MGMT20132 Management Report Sample

MGMT20132 Management Report

Assessment Task and Criteria

This assessment must be completed by students individually. This assessment builds on the novel value proposition developed for an existing business or organisation in assessment 1. Adjustments to the novel value proposition can be made based on assessment 1 reflection and feedback. The assessment is designed for students to develop innovation insight and practices by developing a novel business proposition for an existing business or organisation and aligned with the strategic trajectory of the business or organisation. The assessment is also designed for students to self- reflect on their own innovation capabilities and practices. The assessment involves writing a 2000- word report for a novel business proposition within an existing business or organisation.

You are required to write a 2000-word report for a novel business proposal you have developed.

The business proposal must build upon your novel value proposition developed in assessment 1 for the existing business or organisation selected in assessment 1. 

You must use appropriate headings to structure the body of the report.

Your report will be assessed according to the following criteria.

Your report must demonstrate:

1. Logical and persuasive articulation of business model description; value proposition development; business operations development; supply chain development; competitive advantage; financial value capture and strategic fit. (This includes elements number 3 to 9 in the business model canvas by Osterwalder & Pigneur (2010, pp. 16-17). The canvas elements 3 to 9 are channels; customer relationships; revenue streams; key resources; key activities; key partnerships; and cost structure. The canvas elements number 1 customer segments and number 2 value proposition were addressed in assessment 1). 40% 

2. Logical and persuasive argumentation regarding how business model assumptions and uncertainties related to customer desirability; technical feasibility; and financial viability as described by Bland & Osterwalder (2020, pp. 32-33) have been addressed to date and how they will be addressed in the future. This must be described in a phased development plan. 16%

3. Logical and persuasive argumentation for the selection of the innovation tools and techniques used to support the report findings and recommendations. These must be beyond the tools and techniques used in assessment 1. Minimum five (5) additional tools and techniques must be selected from the provided list of tools and techniques. 

4. Logical and persuasive application of the selected innovation tools and techniques used to support the report findings and recommendations. These must be different from the tools and techniques used in assessment 1. Minimum five (5) additional tools and techniques must be applied from the provided list of tools and techniques. 8%

5. Candid and critical self-reflection about assessment 2 tasks including a reflection on the development of personal innovation capabilities and practices; identification of own personal strengths and weaknesses; supported with suggestions for self-improvement. 10%

6. Clear flow of thought throughout the report with a convincing executive summary; clear and succinct purpose described in the introduction; relevant structure and content within the body of the report; and a clear and succinct conclusion. 6%

7. Critical review skills and integration of relevant academic and professional literature. A minimum of ten (10) academic and professional references must be used. 4%

8. Appropriate in-text referencing and reference list. Adherence to CQUniversity APA reference style. 4%

9. Clarity of expression, grammar and spelling. Appropriate report format with good use of bullet points, illustrations and figures. Within ±10% of the word limit for the report, excluding references and appendices: 2000 words. 6%

Assessment illustrations and references

The following figures are for illustration purposes only. They are provided to help you understand the assessment task and marking criteria. You find the following references in the CQUniversity library available for free.

Aulet, B. (2017). Disciplined entrepreneurship workbook. Hoboken, New Jersey: Wiley.

Bland, D. J., & Osterwalder, A. (2020). Testing business ideas. Hoboken, New Jersey: Wiley.

Osterwalder, A., & Pigneur, Y. (2010). Business model generation. Hoboken, New Jersey: Wiley.

Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014). Value proposition design. Hoboken, New Jersey: Wiley. 

Solution

Introduction

Electric motors and control quality of mechanism from Siemens was being productively and used approximately around the global. Here, the customers can practice the everyday uses of Siemens electric motors, used in an extensive collection of appliances. The innovative Artificial Intelligence in Siemens in electric motors is used to develop the control process. The report is going to evaluate the solutions which are going to address. After that, it will focus on the Business model description and value development (Cozmiuc, & Petrisor, 2020).

Further to this, it will discuss the development in the Business operation of quality control of Smart Artificial Intelligence (AI). Then, the report will analyse the supply chain of intelligent AI in the quality control of electric motors. As per the MBA Assignment Experts After this, it will access the competitive advantages and financial value capture of smart AI in Siemens in the quality control process. Then it will access the strategic fits in quality control. After that, the report will evaluate the business model assumptions and challenges in the quality control process. Eventually, it will select the innovative tools for the quality control process and the possible findings and recommendations for the issues addressed. At last, this will analyse the critical reflection of the report. On the other hand, besides the supply chain, the customer service and selling of the electric motors can be improved and get fastened. By doing this, more customers can purchase the Siemens Artificial Intelligence electric motors that will improve to permit them to make all the acquirements at once. Hence, more purchases of smart AI of Siemens electric motors will be made, and it could also control the quality process. With SIMOTICS motors of Siemens, they access the most wide-ranging variety of low power motors international.

Addressing The Problem 

The Siemens Company, SIMOTICS are the electric motor that creates an impact on the customers and better performance in quality control. There can be several electric motor setbacks, such as low resistance, which is the most frequent reason for the breakdown in electric motors and quality control (Cozmiuc, & Petrisor, 2021). The use of Smart Artificial Intelligence of Siemens needs to control the quality of electrical machines mainly to make it safe for the customers and as well as for the technology sectors. The other factor could be overheating, affected by a high temperature in the operating setting in control quality. A significant disadvantage of the smart Artificial Intelligence of Siemens in the quality control process in electric motors is somewhere affects the overall technology process. Artificial Intelligence (AI) is based on the data approaches, which turn out to be increasingly trendy by 84.5%, for the high performance in quality control of electric machines (Petrisor, & Cozmiuc, 2020). The effective upgrading in smart Artificial Intelligence has come across increasingly appropriate tools, but it also has stricter quality control requirements for the customers. Initially, these problems have occurred enough by 77% in the technology sectors, and hence this needs to defect the issues of a well-organised Artificial Intelligence (AI) algorithm. These problems could also be affected the environment. Smart Artificial Intelligence has would-be to speed up the environment in a dreadful condition.   

Solutions Proposed 

The possible solutions have been proposed to control the quality process of electrical motors of Siemens and for the development and implementation to improve the product and for the benefit of the customers. Siemens SIMOTICS electric motors are comparable with the excellent quality, advancement and the highest competence. They try to cover up the entire assortments of manufacturing motors to regulation in the electric motors throughout the servomotor for the proposal of quality control appliances up to high electrical energy and the Direct Current (DC) vehicles (Křenková, Rieser, & Sato, 2021). With SIMOTICS motors of Siemens Company, they access the most wide-ranging variety of low power motors international. The customers can able to get a practical benefit from Smart Artificial Intelligence. The Smart Artificial Intelligence in Siemens electric motors are an essential element of Digital Enterprises' quality control process.   

Business Model Description 

The business model description of Siemens quality control for electric motors reflects the relevant models for the growth of customer services. Currently, Siemens follows B2B Business Model to gain popularity in the German market (Cozmiuc, & Petrisor, 2020). By following the B2B business model, its Facebook page of Siemens has activated a messenger option. The interested dealers can communicate with the company through the platform Facebook, rather than preferring to go through the traditional communication channel. 

Value Proposition Development 

Eventually, Siemens Company has developed numerous approaches, which combine integrated the particular demands of their customers. These approaches can evaluate the relationship between their consumers (Orloff, 2020). Siemens make unique motors to offer their customers based on their demands, and hence it can expand its relationship with the customers. The Siemens company carry out the business honestly and transparently with their competitors and as well as for the stakeholders. 

Business Operation Development 

Initially, the uses of electric motors were essential to modern-day life and are being used to manufacture fax machines. The Siemens Company implements several opportunities to optimise the customer's behaviour. The following approach of Siemens Company is to build up new technology concepts to enhance their marketing strategy. It also develops several practices for quality control in electric motors (Stephenson, 2018). The Siemens Company sustained its growth trail and had achieved strong business operational performance. It mainly focuses on the demanding process of its customers. The Siemens Company develops their field service and enlarges customer value co-creation uses. 

Supply Chain Development

The Siemens Company offers more productive electric motors to influence the supply chain effectively. Eventually, different approaches will be essential to improve the supply chain of Siemens Company (Orloff, 2020). They provide the most effective technology and supply chain to make reliable market intelligence and support good decision-making. The Siemens Company brings out the new technologies to expand their marketing planning for the future times. 

Competitive Advantage

The Siemens SIMOTICS motors are proposed to induct innovative IQ methods for its digital electric motors, which will reduce the overall effort of the components, thus lessening the delays in its operations. The technological shift towards the intelligent integration of Artificial Intelligence for quality control purposes has further enabled Siemens electrical motors to locate the technical errors and implications. The introduction of such allowed the company to predict the future consequences. The quality check of the motors has also inclined the energy management for its screw compressors. Moreover, it gave an edge over its prominent competitors like 3M, Hitachi, and Schneider Electric. 

Financial Value Capture

The Siemens financial value capture is currently based on the financial framework that aims to attain the desired target revenue growth of 4 per cent to 5 per cent in a subsequent timeframe of three to five years. Hence it has been observed practical from the fiscal year of 2022. In the fiscal year of 2021, the margin of the financial income, net and paybacks of intangible assets were being calculated and amortised in 2022 as it has resulted in the analysation of purchase price allocation accounting after EPS to provide clarity in the operations, with a goal of high single-digit annual growth in EPA pre-PPA in a timeline next three to five years.

Strategic Fit 

The integration of smart AI in its machine learning process of electrical motors can result in effective and efficient, providing an array of strategic merits for fast growth, hence the less time taken to achieve the desired target of revenue growth and market capture. 

Business Model Canvas 3 to 9 Element

Analysing Business Model Assumptions and Uncertainties 

Customer Desirability

Since the technological shift of this generation, to satisfy the growing demand for electric motors in large industries to run their business, the implementation of AI technology has made the functionality of the equipment more efficient and productive. To keep track of the performance of the motor, the development of SIMOTICS digital data application has supported the operators and perform the work.

Technical Feasibility

The quality check aspect using the AI machine learning technology has enabled the manufacturers better to understand the complications and issues during the process. In addition to it, the technical part of using such technology has amplified the work performance by implicating SIMOTICS SD gen-next electric motor, as it boosts the purpose of the use and optimises the process after lessening the delays (Gerőcs, & Pinkasz, 2019).

Financial Viability

The company's financial viability is expecting a 4 to 5 per cent growth in the coming three to five years, and thus the result is coming into effect in 2022. Developing a financial framework will allow Siemens to understand the market statistics and perform as per the needs and requirements (Bohnsack et al., 2020).

Phased Development Plan

Since the growing demand for modern needs that reduce the overall time and enhance the machine's performance, it requires an amplified electric motor (Zagel, & Tarhonskyi, 2020). Here the Siemens electric motors have supported the context by improving the performance of motors. Moreover, the integration of AI has improved the exercise after locating the existing errors.

Selection of Innovative Tools and Techniques 

1. Customer journey canvas

2. Business environment map

3. Value chain analysis.

Figure 1: Value Chain Analysis
(Source: Made by Author)

4. Brainstorming ideas

The application of smart AI for quality control has ensured the better management of its components and identify the present complications in the overall process. After assessing the complications, it will set the objective to be corrected for the future. In addition, the idea of implication will enhance the decision-making structure of Siemens and enhance the competitive advantage of the company to back off its competitors. 

5. Value proposition canvas

Figure 2: Business model Canvas
(Source: Made by Author)

Findings

It has been identified that the implementation of AI technology for quality control purposes has allowed us to understand the current issues and make the outcome more efficient. The optimised performance has made the customers loyal to their company and supported the members to work efficiently and productively (Shcherbinin, 2019, March).

Recommendation

After assessing the above report, it can be recommended that:

• AI application assessments for the components.
• Use of machine learning and service of vast information for other operational purposes
• Smart application for mobile to check and manage the functionality. 

Critical self-reflection

During the study of this report, I have got to understand the benefit of Artificial Intelligence in Siemens Company of electric motors. I have identified various issues related to its quality control process. At the end, by keeping those issues in mind, I have proposed suitable solutions. 

Conclusion

The report has the development in the Business operation of quality control of Smart Artificial Intelligence (AI). Then, it has analysed the supply chain of smart AI in the quality control of electric motors. After this, it has access to the competitive advantages and financial value capture of smart AI in Siemens in the quality control process. 

References

Bohnsack, R., Kolk, A., Pinkse, J., & Bidmon, C. M. (2020). Driving the electric bandwagon: The dynamics of incumbents' sustainable innovation. Business Strategy and the Environment, 29(2), 727-743. https://onlinelibrary.wiley.com/doi/pdf/10.1002/bse.2430

Cozmiuc, D. C., & Petrisor, I. I. (2020). Innovation in the age of digital disruption: the case of Siemens. In Disruptive Technology: Concepts, Methodologies, Tools, and Applications (pp. 1124-1144). IGI Global. https://www.igi-global.com/chapter/innovation-in-the-age-of-digital-disruption/231235

Cozmiuc, D. C., & Petrisor, I. I. (2020). Siemens' Customer Value Proposition for the Migration of Legacy Devices to Cyber-Physical Systems in Industrie 4.0. In Disruptive Technology: Concepts, Methodologies, Tools, and Applications (pp. 955-978). IGI Global. https://www.researchgate.net/profile/Diana-Cozmiuc/publication/326393413_Siemens%27_Customer_Value_Proposition_for_the_Migration_of_Legacy_Devices_to_Cyber-Physical_Systems_in_Industrie_40/links/60e59ec0a6fdcc3486421693/Siemens-Customer-Value-Proposition-for-the-Migration-of-Legacy-Devices-to-Cyber-Physical-Systems-in-Industrie-40.pdf

Cozmiuc, D. C., & Petrisor, I. I. (2021). The Siemens Digitalization Strategy in a Value-Based Management Framework. In Managerial Issues in Digital Transformation of Global Modern Corporations (pp. 183-209). IGI Global. https://www.igi-global.com/chapter/the-siemens-digitalization-strategy-in-a-value-based-management-framework/286206

Gerőcs, T., & Pinkasz, A. (2019). Relocation, standardisation and vertical specialisation: core-periphery relations in the European automotive value chain. Society and Economy, 41(2), 171-192. https://akjournals.com/downloadpdf/journals/204/41/2/article-p171.pdf

Křenková, E., Rieser, K., & Sato, A. (2021). How can software robots facilitate the procurement process? A case study of Siemens in the Czech Republic. Entrepreneurial Business and Economics Review, 9(3), 191-203. https://www.eber.uek.krakow.pl/index.php/eber/article/view/1107/692

Orloff, M. A. (2020). Modelling the Great Inventions of Carl and Werner von Siemens with Modern TRIZ. In Modern TRIZ Modeling in Master Programs (pp. 386-429). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-37417-4_10

Petrisor, I., & Cozmiuc, D. (2020). Global supply chain management organisation at siemens in the advent of industry 4.0. Supply Chain and logistics management: Concepts, methodologies, tools, and applications (pp. 1095-1114). IGI Global. https://www.igi-global.com/chapter/global-supply-chain-management-organization-at-siemens-in-the-advent-of-industry-40/239318

Shcherbinin, N. (2019, March). The Siemens Company Innovative Activity in Electrical Engineering in Russia (1880-1886). In 2019 International Conference on Engineering Technologies and Computer Science (EnT) (pp. 120-123). IEEE. https://ieeexplore.ieee.org/abstract/document/8711964/

Smith, J., & Pelliccione, A. (2019). 2019 PRODUCT of the Year FINALISTS: Advance manufacturing through innovation. Plant Engineering, 73(9), 29-55. https://go.gale.com/ps/i.do?id=GALE%7CA612695422&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=0032082X&p=AONE&sw=w

Stephenson, W. D. (2018). The Future is Smart: How Your Company Can Capitalise on the Internet of Things--and Win in a Connected Economy. AMAZON. https://books.google.com/books?hl=en&lr=&id=ykRiDwAAQBAJ&oi=fnd&pg=PP1&dq=smart+AI+of+siemens+in+electric+motors&ots=GndOw0F92z&sig=5QVQ6QdxJ_QN_KZCRn1U3yXLiD8

Stokić, M., & Đogatović, M. (2021). DEVELOPMENT AND VALIDATION OF THE E-BUS SIMULATION MODEL. International Journal for Traffic & Transport Engineering, 11(2). http://ijtte.com/uploads/2021-04-14/7f000001-a42b-aa68ijtte.2021.11(2).04.pdf

Zagel, F., & Tarhonskyi, V. (2020). How do German industrial leaders evolve their business model towards sustainability: A case study of Adidas AG and Siemens AG. https://www.diva-portal.org/smash/get/diva2:1450111/FULLTEXT02