This assignment is intended to help you use leadership skills to gather project members from cross-functional departments and skill sets and lead them in the fulfillment and implementation of a mock project.
Discover the various responsibilities of a project manager by organizing a project. See Chapter 19, sections 19.9 and Cases. 
Apply project management tools and a PM outline type of your choice to structure and plan the project by defining, planning, and controlling. The project will be a continuation of how to improve the process you chose in Weeks 1 and 2.
Create a presentation (supported by Excel and Word as needed), with detailed speaker notes, that includes the following:
Project description
Project Management Charts (Critical Path, Gant Chart, etc.)
Improved Process Flowchart from Week 1
Meeting cadence/rhythm and timing
Metrics to measure the project’s success

Financial and budgetary considerations
Description of the project reporting structure
Cite references to support your assignment.
Process Improvement Flowchart
University of Phoenix
OPS/574
As-Is Process Flowchart Evaluation
As-Is Process Flowchart
Below is the as-is process flowchart for the online order fulfillment process at Walmart: Below is
the as-is process flowchart for the online order fulfillment process at Walmart:
Customers place
an order online
Order is received
by Walmart
Order is recorded
in the system
Fulfilment centre processes the order
Items are picked from inventory
Items are packed for shipping
Order is shipped to the
customer
Order is sent to the
nearest fulfilment
centre
Evaluation of As-Is Process
The As-Is process was made subject to process improvement techniques, including Value Stream
Mapping (VSM) and Root Cause Analysis (RCA), to evaluate its effectiveness. Here are the key
findings:
Bottlenecks: The bottleneck in the order processing time due to the prolonged periods spent at
the fulfillment centers is an obvious but unavoidable problem.
Redundancies: Some packing operations contain repetition, including
redundancies, hence causing inefficiencies.
Errors: An error in order picking is usually observed, and wrong items shipped to the customer
arise.
Process Improvement Flowchart
Improved Process Flowchart
According to this evaluation, automation can enhance procedures, and the implemented steps can
be minimized.
Below is the process improved flowchart:
Order is received
by Walmart
Customers place
an order online
Order is recorded
in the system
Order is sent to the
nearest fulfilment
centre
Fulfilment centre processes the order
Items are picked from
inventory
Items are packed for shipping
using automated system
Order is shipped to the
customer
Customer Receives
the order
Automated
feedback system
collects customer
feedback for
continuous
improvement
Executive Summary
The process under examination is Walmart’s online order completion process. It consists of
several stages of processing the employee’s orders, picking, and packing the products, which
causes overflows, keeping the elemental cycles, and mistakes. These types of inefficiencies have,
therefore, a negative influence on the overall satisfaction level of customers as well as the longterm performance of the company, which consequently calls for an in-depth analysis and reform
of the process (Lukinskiy, et al., 2023)
Applying the Valued Stream Mapping (VSM) and Root Cause Analysis (RCA), fundamental
problem points were detected in the current process. The biggest challenges we face are shipping
and fulfilling the order because it takes very long administrative actions. It is also the reason why
the process of packing could be more active, and we have frequent mistakes in picking. These
inefficiencies lead to an increase in the order processing times, less order accuracy, and
dissatisfied customers via the process of customer satisfaction.
To tackle these issues, a process aimed at automation was introduced as a solution. Many
systems had automation that handled diverse tasks, for example, order allocation, picking, and
packing. By utilizing advancing technologies like robotics in picking and packing processes, the
overall steps are easily managed, decreasing the reliance on human labor and error-prone human
workmanship (Orman et al., 2022).
The new process flow chart illustrates a faster system with a higher degree of efficiency, where
automation is a core element in speeding up the process and improving the accuracy level. The
indicators employed to assess the process cover order processing time, order accuracy
percentage, and customer satisfaction rate. These metrics will only present a comprehensive
picture of the process and the critical elements for further enhancement.
Metrics and Measures
Order Processing Time: This measures the length of the delivery process, from customers’ order
placement to the end of fulfillment.
Order Accuracy Rate: Learn the ratio of orders handled and quoted without spelling mistakes
or other errors.
Customer Satisfaction: The level of customer fulfillment is checked using delivery surveys,
which help us gather such data.
Order Processing Time: Implementing such technology will directly affect the time between
handling the order process (allocation, picking, and packing) and having the order shipped to the
customer. Delivery time speed and the capacity for the system to handle a number of orders
within the same time duration further enhance performance.
Order Accuracy Rate: Automation helps eliminate risks of human error that may result in hightech accuracy rates. This results in fewer discrepancies, whereby wrong items are not shipped to
customers. Ultimately, this improves customer satisfaction and saves on the costs of returns and
corrections.
Customer Satisfaction: Shorter processing times and more precise results are also significant
components of this highest level of customer experience. Satisfied customers have a much higher
probability of returning and suggesting the service to others; businesses prosper.
The process improvement project involves several key steps:
Implementing Automated Systems: The key to the outcome is replacing manual order picking
and packing procedures with automatic systems. This integration should be effected so that the
newly designed system merges with the existing ordering system.
Training Staff: Employees will require training to work and maintain the new automatic system
installations. This training comprises various tasks such as exploring the technologies, resolving
issues, and ensuring the operations’ functionality.
Continuous Monitoring: The critical factor is ensuring the performance metrics are monitored
continuously to detect new problems or issues. Customer feedback should be taken seriously and
utilized to improve our product continually.
Hence, Walmart is able to improve the order fulfillment process by being more precise and
efficient and putting the client first. The planned benefits, such as improved processing time,
accuracy, and customer satisfaction, will go a long way in the company’s operations and will also
give the company a competitive advantage.
References
Lukinskiy, V., Lukinskiy, V., Ivanov, D., Sokolov, B., & Bazhina, D. (2023). A probabilistic
approach to information management of order fulfilment reliability with the help of perfect-order
analytics. International Journal of Information Management, 68, 102567.
Orman, K., Romano, G., Tee, A., Thornburgh, J., & Turner, M. (2022). Walmart: Predictive
planning, ordering, and monitoring.
Theory of Statistical Process Control and Process Improvement
Theory of Statistical Process Control and Process Improvement
Statistical process control (SPC) is one type of quality control that utilizes statistical tools
to monitor a process. SPC assists in keeping the process at optimal performance by generating
more goods according to specifications with less material quantity classified as spoils or
reversion. SPC involves measuring any method with a measurable output that is a ‘conforming
product,’ which is a product that meets specifications.
Lean Concept and Waste Elimination
Lean manufacturing, as opposed to lean production, can be described as an efficient
means of eliminating wastage in the manufacturing system without compromising on the
system’s efficiency. Lean also defines overburden as another type of waste, and the condition
where the workload is not evenly distributed is another type of waste. Lean manufacturing aims
to reduce the amount of waste and promote efficiency and effectiveness or improvement through
continuous improvement methodologies. Lean on the concept of many tools and techniques,
among them Value Stream Mapping (VSM), which maps and analyses the flow of material and
information.
Use of Six Sigma for Defect Mitigation
Six Sigma is defined as a methodology used to provide solutions for improving
organizational processes. Six Sigma was pioneered by Motorola engineer Bill Smith in 1986
while developing the technology for enhancing semiconductor yields (Tran et al., 2022). Six
Sigma methods aim to address the quality of the output of a process by eradicating the sources of
deviations and reducing the extent of fluctuations in manufacturing; this is done with statistical
analysis and by training and assigning specially created roles within the organization (Black
Belts or Green Belts) to experts in these techniques. By definition, every Six Sigma project in an
organization has phases and instructions to be followed sequentially. In contrast, the project’s
goal or objective is expressed in monetary values, such as cost reduction or profit improvement.
Using SPC methods- Process evaluation
In evaluating a process using SPC, several steps are involved:
Identify Critical Quality Characteristics (CQCs): These are the main factors that should be
controlled and tracked closely to ensure that the overall process stays under control.
Data Collection: Gather information about the above-stated CQCs; this can be done through
sample techniques. In this regard, the view held here is that the generalization from sample to
population is achievable in one of the following ways:
Create Control Charts: Control charts are essential tools of SPC. They plot specific variable
data over time to look for evidence that the process is out of control. The X-bar, R, and S charts
are the most commonly used control charts in organizations.
Analyze Control Charts: Check the control chart to note any sign of a process or pattern that
might indicate it is out of control. Such can imply looking for trends, shifts, or any other point
that may lie beyond the control limits.
Implement Process Improvements: The conclusion from the above analysis includes making
necessary adjustments to ensure that the process is brought back into control and that the
capability is improved.
Control Chart and Process Metrics: Control charts and other process metrics are used as
process control tools to monitor and regulate process performance.
The Use of SPC Methods to Evaluate Control Charts
Calculation of Metrics: Real numbers like mean, range, and standard deviation, as well as
process indices like Cp and Cpk, should be computed.
Developing Control Charts: Control charts should be constructed for the above process metrics
on motor parameters. For example, an X-bar chart can be used to observe the changes in the
process mean over time, while an R chart can be used to observe the range or variability.
Evaluating the Control Chart: Control chart analysis involves looking at plotted points about
the control limits to determine whether they allow process control. Patterns within the control
limits and points outside the control limits can be explicit indicators of particular cause
variations.
Process Capability Analysis: A process’s center line measures the data’s average in process
capability analysis. At the same time, the range determines the spread of the process data to the
specification limits. These measurements also assist in establishing how well the process will
perform given specifications.
Process Improvement Flowchart
Therefore, a process improvement flowchart should be developed and worked on to
facilitate a schematic view and analyze the process stages. Thus, this is useful for determining
where changes can be made within a process, whether that change can be made by an automated
system, whether a step can be removed, and whether the number of errors in a process can be
reduced.
Lean and SP (Statistical Process) Control Techniques – SN2 Executive Summary
Summary of Process Evaluation: Lean was applied to analyze which process steps added value
for the consumers and defined inefficiencies as wastes. Essential zones to focus on include
incorporating waste within the packing process cycle and mistakes of order picking.
Evaluation of Control Chart and Process Metrics: Other tools used included control charts,
which were established for significant measures such as the order processing time, the rate of
order accuracy, and the level of customer satisfaction. By scrutinizing these beans, it was easy to
identify points in the process that needed to be controlled.
Use of Six Sigma and Lean Tools: A study was conducted to determine the degree of Six
Sigma and Lean in the process, and it was identified that the process leverages benefited greatly
from these tools. These tools supported the reduction of variability and the number of defects,
better process control, and increased capability and effectiveness.
SPC Project and Recommendations: The various technical solutions in the SPC project
included the mechanical replacement of manual order picking and packing. Other measures
included training staff on new systems implemented for the organization and monitoring the
process to enhance the process for the future (Yuan et al., 2020). The benefits derived from the
improvements were improved response time, reduced probability of order errors, and improved
customer satisfaction.
References
Yuan, C. C., Chung, W. H., Cai, C., & Sung, S. T. (2020). Application of statistical process
control on port state control. Journal of Marine Science and Engineering, 8(10), 746.
Tran, P. H., Ahmadi Nadi, A., Nguyen, T. H., Tran, K. D., & Tran, K. P. (2022). Application of
machine learning in statistical process control charts: A survey and perspective.
In Control charts and machine learning for anomaly detection in manufacturing (pp. 7–
42). Springer, Cham.

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