Analysis of Drug Forecasting with Single Moving Average and Single Exponential Smoothing Approach (Case Study in Jombang Regency 2017-2019)

Restyana, Anggi; Savitri, Lisa; Laili, Nur Fahma; Probosiwi, Neni

doi:10.1088/1742-6596/1899/1/012100

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Penelitian ini penting untuk toko Vape Industrial karena memiliki karakteristik yang berbeda dan dapat mempengaruhi hasil prediksi stok produk [9]. Diharapkan penelitian ini memberikan solusi yang tepat dalam mengelola persediaan stok etalase produk [10]. Dengan menggunakan metode peramalan yang akurat, toko Vape dapat meminimalkan risiko kelebihan atau kekurangan stok, meningkatkan efisiensi, dan memenuhi kebutuhan pelanggan.…”

Section: Pendahuluanunclassified

Prediksi Pengadaan Stok Produk Menggunakan Algoritma Single Moving Average Dan Single Exponential Smoothing

Andryana,

Nasution,

Rubhasy

2023

SMATIKA

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Sistem dan teknologi informasi memiliki peran krusial dalam kehidupan masyarakat karena membantu pengambilan keputusan yang efektif. Kehandalan dalam memprediksi penjualan Vape pada produk liquid menjadi prioritas untuk meningkatkan pelayanan dan keuntungan toko Vape. Namun, Vape Industrial belum memiliki metode untuk meramalkan penjualan barang liquid di masa depan, menyebabkan beberapa liquid mengalami kelebihan atau kekurangan. Dalam penelitian ini, dikembangkan aplikasi untuk memprediksi jumlah stok yang dibutuhkan pada periode berikutnya. Data transaksi konsumen pada periode sebelumnya dianalisis menggunakan algoritma Single Moving Average dan Single Exponential Smoothing. Dari kedua algoritma ini akan dibandingkan berdasarkan hasil perhitungan error memilih algoritma yang tepat untuk prediksi stok produk. Hasil pengujian menunjukkan bahwa aplikasi ini berhasil memprediksi stok liquid Besti Matcha dengan baik. Untuk periode harian, algoritma Single Exponential Smoothing dengan metode alpha 0.2 memberikan hasil ramalan yang tepat, dengan prediksi penjualan 1 produk dan nilai error 0%. Sedangkan untuk periode mingguan, algoritma Single Moving Average dengan 5 periode memberikan hasil prediksi 5 produk terjual dan nilai error 25%. Untuk periode bulanan, Single Exponential Smoothing dengan alpha 0.2 memberikan hasil ramal 21 produk terjual dan nilai error 10.5%. Dengan adanya aplikasi ini, diharapkan Vape Industrial dapat efektif dalam memprediksi kebutuhan stok untuk periode berikutnya dan menghindari kerugian produk berlebih.

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Section: Pendahuluanunclassified

Prediksi Pengadaan Stok Produk Menggunakan Algoritma Single Moving Average Dan Single Exponential Smoothing

Andryana,

Nasution,

Rubhasy

2023

SMATIKA

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“…Restyana et al ( 41 ) applied Single exponential smoothing and moving average for prescriptive drugs: paracetamol and vitamins, however, do not provide MAPE value.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Model: The Case of the Pharmaceutical Retail

Burinskienė

2022

Front. Med.

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IntroductionMany forecasting methods are used to predict sales, such as the moving average method, naive method, exponential smoothing methods, Holt's linear method, and others. The results brought by these models are quite different. Forecast delivered by the naive method is entirely accurate for an extended period, like 3–5 years, Holt's methods are bringing accurate one-year period forecasts. The planning decisions have several levels, meaning different forecasting results. However, the authors that are testing various forecasting methods are not discussing results researched in different planning levels (retail chain and different pharmacies). The study is given to the construction of the forecasting model covering both planning levels, which later is empirically tested for the Lithuania retail case:PurposeThe development of the forecasting model for reduction of shortages in drug supply. To achieve this goal, the author revises the improvement of drug availability weekly.Research MethodologyThe construction of the forecasting model is incorporating outliers' detection methods and sales by pharmacies to minimize shortage. In the forecasting model, the author uses Theil's U2 test to evaluate forecasting accuracy.FindingsDuring analysis, the author constructs the model application for forecasting drug sales where weekly availability is highly recommended. The results show that forecasting on individual pharmacies level using the integration of these plans approach leads to higher accuracy.Research LimitationsThe research covers 3 months of sales data. Das and Chaudhury suggest for short-sales period products use 36 days' time horizon. Ayati et al. discuss short and long-term time horizons for planning sales of drugs. Kanyalkar and Adil analyzed multi-site production and suggest that the time horizon should cover the longest lead time required for delivery of raw material, which is 12 weeks, and select 3 months (i.e., 13 weeks) as short-term time period horizon. Wongsunopparat and Chaveesuk forecast drug sales for 1-month and 12-month periods and compare the results. In this study, the focus is on short-term time-horizon, which is considered as 3 months period and also represents the longest lead-time. In the future, the study could review other periods. The author has incorporated the review of eight forecasting methods into the study by leaving other forecasting methods unresearched. Future studies could also incorporate different ARIMA methods into shortage reduction case analysis.Practical ImplicationsPresented forecasting model could be useful for practitioners, which analyze the reduction of the shortage of prescribed drugs. There the revision of repeated purchases is recommended for national authorities, wholesalers, and pharmacies aiming to minimize shortage.Originality/ValueThe analysis to reach the highest forecast accuracy and identification of a forecasting approach which responds to the fluctuation of weekly sales for the whole pharmacy chain and separate pharmacies. The study contributes to drug sales review, where most authors analyze the total volume, which is not separated by pharmacies.

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“…Quantitative forecasting techniques used in various researches are linear regression analysis, moving average, exponential smoothing, seasonal method, and Holt Winters seasoning [9]. The recent literature reviews carried out by Restyana et al [10] focus on forecasting medicine using single moving averages and single exponential smoothing methods. Wettermark et al [11] focus on linear regression analysis to aggregate sales data on hospital sales and dispensed drugs in ambulatory care.…”

Section: Introductionmentioning

confidence: 99%

“…Tolerance measurement methods include: mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percent error (MAPE) [7]. The MAD and MSE were used in [10], [14], [15]. The best forecasting method of spare parts is chosen based on MSE, MAD, and MAPE the smallest [16].…”

Section: Introductionmentioning

confidence: 99%

Purchasing planning for pharmaceuticals inventory: a case study of drug warehouse in hospital

Yawara,

Supattananon,

Siwapornrak

et al. 2023

IJEECS

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Lack of purchasing planning and proper demand forecasting causes hospitals to suffer from drug inventory mismatches with actual demand; in other words, the inventory management cost is high if the quantity exceeds or less the demand. Therefore, this research aimed to plan an appropriate inventory purchase to reduce inventory costs and effectively meet the hospital's pharmaceutical inventory needs in a case study: i) demand forecasting for 29 AV drugs using Minitab 19, ii) economic order quantity (EOQ) and Newsboy form when drug demand is stable and non-steady, respectively, and iii) design a ready-made program using Excel program to help control, make purchase decisions and be easy to use. There were 5 forecasting methods used. Each drug forecasting method was selected from the one with the slightest error. Twenty-four drugs and five drugs were determined using EOQ and Newsboy forms for re-order point (ROP), safety stock (SS), and total costs. The total cost of drug inventory management per year was 1,780,336.98 baht; compared with the current method, it reduced the cost by 506,569.10 baht per year or a 22.15% reduction.

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Analysis of Drug Forecasting with Single Moving Average and Single Exponential Smoothing Approach (Case Study in Jombang Regency 2017-2019)

Cited by 3 publications

References 12 publications

Prediksi Pengadaan Stok Produk Menggunakan Algoritma Single Moving Average Dan Single Exponential Smoothing

Prediksi Pengadaan Stok Produk Menggunakan Algoritma Single Moving Average Dan Single Exponential Smoothing

Forecasting Model: The Case of the Pharmaceutical Retail

Purchasing planning for pharmaceuticals inventory: a case study of drug warehouse in hospital

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