Analysis Control Of Inventory Of Non Subsidy Fertilizer Merchandise With Economic Order Quantity (EOQ) Method In CV. Dohar Agro Mandiri

Economic Order Quantity (EOQ) is a mathematical model that determines the number of items that must be ordered to meet the projected demand, with minimized inventory costs. The types of costs to run the business cycle are storage costs and ordering costs. The purpose of this research is to analyze "Analysis of Non-Subsidized Fertilizer Inventory Control with Economic Order Quantity (EOQ) Method at CV. Dohar Agro Mandiri. The total inventory obtained using the EOQ method is 11,003 with a purchase frequency of 3 times. Using the EOQ method the company must reorder when the merchandise inventory is 1,015 zak, with the EOQ method the maximum inventory that the company must maintain is 12,018 per day. The total inventory cost of fertilizer merchandise inventory is 24,648. In other words, inventory control using the EOQ method can help companies achieve an optimal level of inventory ordering and ordering frequency.

Analysis Of Stock Control Of Raw Materials Of Dzohir Noodles In Bengkulu

This study aims to analyze the stock inventory control of Mie Dzohir's raw materials in Bengkulu. The analytical method used is Economic Order Quantity (EOQ), Reorder Point (ROP), Total Inventory Cost (TIC) and Safety Stock (SS). The results showed that to meet the raw material needs of 91,375 kg during the research period, from January 2020 to December 2020 (12 months), the number of economical purchases/EOQ of wheat flour raw materials that had to be made by the Mie Dzohir factory in Bengkulu was as much as 5,372 kg for each order with a purchase frequency of 17 times for a period from January 2020 to December 2020. Reorders or reorder points (ROP) can also be determined, namely reordering should be done when 345 kg of inventory is in warehouse, with a lead time of 1 days so as not to hamper the company's production process. The total inventory cost (TIC) can also be determined, namely the total cost of raw material inventory of Rp. 3.572.188, - therefore, it can save on inventory costs of Rp. 1.051.697,- .Safety stock (SS) of 95 kg which must be in the warehouse, this is intended therefore, there is no shortage of wheat flour raw materials if there is a delay in the delivery of raw materials.

Analisis Pengendalian Persediaan Bahan Baku dengan Metode Economic Order Quantity (EOQ) pada Layla Bakery Jember

Layla Bakery merupakan usaha agroindustri yang terletak di Jalan Ambulu No.110, Kecamatan Balung, Kabupaten Jember. Produk dari Layla Bakery Jember antara lain roti pisang, donat, roti tawar, roti sisir dan lain-lain. Penelitian ini bertujuan untuk menentukan jumlah persediaan bahan baku tepung optimal yang seharusnya dilakukan Layla Bakery, kapan waktu yang tepat untuk melakukan pemesanan kembali bahan baku tepung dan berapa total biaya pemesanan optimal untuk melakukan pengadaan bahan baku tepung. Alat analisis yang digunakan dalam penelitian ini adalah EOQ (Economic Order Quantity) yang meliputi perhitungan Quantity untuk mengetahui jumlah pemesanan bahan baku yang optimal, alat analisis ROP (Reorder Point) untuk mengetahui kapan waktu yang tepat untuk melakukan pemesanan bahan baku kembali, alat analisis Safety Stock untuk mengetahui persediaan pengaman dan TIC (Total Inventory Cost) untuk menghitung total biaya pemesanan bahan baku. Berdasarkan hasil analisis dengan metode EOQ, jumlah pesanan bahan baku yang optimal yaitu sebesar 3.179 Kg dengan frekuensi pembelian 18 kali dalam satu tahun dengan pemesanan ulang bahan baku jika persediaan mencapai 704 Kg dengan total biaya persediaan sebesar Rp. 351.827.600.

Analisis Penerapan Manajemen Persediaan Bahan Baku Arm Rear Brake Kyea dengan Metode EOQ

Inventory management is an essential aspect of supporting good production planning in company business activities. The implementation of inventory management in the company is a primary need. If there is a problem in the supply of raw materials, all the company's operational activities will be disrupted. Inventories in manufacturing usually consist of raw materials, semi-finished goods, and finished goods or final products. Inventories of arm rear brake materials for the production process often experience shortages due to immature controls. This study aims to determine the inventory control of rear arm brake raw materials using the Economy Order Quantity (EOQ) method at PT. Ciptaunggul Karya Abadi. Based on the discussion results, it was found that ordering raw materials was carried out two times a year with a total order of 621 pieces with 86 pieces of safety stock and will be ordered again when raw materials collected 21 pieces. Total Inventory Cost (TIC), which was initially Rp110.264.235,00, can be saved to Rp70.598.399,00 so that the total savings that occur are Rp39.674.899,00 which is equal to 33.26%.

Multi-Criteria Inventory Optimisation of University of Ibadan, Ibadan, Nigeria’s Bakery Using Goal Programming Approach and Flour as the Major Raw-Material

Asides inventory cost, which is being used as the only inventory performance measure at the University of Ibadan bakery, a number of other criteria, such as inventory cost, service level, inventory turnover and delivery lead time which influence the performance of an inventory system have surfaced over the years. Hence, there is the need to examine all these criteria-objectives altogether. Therefore, this research was centred towards optimising the inventory system of University of Ibadan bakery, putting multiple criteria into consideration. Data on 17 raw materials: their costs, suppliers, usage rate, lead time, storage space and available capital were collected by means of interviews, past records and observations. The weighted goal program algorithm was adopted to find the best compromise between fulfilling the four objectives by minimising the sum of the deviation from the target values of the goals. Subsequently, Lingo 17.0 and Tora 1.0 optimisation software packages were used to solve and compare the model generated, while putting into consideration storage space constraint and budgeted capital. The developed model from the goal programming algorithm exhibited four goals (combined into one objective function). Same solutions were obtained from Lingo 17.0 and Tora 1.0. While Lingo 17.0 gave a uniform service level of 100% , a turnover ratio greater than 1(>1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material, Tora 1.0 gave a uniform service level of 100% , a turnover ratio greater than 1 (> 1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material. Implementation of the developed model will eliminate unnecessary waiting time between production, thereby ensuring effective and efficient utilisation of raw materials in production which will lead to reduced cost of holding inventory, elimination of unnecessary overall cost and wastages, and also improvement of the productivity and profit on the long run.

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.

A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

In this article, the parabolic dense fuzzy set is defined, and its basic arithmetic operations are studied with graphical illustration. The lock set concept is incorporated in a parabolic dense fuzzy set. Then, it is applied to the problems of fishery culture via the modeling of an economic order quantity model. Here, the fingerlings are fed to reach the ideal size to fulfill the customer’s demand. The growth rate of the fingerlings is assumed as a linear function. After the sales of all fish, the pond is cleaned properly for a new cycle. Here, the model is solved in a crisp sense first. Then, we fuzzify the model considering the demand rate as a parabolic dense lock fuzzy number and obtain the result in a fuzzy environment. The main aim of our study was to find the quantity of the ordering items such that the total inventory cost gets a minimum value. Lastly, sensitivity analysis and graphical illustrations were added for better justification of our model.

ANALISIS PENGENDALIAN PERSEDIAAN AUX RAW MATERIAL MENGGUNAKAN METODE MIN-MAX STOCK DI PT. MITSUBISHI CHEMICAL INDONESIA

PT. Mitsubishi Chemical Indonesia merupakan perusahaan yang bergerak di bidang industri polyester yang memproduksi Purified Terephthalic Acid (PTA) terbesar di Indonesia. PTA adalah suatu senyawa yang tidak tersedia di alam yang dibuat dari sintesa kimia. Terdapat auxiliary raw material yang diperlukan untuk membantu proses produksi PTA agar dapat berjalan dengan lancar, seperti Hydrobromic Acid (HBr) dan Soda Ash Dense (Na2CO3). Perusahaan belum memiliki jumlah safety stock padahal perusahaan harus mengendalikan persediaan auxiliary raw material agar dapat menghindari kekurangan dan kelebihan bahan baku yang menyebabkan perusahaan dapat mengeluarkan biaya lebih banyak. Hasil perhitungan memperlihatkan bahwa metode min-max stock menunjukan jumlah safety stock bahan baku Hydrobromic Acid yaitu sebesar 17,5 ton dan untuk Soda Ash Dense yaitu sebesar 5,41 ton. Penentuan jumlah persediaan antara kebijakan perusahaan dengan hasil perhitungan metode min-max stock memiliki beberapa perbedaan. Dari perbedaan tersebut, perusahaan dapat menghemat total biaya persediaan sebesar Rp 7.550.000,00 untuk Hydrobromic Acid dan Rp 11.221.224,16 untuk Soda Ash. Frekuensi pemesanan yang terlalu sering dengan ukuran pemesanan yang besar menyebabkan total biaya persediaan menjadi tinggi. Dari hasil tersebut perusahaan perlu menerapkan metode min-max stock untuk mengendalikan persediaan bahan baku supaya dapat menghemat biaya pengeluaran. Abstract[Inventory Control Analysis of Aux Raw Material Using Min-Max Stock Method in Mitsubishi Chemical Indonesia Company] Mitsubishi Chemical Indonesia is a company engaged in the polyester industry that produces the largest Purified Terephthalic Acid (PTA) in Indonesia. PTA is a compound that is not available in nature, so it is made by chemical synthesis. There are auxiliary raw materials that are needed to help the PTA production process, so it can run well, such as Hydrobromic Acid (HBr) and Soda Ash Dense (Na2CO3). The company don’t have the safety stock even though the company should control the aux raw material inventory to avoid the company to run out of stock or overstock, that causes the company to spend a lot of money. The calculations result shows that the amount of the safety stock for Hydrobromic Acid is 17.5 tons and for Soda Ash is 5.41 tons. The determined number of inventories between company policy and the calculation of the min-max stock method have several differences. From those differences, the company can save the total inventory cost amounted at IDR 7.550.000,00 for Hydrobromic Acid and IDR 11.221.224,16 for Soda Ash. High frequency of orders with a large order size can cause the total inventory cost to be high. From that result, the company needs to apply a min-max stock to control inventory and to save money on expenses.Keywords: inventory; min-max stock method; out of stock; overstock; TIC

Multi-item inventory policy with time-dependent pricing and rework cost

The price of broiler chickens at the consumer level varies daily. The price can be very low or otherwise. The price has resulted from the imbalance between the availability of chicken from suppliers and the market demand. As a result, demand will also fluctuate because it is influenced by consumer purchasing power. When the price of live chickens is low, the carcass company will usually buy in large quantities and expect to sell them at a higher price. The problem arises when the chicken overstock company will risk product damage due to product buildup in the refrigerated warehouse, so rework is necessary. In this paper, we will be developed a multi-item inventory model that considers material prices that vary to time, probabilistic demand, and rework costs. The aim is to determine the right policy for controlling frozen chicken products' inventory to minimize losses and total inventory costs. This model can evaluate the best time to order broiler chickens, how much to order, how long the interval between orders, and the optimal number of orders, resulting in minimum total inventory cost per period. The model solution is carried out with an optimization approach based on the parameters that affect the model. A numerical example is given at the end of this paper for model validation and illustrates the model solving algorithm.

A Scatter Search Algorithm for Multi-Criteria Inventory Classification with Maximize the Satisfaction Level

Inventory management requires thousands or millions of individual transactions each year. Classification of the items influences the results of inventory management. Traditionally, this is usually classified with considering an annual dollar usage criterion but maybe other criterias such as lead time, criticality, perishability, inventory cost, and demand type can be affected on that classification. The objective of this study is to determine the multi-criteria inventory classification (MCIC) of the inventory items to minimize the total inventory cost and also dissimilarity of classes. Because of the two objectives is considered to solve with together, the maximization of satisfaction level is described to solve the multi-objective problem. This study introduces a Mixed Integer Nonlinear Programming (MINLP) model of the MCIC problem by giving two objectives. A Scatter Search Algorithm (SSA) is used to solve the MINLP model for obtaining high-quality solutions within reasonable computation times. Finally, we illustrate an example and compare our results with other studies in previous literature.