scholarly journals Statistical improvements to the Advanced Planner & Optimizer Module of a Food Production Company

2017 ◽  
Author(s):  
daniel sarcos
Keyword(s):  
Food Production ◽  
Planning Process ◽  
Historical Data ◽  
Demand Forecasting ◽  
Model Parameters ◽  
Demand Planning ◽  
Historical Data Analysis ◽  
Forecasting System ◽  
Definition Of ◽  
Production Company

Objectives: An analysis of the SAP-APO demand forecasting system of a food company is carried out, describing how the demand-planning process is carried out from start to finish, with emphasis on the Demand Planning (DP) tool, Found in the system, which is responsible for generating demand proposals for future months.Methods/Statistical analysis: It was divided into definition of the information system, collection of historical data, analysis of demand forecasting, adjustment of model parameters and implementation of improvements.Findings: Redefining the parameters of the statistical models of the SAP-APO demand moduleApplication/Improvements: The percentage of products to be manually adjusted decreased.

scholarly journals An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Zeynep Hilal Kilimci ◽  
A. Okay Akyuz ◽  
Mitat Uysal ◽  
Selim Akyokus ◽  
M. Ozan Uysal ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Support Vector Regression ◽  
Historical Data ◽  
Demand Forecasting ◽  
Support Vector ◽  
Series Analysis ◽  
Integration Strategy ◽  
Forecasting System

Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.

Mind the gap – Assessing maturity of demand planning, a cornerstone of S&OP

2018 ◽  
Vol 38 (8) ◽  
pp. 1618-1639 ◽  
Author(s):  
Ann Vereecke ◽  
Karlien Vanderheyden ◽  
Philippe Baecke ◽  
Tom Van Steendam
Keyword(s):  
Performance Management ◽  
Online Survey ◽  
Planning Process ◽  
Empirical Work ◽  
Assessment Model ◽  
Forecast Performance ◽  
Content Type ◽  
Demand Planning ◽  
Maturity Assessment ◽  
Forecasting System

Purpose The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations. Design/methodology/approach The authors developed a maturity assessment model for demand planning through iterations of theoretical and empirical work, combining insights from literature and practitioners. An online survey is developed to validate the model using data from different industries. Findings The authors identify six dimensions of demand planning maturity: data management, the use of forecasting methods, the forecasting system, performance management, the organisation and people management. The empirical study indicates that demand data are well managed and organisation readiness is high, yet improvements in the forecasting system and the management of forecast performance are needed. The results show a positive relationship between the size of an organisation and its demand planning maturity. Practical implications The contribution of this work is to propose an assessment model and survey instrument for demand planning maturity. This will help the practitioner to understand the current level of maturity of the demand planning process, reflect on the desired level and develop action plans to close the gap. Originality/value There is broad literature on process maturity assessment in general and on sales and operations planning (S&OP) maturity in particular. However, there is no comprehensive model for assessing the maturity of demand planning, which is a specific and critical process within the overall S&OP process. The authors fill this gap by offering a demand planning maturity model.

scholarly journals Perencanaan Produksi dan Pengendalian Persediaan pada Perusahaan Manufaktur

2021 ◽  
Vol 8 (01) ◽  
pp. 14-27
Author(s):  
Myra Beatrice Soeltanong ◽  
Catur Sasongko
Keyword(s):  
Inventory Control ◽  
Production Planning ◽  
Planning Process ◽  
Historical Data ◽  
Demand Forecasting ◽  
Secondary Data ◽  
Production Schedule ◽  
Safety Stock ◽  
Resource Requirements ◽  
Demand Patterns

ABSTRACT This research was conducted with the aim of observing the production planning process applied by PT X then designing a comprehensive production planning and inventory control so that it can overcome the occurrence of shortages and excess of inventory in the company. The research was conducted by observing a springbed manufacturing company PT X in Makassar. The data used are primary and secondary data, using instruments such as interviews, conducted on staffs and management of PT X, site visits, as well as company historical data. The results of the research were in the form of production planning, through demand forecasting, master production schedule, and planning for resource requirements. In addition, the researcher also established an inventory control system that could support the company's production process using the EOQ method, safety stock, and reorder points.  The results of the research are not necessarily applicable to other companies with different demand patterns or to companies with different industries. In addition, the methods used in production planning and inventory control are limited in this study. ABSTRAK Penelitian ini dilakukan dengan tujuan untuk mengamati proses perencanaan produksi yang diterapkan perusahaan manufaktur PT X, merancang perencanaan produksi dan pengendalian persediaan yang komprehensif sehingga dapat mengatasi terjadinya kekurangan maupun kelebihan persediaan pada perusahaan. Penelitian dilakukan dengan melakukan observasi pada perusahaan manufaktur PT X, perusahaan manufaktur yang memproduksi kasur per di kota Makassar. Data yang digunakan merupakan data primer dan sekunder, berupa wawancara yang dilakukan pada jajaran staf dan manajemen PT X, serta data historis perusahaan. Hasil penelitian berupa perencanaan produksi yang melewati tahap peramalan permintaan, pembentukan jadwal induk produksi, serta perencanaan kebutuhan sumber daya. Selain itu, peneliti juga membentuk sistem pengendalian persediaan yang dapat mendukung kelancaran proses produksi perusahaan dengan metode Economic Order Quantity (EOQ), persediaan pengaman, dan titik pemesanan kembali. Hasil penelitian belum tentu dapat diterapkan pada perusahaan lain dengan pola permintaan maupun pada perusahaan dengan industri yang berbeda. Selain itu, metode yang digunakan dalam pembentukan perencanaan produksi dan pengendalian persediaan pun menjadi keterbatasan dalam penelitian ini.

Teaching Computer Based Historical Data Analysis

1994 ◽  
Vol 6 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Charles Anderson ◽  
Robert J. Morris
Keyword(s):  
Data Analysis ◽  
Social History ◽  
Historical Data ◽  
Good Practice ◽  
Computer Assisted ◽  
Historical Data Analysis ◽  
University Of Edinburgh ◽  
Computer Based ◽  
The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

scholarly journals Estimation of the return period of rockfalls according to the block size

2016 ◽  
Author(s):  
Valerio De Biagi ◽  
Maria Lia Napoli ◽  
Monica Barbero ◽  
Daniele Peila
Keyword(s):  
Return Period ◽  
Statistical Approach ◽  
Historical Data ◽  
Risk Estimation ◽  
Block Size ◽  
Return Time ◽  
Correct Definition ◽  
Protection Devices ◽  
Definition Of ◽  
Rockfall Protection

Abstract. With reference to the rockfall risk estimation and the planning of rockfall protection devices one of the most critical and most discussed problems is the correct definition of the design block taking into account its return period. In this paper, a methodology for the assessment of the design block linked with its return time is proposed and discussed, following a statistical approach. The procedure is based on the survey of the blocks already detached from the slope and accumulated at the foot of the slope and the available historical data.

scholarly journals The impact of implementing a demand forecasting system into a low-income country's supply chain

Vaccine ◽  
2016 ◽  
Vol 34 (32) ◽  
pp. 3663-3669 ◽  
Author(s):  
Leslie E. Mueller ◽  
Leila A. Haidari ◽  
Angela R. Wateska ◽  
Roslyn J. Phillips ◽  
Michelle M. Schmitz ◽  
...  
Keyword(s):  
Supply Chain ◽  
Low Income ◽  
Demand Forecasting ◽  
Forecasting System ◽  
The Impact

scholarly journals Forecasting telecommunication new service demand by analogy method and combined forecast

2005 ◽  
Vol 15 (1) ◽  
pp. 97-107 ◽  
Author(s):  
Feng-Jenq Lin
Keyword(s):  
Planning Process ◽  
Historical Data ◽  
Optimal Investment ◽  
Telecommunication Industry ◽  
Market Demand ◽  
Analogy Method ◽  
Service Demand ◽  
Forecast Field

In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

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