Posted: January 16th, 2023

BU7763 Artificial Intelligence (AI) in supply management assignemnt

BU7763 STRATEGIC SUPPLY CHAIN MANAGEMENT

ASSIGNMENT
January 2023

Your Brief

This assignment accounts for 100% of the module assessment.

Your assignment is to produce a Report building upon the Group Research and Presentation Project you conducted in class on Day 4. Therefore, your assignment will be an exploration of the application of one of the following supply chain technology and collaboration concepts:
– Artificial Intelligence (AI) in supply management
Artificial Intelligence (AI) is a rapidly growing technology that has the potential to revolutionize the way organizations operate, including the field of supply management. AI can be defined as a branch of computer science that deals with the creation of intelligent machines that can perform tasks that typically require human intelligence, such as decision making, problem solving, and learning.

In supply management, AI can be used to automate and optimize various processes, such as demand forecasting, inventory management, and supplier selection. For example, AI-based systems can analyze vast amounts of data to make accurate predictions about future demand, allowing organizations to better manage their inventory levels and reduce the risk of stockouts. Additionally, AI can be used to evaluate and select suppliers based on various criteria, such as cost, quality, and delivery performance, which can help organizations to improve their supply chain performance.

One of the main benefits of AI in supply management is that it can help organizations to make more informed decisions. AI-based systems can process large amounts of data and identify patterns that humans may not be able to see. This can help organizations to identify opportunities for cost savings, improve their efficiency, and respond more quickly to changing market conditions. Additionally, AI can help to reduce the risk of errors, as it can automate repetitive tasks and make decisions based on facts and data, rather than human biases.

However, there are also potential challenges associated with the use of AI in supply management. One of the main challenges is that AI-based systems require large amounts of data to be trained and operate effectively. This can be a problem for organizations that do not have access to sufficient data, or that have data that is not of high quality. Additionally, there are also concerns about the security of AI-based systems, as they may be vulnerable to cyber attacks.
Your individual assignment task is to:
Critically analyse the application of the concept you researched and presented in your class-based group project for an organisation of your choice.

Before you start this assignment you will have to indicate the organisation you wish to use in your analysis: DHL

The title of your assignment should be:

A Critical Analysis of the Application of Artificial Intelligence for DHL

The structure (therefore the subheadings) of your assignment will be as follows:

Executive Summary or Introduction 200 words approx.
This should succinctly indicate the main arguments you raise in your assignment and key recommendation for the organisation. We suggest you might write this as a final task to ensure it matches the content of the main body of the assignment.

1. Characteristics of [Concept] 1000 words approx.
Define the general characteristics of your chosen supply management concept and critically assess its general application, using up to date published literature to support your arguments.

2. Application of Artficial Intelligence in DHL Operations and Supply Network
1000 words approx.
Report the actual and potential further application of your concept in your chosen organisation’s management of it supply network. This part is generally descriptive, but you are expected to provide references and sources to indicate where you have gathered the data and information about your chosen organisation.

3. Critical Analysis of Artificial Intelligence within DHL 1000 words approx.
Report the positive and negative aspects for the application of your concept within your chosen organisation. Here you can consider the following: What have been the benefits? What issues or problems have the organisation encountered. Has the organisation realised the full potential and value of the application yet?

4. Recommendations for DHL 400 words approx.
Provide three key concise recommendations for the organisation how they might improve the effectiveness of their application of the supply chain concept you have been evaluating.

5. Conclusion 400 words approx.
Summarise your main arguments and findings from the main body of your assignment.

References – in the standard APA(7) style please.

See further notes on next page ……..

Notes:
1. The overall word count must be between 3600 and 4400 words, excluding words on any diagrams and the references section.
2. Please adhere to the word count requested for your answer to each question.
3. Use course texts and other reputable literature to back up and inform your arguments and discussion.
4. Top marks will be awarded to those assignments which indicate clear competencies in critical analysis of supply chain management principles, concepts and practices.
5. The assignment must be submitted as a Word document – any other format will not be accepted or marked.
6. This assignment accounts for 100% of overall module assessment.

Assessed work to be submitted through Turnitin.
The deadline for submission is Monday 23rd January 2023, 12:00PM (midday).

Marking Criteria
For the report, in terms of marking allocation, the following gives an indication of how marks are allocated:
• Knowledge (20%)
• Sources (Readings) (30%)
• Analysis (30%)
• Communication (20%)
Please refer to the marking rubric grid on the next page for a fuller explanation of the assessment criteria.

MARKING
RUBRIC Module Title: BU7763: Strategic Supply Chain Management Level: 7
Assessment Title: Reflective/Critical Analysis of a Case Organisation Weighted: 100% – Individual Report

CRITERIA Distinction
90–100%
Evidence of… Distinction
80-89%
Evidence of… Distinction
70-79%
Evidence of… Merit
60-69%
Evidence of… Pass
50-59%
Evidence of… Fail
40-49%
Evidence of… Fail
30-39%
Evidence of… Fail
20-29%
Evidence of… Fail
10-19%
Evidence of… Fail
0-9%
Evidence of…
Knowledge
Knowledge and understanding of the academic discipline, field of study, or area of professional practice.
Critical engagement with the primary & secondary sources used to answer the question. (20%)
Insightful and sophisticated engagement with research and/or practice pertaining to field(s) and disciplines of study.
Advanced engagement with research and or practice pertaining to the field(s) and disciplines of study.
A high degree of engagement with research and/or practice pertaining to field(s) and disciplines of study.

Sustained engagement with research and/or practice pertaining to disciplines of study.

Engagement with relevant knowledge pertaining to discipline and key issues.
Unsatisfactory engagement with relevant knowledge pertaining to discipline and key
issues.

Inadequate coverage of relevant issues, inconsistent understanding shown.

Lack of relevant research and little understanding shown.

Severely lacking in relevant research and underpinning knowledge.

Entirely lacking in relevant research and underpinning knowledge

Sources
Reading and use of appropriate sources.
Accurate and consistent acknowledgment and referencing of sources.
(30%)
Unparalleled standard of research both in breadth and depth, which demonstrates a very high intellectual engagement and rigour.
Extremely well referenced research both in breadth and depth, which demonstrates high intellectual engagement and rigour.

Well-referenced research both in breadth and depth, which demonstrates clear intellectual rigour.
Very good referencing in breadth and/or depth, which shows a very good level of intellectual rigour.
Sources generally acknowledged according to academic conventions. May contain minor errors and
limited in breadth, depth and intellectual rigour.

Sources not acknowledged in line with academic conventions of referencing.

Sources inaccurately referenced.

Inconsistent and/or limited referencing of sources.

Sources either not present and/or not referenced.

No indication of source reading.

Analysis
Critical analysis and interpretation.
Appropriate analytical discussion and interpretation of source material (30%)
A sophisticated cogent argument offering new and original contributions to knowledge. A highly developed cogent argument with the potential to bring new and original contributions to knowledge.

A sustained argument with the possibility for new insights to knowledge.
A developed and sustained argument with the possibility for new insights to knowledge.
Ability to devise a coherent
critical/ analytical argument is supported with evidence.
The ability to construct an argument is underdeveloped and not supported fully with evidence.
Weak interpretation of research and work is not supported with evidence.

Episodes of self- contradiction and/or confusion

Slight indication of ability to deal with key issues.

Negligible coverage of learning outcomes.
Communication
Communication skills: creative, written and presented.
Communication of intent, adherence to academic subject discipline protocols. (20%)
A sophisticated response, the academic form matches that expected in published and professional work.
Persuasive articulation, where
the academic form largely matches that expected in published work.
A high degree of skill, the academic form shows exceptional standards of presentation or delivery.
Secure and sustained expression, observing appropriate academic form.

Good expression, observing appropriate academic form.
Unsatisfactory demonstration and application of key communication skills.

Significant errors evident in the academic form.

Very weak observation of academic conventions.

Slight observation of academic Conventions.

Negligible observation of academic conventions.

One of the key areas where DHL is using AI is in warehouse automation. The company has implemented a system called “Pick-by-Vision,” which uses AI and computer vision technology to guide warehouse workers through the picking process. This system has been reported to have increased the accuracy of picking by up to 25% and increased the efficiency of the process by up to 15%. Additionally, DHL is using AI-powered robots to Help in the picking and packing operations, further improving efficiency and accuracy.

Another area where DHL is using AI is in logistics optimization. The company has implemented a system called “Predictive Analytics for Transportation,” which uses machine learning algorithms to analyze data from various sources such as GPS, weather and traffic information to predict potential disruptions and proactively take actions to mitigate them. This system helps DHL to optimize the routes of its delivery trucks and predict maintenance schedules of vehicles, which leads to a reduction in transportation costs and improved delivery times.

DHL is also using AI to automate customer service processes, allowing customers to get quick answers to their questions and tracking of their orders. The company has implemented AI-powered chatbots that can handle customer queries, reducing the need for human intervention. This improves customer satisfaction and reduces operational costs.

Furthermore, DHL is using AI to monitor and analyze the performance of its warehouses and logistics operations, identifying bottlenecks and areas for improvement. This allows the company to detect and predict defects and anomalies in the supply chain, which leads to improved quality and reduced costs. Additionally, DHL is using AI to optimize the use of its resources, such as vehicles and personnel, by predicting demand and adjusting the deployment of resources accordingly.

In conclusion, DHL is using AI in various ways to optimize its supply chain management operations, including warehouse automation, logistics optimization, customer service automation, performance monitoring, anomaly detection, and resource optimization. These applications of AI have led to significant improvements in efficiency, accuracy, and cost-effectiveness for DHL. However, it’s worth noting that AI is a rapidly evolving technology and there’s always a risk of errors, biases and the need of human oversight, thus it’s important for the company to continuously monitor and evaluate the performance of its AI systems, and make adjustments as necessary to ensure that they continue to deliver value to the company and its customers.

DHL Supply Chain UK is also using AI-powered chatbots to automate customer service processes, allowing customers to get quick answers to their questions and tracking of their orders, reducing the need for human intervention

DHL is also using AI to monitor and analyze the performance of its warehouses and logistics operations, identifying bottlenecks and areas for improvement, as well as to detect and predict defects and anomalies in the supply chain.

One specific example is the use of AI-powered robots in warehouses to improve efficiency and accuracy of picking and packing operations. DHL’s Innovation Center in the UK has implemented a system called “Pick-by-Vision” which uses AI and computer vision technology to guide warehouse workers through the picking process. This system has been reported to have increased the accuracy of picking by up to 25% and increased the efficiency of the process by up to 15%.

Another example is the use of AI-powered predictive analytics to optimize logistics operations. DHL has implemented a system called “Predictive Analytics for Transportation” which uses machine learning algorithms to analyze data from various sources such as GPS, weather and traffic information to predict potential disruptions and proactively take actions to mitigate them

Additionally, DHL uses AI in its supply chain management to optimize routes of its delivery trucks and predict maintenance schedules of vehicles.

It’s worth noting that DHL is a global company and it’s using AI in multiple countries, the above examples are from DHL UK specifically and the implementation may vary in other countries.

Furthermore, DHL is using AI to optimize the use of its resources, such as vehicles and personnel, by predicting demand and adjusting the deployment of resources accordingly

Overall, DHL Supply Chain UK is using AI in various ways to optimize its supply chain management operations, including warehouse automation, logistics optimization, customer service automation, performance monitoring, anomaly detection, and resource optimization. It’s worth noting that DHL is a global company and it’s using AI in multiple countries, the above examples are from DHL UK specifically and the implementation may vary in other countries.

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