Search published articles


Improving Alignment Between Supply and Demand Through the Analysis of An Asian Model of Sales and Operations Planning (S&OP)
Hamza Samouche, Abdellah El Barkany, Ahmed Elkhalfi,
Volume 34, Issue 2 (6-2023)
Abstract
Sales and operations planning (S&OP) is considered as an important tool at the planning strategic level. Its models vary depending on industries. The Asian model is known to be very developed. Having several parameters, the Asian model proves to be an effective tool, precisely for the study of capacity. However, after several searches made in various databases, we did not find any concrete model actually used in industry and whose parameters are presented and which defines the analysis logic to better align supply and demand. In this article, we will carry out various simulations on the basis of the data of a model of sales and operations planning used in a wire harnesses factory, in order to explain the decision-making process during S&OP meetings. The parameters of the model and the various constraints that were facing the sales and operations planning team are presented and discussed as well as the financial consequences of certain decisions. As a result of this study, we can notice that S&OP is indeed a powerful tool that makes it possible to detect in advance the various constraints whose resolution concludes in an optimal alignment between customer demand and factory capacity.
 
Single machine preemptive scheduling Considering Energy Consumption and Predicting Machine failures with Data Mining Approach
Ali Qorbani, Yousef Rabbani, Reza Kamranrad,
Volume 34, Issue 4 (12-2023)
Abstract
Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions. 

Modeling the make-to-order problem considering the order queuing system under uncertainty
Hamed Nozari, Maryam Rahmaty,
Volume 34, Issue 4 (12-2023)
Abstract
In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with the League Championship Algorithm (LCA), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA) methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased, and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. By reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the Objective Function Value (OBF) averages among the solution methods, they were prioritized, and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the make-to-order supply chain problem. 

Automated Packaging Machine Analysis with The Overall Equipment Efficiency Method
Iwa Kustiyawan, Mas Rahman Roestan, Catur Riani,
Volume 34, Issue 4 (12-2023)
Abstract
This research aims to identify the initial OEE (Overall Equipment Efficiency) values on automated packaging machines with a 2d barcode track and trace system. Quantitative research methods used to obtain the OEE value, analysis of factors affecting the OEE values, developing a strategy to make improvements, and evaluate these strategies on the level of machine productivity. The importance of the subject lies in the need to improve the efficiency and productivity of pharmaceutical packaging processes. The pharmaceutical industry is facing increasing pressure to optimize operations and reduce waste. Implementing effective performance measurement tools such as Overall Equipment Effectiveness (OEE) can help identify areas for improvement and enhance productivity. This study found that the track-and-trace system was below the company's standard, indicating room for improvement. Then, countermeasures were implemented to increase productivity and machine effectiveness, and the initial OEE value of the automated packaging machine with 2D barcodes improved. Thus, this study demonstrated the effectiveness of the proposed framework in evaluating and improving OEE in pharmaceutical packaging processes, highlighting the significance of digitalization and automation technologies in enhancing productivity.

Page 2 from 2