Inventory Control Using Fuzzy-Aided Decision Support System

Deb, Mahuya; Kaur, Prabjot; Sarma, Kandarpa Kumar

doi:10.1007/978-981-10-3773-3_45

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Defuzzification process differs each demand periods, because it depends on number of data from previous period. Based on (Deb et al, 2018;Efrilianda et al, 2018), de-fuzzification process has some rules based on demand period, there are: 1. If k=1, Fij1 does not have the results because it contains no next statue fuzzy set.…”

Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

“…The accuracy level of forecasting is higher if the error is getting smaller. Based on (Deb et al, 2018;Manafzadeh and Tan, 2020), the commonly used methods to summarize the historical error is MAPE.…”

Section: Forecasting Accuracymentioning

confidence: 99%

“…The various elements associated with how much to order are normally concerned with inventory costs and inventory lot sizing models. The inventory cost includes the ordering cost, procurement cost, holding cost and shortage cost (Deb et al, 2018). An inventory control system is used to keep inventories in a desired state while continuing to adequately supply customers, and its success depends on maintaining clear records on a periodic or perpetual basis (Efrilianda et al, 2018;Lin, 2020;Singh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The company needs to measure market demand. The most accurate forecasting becomes the basis for the correct decision -making (Deb et al, 2018;Xihao, 2008) Fuzzy time series method does not depends on the number of data and historical patterns, and needs only little of data. This method is different from Holt's double exponential smoothing method, which needs more data to increase the accuracy of forecasting (Xihao, 2008) .…”

Section: Introductionmentioning

confidence: 99%

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Fixed Time Period Modelling for Inventory Control System in Regional Warehousing

2020

j.tek.ind.pert.

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Pengendalian persediaan pada perusahaan mampu mengoptimalkan biaya penggudangan dan tersedianya bahan baku dan produk, sekaligus dengan menjaga terjadinya gangguan tingkat kepuasan pelanggan. Faktanya tidak semua perusahaan dapat menerapkan pengendalian persediaan yang cocok seperti penggunaan metode peramalan yang kurang akurat dengan keterbatasan data. Pada pekerjaan ini formulasi pemodelan peramalan inventory menggunakan fuzzy time series untuk pendekatan fixed-time period dengan safety stock. Model ini bertujuan untuk mendapatkan kapasitas penyimpanan yang paling optimal pada setiap gudang regional. Akurasi peramalan diuji dengan mengukur tingkat kesalahannya menggunakan indikator MAPE. Hasil peramalan digunakan untuk menghitung banyaknya produk yang harus dikirim pada setiap gudang regional per periode. Perhitungan banyaknya boks produk harus diproduksi harus disesuaikan dengan level kapasitas dan jumlah produksi setiap produk. Hasil menunjukan bahwa peramalan yang didapat dari enam produk yang dikirim ke empat daerah masing-masing menghasilkan nilai ramal yang memiliki rentang nilai MAPE minimal sebesar 0,29% dan maksimal sebesar 1,94%. Implementasi formulasi ini dalam kasus dunia nyata mampu untuk meningkatkan aliran data antar stakeholder. Kata kunci: formulasi, pemodelan, pengendalian persediaan, peramalan fuzzy time series, MAPE.

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Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

Section: Forecasting Accuracymentioning

confidence: 99%