PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 25, 20260 citationsOpen Access

Methodological Evaluation of Manufacturing Plants Systems in Ethiopia Using Time-Series Forecasting Modelsfor Cost-Effectiveness Analysis

View Full Paper
SDSasane DestaMWMuluqet WorkuAMAnbesa Mamo

Key Points

  • The research aims to assess the cost-effectiveness of manufacturing plants in Ethiopia using time-series forecasting models.
  • Analyzed historical data from selected Ethiopian manufacturing plants.
  • Employed ARIMA model for forecasting future costs.
  • Calculated annual cost savings and confidence intervals.
  • ARIMA forecasts indicate a potential annual reduction of $10,000 in operating expenses per plant.
  • Forecasting model predictions show 95% confidence in cost savings.
  • Identified practical insights for improving operational efficiency in manufacturing.

Abstract

Manufacturing plants in Ethiopia face challenges related to operational efficiency and cost-effectiveness. The study employs ARIMA (AutoRegressive Integrated Moving Average) model for forecasting future costs based on historical data from selected Ethiopian manufacturing plants. ARIMA forecasts indicate an annual reduction of 10, 000 in operating expenses per plant with a 95% confidence interval around the estimate. The ARIMA model effectively predicts cost savings for Ethiopian manufacturing systems, offering insights for improving operational efficiency and reducing costs. Manufacturing companies should implement these forecasting models to optimise their operations and achieve sustainable cost reductions. manufacturing systems, Ethiopia, time-series forecasting, cost-effectiveness analysis, ARIMA model The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Desta et al. (2002) studied this question.

synapsesocial.com/papers/699e91d7f5123be5ed04f99dhttps://doi.org/10.5281/zenodo.18750946
Ask AI
Helpful
Bookmark
Share
View Full Paper