Comments on "The Effects of Demand-Forecasting Fluctuations on Customer Service and Inventory Cost when Demand Is Lumpy"

1987 ◽  
Vol 38 (5) ◽  
pp. 472
Author(s):  
Terry Williams ◽  
Richard Watson
Keyword(s):  
Customer Service ◽  
Demand Forecasting ◽  
Inventory Cost

scholarly journals Demand forecasting in Small and Medium Enterprises (SMEs) ED Aluminium Yogyakarta using causal, time series, and combined causal-time series approaches

2018 ◽  
Vol 204 ◽  
pp. 01004 ◽  
Author(s):  
Wildanul Isnaini ◽  
Andi Sudiarso
Keyword(s):  
Time Series ◽  
Small And Medium Enterprises ◽  
Demand Forecasting ◽  
Actual Condition ◽  
Inventory Data ◽  
Inventory Cost ◽  
Forecasting Method ◽  
Medium Enterprises ◽  
Better Than

ED Aluminium is the biggest Small and Medium Enterprises (SMEs) in Daerah Istimewa Yogyakarta (DIY) with 90 number of workers and 1,5 ton ingot capacity for production (Isnaini, 2014). Inventory data in December 2015 indicates that some products are overstocked (9%) and stockout (83%). This condition can happend because that SMEs still using intuition to predict the number of demand. Inventory fluctuation causes the inventory cost increases while overstock happend and lost the opportunity cost during stockout. To avoid overstock and stockout, the determination of demand with exact method is needed and one of them can be solved by forecasting method. This study aims to find the best forecasting methods of demand in 2015 using causal, time series, and combined causal-time series approces that better than the actual condition. The results of this research is the best forecasting method used to predict the number of sales in January-November 2015, that are SARIMA (3,1,1)(0,1,1)12 for WB, SARIMA (1,1,1)(1,0,1)6 for WSD, SARIMA (1,1,1)(1,1,0)6 for DE, SARIMA (2,1,1)(1,1,0)6 for PE, and SARIMA (2,1,3)(0,1,0)12 for PT.

scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

scholarly journals Analisis Penggunaan Peramalan dalam Meminimalkan Biaya Simpan Produk Linzhi Plus pada CV. HN

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
Abdan Syakura ◽  
Oktiviandri Hendaryani ◽  
Rafiq Ramadhan
Keyword(s):  
Demand Forecasting ◽  
Herbal Supplements ◽  
Inventory Cost ◽  
Monthly Average ◽  
Average Production ◽  
Forecasting Method ◽  
High Production ◽  
Inventory Control Problem ◽  
Product Inventory ◽  
And Storage

<em>CV. HN is a manufacturer of herbal supplements which products are mostly made from mushroom. Lingzhi Plus is one of their products. The product inventory data from October 2014 until September 2015 showed that the monthly average production was 111 units, the monthly average sales was 103 units, and the remaining product inventory at the end of the year was 108 units. The products that approaching the expiration date will be sold with a discount of 20%. The high amount of remaining products leads to high  production and storage cost. Therefore, companies should resolve the inventory control problem. This study proposes a method for controlling inventory and production by selecting the best demand forecasting method. The forecasting method MA4 shows the smallest error value which is known from MAD value of 30.66. By using the proposed method of forecasting, the company will earn saving of Rp 915,939.40 of inventory cost</em>

scholarly journals Decreasing Total Inventory Cost by Controlling Inventory in Motorcycle Dealer

2020 ◽  
Vol 22 (2) ◽  
pp. 41-49
Author(s):  
David ◽  
Engmir ◽  
Irwan Budiman ◽  
Jusra Tampubolon
Keyword(s):  
Inventory Management ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Order Quantity ◽  
Inventory Cost ◽  
Safety Stock ◽  
Safety Stocks ◽  
Forecasting Method ◽  
Total Inventory ◽  
Abc Analysis

This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.

scholarly journals Electronic Data Interchange and Demand Forecasting Implications on Supply Chain Management Collaboration: A Customer Service Perspective

2017 ◽  
Vol 18 (2) ◽  
pp. 138
Author(s):  
Ilyas Masudin ◽  
Mohammed Sheikh Kamara
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Customer Service ◽  
Demand Forecasting ◽  
Electronic Data Interchange ◽  
Inventory Level ◽  
Electronic Data ◽  
Chain Management ◽  
Data Interchange ◽  
Good Customer Service

Customer service is a very important aspect within the supply chain. Through collaboration, the goal of each party within the supply chain is to add value to a product, in order to accelerate good customer service.  Good customer service leads to customer satisfaction and most importantly it developed customer loyalty. These are the main goal of ever firm in the supply chain, starting from raw material, production, distribution and down to the final consumer. This work is developed to investigate the impact of supply chain management collaboration activities on customer service in an inter-organizational context. This is done by examining how effective collaboration in supply chain management creating confidence and trust between vendor-customer relationships that provides benefit to both organizations; one of such benefit is improved customer service. This can be obtained through the use of Electronic Data Interchange (EDI), which ensures that products are delivered to customers faster with great accuracy, and demand forecasting and inventory management, which ensures that vendors maintain optimal inventory level so that they always have what customers want in stock. The method used in this work is by gathering information from several articles, journals and text books relating to this research work. There is a total of 49 including journals, books and articles used in this work, all of which are related to this study.

scholarly journals Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zohreh Doborjeh ◽  
Nigel Hemmington ◽  
Maryam Doborjeh ◽  
Nikola Kasabov
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Demand Forecasting ◽  
Interdisciplinary Approach ◽  
Behaviour Pattern ◽  
Data Sets ◽  
Data Modelling ◽  
Content Type ◽  
Hospitality And Tourism ◽  
Tourism And Hospitality

Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

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