scholarly journals Analisis Bullwhip Effect pada Rantai Supply dengan Model Q Menggunakan Pendekatan Hadley-Within di PT. XYZ Medan

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Martha Purnama Sari Panggabean ◽  
Dimas Akmarul Putera ◽  
Nursafwah
Keyword(s):  
Supply Chain ◽  
Inventory Control ◽  
Distribution System ◽  
Control Policy ◽  
Bullwhip Effect ◽  
Product Distribution ◽  
Soft Drinks ◽  
Inventory Cost ◽  
Inventory Control Policy ◽  
Q Model

PT. XYZ adalah perusahaan yang bergerak didalam pembuatan kemasan botol minum. Pendistribusian produk dilakukan PT XYZ menggunakan data historis berdasarkan jumlah permintaan pada tahun 2013 dan 2014. Data tersebut menunjukkan bahwa terjadinya perbedaan hasil. Tahun 2013 memiliki jumlah permintaan yang lebih rendah dari tahun 2014. Informasi terdapat bahwa perlu dilakukan pengevaluasian karena didalam rantai produksi terdapat bullwhip effect. Terdapat nilai bullwhip Effect menujukan bahwa nilai bullwhip effect untuk distributor Indomaret, Carrefour, dan rantai manufakturnya masing-masing sebesar 0,5303; 0,2967, dan 0,5114. Usulan perbaikan dapat diatasi yaitu dengan model Q yang berfungsi menggendalikan persediaan pada rantai pasok dengan metodeHadley-Within. Perhitungan pengendalian persediaan untuk distributor Indomaret, Carrefour, dan rantai manufakturnya masing-masing sebesar 1,0721; 1,100; dan 1,0714. Hasil dari perhitungan menujukan bahwa terjadi keseimbangan antara penjual dan pembeli sehingga biaya pun dapat dihematkan pada PT XYZ.   PT. XYZ Medan is manufacturing company that produce soft drinks of beverages in containers. In the product distribution system at PT. XYZ Medan, found that the number of orders based on the result of forecasting in 2013 lower than actual orders at distrbutor and manufacturer in 2014. Distorsion of information on this order can evaluate the indication of bullwhip effect in supply chain. Based on the result calculation of bullwhip effect, found that the value of bullwhip effect for Indomaret distributor, Carrefour, and supply chain of manufacturer each of 0,5303; 0,2967, and 0,5114. Proposed improvements to predominate bullwhip effect that is by doing inventory control policy with Q model using Hadley-Within approach. The value of bullwhip effect aftre doing inventory control policy for Indomaret distributor, Carrefour, and supply chain of manufacturer each of 1,0721; 1,100; dan 1,0714. The value of bullwhip effect which is close to one shows that the variance between the number of the order and the number of the demand nearly balanced so as to save the inventory cost at PT. XYZ Medan.

scholarly journals Inventory Control System for a Healthcare Apparel Service Centre with Stockout Risk: A Case Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
An Pan ◽  
Chi-Leung Hui
Keyword(s):  
Control Problem ◽  
Inventory Control ◽  
Lead Time ◽  
Control Policy ◽  
The Elderly ◽  
Inventory Cost ◽  
Product Demand ◽  
Service Centre ◽  
Inventory Control Policy ◽  
Inventory Control Problem

Based on the real-world inventory control problem of a capacitated healthcare apparel service centre in Hong Kong which provides tailor-made apparel-making services for the elderly and disabled people, this paper studies a partial backordered continuous review inventory control problem in which the product demand follows a Poisson process with a constant lead time. The system is controlled by an (Q,r) inventory policy which incorporate the stockout risk, storage capacity, and partial backlog. The healthcare apparel service centre, under the capacity constraint, aims to minimize the inventory cost and achieving a low stockout risk. To address this challenge, an optimization problem is constructed. A real case-based data analysis is conducted, and the result shows that the expected total cost on an order cycle is reduced substantially at around 20% with our proposed optimal inventory control policy. An extensive sensitivity analysis is conducted to generate additional insights.

A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting model

2019 ◽  
Vol 14 (2) ◽  
pp. 385-407 ◽  
Author(s):  
Sanjita Jaipuria ◽  
Siba Sankar Mahapatra
Keyword(s):  
Inventory Control ◽  
Demand Forecasting ◽  
Control Policy ◽  
Bullwhip Effect ◽  
Forecasting Model ◽  
Control Policies ◽  
Content Type ◽  
Inventory Control Policy ◽  
Smoothing Parameters ◽  
Demand Forecasting Model

Purpose The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013). Design/methodology/approach A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies. Findings A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ). Research limitations/implications This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy. Practical implications The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment. Originality/value This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

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