OVERVIEWS

According to the data provided by Cadbury, there are different code types for each non-compliance descriptionfor example, lousy stretch wrap, cancelled lines, late deliveries, damages, etc. Our team aims to perform a detailed analysis of D Mart's non-compliance deductions to determine the root cause of these deductions. Identifying the key drivers of each code type of non-compliance and finally providing recommendations to reduce the costs involved in the business with D Mart.

BACKGROUND

Cadbury has experienced increased total volume and value of non-compliance fines from D Mart. This is driven by many reasons, including but not limited to: changes in the industry & landscape of how/why customers fine Cadbury, increased automation at customer warehouses, late deliveries / missed appointments which carrier issues or internal Cadbury issues can drive, packaging issues, carrier equipment failures (temp recorders), etc. In Q1 2022, Cadbury developed a customer fines dashboard. The new solution brings a holistic view of all Cadbury deduction data, which supports identifying the type of deduction and internal Cadbury root cause code. However, due to varying formats and levels of information available by customers, the dashboard needs to incorporate customer data sets (e.g. D Mart) that can support getting to a more granular root cause level.

PROJECT DESCRIPTION

The client started this project to enhance the efficiency of the supply chain and lower the number of penalties for non-compliance. This project aims to pinpoint and examine the underlying factors contributing to non-compliance penalties and then offer analysis-based conclusions and improvement suggestions. By proactively identifying and avoiding scenarios that could result in fines, improving customer relationships, and mitigating fines, this project will help reduce rework and waste in the process and increase profitability. The project's goals are to conduct a thorough analysis of D Mart's non-compliance deductions, pinpoint the primary factors that contribute to each root cause code type, provide trending information, insights, and suggestions for improving processes and reducing fines, as well as suggest potential changes to strategies that could help Cadbury avoid the levying of these fines from D Mart. A decrease in non-compliance fines, in terms of dollar amount and percentage, could influence supply chain Key Performance Indicators (KPIs). This project's deliverables include reduced rework, process waste, and higher profitability through adequate mitigation.

Improvements in KPIs, such as a decline in non-compliance fines, a drop in rework and waste, and an increase in customer satisfaction, are probably expected due to these advantages. Additionally, this initiative may serve as a foundation for future supply chain process enhancements, with the potential for additional cost reductions.

SCOPE OF WORK

Phase 1: Data Identification - Deadline: Early February

Details
Going through the information from Cadbury Collecting information from our secondary research

Phase 2: Data Analysis - Deadline: Early/Mid-March

Details
We are using excel tools to describe, illustrate and evaluate data.

Phase 3: Root cause Analysis - Deadline: Late March

Details
The information is used from data analysis and secondary research to find the root causes of the issues.

Phase 4: Recommendation - Deadline: Early April

Details
We are using the Minto pyramid to prepare our recommendation presentation.

OUT OF SCOPE

This project is meant to handle something other than implementing or improving the team's systems or analysis. Other departments in the client organization will receive this job to complete. The project addresses nothing outside D Mart's processing issues and non-compliance fines. To solve any more supply chain optimization issues, new initiatives might be required. Additionally, this project's goal is not to thoroughly examine any other customer's non-compliance penalties and processing issues.

This project's scope is meant to concentrate on D Mart processing issues and fines for non-compliance. Therefore, any additional supply chain optimization initiatives or analyses of other clients' non-compliance fines are outside the purview of this project. Maintaining its restricted scope is crucial to achieving the project's goals and objectives.

12 PROJECT REQUIREMENTS

Data sources that we will use for this project:

• Data shared by Cadbury.
• Secondary research (i.e., peer-reviewed academic journals)
• Primary research, if possible

Summary of Key Findings

• Top 80% non-compliance exemplary description based on acceptable types and the sum of charges in 2022:

Late; No show; Short-dated product; Inadequate stretch wrap; Overage; Shipping label missing information; Inadequate paperwork-No 2nd

• Top 80% non-compliance fine description based on the PO department and the sum of charges in 2022:

Late; No show; Short dated product; Overage; Shipping label-missing info; Inadequate paperwork-no 2nd; Temperate issue

• Top 80% non-compliance fine description based on DC types and the sum of charges in 2022:

Late; No show; Short dated product; Inadequate stretch wrap; Overage; Shipping label- missing info; Inadequate paperwork no 2nd; Incorrect information; Inadequate pallets

QUESTIONS:

What will be recommendations for the above project details?