Outline

  • Abstract
  • Research Highlights
  • Keywords
  • 1. Introduction
  • 2. Related Studies
  • 3. Proposed Methodology
  • 3.1. Collecting Data with Rfid
  • 3.2. Olap for Data Analysis
  • 3.3. Rule Based Mechanism for Product Classification
  • 3.4. Artificial Neural Network for Demand Pattern Recognition
  • 4. Exemplary Case
  • 4.1. Tracking Sales Volume and Slow Moving Items with Rfid Equipments
  • 4.2. Removing Rfid Reading Error
  • 4.3. Conducting Market Segmentation with Olap
  • 4.4. A-Item Identification with Rule Based Reasoning
  • 4.5. Training the Data of Demand Pattern
  • 4.6. Monitoring Ann for Convergence
  • 4.7. Testing the Final Neural Network
  • 4.8. Demand Pattern Classification
  • 4.9. Articulating the Replenishment Strategy
  • 5. Conclusion
  • References

رئوس مطالب

  • چکیده
  • کلیدواژه ها
  • 1. مقدمه
  • 2. مطالعات مرتبط
  • 3. متدولوژی پیشنهادی
  • 3.1 جمع کردن داده با شناسایی فرکانس رادیویی
  • 3.2 OLAP برای تحلیل داده
  • 3.3 مکانیزم مبتنی بر قاعده برای دسته بندی محصول
  • 3.4 شبکه عصبی مصنوعی برای تشخیص الگوی تقاضا
  • 4. مورد شایان تقلید
  • 4.1 ردیابی حجم فروش ها و آیتم های حرکت کند با تجهیزات شناسایی فرکانس رادیویی
  • 4.2 برطرف کردن خطای خواندن شناسایی فرکانس رادیویی
  • 4.3 انجام بخش بندی بازار با OLAP
  • 4.4 شناسایی آیتم A با منطق مبتنی بر قانون
  • 4.5 آموزش داده از الگوی تقاضا
  • 4.6 پایش شبکه عصبی مصنوعی برای همگرایی
  • 4.7 تست شبکه عصبی نهایی
  • 4.8 دسته بندی الگوی تقاضا
  • 4.9 بیان صریح راهبرد کالاگیری
  • 5. نتیجه گیری

Abstract

This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today’s globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.

Research highlights

► In the responsive logistics information system, radio frequency identification can provide visibility of product flow and capture the real time inventory data. ► The captured data are analysed by online analytical process to identify the market segment. ► With advert of artificial neural network, the demand pattern is recognized and the corresponding replenishment strategy can be formulated.

Keywords: - - - - -

Conclusions

The responsiveness of the logistics workflow system is expected to be significantly leveraged as OLAP and ANN are included to master the information efficiently. The benefits of the proposed system include power control over inventory so as to respond to customer’s needs quickly. The improvement in efficiency will not simply come because the RFID devices can move data faster. The interlinking of the system, which will lead to better data transmission and recognize the demand pattern, has beneficial effect on the entire supply network. This paper proposes an infrastructural framework, involving various emerging technologies for the development of logistics workflow systems with distinct features of the ability to governing supply chain inventory through understanding the parameters of the distribution patterns. The major contribution of the proposed system is to determine the correct replenishment strategy by automatically classifying the distribution patterns within the complex demand and supply chain. It is recommended for the researchers to utilize the innovation information technologies to create values for distributors, manufacturers and retailers with vendor management inventory concept that can obtain a better quality of the ordering process and inventory management. Further research on the infrastructural framework, particularly relating to the synergetic combination of fuzzy logic is needed in order to leverage the uncertainties in the turbulent market.

دانلود ترجمه تخصصی این مقاله دانلود رایگان فایل pdf انگلیسی