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Imperial motors case study exploring the Issues


Task: Consider imperial motors case study and prepare a report analyzing the challenges encountered by the car manufacturing industry.


Car manufacturers are in plenty and there is cut-throat competition in the arena courtesy of newer and older players toiling hard to gain customer attention. Moreover, the COVID-19 pandemic hasn’t been kind to the automotive manufacturing industry as the demands have significantly decreased. The imperial motors case studyassociated with imperial motors showcases a widerange of threats and challenges faced by the car manufacturing industry (Omar, 2011).

To start with, wastage of parts and other components within the assembly line is something companies are experiencing due to reduced demands. Not just that, it is getting exceedingly difficult to manage assembly lines due to volatile customer requirements. Although Imperial Motors started with the JIT production plan to combat the volatility, it started experiencing issues pertaining to restricted access to parts and shortage of supply due to weather and transportation issues. Just when they thought the worst phase was over, they started getting return and recall orders for the defective cars, and that too in batches. This issue presented in the imperial motors case study escalated the issues from the administrative point of view and even questioned the existing authenticity management and asset management features of the firm.

However, all these issues mentioned in the given imperial motors case study make way for postulated use cases, which, if and when applied can help the relevant car manufacturing setups get ahead of the bottlenecks. In order to mitigate the challenges and make a lasting e-business impression, it is important to concentrate on quality control, analysis of root cause, predictive maintenance, optimization of the supply chain, experience related to the car dealership and retail for better online adaptability, improved customer experience, lower carbon footprint, increased fuel efficiency, and streamlined sales. However, the three main uses cases in regard to better e-business profiling include quality control across diverse platforms, supply chain handling and optimization (Lopez, 2003), and predictive maintenance.

Emerging Technologies that can realign the Car Manufacturing Industry
Machine Learning

When it comes to using data analysis and automated working methodologies for achieving organizational productivity, nothing comes near Machine learning technologies. Put simply, Machine learning is a part of the all-inclusive Artificial Intelligence technology. Moreover, ML technologies can equip an organization in regard to identifying patterns and making decisions based on procured insights. This technology helps firms create models that can uncover patterns and connections. This makes room for better decision making. ML algorithms are best suited to handling massive data warehouses. As evident in the imperial motors case study, the technology has evolved over the past few years thrives on the concepts of supervised, unsupervised, and reinforced machine learning. This technology boasts of enhanced organizational scalability, ensemble modeling, better data preparation abilities, simplified and automated iterations, and more. Machine learning (Learning, 2018) works with neural networks, decision trees, data mining principles, and K-means clustering techniques to make sure the organizational model is robust and capable of working even in inclement environments.

Artificial Intelligence is a much broader technological spectrum that targets a smarter tomorrow. AI concerns building smart machines by using machine learning and even deep learning concepts. In the simplest possible way, this technology aims at complementing human intelligence with machine intelligence, across diverse industry verticals.

The working and capabilities of AI need to be discussed extensively in this imperial motors case study analysis. However, organizations can select from AGI or strong AI and weak AI (E S Soegoto, 2019) for handling the business processes based on requirements. AI aims at making the organizational tools intelligent by making the best use of diverse technologies where the elements are taught on the basis of preferences. However, it is also stated in the imperial motors case study that the scale of the AI system is determined the company requirements.

With AI at the helm, firms can minimize their coding load and allow machines to learn things via automated feedback loops, correct responses, and neural networks.

Internet of Things is one of the more sought-after technologies that involve interconnecting or interrelating operational machines, computing devices, and other objects, in order to make data transfers easier and more productive. IoT enables machines to interact with each other and therefore minimizes human interventions or even HCI inclusions.

As per the readings provided in the imperial motors case study, devices in a specific IoT network (Dhall, 2017) can communicate with each other and perform actions accordingly. Most importantly, the associated devices use NFC, BLE, and sensor technologies to communicate with each other while processing data sets in a highly efficient manner.

Cloud Computing
Organizations that are hard-pressed for security details or hardware-centric storage spaces can rely on cloud computing for increasing productivity. This technology discussed in the given imperial motors case studyallows the company to establish infrastructure, software, and hardware units onto the cloud using PaaS, IaaS, and SaaS services. Besides supporting virtual data storage, cloud technology also makes it easier for professionals to access data while taking care of the scalability (Chen, 2012). Organizations can take care of increased manufacturing and processing demands upon embracing cloud computing technologies.

Predictive Analytics
Every organization requires analytics but it is predictive analytics that offers a pre-emptive power to the management, thereby helping firms formulate strategies on the basis of actionable predictions. Firms use predictive analytics for promoting better sales. Therefore, it is stated herein imperial motors case study analysis thatfor a firm interested in setting up an e-business profile, predictive analytics can really come in handy. The analytics also allows companies to gain management and forecasting insights. The basic design involves pairing historical data with quantitative mathematical models for predicting optimal outcomes.

Deep Learning
Basically a subset of machine learning, this concept deserves a special mention as it comes with a host of empowering capabilities for the business processes. While machine learning allows the systems and gadgets to learn via feedback process, deep learning design comprises of complex neural networks where machines learn in an unsupervised fashion, similarly to the human brand. The premise of deep learning is explored if a machine is looking to gain predictive and identifying capabilities. Therefore, organizations can solve complex problems with deep learning technologies at the helm. Deep learning process design comprises three steps including data gathering (Salakhutdinov, 2009), filtering, and labeling. However, in order to train the models better, individuals need to take the variance and bias into consideration.

3D printing

In regards to the case scenario of imperial motors case study, 3D printing makes way for seamless and usable manufacturing molds and can be used by diverse industry verticals. The designing process is synonymous with the CAD models and allows manufacturing units to process elements on the basis of requirements. This technology allows companies to design any 3D model (Mawere, 2014) by planning the layer addition process. 3D printing allows manufacturers to mitigate product deficiencies by pairing complicated parts almost instantly.

This technology can work wonders for every e-business setup. Most CRM platforms use BI tools (Wali, 2016) for streamlining business processes while improving organizational profitability. Moreover, for online businesses, CRM is a great way to connect with customer needs and preferences. Almost every industry that has plans of going online with the sales, should consider customer relationship management platform for improved B2C relationships. Designing the desired CRM platform depends on the preferences of the customer but it generally thrives on automation, cohorts, and analytics, more than anything else.

Potential of the Enlisted Technologies
Each one of the technologies mentioned in the previous sections of imperial motors case study analysis has the capability of changing the way organizations do business. With AI, machine learning, and deep learning working together to create better and more intuitive machines, the future is expected to show up with highly innovative and intelligent devices. At present, the error percentage of a machine when it comes to identifying shapes and images is only 3 percent and in the future, we might look for perfection in the given arena. According to the research on imperial motors case study, the figures dropped down from 26 percent in 2011 to 3 percent in 2016 and it is expected to drop further by the end of this year. In addition to that Machine learning and AI in cohesion are bringing in the concepts of Natural Language Processing or NLP that will allow machines and bots to interact with clients, concerning the given e-business. The future trends for ML, AI, and Deep learning hint towards improved security systems as patterns will be the only way to access systems. Moreover, for businesses looking to create online identities, these technologies are slowly blurring the demarcations between physical and digital worlds.

IoT, on the other hand, will make the manufacturing process simpler for industries. Needless to say, cars and other automobiles are expected to become smarter. Not just that, by 2025 it is predicted that almost 21 billion devices with being interconnected, thereby taking the concept of automation to a whole new level. Moreover, AI technologies like ML and Deep learning can also make these devices intelligent and it might eliminate human intervention quite considerably. Future applications of IoT will comprise of smart grids and smart networks.

Security and scalability challenges can be thwarted with the future cloud computing technologies. E-Business will hardly require physical units to function and besides manufacturing, nothing will be required to have an offline identity. Cloud computing in the future will make room for better internet performance courtesy of IoT proliferation. Moreover, with devices connecting between them at every single point, cloud computing will also improve the storage options and bring forth modular software units for the businesses.

CRM and Predictive analytics based on the analysis on imperial motors case study will transform the future of e-businesses. With companies getting all the possible details regarding the existing and presumed future behavior, it will become easier to create and develop products and strategies for eliminating the pain points. These technologies will make way for customizable products and even improve data visualization standards. Last but not least, they can connect with the IoT for gaining insights about the usage patterns without having to instruct humans for doing the job.

3D printing will also play a major role in amplifying the manufacturing side of e-businesses. Propelling three-dimensional manufacturing processes are nothing less than technological breakthroughs and they can help companies create innovative products that aren’t even possible to envision. Future applications of 3D printing (Saxena, 2016) include the endless possibilities in regard to serial production, better hardware-software integrations, improved role in production technology, and metal printing. Each one of the technologies mentioned in the given imperial motors case study can pair up with each other to form an interconnected layout for increasing the productivity of a manufacturing-centered e-business initiative.

Relevance with the E-Business Use Cases
Use Case 1: Quality Control

Based on the imperial motors case study, Imperial Motors need to concentrate on quality control for taking care of the fuel efficiency, carbon footprint, quality of the products, and detection of faulty and duplicate parts. This is where technologies like machine learning, AI, and related algorithms come into the mix. Not just that, this use case also includes predictive analytics. The entire feature set of machine learning becomes relevant while handling quality control for imperial motors. The inclusion of the mentioned technologies can help the company weed out the faulty parts before they enter the manufacturing workflow. Smallest of the defects can be identified in the quality control use case. In addition to that, quality control use case issue concerning replacing and eliminating select parts can also be handled with these technologies. Root cause identification and analysis are also possible with AI, ML, Deep learning, and predictive analytics technologies. While quality control takes care of the reason for the defect, root cause identification identifies the reasons via IoT and machine learning algorithms. Therefore, it becomes easier to diagnose issues and nip them in the bud. 3D printing technology also comes into the play by fixing issues regarding parts, if and when they show up.

Use Case 2: Supply Chain Management and Optimization
Imperial motors started facing supply chain issues since the time the customer requirements started dropping, slowly. To start with the supply chain optimization issues were pretty prevalent as the company started facing losses in production. There wasn’t any real-time information available and even the JIT production process didn’t help. The inclusion of predictive analytics and IoT in the given context of imperial motors case studycan take care of these issues followed by unsupervised machine learning algorithms. (Learning, 2018) The products and parts can then be imported on the basis of demands and even the weather conditions, as monitoring becomes easier. Machine learning and IoT can also streamline product recalls for manufacturers. Cloud computing helps the companies store workflows without the requirement of dedicated hardware.

Use Case 3: Predictive Maintenance
Imperial Motors aspires to grow as e-business and therefore, it must give adequate attention to predictive maintenance (Lee, 2019). This use case takes care of customer satisfaction levels and uses analytics to understand user behavior. Manufacturers can understand the aspirations and preferences of the users and can offer several add-ons to them. Moreover, predictive maintenance also minimizes the returns and recalls as the machine learning and predictive analytics help manufacturers determine issues beforehand. While IoT comes in handy in notifying manufacturers (Wali, 2016) about the condition of the in-device equipment, the CRM platform works on the digital facet of the business by keeping an eye on the customers that are looking to order online or connect back with complaints. CRM takes care of every detail of the customer-manufacturer interactions and helps companies make better strategies.

Addressing Use Case Requirements
All the mentioned technologies provided within the imperial motors case studywork with each other and interoperate to make e-business transition easier for Imperial motors. To start with the machine learning and AI offers anomaly detection and image recognition technologies to assess the nature of the part defects. Once the defects are identified, predictive analytics reveal whether they need to be replaced or eliminated from the workflow. Machine learning models along with deep learning make sure that the selection and elimination process are far more intelligent and highly automated. The CRM (Wali, 2016) takes care of the data that is necessary and must be fed into the Machine learning models.

However, flaw identification isn’t all-compassing and businesses like Imperial Motors must understand the value of flaw prevention. Machine learning along with IoT helps manufacturers analyze sensor measurements, testing data, and parameters for manufacturing, in order to identify the root cause of the problems. Anomaly detection comes in handy and it can take every unstructured data set into consideration. For instance, images taken during quality control can be fed into the systems to correlate existing flaws with the failure modes. Not just that, there are several written and unstructured notes from the service providers and part suppliers which can be deciphered using NLP and text recognition technologies, stacked within the ML module (Lee, 2019).

Now that imperial motors have taken care of the manufacturing and the issues plaguing the firm in the concerned department, it is time to shift focus to the supply chain constraints that interfere with timely supplies, lack of real-time information, weather-specific threats, and an eventual breakdown in the manufacturer’s assembly line. Optimization of the supply chain can minimize the issues related to capital erosion and overproduction. However, for that, imperial technologies require real-time intimations into consignment deliveries. Artificial intelligence, machine learning, deep learning, and predictive analytics can take care of real-time forecasting and assembly line replenishments. IoT clubbed with cloud interface and deep learning makes way for intelligent devices that can decide the supply chain pathway by themselves. Herein imperial motors case study, all the associated technologies work together to find the gap between the predicted and actual inventory levels. While better quality control automatically minimizes batch-wise return of the cars, these recalls also comprise of the supply chain network. Attending to this requires IoT as the parts and equipment can interact with each other to notify the manufacturer of the issues. Even when the products are returned, the IoT and deep learning technologies can help manufacturers track the location and consignment, better than before. Lastly, technologies like AI and Machine Learning increase the data evaluation and processing capabilities of the systems, in a considerable manner (Hmian, 2019).

Predictive management is the one segment that entails servicing and post-sale services. In regard to direct manufacturing, IoT, clubbed with LPWANs or Low Power Wide Area Networking technologies can actually come in handy. LPWAN, besides detecting, also helps auto manufacturers maintain ambient working and warehousing conditions. In the present context of imperial motors case study, this technology also delves deep into the deviation and equipment anomalies; thereby avoiding asset failure and increased downtime. The working conditions are also tracked and monitored for safety. Hybrid cloud computing models come handy in this regard followed by CRM that offers other relational analytics apart from predictive ones.

Cloud technologies, predictive analytics, IoT, and deep learning form the back-end of Imperial Motor’s e-business, thereby helping the company offer better maintenance services to the clients.

The overall conclusion basis the imperial motors case studysignifies that the existing automotive manufacturing sector is a keenly contested arena. With AI and Machine learning making way for intelligent devices that are connected better with IoT, it is only a matter of time that manufacturers like Imperial Motors need to include these technologies into the mix. The use cases (Unfoldlabs, 2020) associated with the technologies present opportunities for growth and increased revenue.

Chen, L.a., 2012. Cloud computing as an innovation: Perception, attitude, and adoption.Imperial motors case studyInternational Journal of Information Management, 32(6), pp.533-40.

Dhall, S., 2017. An IoT Based Predictive Connected Car Maintenance Approach. Computer Science IJIMAI.

E S Soegoto, R.D.U.a.Y.A.H., 2019. Influence of artificial intelligence in automotive industry. Journal of Physics: Conference Series, 1402(6).

Hmian, 2019. [Online] Available at: [Accessed 29 May 2020].

Learning, N., 2018. [Online] Available at: [Accessed 29 may 2020].

Lee, W.K.Y.J.a.S., 2019. Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. Procedia CIRP, 80, pp.506-11.

Lopez, Y.a.G., 2003. A model predictive control strategy for supply chain optimization. Computers & Chemical Engineering, imperial motors case study27(9), pp.1201-18.

Mawere, C., 2014. The Impact and Application of 3D Printing Technology. International Journal of Science and Research (IJSR)..

Omar, M., 2011. New Concept in Automotive Manufacturing; a System-Based Manufacturing. New Trends and Developments in Automotive Industry.

Salakhutdinov, 2009. Learning Deep Generative Models. University of Toronto.

Saxena, A., 2016. A Comprehensive Study on 3D Printing Technology. 3D Printing Technology. , 6, pp.63-69.

Unfoldlabs, 2020. [Online] Available at: [Accessed 29 May 2020].

Wali, A.&.W.L., 2016. Customer relationship management and service quality: Influences in higher education.Imperial motors case study. Journal of Customer Behaviour, 15, pp.67-79.


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