What question did this study set out to answer?

The aim is to develop a forecasting model to evaluate and predict cost-effectiveness in manufacturing plants.

March 13, 20260 citationsOpen Access

Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness in Senegalese Manufacturing Systems

MNMoussa NdiayeCouncil for the Development of Social Science Research in Africa ADAliou DiopUniversité Alioune Diop de Bambey FSFatou SarrCouncil for the Development of Social Science Research in Africa

Key Points

The aim is to develop a forecasting model to evaluate and predict cost-effectiveness in manufacturing plants.
Analyzed longitudinal dataset of operational and financial metrics.
Developed a SARIMA model to forecast cost-effectiveness ratios.
Estimated model parameters using maximum likelihood estimation.
Quantified forecast uncertainty with 95% prediction intervals.
The SARIMA(1,1,1)(0,1,1)₇ model achieved a mean absolute percentage error of 4.7%.
Material cost volatility accounted for approximately 60% of the forecast variance.
Forecasts indicated a stable but marginal improvement in cost-effectiveness ratios over time.

Abstract

{ "background": "Manufacturing systems in West Africa face persistent challenges in achieving cost-effectiveness, with a recognised need for robust analytical frameworks to support operational decision-making. Existing evaluations often lack the integration of systematic forecasting techniques tailored to local industrial contexts. ", "purpose and objectives": "This study aims to develop and validate a time-series forecasting model to measure and predict cost-effectiveness in manufacturing plants. The objective is to provide a methodological tool for evaluating system performance and informing capital allocation. ", "methodology": "A longitudinal dataset of operational and financial metrics from multiple plants was analysed. The core methodological innovation is a seasonal autoregressive integrated moving average (SARIMA) model, specified as \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \, where yt is the cost-effectiveness ratio. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals. ", "findings": "The SARIMA (1, 1, 1) (0, 1, 1) ₇ model provided the best fit, with a mean absolute percentage error (MAPE) of 4. 7% on the test set. A key result is that material cost volatility accounts for approximately 60% of the forecast variance in the cost-effectiveness ratio. Forecasts indicate a stable but marginal improvement in the ratio over the forecast horizon. ", "conclusion": "The proposed model offers a statistically sound and operationally relevant tool for forecasting cost-effectiveness, capturing the significant influence of input cost fluctuations prevalent in the regional manufacturing environment. ", "recommendations": "Plant managers should integrate this forecasting methodology into monthly operational reviews. Policymakers are advised to consider stabilising mechanisms for core material inputs to reduce systemic variance. ", "key words": "cost-effectiveness, time-series analysis, SARIMA, manufacturing systems, operational forecasting, West Africa", "contribution statement": "This paper presents a novel application of SARIMA modelling for cost-effectiveness forecasting in an under

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