A smart scale for efficient inventory management based on design science research principles

Chou, Tung-Hsiang; Chen, You-Sheng; Pan, Chung-Wei; Chang, Hung-Hsuan Article A smart scale for efficient inventory management based on design science research principles Contemporary Economics Provided in Cooperation with: VIZJA University, Warsaw Suggested Citation: Chou, Tung-Hsiang; Chen, You-Sheng; Pan, Chung-Wei; Chang, Hung-Hsuan (2024) : A smart scale for efficient inventory management based on design science research principles, Contemporary Economics, ISSN 2300-8814, University of Economics and Human Sciences in Warsaw, Warsaw, Vol. 18, Iss. 2, pp. 153-170, https://doi.org/10.5709/ce.1897-9254.531 This Version is available at: https://hdl.handle.net/10419/312947 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ www.ce.vizja.pl 153 This work is licensed under a Creative Commons Attribution 4.0 International License. Even in the context of Industry 4.0, conventional warehouse management continues to encounter challenges. Amidst these obstacles, innovative solutions are imperative. Order picking, a critical process with significant implications for customer service, remains labor-intensive within warehousing operations. Current manual methods result in prolonged inventory cycles and inherent accuracy complexities. To address these issues, this research has used the Design Science Research (DSR) method for developing a device called the Smart Scale, which helps optimize warehouse inventory management. This device, which is tailored specifically for lightweight items such as chewing gum, screws, and fasteners, uses weight sensors to calculate real-time quantities dynamically. By reducing human error and enhancing item supervision, the Smart Scale improves inventory precision and time and cost efficiencies. 1. Introduction1. Introduction Industry 4.0 has existed for a while, yet traditional issues such as inventory problems persist in warehouse management. The rise of e-commerce has led to increased production speed and an increase in the number of orders for some factories, resulting in reduced lead times, shorter product lives, and increased inventory turnover rates, all of which increase the pace of work in warehouses (Tompkins & Smith, 1998). Order picking—which is the process of retrieving a product from a warehouse in response to a customer request—is a pivotal procedure consuming a significant amount of labor and directly affecting the service quality perceived by downstream customers (Bartholdi & Hackman, 2019). Most labor-intensive operations in warehouses with manual systems involve high operational costs and are time-consuming (Deshpande & Kumar, 2020). To address these problems, warehouse managers can choose from various technologies. According to Koster (2008), warehouses that must quickly handle many customer order lines can use techniques involving a “split-case order-picking” system (i.e., a picker-to-parts system). There is no one best technology for split-case order picking. Pick-to-light systems, which use electronic labels and related software, are standard in the inventory industry driven by the Internet of Things (IoT) (Su et al., 2019). Other technologies used in split-case order picking include radio-frequency systems, barcode scanners, vision picking, put-to-voice systems—each having a distinct set of applications (Berger & Ludwig, 2007; Fang & An, 2020). Thanks in part to its reputation for high-quality production and fast delivery, Taiwan has estab lished A Smart Scale for Efficient Inventory Management Based on Design Science Research Principles ABSTRACT C80, L86. KEY WORDS: JEL Classification: Industry 4.0, DSR, IoT, intelligent warehousing, order picking. Department of Information Management, National Kaohsiung University of Science and Technology, 1 University Rd, Yanchao District, Kaohsiung City, Taiwan Correspondence concerning this article should be addressed to: Tung-Hsiang Chou, Department of Information Management, National Kaohsiung University of Science and Technology, University Rd, Yanchao District, Kaohsiung City, Taiwan. E-mail: [email protected] Tung-Hsiang Chou , You-Sheng Chen , Chung-Wei Pan , Hung-Hsuan Chang Primary submission: 22.01.2024 | Final acceptance: 03.02.2024 154 Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang 10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS Vol. 18 Issue 2 153-1702024 itself as a significant manufacturer in the global market for screws and nuts (Huang, 2021). The screws and nuts stored in Taiwanese warehouses are diverse and numerous, making the counting of these products both costly and prone to error. Indeed, throughout the world, efficient inventory management remains a problem for industries dealing with small, lightweight materials. To tackle these issues, a common practice is to divide the materials into batches packaged in plastic bags or boxes. However, counting errors still occur in the production department owing to the difficulty of precisely counting small objects. Also, not all products are able to have an attached radio-frequency identification (RFID) tag. In short, there is no precise and fool-proof method for counting small objects individually in warehouse inventory. Most existing research on order picking focuses on reducing travel and balancing problems by optimizing routing. For example, the retrieval-order request in a warehouse can be viewed as a traveling salesperson problem (TSP) that can be efficiently solved with computer algorithms (Hall, 1993; Roodbergen & Koster, 2001). Another popular topic of research is the bucket-brigade system, where each worker passes an item from one station to another and from one stage of processing to another (Alligood et al., 1996; Bartholdi & Eisenstein, 1996). These and other studies have offered valuable insights into order-picking problems and solutions, but intricate challenges persist with respect to the management of small-item inventories (Li et al., 2023; Mugoni et al., 2023). In the present study, its central aim is to show how both IoT technology and cloud technology, when based on the DSR method, can improve the orderpicking process for small materials. This study revolves around the following research question: how can companies leverage the DSR method to develop a technical service that reduces manual-picking errors and that improves real-time calculations of small-item inventory? To answer this question, this research has set out to achieve three primary research goals: 1. Use the DSR method to develop the Smart Scale, which is a device that should enable precise, real-time calculations and tracking of small items in an inventory. 2. Assess the effectiveness of the Smart Scale regarding warehouse efficiency and operational costs while iteratively optimizing the design of the Smart Scale according to expert opinions. 3. Investigate the economic value of implementing the Smart Scale in smalland medium-sized enterprises (SMEs) in Taiwan. This study proposed Smart Scale should encompass real-time management functionalities and data-inquiry capabilities, facilitating instant information dissemination and manipulation through a web or mobile interface. The algorithm embedded within the device should help to reduce both human errors and labor costs. In the end, this research hopes to address managerial and economic issues in ways that benefit SMEs and consumers alike (Chaopaisarn & Woschank, 2021; Özşahin et al., 2022). The structure of this paper is as follows: In Section 2, this research reviews the literature on related topics. In Section 3, this research discusses the research method. In Section 4, this research discusses the requirements and the architectural design for its proposed Smart Scale. In Section 5, this research reports the results of the initial implementation of Smart Scale. Finally, in Section 6, this research concludes the paper, point out limitations of its findings, and suggest directions for future research. 2. Literature Review2. Literature Review In this study, it explores the dynamic interplay between traditional and cutting-edge methods in the contemporary landscape of inventory management. This section comprehensively examines three distinct yet interconnected domains pivotal to the evolution of contemporary inventory control: intelligent warehousing, counting-scale technology, and DSR. 2.1. Intelligent Warehousing and Order Picking The term ‘warehousing’ refers to the largescale storing of goods that are not immediately used or sold (Kadwe & Saha, 2018). The term ‘intelligent warehousing’ refers to a storagemanagement concept that integrates information and communication technology (ICT) through the IoT, cloud computing, and mechanical circuits in www.ce.vizja.pl 155 A Smart Scale for Efficient Inventory Management Based on Design Science Research Principles This work is licensed under a Creative Commons Attribution 4.0 International License. ways that reduce storage costs, improve efficiency, and improve the ability of management to respond quickly to changes in demand. Order picking is a critical sub-process of warehousing and typically requires that warehouse workers traveling to a specific location in a warehouse, find the desired item, and retrieve it. Even with the introduction of robots to decrease labor and streamline the search and retrieval of items in aisleways, human workers are still responsible for executing straightforward physical and cognitive tasks throughout warehouses, including in packing stations (Su et al., 2019). As a result, the travel time required to retrieve an order is a non-value-adding expense and wastes labor hours. Much research has been conducted on reducing travel time by optimizing routing algorithms that consider the geometric layout of a warehouse and that quickly identify optimal TSPbased solutions to travel-time problems (Hall, 1993; Roodbergen & Koster, 2001). Xu et al. (2020) argue that management can streamline warehouse operations by integrating picking tasks and storage-location tasks into a single process without altering the given picker’s existing walking path. Some scholars have suggested that methods using differential equations can optimize workflows in complex, dynamic systems (Alligood et al., 1996; Bartholdi & Eisenstein, 1996). 2.1.1. The Split-Case Order-Picking System Management systems and order-picking systems are crucial for warehouses that need to connect with the world affordably. For order picking, most warehouses rely on human workers who retrieve one or more unit loads and bring them to a designated warehouse location by foot or by vehicle. To improve the efficiency and accuracy of order picking, many warehouses use multiple methods, such as split-case order picking (i.e., each picking, piece picking, or break case; Bartholdi & Hackman, 2019), which involves selecting individual units that are then packed into a carton. Multiple technologies can augment the efficiency Table 1 Types of Split-case Order-picking Systems Type Operation Features Study citations pick-to-light (PTL) systems Uses software, electronic labels, and illuminated digital instructions in place of a traditional paper system to guide employees. (1) Is suitable for numerous orders involving a small product count. (2) Can process multiple orders simultaneously. (3) Supports the grouping of orders. (Su et al., 2019; Swenja et al., 2022) voice-picking (put-tovoice) systems Provides warehouse workers instructions through headsets for picking tasks, followed by system confirmation. (1) Permits hands-free and user-friendly interaction between warehouse workers and the system. (2) Decreases training time and standardizes workflows. (3) Facilitates flexible personnel scheduling. (de Vries et al., 2016; Lager et al., 2021) 156 Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang 10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS Vol. 18 Issue 2 153-1702024 and accuracy of order picking, but the suitability of a particular technology varies according to the scenario. Research has identified five types of splitcase order-picking systems: pick-to-light (PTL) systems, voice-picking (put-to-voice) systems, radio-frequency (barcode) systems, vision-picking systems, and goods-to-person (GTP) systems. Each one presents distinct operational methods and features suitable for diverse scenarios and needs, as shown in Table 1. Through IoT technology, an intelligent orderpicking service can use sensors to obtain real-time information on the quantity, type, temperature, humidity, weight, and location of goods in a warehouse. The data are then transferred to a cloud system, allowing real-time and remote monitoring of goods and orders. With advances in IoT, robots, and big-data technology, warehouse management systems can track and process goods and orders with ever increasing efficiency. This research will demonstrate, in this paper, that Smart Scale stands out for its ability to enable both real-time calculations and the precise, streamlined tracking of small inventoried items. 2.2. Counting Scale Smart Scale relies chiefly on a weight sensor with an electronic counting scale. This scale accurately calculates quantities of inventoried items, improves the efficiency of warehouse labor, and reduces labor costs. 2.2.1. Internet of Things (IoT) The IoT refers to interconnected digital devices that handle large amounts of data in communication networks such as the internet. IoT technology has widespread applications in various industries and fields, including smart homes, logistics, agriculture, and healthcare. To tap into the potential of the IoT, current research focuses on addressing key issues such as data collection, management, security, and privacy Moreover, emerging technologies, such as artificial intelligence, blockchains, and edge computing have greatly enhanced the functionality and performance of IoT systems. 2.2.1. Weight Sensors Table 1 Types of Split-case Order-picking Systems (Continued) Type Operation Features Study citations radio-frequency (barcode) systems Enables employees to scan RFID or barcodes from multiple locations to ensure the correct selection and packaging of items. (1) Enhances the userfriendliness of warehouse operations. (2) Decreases training time. (3) Simplifies shipping processes. (Kubáňová et al., 2022; Sarkar et al., 2022) vision-picking systems Employs software-based augmented-reality (AR) technology to facilitate warehouse tasks dependent on precise inventory locations and times. (1) Requires minimal training. (2) Rapidly locates inventory. (3) Facilitates simple tracking operations. (Fang & An, 2020; Gialos & Zeimpekis, 2020) goods-to-person (GTP) systems Permits the roller-conveyor transport of containers to selectors for retrieval of required items. (1) Reduces picking errors. (2) Offers ergonomic handling. (Ashgzari & Gue, 2021; Bozer & Aldarondo, 2018) www.ce.vizja.pl 157 A Smart Scale for Efficient Inventory Management Based on Design Science Research Principles This work is licensed under a Creative Commons Attribution 4.0 International License. The load cell inside a weight sensor uses a strain gauge, which measures the stress and force—and ultimately the weight—applied to surrounding metal components. Specifically, the resistance value of the strain gauge changes in relation to the degree of bending and deformation that the metal components undergo. The strain gauge then calculates weight according to the Wheatstone bridge principle (Nachazel, 2020). The following diagram is an example of how weight sensors with strain gauges work: R1, R2, R3, and RX are four resistors in a Wheatstone bridge circuit. Three resistors (R1, R2, and R3) have fixed resistance values. When the fourth resistor (RX) changes, the voltage of the circuit between points A and B changes. By measuring the difference in voltage between A and B (VAB), the strain gauge can determine changes in physical weight and quantity in the environment and can thus achieve the measurement objective. For example, assume that the current flow through R1 and R2 is I1 (Equation 1), the current flow through R3 and the sensor (RX) is I2 (Equation 3), and the bridge supply voltage is VCC. These relationships are expressed in Equations (1) through (5), as shown below (Wasson, 2000). Using Ohm’s law, one can calculate the voltage at each end of the resistors. The R1 and R2 circuits divide the voltage of the voltage common collector (i.e., the VCC), and the voltage at both ends of the R2 resistor is VA (Equation 2). In the R3 circuit and the sensor (RX), R3 and RX divide the voltage of the VCC, and the resulting voltage at both ends of the R3 resistor is VB (Equation 4). Let us use Ohm’s law to calculate VA and VB here: (1) (2) (3) (4) (5) The voltage is in equilibrium if VA − VB = 0, meaning that R1 = R2 = R3 = RX. In this state, one can measure a physical quantity by measuring the change in the voltage difference VAB, as described above in Equation (5). This concept originates from bridge-circuit research conducted by Wasson (2000). Any alteration in the resistance values results in an imbalance in the bridge and leads to a detectable change in the voltage difference. This change is directly proportional to the variation in the measured physical quantity. The resistance-bridge principle is a reliable and accurate method for measuring physical quantities because it exploits the balanced voltage equilibrium in a circuit. The implementation of this principle rests on Figure 1 The Configuration of the Wheatstone Bridge Circuit Used in this Study for Sensor Analysis and Voltage Calculation Note: (Adapted from Plunkett & Cross, 2014; Wasson, 2000) 158 Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang 10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS Vol. 18 Issue 2 153-1702024 a combination of a microcontroller unit (MCU), such as the LinkIt7697 by MediaTek Inc., and an analog-todigital (A/D) converter. Together, these components enable the conversion of analog signals into digital data, facilitating efficient data processing, calculation, and interfacing with external systems. When integrated into an IoT system, weight sensors that are equipped with environment sensors can facilitate the real-time analysis of not just weight and quantity but also type of product, temperature, humidity, and location. The data, upon being transferred to a cloud system, can promote real-time and remote warehouse management. In summary, counting scales accurately measure quantities by employing a weight sensor and by following the Wheatstone bridge principle. When integrated into weight sensor, components like MCUs and A/D converters enable efficient data handling and interfacing, providing an effective solution to the challenges of warehouse management. 2.3. Design Science Research (DSR) Design science research (DSR) is the systematic development and evaluation of solutions to problems that arise in real-world scenarios (Hevner et al., 2004). Because DSR is a science, it promotes the sharing of research findings with the broader academic and professional community. This exchange of data and the repeatability of DSR experiments benefits the generation of rigorous knowledge. DSR is an approach to research that focuses on the development of innovative artifacts, such as new technologies, systems, or processes. Like any research method, DSR has its advantages and disadvantages. The DSR has some advantages, it emphasizes the creation of practical solutions to real-world problems and encourages innovation by promoting the development of novel artifacts. Researchers have the opportunity to design and implement creative solutions that address specific challenges in various domains. DSR also is problem-centric, meaning it starts with identifying and understanding a problem before proposing and creating a solution. This ensures that the research is relevant and addresses actual needs. DSR often involves an iterative process of designing, building, and evaluating artifacts. This iterative cycle allows researchers to refine and improve their solutions based on feedback and testing. However, DSR may produce artifacts that are specific to particular contexts, making it challenging to generalize findings to other settings. The focus on solving specific problems may limit the broader applicability of the developed solutions. The design process involves subjective decisions, and the researcher's background, experiences, and biases can influence the outcome. This subjectivity may raise questions about the validity and reliability of the research. Designing, building, and evaluating artifacts can be time-consuming. The iterative nature of the process, while valuable, may extend the duration of the research project. DSR may require significant resources, including financial, technological, and human resources. Access to specialized expertise and equipment may be necessary for the successful implementation of designed artifacts. It's important to note that the advantages and disadvantages of DSR depend on the specific goals of the research and the context in which it is applied. Therefore, this research need to consider these factors when choosing the appropriate research methodology for this study. In summary, Design Science Research in management focuses on creating practical solutions to organizational challenges through an iterative and stakeholder-involved design process. While it offers advantages in terms of innovation and tailored solutions, challenges related to complexity and resource requirements should be carefully considered. Successful DSR in management can lead to improved managerial practices and organizational outcomes. 3. Methodology3. Methodology Drawing on the DSR approach described by Hevner et al. (2004), this research has developed the Smart Scale, which facilitates the real-time calculation of small-item inventory quantities and reduces manual picking errors. This technological innovation directly optimizes warehouse efficiency, reduces costs, and improves customer satisfaction. All of these outcomes have huge potential commercial value. In developing the Smart Scale, it generally adhered to a widely accepted process consisting of six stages: problem identification, objective identification, design and development, demonstration, evalu- www.ce.vizja.pl 159 A Smart Scale for Efficient Inventory Management Based on Design Science Research Principles This work is licensed under a Creative Commons Attribution 4.0 International License. Figure 2 The Application of DSR to the Environment and Knowledge Contexts of the Proposed Smart Scale Note: Revised from Hevner et al. (2004), p. 80. ation, and communication (Sarkar et al., 2022). The only change that this research made to this process was to turn the communication stage into a guideline for its demonstrations and evaluation stages. Thus, in this study, Smart Scale rested on five, not six, stages. Also, this research should note that DSR architecture addresses two contexts: the environment context and the knowledge context. In this study, the environment context (i.e., relevance) concerns the order-picking challenges faced by Taiwanese SMEs with respect to inventories; and the knowledge context (i.e., rigor) encompasses system analysis applied to current developments in the IoT, industry 4.0, intelligent warehousing, and split-case order-picking systems. Figure 2 illustrates these two contexts in a DSR framework . In conducting system analysis (SA), it used unified modeling language (UML) notation to present system structures and processes with greater clarity (Dennis et al., 2020). One of the primary advantages of UML notation is that it provides standardized symbols and diagrams that effectively convey the relationships among different elements within a system. it used UML notation to construct use-case diagrams that precisely define system functions. This research also used two types of interaction diagrams: it used sequence diagrams to illustrate the interplay between various objects, and it used activity diagrams to illustrate the interplay between various actions. As it noted, the DSR process in this study consists of five stages: problem identification, objective identification, design and development, demonstration, and evaluation (with communication serving not as a stage but as a guideline for the last two stages). In the problem-identification stage, it identified errors related to both manual order picking and the realtime calculation of small-item inventory quantities. In the objective-identification stage, this research established goals that would guide development of the Smart Scale Service. In the design-and-development stage, it created the architecture for the Smart Scale Service. Then, it undertook the final two stages— demonstration and evaluation—to validate the research findings. Table 2 shows how this research applied the five DSR stages to the current study. 160 Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang 10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS Vol. 18 Issue 2 153-1702024 Table 2 The Six DSR Stages in Development of the Smart Scale Service DSR Stages Description DSR-Stage Methods DSR Guidelines Source Stage 1: Problem Identification Defining challenges in a system. Perform a literature review and follow the AEIOU framework. Guideline 1: Conceptualize the problem. Guideline 2: Assess the relevance of the problem. Academic literature Case studies Company data Stage 2: Objective Identification Defining ways of overcoming challenges in a system. Consider previous prototypes and consult stakeholders. Guideline 3: Conceptualize the objective. Guideline 4: Review the literature. Company feedback Stage 3: Design and Development Creating a service— in this study, the Smart Scale—that will achieve the defined objectives. Methodically apply knowledge to the creation of the service. Guideline 5: Follow rigorous R&D methods. Guideline 6: Focus on search processes. Existing theories Our own problem-solving knowledge Stage 4: Demonstration Performing the service. Present the device to stakeholders and seek their assessment of the device. Guideline 7: Communicate research. Communicate research Stage 5: Evaluation Assessing the service performance. Note: revised from Hevner et al. (2004) and van der Merwe et al. (2020) 4. The System Architecture4. The System Architecture The following section describes the DSR process that this section followed when designing the initial framework for the Smart Scale Service. 4.1. Problem identification: Initial Conceptual Design In designing Smart Scale, it conducted field visits to a logistics company that was located in Kaohsiung, Taiwan and that relied on PTL systems. (Owing to confidentiality concerns on the part of the company, this research did not have access to video recordings of the operation.) This research identified company requirements and prevalent problems with company operations by using the AEIOU framework. It served as a design-conceptualization tool for interpreting and coding data collected during its ethnographic research of the Taiwanese company (Xu et al., 2020). More specifically, the AEIOU framework identifies five types of elements to be coded in research: activities (usually goal-oriented actions), environments (the contexts in which the activities occur), interactions (people’s activities as they relate to other people and objects in the environment), objects (the non-human things that people use in activities), and users (the people who carry out the above activities). Table 3 presents application of the AEIOU framework to the Kaohsiung-based company whose operations this research observed for the present study. Author A of the present study visited the Kaohsiung company in March 2019 and observed the challenges faced by employees dealing with warehouse items. Author A noted that employees who packed items faced the challenge of counting small items. Order picking is a standard aspect of PTL systems and it helps employees quickly identify the items on order and retrieve the correct number of items from the relay area. The logistics company it visited required a relatively large workforce to count items www.ce.vizja.pl 167 A Smart Scale for Efficient Inventory Management Based on Design Science Research Principles This work is licensed under a Creative Commons Attribution 4.0 International License. Smart Scale stands out as a major technological asset for warehouse management. The device offers distinct advantages over established technologies: it reduces search times and calculation errors while improving data transmission, worker well-being, and overall efficiency (Żebrowska-Suchodolska & Karpio, 2022; Zhao et al., 2022). The scalability and adaptability of the Smart Scale make it a viable solution for addressing the evolving demands of contemporary warehouse management. The present study achieved three primary objectives: 1. Development within the DSR Framework: This study’s first objective was to create the Smart Scale according to DSR principles. Through this approach, it successfully engineered a scale capable of conducting real-time calculations and of accurately tracking small-item inventory. 2. Evaluation and Iterative Optimization: This study’s second objective was to evaluate and optimize the Smart Scale’s operational and cost effectiveness. Through rigorous assessments and expert feedback, this research identified areas needing improvement and optimized the device’s design accordingly. 3. Exploration of Commercial Viability: This study’s third objective was to evaluate the commercial and economic potential of implementing the Smart Scale in the context of Taiwan-based SMEs. Its engagement in industry exhibitions, along with constructive feedback from industry experts, showcased the practicality and applicability of the Smart Scale in authentic logistics environments. In summary, this research successfully developed the Smart Scale within the DSR framework, evaluated the device’s effectiveness, and iteratively optimized the device’s design, which proved to have commercial viability at least in the context of Taiwan-based SME warehouses. The Smart Scale aligns with the growing need for efficient, accurate, and technologically driven solutions in warehouse operations, and is thus a promising tool for meeting dynamic market demands and enhancing customer service. Future research in this area would do well to further explore both the managerial aspects of technology-driven warehouse operations and the potential roles played by DSR therein. 6. 6. ConclusionsConclusions In the present study, it has presented a Smart Scale device that—on the basis of DSR principles, UML tools, and IoT and Industry 4.0 technology— facilitates split-case order picking for small inventoried items. The Smart Scale received positive feedback from a variety of manufacturers. Specifically, the device has the potential to improve the real-time monitoring of inventories, streamline supply-chain processes, reduce manufacturing costs, and enhance customer service (Brzeziński & Bitkowska, 2022). 6.1. Limitations and Future Research As with any study, its current one has its share of limitations. One limitation was focused on small, lightweight warehouse items. Future research should extend this line of research to larger items and heavier items, which present unique measurement challenges. IoT devices that service such items require powerful—and costly, resourcedemanding—hardware. Another limitation of this study concerns cost effectiveness. Because this research did not extensively explore this aspect of the Smart Scale, further investigation is necessary to assess the economic viability of implementing a large-scale IoT service in warehouse settings. A rigorous evaluation in this direction would necessarily involve the analysis of not only potential savings but also initial investment costs and long-term maintenance costs in terms of both technology and labor. A particularly interesting topic of research would be the effects, positive or negative, that this technology might have on employee morale. For unavoidable reasons, the findings of this study rest on a small set of empirical data. Future research must accumulate comprehensive data sets and analyze them in line with reliable empirical quantitative and qualitative methods. Only in this way can it accurately grasp the impact of smart technology on industrial settings, customer service, and—more broadly—market economies. This research focused current study on warehouse operations. Of course, smart technology has a vast number of actual and potential applications. One area that interests us is the integration of big data and augmented reality (AR) technologies into un- 168 Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang 10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS Vol. 18 Issue 2 153-1702024 manned vehicles. Research in this and other areas may revolutionize the efficiency and accuracy of warehouse operations. Finally, although the setting of this research was Taiwan, the technology that it is proposing has obvious applications across national and international contexts. Future research can explore whether national and regional settings affect the performance of such Smart Scale devices. 6.2. Challenges of DSR in Management The management issues can be complex, and designing effective solutions may require a deep understanding of organizational dynamics, human behavior, and cultural factors. However, implementing new management practices or tools may require integration with existing organizational systems, and this can pose challenges in terms of compatibility and user adoption (Adeel et. al, 2023 and Bachev, 2013) This research developing and implementing designed smart scale may demand significant resources, both in terms of time and financial investment. The design decisions may be subjective and influenced by the researcher's perspectives, potentially leading to biases in the designed solutions. This research implementing a smart scale in management involves integrating technology and intelligent service strategically to enhance warehouse performance across various domains. However, it's important to consider ethical implications, data privacy, and potential challenges associated with adopting advanced technologies in the management context of DSR. AcknowledgmentsAcknowledgments We extend our gratitude to the organizers of the ACIEK 2023 conference and all of its participants. By having the opportunity to share our research findings at the conference, we were able to receive valuable insights and feedback. ReferencesReferences A deel, S., Daniel, A. D., & Botelho, A. (2023). The effect of entrepreneurship education on the determinants of entrepreneurial behaviour among higher education students: A multigroup analysis. Journal of Innovation & Knowledge, 8(1), 100324. Alligood, K. T., Sauer, T. D., & Yorke, J. A. (1996). Chaos: An Introduction to Dynamical Systems. Springer. Ashgzari, M. S., & Gue, K. R. (2021). A puzzle-based material handling system for order picking. International Transactions in Operational Research, 28(4), 1821–1846. https://doi.org/10.1111/itor.12886 Bachev, H. (2013). Risk management in the agrifood sector. 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