Internet of Things in Retail Industry

Module Code CT098 -3-2-RMCT Research Methodology in Computing and Technology Title Project Proposal (Individual) Intake Code UC2F1908IT(IoT) Lecturer Intan Farahana Binti Kamsin Name Grace Ong TP053002 Hand Out Date 19 th February 2020 Hand In Date 4th May 2020 Implementation of Zigbee -enabled RFID Inventory M anagement System in Retail Store s Grace Ong School of Computing , Asia Pacific University of Technology & Innovation [email protected] Abstract - Inventory record inaccuracy (IRI) issues are prevalent across the retail industr ies . In recent years, R adio Frequency Identification (RFID) technology has been considered as a promising solution for IRI and is widely implemented across the retail stores . However, most of the RFID solution s are based on recording the input and output of the inventories to provide a stock situation . In this paper, an effective Io T solution based on the real -time visibility of the inventory items is proposed to solve the IRI issues of fashion retail stores. Firstly, surveys and literature reviews on fashion retail stores will be carried out to further understand the factors generat ing inventory record inaccuracy. Secondly, a prototype of the proposed solution will be built to evaluate its reliability, efficiency, effectiveness, and security. Finally, the implementation of the corresponding potential solutions is suggested along the supply chain for better information flow of the inventories. Index Terms - Internet of Thing, IoT , Zigbee, RFID sensor network , inventory tracking, IRI 1. Introduction As the field of management and business operations becoming more competitive in recent years, the development of a new inventory management system has been increasingly interested among the retail companies. Most of the retail companies re -examine their in ventory management system to identify the opportunity for any improvements to stay competitive in business operations. According to DeHoratius and Raman (2008) , having accurate inventory records in the inventory management system is crucial to the business performance and maintenance of the retail companies as it will affect the decis ion making on forecasting demand and inventory replenishment. A study by Rekik et al. (2019) reported that an average of 54.8% of the audited stoke -keeping units (SKUs) in the fashion retail stores, the inventory records in the inventory management system did not match with the actual physical inventor ies . These discrepancies can cause excess inventory , lead ing to incurring of extra cost s (Chuang & Oliva, 2015) . This paper proposes a new inventory management system based on IoT technolog y such as radio frequency identification (RFID) technology and Zigbee wireless network for identifying the real -time localisation of the inventories to reduc e inventory record inaccuracy (IRI) issues. This research describes the study and the development of an automati c real -time inventory tracking system for a typical fashion retail store which had been cho sen to be the case study in this research . Firstly, a case study in the fashion retail stores will be carried out to understand the problems in managing inventories. Then, a new RFID inventory management system integrated with a Zigbee wireless network will be developed to overcome these issues and provide be tter inventory control. Finally, a pilot test will be conducted to evaluate the performance of the proposed system in terms of its effectiveness, efficiency, security and reliability. 2. Research Background Inventory Management System (IMS) An inventory management system (IMS) is the combination of technology in terms of both software and hardware with operational processes and procedures, that supervises the maintenance and monitoring of stocked products (Gokhale, 2018) . A typical inventory management system consists of the following features (Pontius, 2017) : i. A system that can identify each inventory and its associated information , such as asset tags , barcode or RFID labels . ii. Hardware tools that ca n read the RFID tags or barcode labels on the stocks such as handheld scanners. iii. Software tools that can provide central monitoring of the inventory , including managing the inventory database, generating inventory analysis report, forecasting future demand and so on. Internet of Things (IoT) in Retail Industry The development of the Internet of Things technology has shown an increasing trend in the areas of retail, security and product management in recent years (Valente & Neto, 2017) . This circumstance is because the main enabling capabilities of IoT such as regular sensing , data collection and actuatio n allows new approaches to improve the process of product maintenance and establish long -lasting connection s with client s. (Zaslavsky et al., 2012) . The two leading widely used IoT technologies used in the retail industry are RFID and Zigbee wireless sensor network (Digiteum.com, 2019) . 2.2.1. Radio Frequency Identification (RFI D) Radiofrequency identification (RFID) is one of the standard automatic identification and data collection (AIDC) technolog ies used in the inventory management system . It is a contactless information transmission technology that allows automatic identification and tracking of inventories without a line of sight (Ahsan et al., 2010) . A typical RFID inventory management system consists of three e lements: RFID tags, RFID readers and a software system that can record the information of the inventories . RFID tags can be classified into two categories: active and passive. A comparison between active RFID and passive RFID in details is shown below. Table 1: Comparison table of active RFID and passive RFID (Smiley, 2019) Features Active RFID Passive RFID Frequency 244 MHZ , 2.45GHz 860 -960 MHz Read Range 150m 15m Cost $20 - $50 /unit < $1 /bag Tag Size Smaller than a smartphone Smaller than a business card Asset Size Medium to very large Very small to very large Industry Construction, Oil, Gas, Mining Retail, Healthcare, Manufacturing Location Outdoor Application Indoor/Outdoor Application Attachment Method Rivets, Screws, Zip Ties, Welding Hanging, Epoxy, Adhesives, Zip Tie, Welding Power Internal Battery Powered by RF waves from readers Drawback Batteries only last 3 – 5 years and typically cannot be change d Less effective around metal and water RFID tags also operate in a different range of frequencies. Different frequenc y range s will determine the performance of the RFID tags such as their resistance to interference (Ahsan et al., 2010) . Passive u ltra -high freq uency (UHF) RFID tag s is the preferable choice in the inventory management system as it can perform multiple read rate s, allowing it to identify a large number of tags at a time (Ahsan et al., 2010) . Passive UHF RFID tags can also be printed directly onto the inventories w ith the use of inject -printing technology , which further decre ase the cost (Expósito & Cuiñas , 2013) . 2.2.2. Zigbee Wireless Network Zigbee is a wireless communication standard built on the media access control and physical layer in the IEEE 802.15.4 that features low -power, cost -effective, reliable, products controlling and wirelessly networked monitoring (Yang & Yang, 2009) . Since IEEE 802.15.14 supports most of the communication compo nent s, Zigbee can perform excellent peer -to-peer communication . Zigbee is also known as a sensor network standard which allows up to 65535 nodes in a single network. It has self -healing and self -organizing network structure that can provide multi -hop and routing functions to the packet -based radio protocol (Chandrasekar & Sangeetha, 2014) . Besides that, Zigbee can be used to estimate the coordinate of a target based on the Received Signal Strength Indication (RSSI) (Habaebi et al., 2014) . Barcode IMS VS RFID IMS In this section, two inventory management systems , barcode IMS and RFID IMS will be discussed and analysed in terms of their integration capability with IoT technology to facilitate real -time tracking and localisation of the inventor ies . Figure 1 shows the process of updating the inventories into the IMS database by scanning each of the inventories manually in a barcode IMS. Figure 1: Barcode inventory management system Figure 2 shows the p rocess of updating the inventories into the IMS database by scanning a batch of inventories at a time in an RFID IMS . Figure 2: RFID inventory management system According to both Figure 1 and Figure 2, b oth IMSs serve the purpose of recording the inventories into the system . However, IRI issues can still occur due to execution errors . For example, some of the stocks might get left out during the scanning process . To further understand the characteristics and features o f these systems, a comparison between barcode IMS and RFID IMS in details is shown below . Table 2: Comparison table of Barcode IMS and RFID IMS (Mansuri, 2018) (Archit, 2016) Features Barcode IMS RFID IMS Line of sight Required Not required Read/Write capability No Yes Read Rate Very low throughput. Tags can only be read manually, and one at a time High throughput. Multiple (>100) can be read simultaneously Security level Low . Easily damaged or removed. Cannot be read if greasy or dirty High . Difficult to replicate. Information stored in a much more secure environment Tag Information Storage < 100 char (1D ) < 2000 char (2D) 256 bits Real -time tracking capability No Yes Localisation capability No No Event Triggering capability No Yes Deployment Cost $100,400 $109,200 According to the table above, barcode IMS cannot integrate with IoT technology as it will disable the sensing feature due to its incapability to trigger an event . Although RFID IMS is better than barcode IMS in overall, the scanning of the inventories only occurs during the input and output of the inventories. Both barcode IMS and RFID IMS cannot ensure an accurate inventory record in IMS until the workers perform stocktaking. Therefore, this paper proposes a solution that is capable to detect the real -time location of the inventories with the integration of Zigbee wireless network in an RFID IMS . The proposed solution can be furt her enhanced with the implementation of RFID sensors on the shelves to enable real - time tracking . An illustration of the proposed system is shown below. Figure 3: Zigbee -enabled RFID IMS 3. Problem Statement Inventory record in accuracy (IRI) has always been a significant operational issue in the retail supply chain that indirectly affects financial performance (Fan et al., 2014a) . IRI may stem from multiple factors such as transaction errors, misplacement errors, and shrinkage (Rekik, 2011) . Rekik et al. (2009) reported that shrinkage including spoilage or theft is the main factor generating negative discrepancies between the actual physical inventory level and the inventory record i n the inventory management system (IMS) . Since the inventory shrinkage can be translated as permanent item loss, this phenomenon can cause an accumulation of discrepancies over time. If the shrinkage problem is not corrected, t he situation will cause the a utomatic inventory replenishments in the IMS to be made too late, increas ing the stockout risk which lead s to more sales loss over time (Rekik et al., 20 19) . Several attempts had been made by most of the retail store s to solve IRI issues . For example, retail stores will spend almost 10% of the sales on extra labour expenses to perform a more regular stocktake (Workforce.com, 2004) . However, this solution will dimi nish the profit and also defeat the purpose of using an inventory management system to forecast demand and replenish the inventory automatically . According to Fan et al. (2014b) , Radio frequency identification (RFID ) technology has the potential to eliminate shrinkage problems . However, the current available RFID inventory management system in the market can only provide the inventory situation based on recording the inventory during input and output which technically cannot reduce shrinkage problems (Anssens et al., 2011) . Therefore, investigating another approach of solving IRI issues using the Internet of Things (IoT) technology in providing real -time visibility of the inventory will have practical benefits to the retail stores. 4. Aims and Objectives This research aims to propose an effective IoT solution to solve the inventory record inaccuracy issues of fashion retail store s by focusing on the following specific objectives: i. To implement an RFID inventory management system in the fashion retail stores such as Uniqlo to improve IRI ii. To enable real -time tracking of inventories iii. To enable localisation of the inventories 5. Research Questions i. How RFID inventory management system improves the inventory accuracy of a retail store ? ii. How the deploy ment of multiple RFID sensors on the shelves of retail stores enable the real -time tracking of the inventories ? iii. How the integration of Zigbee wireless network with RFID inventory management system achieve the localisation of the inventories? 6. Significance of The Work The findings of this research will redound to the benefit of retail sectors and supply chains , understanding that inventory record inaccuracy plays an essential role in financial performance. The higher the d emand on inventory management system in providing accurate inventory record for forecasting demand and automatic replenishment justify the need for solving the IRI issues in a more practical approach. Thus, retail stores that apply the recommended IoT solu tion derived from the result of this research will be able to reduce the IRI issues of the retail store. Managers of the retail stores will be guided on how to use the new proposed system so the workers can fully utilise the potential of the inventory man agement system in managing and monitoring the inventories in the retail stores. For the researcher s, the study will help them uncover the potential in using IoT technology to solve IRI issues practically that many researche rs were not able to explore. Thus, a new inventory management system on solving IRI issues may arrive . 7. Methodology This research will use both quantitative and qualitative research approaches to study an in -depth exploration of the factors causing inventory record inaccuracy in fashion retail stores . A survey that consis ts of 5 multiple choice questions and 10 questions with 10 -point Likert scale s about the current inventory management system installed will be given to the manager of the retail store . This survey will be conducted with 15 retail managers of Uniqlo registered around Kuala Lumpur. The quantitative data generated from this survey will be monitored first to eliminate missing data and outliers. Th e data will then be analysed using descriptive statistics including mean, mode and percentage distribution. Besides that, a literature review on the causes of inventory record inaccuracy from both primary and secondary data will be conducted to help in understanding the behaviours of the data collected in the survey. Transcription of the quantitative data collected in the survey will be reported later. After that , the quantitative experimental study will be used to evaluate the feasibility of RFID inventory management system in tracking the inventories in real -time and enabling the localisation of inventories . A prototype will be built based on the RFID inventory management syst em . The environment of the fashion retail store will be replicated in a laboratory with the dep loyment of UHF RFID tags on each inventor y and RFID sensors on the shelves . Theses setups are crucial to mimic the real -world situation of a fashion retail store. Afterwards , the effectiveness of the whole prototype system on detecting the physical invento ry level in real -time will be validated by comparing information received from the RFID sensors with the actual inventory level and the consistency of the information received continuously . Then, a Zigbee wireless network will be integrated into this prototype system to investigate its feasibility in achieving localisation of the inventories. The localisation of the inventory feature will then be evaluated on its consistency of providing accurate coordinates of the inventories in the shelves . The quantitative data generated will be further analysed using descriptive statist ics. Multiple regression will also be used to explain the relationship between the independent and dependent variable . Although a lab -based experiment with the implementation of a prototype cannot completely simulate the real -world situation and behaviours , it helps in testing the minimum feasibility requirements of the proposed system effectively. 8. Overview of the Proposed System Figure 4: Use -case diagram of Zigbee -enabled RFID IMS In this section, an overview of the proposed RFID IMS with a Zigbee -enabled wireless network will be discussed. A user -diagram of the proposed system is shown above. According to the user -diagram, t he basic features of IMS will be implemented in this proposed system such as user administration, inventory management, inventory level checking, future inventory demand forecasting , report generation and automatic inventory replenishment according to the in ventory records. The new feature that is included in the proposed system is inventory coordinate checking . Th e coordinates of a particular inventory will be shown in the IMS according to the location of the shelves, columns and rows. The inventory records can be refreshed by just clicking a button in IMS to activate the scanning of all RFID tags on the inventories after the implementation of Zigbee -RFID readers on each of the shelves of the fashion retail sto res . Hence, this feature can fully replace the manual stocktaking process and ensure that the inventory records in the IMS match the actual physical inventories in the fashion retail store. 9. Conclusion This paper propose d integration of Zigbee wireless network with the RFID inventory management system to so lve inventory record inaccuracy issues. By identifying the location of the inventories in real -time , it not only able to maintain an accurate inventory record in the inventory management system but also supports the users to track the physical location of the inventories . 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