to My Project

08

Quick Cite

(2023). Assessing the Outcomes of Digital Transformation Smartization Projects in Industrial Enterprises: A Model for Enabling Sustainability. Sustainability. https://doi.org/10.3390/su151914075 Retrieved from https://designdex.org/study/b16db106-8cfd-414d-b6a8-95809f035794/economic-mathematical-model-optimizes-smartization-projects-for-industrial-sustainability

Paragraph starter

The evaluation of industrial digital transformation projects can be significantly enhanced by employing economic-mathematical models that optimize for cost minimization, reduced deviations from planned business indicators, and minimized production rhythm disruptions, as demonstrated by Bashynska et al. (2023). Such models provide a quantitative framework for assessing project effectiveness and sustainability.

09

Source

Sustainability

Assessing the Outcomes of Digital Transformation Smartization Projects in Industrial Enterprises: A Model for Enabling Sustainability

journal · 2023

View source

Questions about this research

What does the research say about economic-mathematical model optimizes smartization projects for industrial sustainability?
Integrate a multi-objective economic evaluation framework into the design and implementation phases of industrial smartization projects to ensure alignment with cost, performance, and sustainability objectives. Evidence: Sustainability (2023).
Why does "Economic-Mathematical Model Optimizes Smartization Projects for Industrial Sustainability" matter for design?
Implementing digital transformation and smartization requires significant investment. A robust evaluation model is essential to ensure these projects deliver tangible benefits and contribute to long-term sustainability goals, justifying the investment and guiding future strategic decisions.
How can designers apply this research?
Integrate a multi-objective economic evaluation framework into the design and implementation phases of industrial smartization projects to ensure alignment with cost, performance, and sustainability objectives.
What were the main findings?
A matrix for selecting indicators to diagnose the results of digital transformation smartization projects was proposed.. A two-level economic evaluation model was developed to assess the effectiveness of these projects at various stages.
What research method was used?
Statistical analysis and economic-mathematical modelling.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2023 journal from Sustainability.
What should I do differently in my next project?
When designing or evaluating a smart factory initiative, define clear metrics for cost, productivity deviations, and production flow stability, and use an optimization model to balance these objectives.
What are the limitations?
The model's effectiveness may depend on the accuracy and availability of data from industrial enterprises, and its applicability might vary across different industrial sectors or project complexities.
Is there evidence that smartization projects affects design outcomes?
The research provides a structured approach to selecting key performance indicators and an economic model to evaluate the success of digital transformation projects in industry, allowing for assessment at any project phase. Implementing digital transformation and smartization requires significant investment. A robust e Source: Sustainability (2023).
Where does this digital transformation research apply?
Industrial enterprises undergoing digital transformation and smartization projects. It sits within commercial production research on designdex.org.

Related research topics

smartization projects design research · evidence on smartization projects · does smartization projects improve design outcomes · digital transformation studies for designers · smartization projects and digital transformation findings · commercial production research evidence