Analysis of Empty Containers Supplies in the Shipping Network: Case Study at National Shipping Company

National Shipping Company (NSC) is a national shipping lines servicing domestic routes and a logistic key player of sea transport in Indonesia. Focus of this research is to determine total containers inventory for all routes, containers safety stocks at primary ports as well as optimum empty containers reposition strategy based on market’s requirement, all with purpose to avoid potential empty containers shortage. Unbalanced container turnover between inbound and outbound is a global issue in the shipping industry. A very common method applied by shipping operators in balancing supply and demand is repositioning excess containers to deficit areas. Determination of total container inventory begins with the calculation of safety stock of container inventory in Surabaya and Jakarta as a demand location port. Main parameter forming the calculation model is forecast cargo at home base port per route, service schedule as well as swiftness of cargo stuffing and stripping. Furthermore, determining the best routes combination shall reduce amount of container inventory. Calculation model of producing total container inventory which should be provided in 2018 is 46,382 TEUs while total existing NSC’s inventory in 2018 is 48,567 TEUs. From the total existing inventory, 2,516 TEUs turned out to be inactive (idle) for more than 30 days, meaning the active ones were 46,051 TEUs. From this comparison, calculation model is able to describe existing condition at 99.3%. Using the same model, a change in route combination with a pendulum nusantara service was carried out, which resulted in 968 TEUs reduction of container potential and reduction of one vessel allocation. This leads to potential efficiency of IDR 40,624,648,190 (forty billion six hundred twenty four million six hundred forty eight thousand one hundred and ninety Rupiah) for operational of year 201

Analysis and Mitigation of Strategic Risk Business Process by Considering Relationship Between Risk Case Study in Electricity Generation Companies

In this study an analysis of the linkages between the causes of strategic risks of business processes which refers to the balance scorecard perspective in company X one of electricity generation company. Where at the initial stage prioritization of 87 risk causes identified at the outset using the House of Risk 1 method and the Pareto principle so that 17 dominant risk causes were obtained, which were then analyzed using the ISM method and then weighted using the analytic network process (ANP) method to obtain the new ARP value causes risk that has accommodated the relationship between the causes of risk.To make it easier for companies to prioritize the handling of the 17 risk-causing agents, a mitigation analysis is then carried out using the House of Risk 2 by considering the ranking of existing Effectiveness to Difficulty so that 8 strategies for handling agents that cause risk are considered effective was chosen

Re-Design Layout and Allocation of Raw Material Warehouses Using Simulation Methods to Minimize the Handling Cost from Port to Warehouses

The high cost of raw materials handling from the port to the warehouses at PT. Petrokimia Gresik has encouraged this research to find solutions to reduce costs in raw material unloading activities. The location of factories and raw material warehouses that are separated from each other, the number of warehouses that are not yet connected with conveyor belts as well as the diverse types of raw materials become its own challenges in operating unloading activities to be more efficient. The layout and allocation of raw materials in the existing warehouses creates high cost of raw materials handling because slow moving raw materials are close to the production location, while fast moving raw materials are far from the production location. In addition, the differences in the existing unloading methods also affect the cost of raw materials unloading, the use of vessel cranes and dump trucks will increase costs, otherwise the use of conveyor belts will minimize the cost of raw materials handling. This research is important to find the layout and allocation of raw materials that have the most efficient handling costs. The steps in this study include (1) data collecting and processing; (2) making conceptual and simulation models, (3) verification and validation tests; (4) developing alternative scenarios; (5) running simulations based on alternative scenarios; (6) comparing scenarios using anova test and cost and benefit analysis. The simulation is done using Arena 14.0 software. The simulation results show that the re-design layout and allocation of 5 warehouses in Factory 2A and Factory 2B is the best alternative scenario. This scenario is proven to be able to minimize the cost of raw materials handling with savings of Rp. 10.958.028.455 per year with an ROI of 108% and a Payback Period of 0,48 years

Pricing and Warranty Level Decisions for New and Remanufactured Short Life-Cycle Products

Remanufacturing has become more prominent as a recovery process to mitigate the massive disposal of short life-cycle product at its end-of-use. However, remanufactured product is often perceived to be inferior to new product, and it has lower value in consumer’s willingness to pay. To increase the perceived quality of the remanufactured product, manufacturer offers a warranty, since one of the three roles possessed in warranty is being a signal to product reliability. This paper studies the pricing decisions and warranty level decision for new and remanufactured products in a closed-loop supply chain consists of a manufacturer and a retailer. The optimization modeling is performed under Stackelberg game with manufacturer as the leader. We found that higher expansion effectiveness coefficient would increase the supply chain profit. Also, there is an interval of demand’s speed of change, where the total profit would be at its highest. The optimum warranty level can be achieved regardless the initial warranty level set at the beginning of retailer’s optimization. Furthermore, the remanufactured product’s wholesale and retail prices are influenced by the expansion effectiveness coefficient

Simulation of In Bag Fertilizer Loading Process In Port of Petrokimia Gresik to Increase Loading Rate

Pelabuhan Petrokimia Gresik is one of the element to support the goverment regarding to maintain stability of fertilizer loading speed. The issue that occurs at Pelabuhan Petrokimia Gresik is the low speed of loading of fertilizer in the bag for the past 4 years. The low speed of loading fertilizer in bag tends to affect its cost. This study aims to simulate the process of loading fertilizer in bag at the Port of Petrokimia Gresik in order to find out the factors which influence the speed deterioration of fertilizer bagging process and applicable alternative scenarios to improve the process of loading fertilizer in bag. Simulations were executed using discrete simulation methods. The results show that loading speed downturn are influenced by significant factors as follows: high loading preparation time at the dock, which consumes 29,35% of loading activity; the high loading time at the dock, which reaches 23,15% of loading activity; also the extensive duration of truck commute from the warehouse to the pier which utilizes 22,95% of loading activity. This study evaluate 7 alternative improvement scenarios. According to ROI perspective, it is preeminent to additionally put field internal control personnel at each shift when loading fertilizer in bag, which can increase the loading speed to 668,29 tons / day, of investment (ROI) of 68% will be obtained within 1 year and the payback period will approximately result in 0,60 years. The best scenario in terms of loading speed is to add internal control personnel and apply a new method of loading fertilizer using a sling bag with an increase in loading rate to 804.17 tons / day, following ROI value of 23% within one year and a PP value of 0.81 year

Continuous Handling of Uncertainty in Food Chains: Using the House of Risk Model in Ecosystems

The house of risk model represents an approach to mitigate risk through systematically analysing data risk agents based on empirical findings through prioritizing them. Food production is associated with uncertainty both within the production system as well as in environment. Given the state of current technology, including its rapid development impacting on connectivity in supply chains, the house of risk model is considered through this conceptual study applying an ecosystems approach on how to mitigate risk in food chains in their many-faceted environmental setting. Ecosystems thinking is rooted in a normative quest to secure sustainability. It also is at the operations level a complex system. It is pointed out that an ecosystems approach encompasses mixed methods, including both deterministic and complex systems. The nature of this complementarity is discussed. The study provides a list of four issues regarding using the house of risk model within an ecosystem: (1) ethical, (2) development, (3) operations and (4) discourse

Resource-constrained project scheduling with ant colony optimization algorithm

Resource allocation commonly becomes one of the critical problems in project scheduling. This issue usually occurs because project managers estimate the schedule of activities and network time without considering resource availability. Resource-Constrained Project Scheduling Problem (RCPSP) links to the allocation of resource or set of resources into certain activities in order to accomplish particular objectives. Various approaches have been performed to overcome RCPSP, including the heuristic approach. In this research, we used the Ant Colony Algorithm in solving RCPSP. We used 11 examples of projects with dissimilarity in-network and several activities. The implementation of the Ant Colony Algorithm resulted in the percentage of a near-optimal solution of 63.64%. Besides, the duration obtained from the algorithm above the manual scheduling (assumed optimal) was only 4.29%. Sensitivity analysis was performed to understand how substantial the changes of ACO parameters influenced the result obtained from the algorithm. Based on the result, we could conclude that the parameters of ACO have no significant effect to project duration

A multi layer recency frequency monetary method for customer priority segmentation in online transaction

Customer segmentation is a critical step toward appropriately differentiating services to different customers. One common way of segmenting customers is by using what is called Recency, Frequency, Monetary (RFM) approach, where customers are classified based on the recency of their transactions as well as how often they purchase goods and services and how much money they spent. However, this approach is not able to fairly differentiate customers especially when it comes to the cases where old customers have decreased or stopped their purchases and the new customers just started buying. In order to overcome this, we proposed what is called Multi Layer Recency, Frequency, and Monetary (MLRFM) approach. In this approach, we divide time periods into multiple layers and the recency, frequency, and monetary values are analyzed considering these different segments. Our numerical examples show that this multi layer approach can provide a good alternative for the companies that sell products online and customers are behaving very dynamically

The Establishment of Decision Making Support Tool for Inventory Control and Production Planning with Periodic Review System and Linear Programming Approach

PT. Mortars is an instant cement producer with many product variants. Some problems that PT. Mortars encountered are uncertainty of demand, limitation on production capacity, and inventory capacity, which caused product shortages and high inventory costs. The production scheduling process is produced manually so it requires a long time, relying on the ability and experience of the production planner which is vulnerable to mistakes. Therefore, new methods and tools are needed for inventory control and production scheduling to make sure goods are delivered to customer just in time while maintaining operational efficiency. Periodic review system (R,S) and (R, s, S) methods are proposed in this study to improve inventory parameters and reorder systems that have an impact on service levels and inventory costs. The tools are designed using Microsoft Excel software with solver add-ins functions and linear programming approaches to generate optimal production schedule decisions automatically. In order to determine the level of service and inventory costs generated by each method, a simulation is conducted by using the tools that have been made. The simulation of the existing method produces a service level of 98,64% and a total inventory cost of Rp. 1.167.160.494. The periodic review system (R, S) method resulted in increasing service level of 0,96% and lower inventory cost of Rp. 15.130.801 while the periodic review system (R, s, S) method resulted in increasing service level of 1,29% and a lower inventory cost of Rp. 448.653 compared to the existing method. The periodic review system (R, S) method produces the lowest inventory costs while the method (R, s, S) produces the highest service level compared to other methods