ANLY482 AY2017-18T2 Group27 : Project Overview
| Description | Data | Methodology |
1.0 Sponsor Background
Company X is the largest logistics firm in the industry today. Company X currently operates in over 220 countries and regions. Their global presence is part of the vision to become the global logistics company of the world. To fulfill its vision, Company X has incorporated 4 elements into their mission statement: simplifying the lives of our customers, contributing the successes of our customers, employees and investors, significantly contributing to the success of the world, showing respect when trying to meet targets.
Company X focuses on 3 areas - parcel, document & international mail shipping, freight shipping and solutions & special expertise. They are particularly dominant in sea and air mail. Their 2 main competitors are Fedex and UPS, but several local brands are a threat to Company X in their domestic territory.
2.0 Motivation
Kanchen's Motivation
Being able to work with Company X will certainly be challenging. They have extensive data which is currently untapped. This project will allow me to apply whatever I have learnt in school to a real world example. Despite not having background in operations management, the business problems that Company X faces is interesting and important to their internal and external stakeholders. Our work will definitely reveal new insights into an account and potentially make a tangible impact on the company and the customer. This project will give me a unique opportunity to explore the logistics industry and also, help improve the business process of Company X.
Sonea's Motivation
As a final year analytics student, I am interested to use the skills I developed in university to solve a current business problem. This current project looks into the effectiveness of shipping lanes and delivery as well as forecasting of capacity in a supply-chain. Thus, it yields aspects that I am curious about and would want to develop especially in a Logistics environment which I am currently unfamiliar. With a background in Accountancy, I believe I will be able to add value towards this project when it comes to looking to looking at variables that involves accounting concepts and providing a different perspective on building the linear regression model.
Jozanne's Motivation
As a final year economics and analytics student, I am keen to practice my skills and understanding on a real-life huge dataset. Having some insights of the logistics industry from previous modules, I am interested in understanding more of the logistics industry and the application of analytics to it.
In this project, our group will attempt to improve service levels by understanding trends and predicting shipping volumes. Subsequently, with a good prediction, the company can prepare sufficient resources for the shipments. This can be used to support identified factors affecting customers’ shipping volume. Our motivation is to utilise the given shipping data, to identify seasonality and density in shipping lanes, to come out with a feasible 3-month prediction of shipping data.
3.0 Business Problem
To drive growth and have meaningful conversations with an account, the Group Account Managers (GAM) analyse the past data to gather relevant insights on airfreight for a account. However, within this process, Company X's team faces 2 prominent business problems.
2.1 Lack of All-In-One Visualisation Tool
Currently, GAMs from the Company X's team are currently using excel as their primary analysis tool to analyse the cost and revenue for each account. For each specific measure or investigation, they have an individual excel spreadsheet. As such, there is a lack of standardisation of parameters across the spreadsheets. This results in fragmented insights and sometimes, they miss out on key insights which could potentially improve business decisions.
2.2 Tracking of Commercial Insights
Despite the sheer amount of data Company X has, Company X has not been tracking its operational performance effectively. They are unable to do a quick diagnostic of their performance and spot trends.
Currently, the GAMs have a lack of commercial insights with regards to cost and revenue when engaging with accounts. Below are the commercial insights lacking:
Rate Per Kg (RpK) Company X has different rates for different customers and different routes. These rates are based on air freight services provided by external airlines, which are also dependent on the routes, weight and volume. To retain customers, Company X offer contractual rates. These rates are based on loyalty of customers, their shipping volume, weight and their frequency. Also, any ad-hoc delivery requested by Company X’s customers will be charged at a higher rate.
Density One issue faced by Company X is the optimisation of the cargo mix for the client. Currently, Company X relies solely on the transactional data provided by its clients to buy up cargo capacity. This is based on the forecasted weight and volume of goods. However, when there is an over or under shipment of cargo, Company X would have to bear the additional cost of the unused capacity. Since Company X engages external airlines, these costs are chargeable; an extra charge for dense shipments and volumetric shipments that have an unused capacity is a sunk cost. Without an efficient tracking system on the density of its cargo shipped, Company X is unable to determine how much additional costs it is incurring and hence, it faces difficulty in reducing these costs.
Ship To Profile (STP) For every account, there is a stipulated projected shipment volume in the contract for each country lane. Despite the stipulated contractual amount, there are always discrepancies in the projected shipment volume and the actual shipment volume. In some country lanes, the account ships drastically more or less than the projected amount. The GAMs inability to spot volume trends and monitor actual shipping amount will result in additional costs for Company X.
4.0 Project Objectives
Considering the business problem mentioned above, this practicum seeks to achieve substantial analysis on the following areas through the use of PowerBi. The account used in this practicum is will be kept confidential but it includes all its business units and worldwide locations for its air freight.
The objective of the practicum will be to build a comprehensive, fully functional visual dashboard that will enable GAMs to derive meaningful insights on the following key areas:
1. Rates per Kg (RpK)
RPK seeks to visualise rates per kg at each shipping lane for each customers. Furthermore, it also seeks to shed insights on the rates offered by each contract and how these can be further maximised.
2. Ship to Profile (STP)
Ship to profile aims to track the shipment volume of each business unit and country lane. This will be tracked against the projected amount to monitor the performance and behaviour.
3. Density
This gives an in-depth view of the range of goods shipped in Company X’s containers and they will be grouped into the standard metric density ratio scale. From this, there will be a further analysis on the cargo mix of a container.
It will also contain features like a dynamic map and dynamic charts that will respond accordingly to the common slicers set.
5.0 Scope of Work
The main deliverables of this project will be a
- Dashboard that includes data visualisation of RpK, shipping density and delivery routes’ seasonality
Phase 1: Case Context
In the first phase, we will study the dataset and existing literature to identify the relevant predictors. Then, we will proceed to prepare the data for analysis. This includes:
- Identifying the variables that affect the company’s delivery
- Identifying the variables that affect the customer’s delivery
Phase 2: Data Preparation
In the second phase, we will prepare the data for exploratory analysis and regression analysis. These include:
- Obtaining data for any additional variables, apart from the shipping data that is provided to us by the company
- Rectify any missing or invalid data
- Transform any data if necessary
- Removing any duplicates
- Removing any outliers
- Stating assumptions
Phase 3: Data Exploration
Upon preparing the dataset, we will proceed to explore the data using Power BI. The analysis will include:
- Summary statistics of data
- Single variate analysis of variables
- Slicers for analysis
We will then incorporate these commercial insights into our dashboard.
Phase 4: Dashboard Design
After exploring the data, we will proceed to construct and design the dashboard appropriately by incorporating Stephen Few's principles and relevant visualisations such as:
- Stephen Few's - Dashboard Design
- Chord Diagram
- Quadrant Analysis
