SAP SYSTEM DRILLING BLOCK, CLASSIFICATION AND CREATION OF APPROPRIATE PLATFORMS

2021 ◽  
Vol 66 (05) ◽  
pp. 225-227
Author(s):  
Ləman Elxan qızı Ağayeva ◽  
Keyword(s):  
Key Words ◽  
Inventory Management ◽  
Resource Planning ◽  
Spare Parts ◽  
System Management ◽  
Maintenance Planning ◽  
Management Planning ◽  
Block Classification ◽  
Management Processes ◽  
Procurement Cost

For companies wishing to monitor inspection and repair processes through the system, the module proposed by SAP as part of the enterprise source planning application is the SAP PM (Plant Maintance) module. The PM module manages all equipment faults and scheduled repairs. The module, which is integrated with procurement, cost accounting, financial and inventory management processes, can automatically order for technical needs, track your current and overhaul costs immediately, and prepare spare parts. Purchase and consumption operations related to the warehouse can be carried out through tracking and system. These are the functions of the SAP PM module: Planning and implementation of repair activities; Repair for backup; Repair for the purpose of correction; Repair of spare parts in case of critical development of material in the warehouse; Capacity and resource planning; Maintenance planning in addition to repairs and etc. Key words: SAP system, management, planning, drilling, repair, control, organization

Cutter Suction Dredger Spare Parts Joint Inventory Management Research

2013 ◽  
Vol 421 ◽  
pp. 889-893
Author(s):  
Quan Wen ◽  
Shi Dong Fan ◽  
Ting Xiong ◽  
Shou Hui He ◽  
Xiao Wei Xu
Keyword(s):  
Inventory Management ◽  
Construction Project ◽  
Spare Parts ◽  
Management Mode ◽  
Management Research ◽  
Total Cost ◽  
Basic Criterion ◽  
High Efficient ◽  
Procurement Cost ◽  
Safety Operation

This paper aims to study inventory management mode optimization, meanwhile, combined with the best spare parts procurement cost as a basic criterion to explore a way of how to reduce the spare parts cost in routine operation of channel construction units by using joint inventory management. In the terms of this purpose, this paper concentrates on ensuring high efficient safety operation of construction project and making further reduce total cost.

scholarly journals Customer Claims in Automobile Spare Parts Industry – A 3PL Perspective

2019 ◽  
Vol 8 (4) ◽  
pp. 7810-7813
Keyword(s):  
Inventory Management ◽  
Enterprise Resource Planning ◽  
Automobile Industry ◽  
Processing System ◽  
Resource Planning ◽  
Spare Parts ◽  
Stock Management ◽  
Warehouse Management ◽  
Customer Order ◽  
Wear And Tear

Automobile OEM (Original Equipment Manufacturer) has two key lines of business. One being the main equipment sales and the other being spare parts associated to the original equipment. This line of business is either called as After Sales Market / After Market / Spares Market. As part of wear and tear of spare parts of original equipment service and replacement of spare parts comes into picture. A 3PL organization is selected by OEMs to manage their after sales service. Primary responsibilities of a 3PL includes warehouse management where the spare parts of OEMs are received, stored, order processed and delivered to OEM’s end customers. Hence, 3PL companies have major share in ensuring accurate delivery of spare parts to OEM’s end customers. Any wrong or shortage or excess delivery (which is termed as Customer Claims) impacts the customer satisfaction level and adds up logistics cost to both 3PL organization and OEM. This paper aims at evaluating the core reasons behind customer claims. In completing this research, the population selected for study is from 3 leading automobile OEMs and 1,00,000 samples. The results have shown that the key drivers of customer claims are due to stock management in warehouse managed by 3PL organization in following areas of operation.  Inbound (Gate entry of vehicles, Unloading, Physical verification of unloaded stock, Physical putaway of parts in warehouse storage locations)  Outbound (Customer order registration, Picking instructions from the sales order processing system, Physical picking of parts, Packaging of picked parts, Despatch and Delivery at customer end) In the above-mentioned key process areas ERP (Enterprise Resource Planning) application or WMS (Warehouse Management System) or Inventory Management application plays a significant role in avoiding or minimizing customer claims. Apart from applications and Information Technology systems, skill of staff and operatives, discipline of operatives, system controls influence extensively in customer claims to be generated. Background/Objective: To investigate deeper into causes that impact customer claims from 3PL service provider view. Methods / Statistical analysis: The results are based on about 1,00,000 samples for a periodicity of 6 years from 3 major automobile industry linked 3PL organization. Results/findings: The claim samples showed significantly higher due to pick issues ~ 41.7% and wrong labelling issues ~ 42.34%. Other reasons contribute about 15.96% Conclusion: The analysis offers a clear distribution of root causes in the collected samples.

Slow moving stock problem: empirical evidence from Malaysia

2018 ◽  
Vol 60 (5) ◽  
pp. 1148-1162
Author(s):  
Anuar Nawawi ◽  
Ahmad Saiful Azlin Puteh Salin
Keyword(s):  
Inventory Management ◽  
Internal Control ◽  
Management System ◽  
Spare Parts ◽  
Automotive Sector ◽  
Stock Management ◽  
Academic Journal ◽  
Content Type ◽  
Management Processes ◽  
Stock Management System

Purpose The purpose of this study is to identify weaknesses in the internal control of the stock management processes and provide recommendations to improve those weaknesses. The study also analyses whether the current stock management processes are adequately documented and updated regularly. Design/methodology/approach One company involved in the automotive industry was selected as a case study and its stock management examined, focusing on the processes involved and the reliability of the current inventory system. Data were collected via interviews with the selected staff and document analysis on various financial and non-financial company records. Findings The results show that there are loopholes and weaknesses in the current stock management system. This is because of poor technology and a lack of exposure and knowledge of staff involved in the stock management system. Research limitations/implications As this research is based on one company as a study case, generalization must be done with precaution. Access to certain important documents was denied because of the confidentiality. Practical/implications This study will help companies in the automotive sector to improve their stock management process, allowing for more efficient cost control and better services to customers. Specifically, this study identifies weaknesses in the current inventory management and provides recommendations to overcome those weaknesses. This will indirectly prevent fraud and theft and safeguard the cash flow of the company by improving the operation of the procurement and spare parts stock control department. Originality/value This study is original, as it focuses on a company that operates in the automotive sector, which is rare in the internal control literature, particularly in developing markets such as Malaysia. It contains examinations of various internal documents that are generally difficult to be accessed by researchers for the publication in an academic journal.

scholarly journals Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

2021 ◽  
Vol 11 (15) ◽  
pp. 7088
Author(s):  
Ke Yang ◽  
Yongjian Wang ◽  
Shidong Fan ◽  
Ali Mosleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Hierarchical Classification ◽  
Critical Issue ◽  
Spare Parts ◽  
Deep Convolutional Neural Network ◽  
Learning Ability ◽  
Second Phase ◽  
Good Decision

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

Inventory Management, a Decision Support Framework to Improve Operational Performance

2012 ◽  
pp. 581-600
Author(s):  
Jan van den Berg ◽  
Guido van Heck ◽  
Mohsen Davarynejad ◽  
Ron van Duin
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Performance Improvements ◽  
Decision Support Framework ◽  
Comparable Performance ◽  
Potential Benefits

Enterprise Resource Planning systems have been introduced to support the efficient and effective execution of business processes. In practice, this may not fully succeed. This also holds in particular for inventory management (IM), which forms a part of supply chain management. Within this research, by analyzing the IM business process theoretically, eleven potential benefits are indicated. Next, by using a Business Intelligence approach, key performance indicators (KPIs) are selected to measure the performance of IM sub-processes. Integration of these approaches yields an IM performance decision support framework that can be used to obtain a generic, coherent picture of the fundamental IM processes in an organization. In addition, by tracking and analyzing KPI measurements, adequate decisions can be prepared towards the improvement of the operational IM performance. The proposed framework is validated using experts’ opinions and a comparative case study. The experts’ comments yielded a list of top-10 KPIs, based on the measurements of which a set of quick wins can be determined. The case study results show that some of the identified potential benefits are also observed in practice. Future research may reveal that comparable performance improvements are possible in other IM environments (and even in other supply chain domains) based on similar decision support frameworks.

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