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Proceedingsof the 2nd African International Conference on Industrial Engineering and Operations Management
Harare, Zimbabwe, December 8-10, 2020
Paper Title Here
Abstract
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Keywords (12 font)
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1. Introduction (12 font)
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1.1 Objectives (11 font)
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2. Literature Review (12 font)
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3. Methods (12 font)
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4. Data Collection (12 font)
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5. Supply Chain Network Modeling (12 font)
6. Supply Chain Improvements
7. Results and Discussion (12 font)
7.1 Numerical Results (11 font)
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7.2 Graphical Results (11 font)
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6. Conclusion (12 font)
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References
(12 font)
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Rahman, M. A., Sarker, B. R., and Escobar, L. A., Peak demand forecasting for a seasonal product using Bayesian approach, Journal of the Operational Research Society, vol. 62, pp. 1019-1028, 2011.
Reimer, D., Entrepreneurship and Innovation, Available: http://www.ieomsociet.org/ieom/newsletters/, July 2020.
Reimer, D., and Ali, A., Engineering education and the entrepreneurial mindset at Lawrence Tech, Proceedings of the 3rd Annual International Conference on Industrial Engineering and Operations Management, Istanbul, Turkey, July 3 – 6, 2012, pp. xx-xx.
Reimer, D., Title of the paper, Proceedings of the 5th North American International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10-14, 2020, pp. xx-xx.
Shetty, D., Ali, A., and Cummings, R., A model to assess lean thinking manufacturing initiatives, International Journal of Lean Six Sigma, vol. 1, no. 4, pp. 310-334, 2010.
Biography
(12 font)
Add each author biography – limited to 250 words. (10 font)
Literature Review
If author is mentioned at the beginning for the citation:
Rener (2020) developed the SC network with uncertainty. – For Single author
Rener and Ali (2020) developed the SC network with uncertainty. – For two authors
Rener et al. (2020) developed the SC network with uncertainty. – For more than two authors
or if author is mentioned at the end for the citation:
SC network was developed with uncertainty (Rener 2020). – For single author
SC network was developed with uncertainty (Rener and Ali 2020). – For two authors
SC network was developed with uncertainty (Rener et al. 2020). – For more than two authors
Full details should be provided at the end for Reference Section:
Rener, A., Optimization of the supply chain network using uncertainty, International Journal of Industrial Engineering and Operations Management, vol. xx, no. xx, 2020.
Rener, A., and Ali, A., Optimization of the supply chain network using uncertainty, International Journal of Industrial Engineering and Operations Management, vol. xx, no. xx, 2020.
Rener, A., Ali, A., and Reimer, D., Optimization of the supply chain network using uncertainty, International Journal of Industrial Engineering and Operations Management, vol. xx, no. xx, 2020.
Few aspects to be considered to prepare a literature review:
· Introductory write up for literature review
· Make sure to add some recent references
· Avoid paper-by-paper review. It should be based on category. Similar topics, applications or tools could be added in one paragraph. Few citations should be in a paragraph.
· A summary paragraph should be added.
Figures
· Texts of figure should be readable
· Original high quality pictures
· Center justification
· Title of Figure should be in center and it must be mentioned as “Figure x: …”
· Title of figure should be sentence case with center justification and 10 font
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· One space between texts and figure, figure and title of the figure and title of the figure and texts.
Figure 1. Name of the figure
Tables
· Texts of table should be readable
· Center justification
· Title of table should be in center and it must be mentioned as “Table x: …” It should be added before table.
· Title of table should be sentence case with center justification and 10 font size
· All table numbers must be mentioned in the body of the paper.
· One space between texts and table, table and title of the table and title of the table and texts.
Table 1. Name of the table
References
· References title – 12 font with bold and left justification
· References texts – 10 font
· No numbering should be used for reference title
· Last name and year should be used for any reference citation. Last name and year should be used for single author and double authors. For more than two authors, last name of the first author and “et al.” with year should be used. For examples: Reimer (2009), (Reimer 2009), Reimer and Ali (2009), (Reimer and Ali 2009), Reimer et al. (2009) and (Reimer et al. 2009). Number is not allowed in the reference citation.
· All references must be cited in the paper.
· Journal and conference names should in in italic.
· Title of the book should be in italic.
· All lines after the first line of references list should be indented one-fourth (1/4) inch from the left margin. This is called hanging indentation. .
· Last name and first initial should be should in the reference formatting like below examples.
Chang, T., Wysk, R., and Wang, H., Computer-Aided Manufacturing, 3rd Edition, Prentice Hall, New Jersey, 2006.
Cook, V., and Ali, A., End-of-line inspection for annoying noises in automobiles: trends and perspectives, Applied Acoustic, vol. 73, no. 3, pp. 265-275, 2012.
Khadem, M., Ali, A., and Seifoddini, H., Efficacy of lean metrics in evaluating the performance of manufacturing system, International Journal of Industrial Engineering, vol. 15, no. 2, pp. 176-184, 2008.
Krstovski, S., IEOM Lean Six Sigma Global Competition: COVID-19 Pandemic, IEOM Society International, http://ieomsociety.org/ieom/covid-19/, Accessed Day: June 9, 2020.
Pandian, A., and Ali, A., Automotive robotic body shop simulation for performance improvement using plant feedback, International Journal of Industrial and Systems Engineering, vol. 7, no. 3, pp. 269-291, 2011.
Rahim, A., and Khan, M., Optimal determination of production run and initial settings of process parameters for a deteriorating process, International Journal of Advanced Manufacturing Technology, April 2007, vol. 32, no. 7-8, pp. 747-756, 2007.
Rahman, M. A., Sarker, B. R., and Escobar, L. A., Peak demand forecasting for a seasonal product using Bayesian approach, Journal of the Operational Research Society, vol. 62, pp. 1019-1028, 2011.
Reimer, D., Entrepreneurship and Innovation, Available: http://www.ieomsociet.org/ieom/newsletters/, July 2020.
Reimer, D., and Ali, A., Engineering education and the entrepreneurial mindset at Lawrence Tech, Proceedings of the International Conference on Industrial Engineering and Operations Management, Istanbul, Turkey, July 3 – 6, 2012.
Shetty, D., Ali, A., and Cummings, R., A model to assess lean thinking manufacturing initiatives, International Journal of Lean Six Sigma, vol. 1, no. 4, pp. 310-334, 2010.
Srinivasan, G., Arcelus, F.J., and Pakkala, T.P.M., A retailer’s decision process when anticipating a vendor’s temporary discount offer, Computers and Industrial Engineering, vol. 57, pp. 253-260, 2009.
Biography
Ahad Ali is an Associate Professor, and Director of Master of Engineering in Manufacturing Systems and Master of Science in Industrial Engineering in the A. Leon Linton Department of Mechanical Engineering at the Lawrence Technological University, Michigan, USA. He earned B.S. in Mechanical Engineering from Khulna University of Engineering and Technology, Bangladesh, Masters in Systems and Engineering Management from Nanyang Technological University, Singapore and Ph.D. in Industrial Engineering from University of Wisconsin-Milwaukee. He has published journal and conference papers. Dr. Ali has completed research projects with Chrysler, Ford, New Center Stamping, Whelan Co., Progressive Metal Manufacturing Company, Whitlam Label Company, DTE Energy, Delphi Automotive System, GE Medical Systems, Harley-Davidson Motor Company, International Truck and Engine Corporation (ITEC), National/Panasonic Electronics, and Rockwell Automation. His research interests include manufacturing, simulation, optimization, reliability, scheduling, manufacturing, and lean. He is member of IEOM, INFORMS, SME and IEEE. Dr. Ali is elected as a Fellow of IEOM Society International.
Donald M. Reimer is the managing member of The Small Business Strategy Group, L.L.C and serves as an adjunct professor at Lawrence Technological University. Mr. Reimer holds a Bachelor of Science degree in Industrial Management from Lawrence Technological University and a Master of Arts degree in Political Science from University of Detroit/Mercy. He has been recognized as a professional management consultant with over 45 years of experience in working with closely-held businesses. He has taught courses in entrepreneurship, management and corporate entrepreneurship and innovation for engineers. Mr. Reimer served as member of the Minority Economic Development Committee of New Detroit. He has served as a KEEN Fellow for The Kern Family Foundation. He is member of the Lawrence Tech Alumni Board of Directors and has elected a Fellow of the IEOM Society International. Mr. Reimer is a faculty advisor of the IEOM Student Chapter at Lawrence Tech.
East 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 20.399999999999999 27.4 90 20.399999999999999 West 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 30.6 38.6 34.6 31.6 North 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 45.9 46.9 45 43.9
© IEOM Society International
SUPPLY CHAIN IMPROVEMENT 1
Supply Chain improvement in healthcare using Big Data Analytics
Abstract
A large number of organizations are faced with the problem of expansion complexity. The problem is a result of increased collaboration between the business market and the products they produce. To address this problem, business organizations are planning to use Big Data Analytics solutions. Many scholars and practitioners are interested in knowing how BDA can be used to solve supply chain management problems brought about by the organization’s expansion. However, there is still missing information about how BDA can be used in supply chain management. Therefore, this paper seeks to address the gap in how Big Data Analytics can be used in supply chain management to solve the problem of large amounts of exchanged data. Organizations are focused on various ways of optimizing supply chain visibility to overcome complexity problem and to enhance decision making for mitigating risks along supply chains. This paper provides a clear classification of how BDA can be applied in an organization, general technologies to be used, and the benefits it will bring for an effective supply chain. It further describes the current problems in supply chain management, their solutions, and successful use case examples for illustration. The paper uses the secondary data content analysis method to address three study areas, namely, areas of SCM where Big Data Analytics can be applied, types of Big Data analysis models used in these cases, and Big Data Analytics techniques used to develop these models. After discussing these three areas, the paper exposes a number of research gaps for future study. The paper summarizes the study of Big Data Analytics on how they can be implemented, evaluated, and applied for efficient and effective supply chain management.
Keywords- Supply Chain, Big Data Analytics, Big Data, supply chain visibility, supply chain management.
Introduction
Organizations are planning to add Big Data Analytics to their set of Information Technology infrastructure to help them manage their increased data volumes. Big companies are projecting big Data as a holistic approach to the process, manage, and analyze the ‘5-Vs’ data-related areas (velocity, volume, value, veracity, and variety). So far, data management in supply chain management has not been satisfactorily done by other IT tools (Ghosh et al., 2015). By managing their enormous data well, health institutions are assured of sustained value delivery, which will give them competitive advantages in the market. Every business is concerned with how it can increase performance by serving the market better wherever corner of the world it might be. Logistical problems and lack of supply chain visibility can make a company lose part of its market share. However, the advent of information technology tools like social media, the Internet of Things, mobile devices, and cloud-enabled platforms has brought a new experience in supply chain management, which has enhanced competitive advantage (Hazen et al., 2014).
The five ‘Vs. ‘ related data problems have forced businesses to shift their attention to Big Data Analytics to support intra-organizational and inter-organizational organizational processes. One of the data concerns is the volume of data, which has increased tremendously due to the business’s expansion in regard to the new collaboration between government organizations, customers, and other businesses. There has been a strain in the available storage spaces for the enormous data in major organizations, and looking for alternative sources is ongoing.
Big Data can be utilized in SCM for predictive, description, and perspective purposes that enhance healthcare organizations to make well-informed decisions for operational and strategic applications. However, some gaps still exist between SC practices and BD theory, like ways to leverage unstructured data. Besides, Big Data can be used in the supply chain through marketing, sourcing, and distribution. Although these operations have been accomplished using older IT tools, Big Data Analysis can do a better job. It was expected that Big Data Analytics would be a game-changer in the industry, but a recent study has shown that it is only 23% of organizations have implemented Big Data into their supply chain management functions.
Literature review
This section discusses relevant literature to put into Big Data Analytics and various ways it can be applied to improve the SCM.
Big Data Analytics
The term as a research field is multidisciplinary and relatively new. Big data is related to business analytics and intelligence, where unstructured data undergo context-specific analysis through sensor-based technologies (Cukier, 2010). From the management perspective, BDA provides actionable insights relevant to making informed decisions that are necessary for performance and competition. From the technological view, BD is essential for data management, collection, and utilization for the main purpose of supporting the business needs, improving existing business processes, and discovering new business models (Fosso et al., 2015). However, Big Data Analytics is different from Business Analytics through the 5Vs, volume, variety, value, veracity, and velocity. Companies and institutions like hospitals are faced with the challenge of data sharing and data integrations.
For data collection, features of data are becoming more complex and diverse. Institutions are logging their activities and transactions, resulting in huge amounts of data that may need to be analyzed and interpreted for better use (Gandomi et al., 2015). Data management needs to be stored in a transparent and safe place, which enables data integration and data sharing. Well-managed data can be used by support businesses, affiliate businesses, as well by the management for decision making (White, 20
12
). On the other hand, for data utilization, data generated is useful in making automated decision making. The analytic insights are essential in identifying problems and possible opportunities for the business as well as highlighting predictive patterns of what will happen in the future.
Big Data and Supply Chain
Big Data is widely used in marketing, business, and other supply chain operations. On the other hand, research establishes the potential, role, and usage of BDA and its application in SCM. Different studies have been conducted to establish the use of data in a company and the analytical potential of SCM, mainly targets on the impact of the application of analytical concepts techniques in SCM planning and execution. The importance of BDA to Supply was highlighted by Fawcett (2013) and defined ‘SC Data management Science’ as “application of qualitative and quantitative methods from various disciplines in relation with SCM theory to solve relevant SCM problems and predict outcomes, taking into consideration availability issues and data quality.” Also, Hazen (2014) studied data quality and highlighted the need to control and monitor data quality and availability in the supply chain processes (Oracle, 2012). He also established that SC professionals are suffering from the challenge of producing, organizing, and analyzing data, which motivates a new way of thinking. As a result, organizations have turned to adopting and perfecting data analytic functions in order to improve SCM performance.
According to Craighead (2009), the competition nowadays has not been any longer between firms but between the individual supply chains. Many companies have been flooded with data, and they try as much as possible to seize data analyzing to achieve competitive advantages. According to Barton (2012), BDA has been transforming the SCM in various ways by enabling data-driven strategies. He also highlighted that BDA helps companies and institutions improve their profitability and productivity by 5% to 6%.
Importance of Big Data Analysis in Supply Chain.
Due to technological advancements in recent years, Supply Chain data has increased at a faster rate. The majority of information is being documented in the form of structured digital data. Big Data has been at the center of large volumes of data being processed and the increased velocity of these data in the supply chain. In addition, many business institutions are deriving huge benefits ranging from customer satisfaction to reducing product delivery time.
Figure 1: percentage ranking of benefits of BDA benefits (Hazen et al., 2014).
Figure 1 shows the results of a study on ten companies concerning the profit and performance benefits of integrating BDA in the supply chains in terms of percentage.
According to statistics in Figure 1, the highest-ranking benefit is the improvement in customers’ demand fulfillment with a percentage of 46%. Also, effective and faster reaction time to SC issues ranked second with 41%. Further, BDA increases supply chain efficiency by 36%. Besides, BDA enhanced greater integration across the supply chain with a 36% mark. Other benefits include optimizing inventory and asset productivity, better customer and supplier relationships, improvement in demand-driven activities, and shortened order-to-delivery cycle times.
According to Ghosh (2015), BDA has offered institutions significant value in areas such as marketing, market demand forecasts, supply decisions, customer feedback, and distribution optimization.
|
Name of business operations |
Contributions to the businesses (%) |
|
|
Risk management |
35 |
|
|
Supply chain operations |
43 |
|
|
sales |
3 8 |
|
|
Product development |
32 |
|
|
IT Analytics |
33 |
|
|
Customer service |
30 |
|
|
Human Resource |
12 | |
|
logistics |
22 |
|
|
Brand management |
8 | |
|
Marketing |
45 |
|
|
Logistics |
||
|
Others |
Table 1: Distribution of supply chain operations (Ghosh, 2015)
The data in Table 1 was collected during the international discourse on healthcare and industrial engineering and operations management in Washington, DC, on September 27, 2018.
Most companies highlighted that they benefited from the use of Big Data Analytics in supply chain operations and marketing.
Sources of Data in the supply chain
The study considered a total of 52 mainstream sources for visualization across the supply chain. The data was divided into structured, unstructured, and semi-structured data based on data volume and data velocity generated for each source.
From the statistical review of the data, the data collected from the point of sale, RFID scans, Sensors, Social media, and Transactions recorded 41%, 42%, 43%, 43%, and 88%, respectively. The data ranked was from eight sources of data analytics and generations. Further, the conclusions were that BDA was essential in supply chain operations like procurement, warehousing, transportation, and marketing.
Methodology
The study used a content analysis methodology to analyze secondary data. According to Rowley (2004), he proposed a 5-step research method to carry out a relevant review, for instance scanning documents, writing notes, structuring and writing down the literature review, and building the bibliography. This paper uses the same methodology for the content analysis of influential works, establishes the recent research areas, and highlights future study areas. The paper also analyzed research tables from other researchers to fully explain the application of BDA to the supply chain for business and marketing. Content analysis methodology helped us rank applications of big data in SCM.
Healthcare supply chain network
Healthcare SC network involves obtaining medical items, managing and delivering healthcare goods and services to physicians and patients. For the process to be successful, physical supplies and information about healthcare services and products normally go through several independent stakeholders, like manufacturers, hospitals, insurance providers, regulatory agencies, and group purchasing businesses (Erevelles et al., 2016). Every healthcare facility is concerned with cutting costs and increasing efficiency in physician and hospital practices. An effective healthcare supply chain network model can help cut costs and proper running of different facility activities. However, the healthcare supply chain is very complex because they involve different stakeholders with different interests. By utilizing data and effective supply chain management, healthcare facilities can efficiently manage inventory and make informed purchasing contracts.
Healthcare supply chain model
As a recommendation, I develop a healthcare supply chain model based on the Causal Loop Diagram. The model focuses more on feedback loops among different healthcare partners. It provides a strategic design for integrating logistic activities like consumption analysis, resource procurement and management, market researches, and asset distribution and planning. The following is a healthcare supply chain model investigating feedback loops in the public healthcare sector.
Healthcare facilities in the same Texas consortium.
Public healthcare yearly expenditure
Number of transactions with suppliers
Risk of failures and uncertainty
Logistics economies of scale
Investments in ICT technologies
Process data control and traceability
Waste reductions like expired drugs
Picking & delivery activities
Purchases economics of scale
Loop 3
Loop 2
Loop 1
Loop4
Jj
Figure 2: Healthcare supply chain network in Texas State consortium showing feedback loops in the logistics networks.
The diagram above involves a consortium of healthcare facilities in Texas State. The diagram aims at communicating strategic loops and links that should be considered in planning and designing healthcare provision to patients at the lowest cost. Figure 2 above shows various loops and dependent variables that capture the whole healthcare system’s behavior.
Improving the supply chain model
Loop 1: It focuses on increasing the number of partners in the Texas state consortium. By increasing the number of partners in the supply chain network and utilizing economies of scale in a logistic hub will make the purchasing unit costs of medical items like medicine and equipment. The suppliers’ transaction cost will reduce, thus reducing the overall expenditure, which will motivate network growth. Loop 1 is meant to reinforce the supply chain and save costs.
Loop 2: This loop focuses on reinforcing the investment of advanced ICT technologies and Automated Data Capture Technologies to capture all relevant supply chain network data. Capturing this data will ensure the reduction of both adverse events and wastages. Adverse effects include adverse drug events, while wastages include the expiring of drugs in the store. This will, in a great way, reduce the public healthcare expenditure and enhance the growth network growth by motivating new members into the network.
Loop 3:By centralizing the logistics processes of different partners in the supply chain network, it will reduce suppliers’ transaction costs. The hub’s presence in the supply chain network will smoothen processes, whereby less time is wasted while integrating different partners into the system. Reducing redundant connections in a supply chain network will reduce the risks of failures in the entire system. The network should be monitored and controlled through detailed risk assessment and efficient performance monitoring. This loop focuses more on balancing the casual loop by reducing the chances of system failures.
Loop 4: In this loop, by increasing the number of partners in the consortium, deliveries and handling processes are going to increase, which brings new economies of scale. The economies of scale in deliveries and picking will reduce public healthcare expenditure. The Healthcare supply chain network will attract new partners in the consortium, which in turn help in the centralization of logistic processes.
The above healthcare supply chain model, if applied, will help reduce the costs of healthcare services and enhance better patient treatment. Streamlining the logistics network in and out of healthcare facilities is the main cause of wastages and risks, which this model solves.
Managing the Healthcare Supply Chain Model
Healthcare organizations require various products essential for the delivery of quality services and care to patients. The effects of interest are needed by the patients, physician providers and other partners serving clients within a locality. For healthcare organizations, partnering with suppliers of the products that are necessary for their institutions requires an understanding of the modes of transportation that is likely to offer them the lowest cost since different modes of transportation attract additional fees. When buying products, healthcare organizations have options of suppliers they can deal with in various transactions. Some suppliers may have warehouses of the needed products in the region that the healthcare organization serves (Ghalehkhondabi, Ahmadi, & Maihami, 2020). Arguably, such product suppliers will use modes of transportation that will attract the lowest costs regarding product acquisition.
Equally, the healthcare model is a consortium of service or product users and relies on the economy of scales to help reduce costs. In this context, a healthcare organization seeking to lower prices of acquisition can join a more extensive network of service or product users. Companies in the logistic and supply management sector locate their warehouses and service facilities in areas that have high demand. Moreover, a more extensive network of healthcare organizations or institutions consuming a product can influence the decision of suppliers to set their warehousing facilities or specific service facility in a region with a high demand of the said products or services. A healthcare organization benefits from the model due to the power of being in a group or consortium that uses certain products and services. Arguably, when suppliers of medical equipment and products establish their warehousing facilities closer to service or product users, healthcare organizations, in such a consortium, benefits in the logistic reduction cost of product delivery. Additionally, products or services are procured when needed and are delivered faster. Consequently, in such transactions, Big Data Analytics becomes an influential tool (Ghalehkhondabi, Ahmadi, & Maihami, 2020). It guide healthcare organizations in evaluating the effectiveness of approaches to acquiring products and services and ultimately deciding on the ones to sustain or discard.
Healthcare organizations require various products essential for the delivery of quality services and care to patients. The effects of interest are needed by the patients, physician providers and other partners serving clients within a locality. For healthcare organizations, partnering with suppliers of the products that are necessary for their institutions requires an understanding of the modes of transportation that is likely to offer them the lowest cost since different modes of transportation attract additional fees. When buying products, healthcare organizations have options of suppliers they can deal with in various transactions. Some suppliers may have warehouses of the needed products in the region that the healthcare organization serves (Ghalehkhondabi et al. 14). Arguably, such product suppliers will use modes of transportation that will attract the lowest costs regarding product acquisition.
Equally, the healthcare model is a consortium of service or product users and relies on the economy of scales to help reduce costs. In this context, a healthcare organization seeking to lower prices of acquisition can join a more extensive network of service or product users. Companies in the logistic and supply management sector locate their warehouses and service facilities in areas that have high demand. Moreover, a more extensive network of healthcare organizations or institutions consuming a product can influence the decision of suppliers to set their warehousing facilities or specific service facility in a region with a high demand of the said products or services. A healthcare organization benefits from the model due to the power of being in a group or consortium that uses certain products and services. Arguably, when suppliers of medical equipment and products establish their warehousing facilities closer to service or product users, healthcare organizations, in such a consortium, benefits in the logistic reduction cost of product delivery. Additionally, products or services are procured when needed and are delivered faster. Consequently, in such transactions, Big Data Analytics becomes an influential tool (Ghalehkhondabi et al. 14). It guide healthcare organizations in evaluating the effectiveness of approaches to acquiring products and services and ultimately deciding on the ones to sustain or discard.
Healthcare organizations can encounter problems concerning products on transit and even the ones in their internal warehouse. When products are lost on transit or misplaced in the warehouse, a healthcare organization suffers greatly. In this context, there is a need for a solution to help handle such situations. Effective coordination between the supplier and the healthcare organization ordering a product or service is necessary. For effective coordination to be realized, a software solution is needed. The software solution (mobile App) synchronizes information to help achieve effective coordination. The technology mentioned must be designed for location and activity tracking and asset tracking. Additionally, the stated software solution will also be a messaging platform. Healthcare organizations need to know the requested product’s status from ordering until it is delivered into the internal warehouse.
Challenges that can be costly to an organization can also be encountered after the products reach the internal warehouse. Warehouse challenges are a consequence of warehouse management errors. A warehouse-management mobile application should be deployed and should use relevant technologies for data collection. Although barcode technology is widely used, a warehouse-management mobile application leaves much to be desired. Moreover, technologies offer healthcare organizations solutions to identify items quickly, optimize stock space, and analyze the products that are prioritized. In this context, technology provides control over segregation, loading, and packaging. Products that are misplaced or lost may be costly to healthcare organizations.
Additionally, healthcare organizations will know the products that are less in the warehouse and needs restocking with the technologies. Equally, the products that are on transit can be tracked, and such data can be useful for healthcare institutions. The duration taken for products to be delivered by a partner can be accurately determined, thereby making the healthcare organization make at times that allows the products to be received early and used where there was a need.
Healthcare organizations encounter problems concerning products on transit and even the ones in their internal warehouse. Statistical analysis has come in handy to provide data for the previous organizations that have adopted new strategies to help them reduce losses. The diagnostic analysis will give an insight into what caused the problems and it will also aid in identifying pattern changes of data. In our case, an application will be the best solution since most organizations have adopted this strategy, and it has proven to be effective. The adoption of the use of the application will be crucial to ensure that products are tracked and this will also help in cutting costs due to losses.
The software solution (mobile App) synchronizes information to help achieve effective coordination. The technology mentioned must be designed for location and activity tracking and asset tracking. Additionally, the stated software solution will also be a messaging platform. Healthcare organizations need to know the requested product’s status from ordering until it is delivered into the internal warehouse.
Challenges that can be costly to an organization can also be encountered after the products reach the internal warehouse. Warehouse challenges are a consequence of warehouse management errors. A warehouse-management mobile application should be deployed and should use relevant technologies for data collection. Although barcode technology is widely used, a warehouse-management mobile application leaves much to be desired. Moreover, technologies offer healthcare organizations solutions to identify items quickly, optimize stock space, and analyze the products that are prioritized. In this context, technology provides control over segregation, loading, and packaging. Products that are misplaced or lost may be costly to healthcare organizations.
Additionally, healthcare organizations will know the products that are less in the warehouse and needs restocking with the technologies. Equally, the products that are on transit can be tracked, and such data can be useful for healthcare institutions. The duration taken for products to be delivered by a partner can be accurately determined, thereby making the healthcare organization make at times that allows the products to be received early and used where there was a need.
References
Cukier, K. (2010). New Rules for Big Data: Regulators are having to rethink their brief. The Economist.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
Ghalehkhondabi, I., Ahmadi, E., & Maihami, R. (2020). An overview of big data analytics application in supply chain management published in 2010-2019. Production, 30. doi:10.1590/0103-6513.20190140
Ghosh, D., & Shah, J. (2015). Supply chain analysis under green sensitive consumer demand and cost sharing contract. International Journal of Production Economics, 164, 319-329.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., &Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108-1118.
Rowley, J., & Slack, F. (2004). Conducting a literature review. Management research news.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. Ghalehkhondabi, Iman, et al. “An overview of big data analytics application in supply chain management published in 2010-2019.” Production, vol. 30, 2020.