Archive for category Lean and Green Operations
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Posted by systems in Agent Based Modeling, Class, Clean and Renewable Energy, Conference, Continuous Event Modeling, Decision Modeling, Discrete Event Modeling, Financial Modeling, Green Economy, Knowledge Area, Lab Development Program, Lean and Green Operations, News, Optimization, Ports, Logistics and Supply Chain, Research Area, Research Highlights, SEMS Day Out, Serious Simulation Gaming SSG, Sustainable Industrial Policy, Technical Area, Technology Policy Transitions, Uncategorized, Urban Industrial Development, Web-Based, Workshop & Training on 03/10/2022
Reducing Cost of Road Maintenance for the Resources Industry
Posted by systems in Lean and Green Operations, News, Optimization, Research Area, Research Highlights, Technical Area on 24/04/2018
Haul road in open mine has short durability. It is because haul road is constructed without asphalt/concrete pavement and passed by big vehicle with heavy load. There is some kind of decreasing quality of haul road such as improper cross section, inadequate roadside drainage, corrugations, potholes, ruts, and loose aggregate. Poor haul road quality will impact on increasing production costs and decreasing mine productivity. Usually open mine use motor grader to maintain the quality of haul road. Way of working of motor grader is to scrap the inadequate haul road surface.
There are some differences among the haul road segments such as characteristic, traffic density, kind of decreasing quality, durability, etc. Therefore, systematically grader route and schedule is needed to minimize the delay of haul road maintenance. Usually grader route and schedule just based on grader’s operator experience. There is no specific approach that can be used in grader route and schedule.
This research focused on grader route and schedule optimization in coal haul road maintenance. Optimization model in this research is designed using Bandit Algorithm. The objective of the optimization model is to minimize the maximum penalty. In this case, penalty is used to describe amount of loss that is caused by maintenance delay on each haul road segment. Grader start from the initial point to a road segment and moves over and over to the other road segment until working hour is over. Determination of he next road segment is based on weight of maintenance delay on each road segment. Greater the weight of the maintenance delay of a road segment, greater the probability of that road segment to be addressed by grader. Grader scraps if the road segment is late maintained and just passes if otherwise. When the working hour is over, grader stops moving and optimization model calculates the objective and records the route as a new solution. The steps are done again as many as have been determined (iteration). Solution with the best objective is chosen as the final solution.
With the probability, grader is not directly addressed to the road segment with the greatest maintenance delay weight to allow for the other road segments to be the next grader destination. This is because short term solutions have effect on long-term solution (whole solution); maybe the best short-term solution is not the best long-term solution. An example in a simpler problem is: we must determine route from city A to city D with 2 possible route that are A-B-D and A-C-D. With the distance between A-B < A-C, A-B is the best first movement. But for the overall movement, maybe A-B-D is not the closest route. Although the distance between A-B < A-C, distance of A-C and C-D can be closer than A-B and B-D.
Optimization model showed a significant cost savings for the mining operations by creating a more effective roads maintenance with reduce cost. With the pressure of low prices in the resources industry, a simple but yet complex optimization can help them stay more competitive.
This research is conducted by Denni and Dr. Komarudin.
How to optimize unequal area facility layout to maximize storage
Posted by systems in Knowledge Area, Lean and Green Operations, News, Optimization, Research Area, Research Highlights, Technical Area on 15/11/2017
Facility layout problem, especially with the unequal departmental area (UAFLP), is one of the problems studied in combinatorial optimization and has received the attention of many researchers in the past decade. The goal of UAFLP is to allocate departments into a facility to obtain the most efficient arrangement. The UAFLP study has a final objective to minimize the total cost of material handling between departments.
Competition in today’s business world is inevitable. Increasing the quality of productivity becomes one of the keys to success in facing competition with business management effectively and efficiently. This can happen by maximizing existing resources ie employees, machines, and other facilities. Therefore, the industry needs to be able to optimize production capability and effectiveness to face competitors. The production process becomes the key that needs to be managed effectively to minimize production costs with higher effectiveness.
Facility layout design has a close reinforcement to the size of the physical arrangement of elements in a manufacturing and service system, such as department, machinery, operational tools, and so on. The purpose of the facility layout design is the design with the minimum material handling cost. With proper facility layout, material handling costs can be reduced. In general, material handling contributes about 20-50 percent of the total. Reduction in the company’s operational costs, along with the increased efficiency of the production system becomes a necessity that every industry needs to do.
The problem of facilities layout that is often the researcher’s attention is Unequal Area Facility Layout Problem (UAFLP). Initially, UA-FLP was developed by Armor and Buffa (1963). They explain that there is a rectangular facility with a fixed Width and Height and several (n) departments that need to be allocated to the facility. There are some problems that have not found the optimal solution and require a long computation time. The goal is to reduce non-feasible solutions to reduce the complexity of possible solutions.
This research will develop a mathematical model using Mixed Integer Programming method based on Flexible Bay Structure. Some additional constraint functions will be attempted to be added to the model. Testing is done by comparing the effect of each additional constraint function that has different approaches in cutting the complexity of possible solutions. The comparison result of the combination of the constraint function used indicates which constraint function has a major influence in reducing the computation time of the model.
This research concludes that all simulated problem sets using additional constraints in the model can provide better computation time than before. The effect that occurs can be the value of the solution becomes not optimum or remain optimum. All computational time gives significant improvement.
This research is conducted by Randa Adi Saputra and Komaruddin
Does the power of Social Media influences E-Commerce Business?
Posted by systems in Decision Modeling, Lean and Green Operations, News, Research Area, Research Highlights, Technical Area on 02/10/2017
Social media has become a great part of someone’s life. People can’t wait to update their social media accounts, or at least get updated about the world simply by watching their timeline. Social media here is defined as every internet-based application that allows you to create and exchange contents with other people around the globe. The number of Internet users in Indonesia has reached 132.7 million people in 2016. With a number this high, Internet also is seen as a potential business platform. In 2016 the total of e-commerce in Indonesia has reached 26.2 million units, with its biggest selling product is in the fashion segment. Internet is not only giving advantages for companies for their business, but also some threats regarding the tougher competition in the market, especially for e-commerce companies.
A study has found that marketing through social media gave significant effect on increasing trust and loyalty of customers towards a brand. Therefore, companies see social media as a potential marketing medium, to retain their competitiveness in the market. By increasing their customers’ trust and loyalty towards their brand, companies can focus on expanding their market and gaining new customers.
According to several papers that this research was based on, there are five social media marketing (SMM) factors, (1) Website Security, (2) Clear & updated website, (3) Social media tools activity, (4) Online advertisement, and (5) Online interactivity. The result of this research shows that three of those factors, which are (1) Clear & updated website, (2) Online advertisement, and (3) Online interactivity, give significant effect on customer’s brand trust. While brand trust gives significant effect on brand loyalty. Therefore it can be concluded that those 3 factors of SMM give also significant effect in brand loyalty indirectly through brand trust.
We develop a structural equation model (SEM) to see which and how each social media factors influencing the buying behavior of the customer. Our models show that there are 3 factors that must be the focus of the e-commerce sites when using the social media platform.
This research is conducted by Annissa Tayara Callista and Akhmad Hidayatno
2nd Asia-Pacific Region System Dynamics Conference
Posted by systems in Conference, Continuous Event Modeling, Knowledge Area, Lab Development Program, Lean and Green Operations, News, Research Area, Technical Area on 24/02/2017
The 2nd Asia-Pacific Region System Dynamics Conference of the System Dynamics Society were held at UTown, National University of Singapore. The theme for this conference is Charting a Path to a Humane and Sustainable Future: Asia’s Leadership Role. The conference brought together various disciplines that have been using Systems Thinking, System Dynamics and Systems Engineering approach in understanding and solving problems. The conference seeks to popularize and extend systems thinking and System Dynamics modelling in the Asia-Pacific Region by facilitating interactions between practitioners from the region and around the world, introducing newcomers to the field, and keeping practitioners aware of current developments.
It brought together so many practitioners in the regions, including group of Indonesian SD practitioners. We hope that in the future, we could establish an Indonesia Chapter of System Dynamics, since currently Indonesia falls into the Australasian Chapters and newly established ASEAN Chapter.
SEMS Lab presented 2 papers in this event that falls in the strategy group of papers.
These are the excerpt of the papers presented by SEMS Lab
1. System Dynamics Approach in Managing Turnover Problem in Profesional Service Firms
Authors: Arry Destyanto, Armand Moeis, Akhmad Hidayatno, Mohammad Rizky Nur Iman
Talented human resources is one of the key factors in a firm’s success to maintain its competitive advantage in the global era. The rising trend of turnover is causing firms to rethink their strategy in maintaining their talented human resource. This issue challenges firms especially that rely on talented human resource such as professional service firms. Turnover has direct and indirect negative impact for firms. Previous researches had been conducted research to understand the underlying problem that causes turnover, and the strategy to overcome it. This research calls for a modeling structure of the turnover phenomena using a system dynamics approach, which can also be used to simulate various strategy. The output of the model is a recommendation of strategy that is most effective and has the best return on investment for organization. The results of simulation study will be discussed at the end of the paper.
2. Scenario Planning using System Dynamics for Reducing Uncertainty on Managing Employee Turnover
Authors: Akhmad Hidayatno, Arry Destyanto, Armand Moeis, Mohammad Rizky Nur Iman
In order to maintain the competitiveness of an organization in the global competition era, the presence of qualified human resources has become one of the key factors. Unlike product quality, qualified human resources have the capacity and talents that are not easy to replicate. Therefore, researchers and practitioners have been looking for the best strategy to manage turnover in an organization. However, the problem with turnover is that it has become more dynamic and changes along time, causing for uncertainties in the impact of the strategy implemented. This research will show how ‘scenario planning’ is used in a system dynamics model to minimize the level of uncertainties. The scenarios generated are not limited to the changes in assumptions, but also includes alternatives of trends that may happen in the future (plausible scenario). This paper focuses on discussing the process of generating and testing the plausible scenarios to the model. The implication of the scenarios is also compared to the initial output of the model.
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