Organization Description
Amazon is a multinational technology enterprise founded by entrepreneur Jeff Bezos in 1994. According to a study by Plotkin (2020), the company has positioned itself across various rapidly growing industry segments since its inception; currently, it provides services in e-commerce cloud computing, digital streaming, and artificial intelligence worldwide. However, the nerve center for the main operation and coordination of these services is handled in its Seattle, Washington headquarters. As of 2021, Amazon had attained its status as the world’s largest online retailer and market-leading cloud services provider, based on its annual report, indicating that the company had an operational scale of more than 1.6 million employees globally with an annual revenue generation of $469+ billion.
Amazon operates within the fast-paced, highly competitive market characterized by synergistic technological and societal innovations; despite this, the company has disruptively capitalized on this field through strategic business strategies that have seen it stay ahead of the competition (Columbus, 2022). Amazon plays a vital role in the global electronic commerce industry; in this industry, Amazon has secured a position as one of the leaders with a global market share that is estimated at approximately 15% (Plotkin, 2020). According to the 2021 annual report released by E-Market, the electronic commerce industry has been forecasted by researchers to reach $27 trillion in total market value by 2030 due to ongoing innovations in artificial intelligence, data analytics, and the Internet of Things that simplify consumer experiences and enable customized recommendations
Amazon pioneered a public cloud services market through its Amazon Web Services (AWS) division, whose fast-paced growth is projected Fortune Business Insights to exceed $1 trillion in total value by 2028; this growth was attributed to the development of digital transformation strategies across enterprises and entire industries, which accelerates the process of migration into the cloud-based infrastructures. (Grand View Research, 2022). As a cloud-based infrastructure services provider, Amazon has retained a clear market leadership with a 32% share as of 2022, ahead of Microsoft Azure with a 22% share based on the 2022 market share results released by Fortune Business Insights.
Additionally, Video streaming is another service offered by the company; this service has provided major growth opportunities since the cloud infrastructure is forecast to reach a $223 billion global market value by 2028, as per Grand View Research analysis. Within this service, Amazon Prime Video has emerged as a highly disruptive competitor as compared to market dominators like Netflix and The Walt Disney Company’s Disney+ platform (Plotkin, 2020)
Amazon creates its value by maintaining relentless customer focus, pursuing continuous innovation in new technologies, and leveraging emerging innovations to stay ahead of the competition; extensive investment in AI research has led Amazon to pioneer recommendation algorithms, supply chain automation, voice-enabled assistants, cashier-less stores, and predictive analytics (Columbus, 2020). The company sustains its value over the long-term through the “Flywheel Effect” strategic framework instituted by the company, which aligns user experience, website traffic, third-party sellers utilizing Amazon’s platform, expanding scope of available products, and continuing enhancement of logistical/technological infrastructure in a manner that reinforces synergies essential to outpacing its rivals (Bouakel & Zerbout, 2021).
Proposal
2.1 Diagnosis of the Core Organizational Challenge
Besides Amazon’s enduring rapid growth trajectory across its major business segments, a formidable challenge has distinctly emerged for the company in recent years concerning substantially escalating rates of product returns by customers after purchase and shipment, which now threatens to dampen profitability and productivity if left unaddressed through strategic interventions (Jadhav, 2021) According to insights revealed in Amazon’s 2021 annual financial filings, the overall percentage of total product shipments to customers that dissatisfied customers subsequently returned climbed to a concerning peak of 30% during 2021, marking a pronounced and potentially alarming increase beyond already elevated return rates witnessed in prior years and substantially surpassing the average return frequency of approximately 20% more typical across the broader e-commerce industry (Amazon, 2022; Optoro, 2022). Each percentage point increase in Amazon’s overall return rate can negatively impact overall company profitability by $1.5 to $2 billion per annum if the issue remains unresolved, indicating the massive profit erosion at stake (Kaur & Sharma, 2018). Moreover, processing volumes of product returns at this current and likely increasing rate also detracts from productivity within Amazon’s extensive fulfillment center warehouse operations and interconnected logistics networks, as employees must devote disproportionate time toward inspecting and processing returned items rather than efficiently fulfilling new orders (Plotkin, 2020). Compounding matters further, the predominant method of manually inspecting and evaluating actual product conditions when items are returned must be revised in inefficiency, inconsistency, and inherent flaws associated with relying upon human subjectivity and limited scalability capacity without advanced automation technologies (Plotkin, 2020).
Multifaceted and interconnected factors largely explain the sizable uptrend in return rates witnessed by Amazon compared to retail industry averages, although pinpointing the exact combination of influences remains complex. Possibilities include intentional abuse of Amazon’s famously lenient and customer-friendly return policies, coordinated schemes such as “bracketing” that exploit the ability to return items after use, intrinsic difficulties for customers assessing products through online descriptions and images rather than in-person examination prior to purchase, consumer expectations for ultra-fast shipping that may increase impulse purchases prone to return, the normalization of free return shipping reducing friction, and simply the broadening range of merchandise now offered by Amazon beyond books and media that are more subject to subjective evaluations by customers post-purchase (Kaur & Sharma, 2018). If Left unchecked, the concerning return rate escalation poses a risk of even further increases in the coming years as Amazon’s sales volumes continue expanding globally; this necessitates a prudent application of emerging technologies capable of optimizing the return process through transformation.
2.2 Executive Summary Overview of Proposed Technology-Driven Solution
In response to the aforementioned organizational diagnosis indicating Amazon’s urgent need to strategically address escalating customer product return rates in order to restore profitability, productivity, and competitive positioning against rival e-commerce enterprises, this proposal sets forth recommendations to pursue an integrated technology-centric solution consisting of targeted deployment of machine learning predictive analytics capabilities leveraged to derive actionable insights from Amazon’s vast repositories of existing customer data, in conjunction with adoption of industrial internet of things automation technologies to optimize current deficient manual processes Used in combination, machine learning and industrial internet of things solutions possess the potential to transform Amazon’s returns management process via intelligently predicting customer return propensity, proactively preventing unnecessary product returns through engagement initiatives, and vastly upgrading the efficiency, accuracy, and speed of required product inspections when items are returned by customers.
More specifically, proprietary machine learning algorithms can be developed through Amazon’s world-class artificial intelligence development resources to ingest the company’s massive historical data sets related to customer identities, behaviors, product categories purchased, seasonal variability, and innumerable other dimensions in order to construct robust customer profiles and predictive models that identify specific customer segments exhibiting an especially high likelihood of product returns based upon correlating common attributes (Columbus, 2018). Once these return-prone customer cohorts are intelligently identified at scale leveraging machine learning, pre-emptive measures can be instituted, such as sending targeted e-mails containing educational content clarifying return policies, product use instructions, sizing guides, and other relevant context aimed at reducing misinformed returns or presenting specialized incentives that dissuade abuse of lenient return practices (Kaur & Sharma, 2018). For returned items that cannot be outright prevented through machine learning interventions, industrial Internet of Things technologies, including computer vision enabled by artificial intelligence algorithms and embedded product sensors, can facilitate automation of inspection, classification, and return disposition processes to accurately evaluate true product condition rapidly at scale without reliance upon slow, inconsistent human appraisals (Kaur & Sharma, 2018).
2.3. Current Business Process (as is)
Figure 1
The Current Business Process
2.4. Comprehensive Scope and Key Elements of the Proposed Technology Solution
In order to successfully leverage the combined potential of machine learning and industrial Internet of Things technologies to mitigate Amazon’s escalating product return rates strategically, prudent scoping of the capabilities required for optimal implementation will prove essential; the technological solution proposed encompasses the following key elements:
Several critical development initiatives must be undertaken regarding integrating machine learning capabilities. Amazon’s world-class artificial intelligence research team needs to leverage the company’s massive repositories of historical customer identity, behavioral, product affinity, seasonal variability, and other transactional data in order to architect proprietary machine learning algorithms capable of constructing sophisticated customer profiles and then apply cluster analysis and correlations to identify specific consumer segments exhibiting exceptionally high propensity for product returns based upon common attributes (Kaur & Sharma, 2018). Implementing unsupervised machine learning in this manner will reveal deep insights into return-prone cohorts since traditional business intelligence lacks the analytical sophistication to uncover them. Additionally, structured datasets capturing past impacts of customer engagement initiatives, economic incentives, and educational content variations will fuel the development of reinforcement learning algorithms that progressively enhance and optimize the strategies applied to dissuade unnecessary returns among targeted customer profiles through continuous learning loops (Abdel-Monem & Abouhawwash, 2022).
On the other hand, integrating industrial Internet of Things capabilities and automation technologies must be pilot-tested and refined to optimize current deficient manual return processes at scale. Installations of computer vision systems powered by deep learning algorithms will accurately inspect returned items for damage rapidly and classify conditions. At the same time, convolutional neural networks can decipher key product details with higher precision than human appraisers, reducing dependency on slow, inconsistent manual inspections (Ramasamy & Kadry, 2021). Passive and active IoT sensors embedded in product packaging can continuously monitor key indicators like temperature, humidity, shocks, and geolocation throughout shipping and handling, building an evidentiary record of any environmental conditions or events that could have led to concealed damage precipitating a return (Ramasamy & Kadry, 2021). Post-sale connectivity of purchased products through Internet of Things integration further informs return triage and disposition decisions based on real-time telemetry detailing product usage patterns, functionality, and remaining lifecycle that will facilitate optimal reselling or refurbishment rather than landfill discarding (Abdel-Monem & Abouhawwash, 2022).
2.5. Future Business Process (to be)
Figure 2
Future business after integration of IIoT and ML in Return Processing
2.6. Benefits Attainable Across Key Performance Indicators
Once implemented successfully, the proposed integrated machine learning and industrial Internet of Things solution can confer both financially quantifiable and competitively qualitative benefits across numerous key performance indicators highly relevant to Amazon’s pursuit of combatting problematic escalation in product return rates. Regarding quantifiable benefits projected through measured reduction of overall product returns, integrating machine learning to predict and steer customer behavior surrounding returns proactively can conservatively yield a 10 to 15 percent decrease in total returns when compared historically, equating to hundreds of millions in cost avoidance relating to processing, logistics, discarding, and lost sales (Banafa, 2023).
Additionally, automation of manual return inspections through industrial Internet of Things-enabled computer vision and sensor technologies can conservatively improve the speed of completing accurate product condition assessments by 20 to 30 percent, thereby alleviating processing bottlenecks (Banafa, 2023). For returned items approved for resale rather than discarding, embedding Internet of Things connectivity, allowing real-time visibility into an item’s cumulative usage and diagnostics post-sale, provides the capacity to increase the percentage of returns subsequently resold by an additional 5 to 10 percent once quality assurance is boosted (Abdel-Monem & Abouhawwash, 2022). From a customer satisfaction perspective, reducing the overall volume of product returns customers require by an estimated 10 to 15 percent through machine learning reductions translates to 2 to 3 percent higher customer retention and loyalty versus historical baselines through minimizing product disappointment events (Abdel-Monem & Abouhawwash, 2022). Financially, if the multilayered solution succeeds in reducing Amazon’s overall product return rate by 4 to 5 percentage points from the current peak of 30 percent down to the target range of 25 to 26 percent, $1.5 to $3 billion in annual cost savings are realizable based on prior data correlating return rates to profitability declines (Clement, 2022). An additional productivity benefit includes increased fulfillment center warehouse throughput and efficiency by alleviating the processing burden imposed by surging returns volumes requiring labor-intensive inspections and handling.
Conclusion
In conclusion, allowing return rates to persist on their current trajectory risks substantial declines in profitability; beyond profitability impacts, increased customer returns have detrimental impacts on warehouse space utilization and workforce morale when processing returned items since more effort and resources are involved in this process, which would otherwise have been dedicated to satisfying new orders. However, the proposed solution leverages both the predictive capacities of machine learning algorithms to implement consumer behavior modifications aimed at reducing unnecessary returns through data-driven engagement and outreach, coupled with the transformative process automation and quality assurance improvements achievable by embedding industrial Internet of Things technologies into returns inspection workflows stands to mitigate these problems considerably. More specifically, with regards to machine learning components of the solution, developing proprietary algorithms capable of analyzing Amazon’s vast historical transaction data archives to reveal actionable insights into customer return propensity drivers enables highly targeted engagement initiatives, such as tailored educational content clarifying return policies sent to return-prone shoppers, which can realistically decrease total returns hence, pursuing rapid implementation of integrated machine learning and industrial internet of things functionalities to transform return processes represents a strategically prudent and potentially highly rewarding investment in Amazon’s continued e-commerce leadership.
Group members takeaways
Member 1:
This project provided valuable insights regarding how machine learning and IoT can be leveraged to tackle real-world business challenges through creative problem-solving that results in optimized future state-enabled strategies. Through this, I learned more about using data to predict customer behavior and drive engagement.
Member 2:
Evaluating Amazon’s returns process highlighted how the industry leaders have opportunities to innovate by supplementing manual workflows with targeted automation, data analysis, and customer engagement powered by AI since through these significant gains in cost, productivity, and satisfaction are achieved, this project showcased how digitally-driven solutions enable competitiveness, and with it, I am inspired by the possibilities and excited to apply similar thinking to other business problems.
Member 3:
This exercise demonstrated Amazon’s customer-centric approach, where even returns present opportunities to learn and improve engagement; I noted that despite the good side of the technology, we should not forget about the negative influence that I learned might come with it, with this, I gained valuable experience in critical and creative thinking, analyzing pain points, envisioning future-state solutions, and articulating compelling business cases; these skills will help me serve well any role requiring complex problem diagnosis and innovative solutions identification.
Member 4:
Working through this case study showcased how IoT and machine learning can optimize physical business processes when applied creatively. Sensor data and intelligent algorithms provide insights that I found unimaginable when performing these processes manually; I am excited to develop further fluency in emerging technologies and experience mapping digitally-driven transformation. These capabilities will prove invaluable as all industries pursue data-centered innovation and automation in the coming decades.
Member 5:
This project highlighted Amazon’s pioneering use of data to restructure critical workflows; with trillions of data collected through the operational activities in every stage of the process, Amazon has enormous opportunities to leverage data analytics and AI for experimentation and value creation. Using technology to preempt returns through targeted engagement can significantly improve sustainability and reduce waste; I gained valuable perspective into how leaders like Amazon approach innovation, seeking use cases for emerging technologies before competitors.
References
Abdel-Monem, A., & Abouhawwash, M. (2022). A machine learning solution for securing the Internet of Things infrastructures. Sustainable Machine Intelligence Journal, 1. https://doi.org/10.61185/smij.hpao9103
Amazon. (2022). Annual reports, proxies, and shareholder letters. Amazon.com, Inc. – Annual reports, proxies, and shareholder letters. https://ir.aboutamazon.com/annual-reports-proxies-and-shareholder-letters/default.aspx
Banafa, A. (2023). The Industrial Internet of Things (IIOT): Challenges, requirements, and benefits. Introduction to Internet of Things (IoT), pp. 5–10. https://doi.org/10.1201/9781003426240-2
Bouakel, M., & Zerbout, A. (2021). Perspectives of Big Data Analytics’ integration in Amazon, Inc.’s business strategy. Big Data Analytics, 201–220. https://doi.org/10.1201/9781003129660-20
Columbus, L. (2022). How Amazon innovates | inc.com. https://www.inc.com/greg-satell/the-secret-behind-amazons-uncanny-ability-to-out-innovate-just-about-every-other-company-on-planet.html
Grand View Research. (2022). IoT Device Management Market Size & Share Report, 2030. IoT Device Management Market Size & Share Report, 2030. https://www.grandviewresearch.com/industry-analysis/iot-device-management-market
Jadhav, A. (2021). Effect of sales returns on retail and e-commerce industry. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3833921
Kaur, J., & Sharma, M. (2018). Extending IoT into the cloud-based platform for examining Amazon Web Services. Advances in Wireless Technologies and Telecommunication, 216–227. https://doi.org/10.4018/978-1-5225-3445-7.ch011
Plotkin, M. J. (2020). History: The struggle for the Amazon. The Amazon. https://doi.org/10.1093/wentk/9780190668297.003.0006
Ramasamy, L. K., & Kadry, S. (2021). Industrial internet of things. Blockchain in the Industrial Internet of Things. https://doi.org/10.1088/978-0-7503-3663-5ch2
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