An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data

Abstract

The problem of missing data is frequently met in time series analysis. If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. In this article, two propositions are presented: (1) a dedicated time series forecasting scheme, which is both accurate and sustainable, and (2) a practical observation of the data background to deal with the problem of missing data and to effectively formulate correction strategies after predictions. In the empirical study, actual tray sales data and a comparison of different models that combine missing data processing methods and forecasters are employed. The results show that a specific product needs to be represented by a dedicated model. For example, regardless of whether the last fiscal year was a growth or recession year, the results suggest that the missing data for products with a high market share should be handled by the zero-filling method, whereas the mean imputation method should be for the average market share products. Finally, the gap between forecast and actual demand is bridged by employing a validation set, and it is further used for formulating correction strategies regarding production volumes.

Sistem Informasi Perencanaan Persediaan Obat Dengan Metode Periodic Review

“…Banyak perusahaan menerapkan metode perencanaan persediaan sebatas peramalan [10], [11] dengan menggunakan rata-rata dari penjualan bulan-bulan sebelumnya untuk menghasilkan rencana satu bulan kedepan. Penggunaan metode ini acapkali mengakibatkan adanya permintaan yang tidak terpenuhi akibat dari kekurangan persediaan barang yang biasa disebut dengan stockout [12].…”

“…)×" (10) Dimana: D = permintaan rata-rata (unit tahun) L = lead time T = periode pemesanan x = permintana rata-rata f(x) = probability density function permintaan x selama L+T H = biaya penyimpanan per unit/tahun C = harga per unit A = biaya per pemesanan π = biaya kekurangan persediaan R = tingkat persediaan maksimum S(x) = kuantitas kekurangan stok per siklus TC = total biaya persediaan Dalam memperoleh R* dan T*, dilakukan dengan langkah-langkah perhitungan berikut:…”