Difference between revisions of "ANLY482 AY2017-18 T1 Group2 Project EZLin Project Overview"

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<div style="background: #fcebea; padding: 12px; font-family: Arimo; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #bf1900 solid 32px;"><font color="#bf1900">Introduction</font></div>
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The supply chain network forms the backbone in the movement of any goods or services for any organisation with a tangible product. The ability to accurately identify the cost incurred at each stage of the network could potentially create substantial benefits in terms of cost savings and efficiencies for the company. Given that most organisations always regard the supply chain network as a cost centre, not much has been done in fully leveraging on the power of analytics to derive insights that is able to contribute to the profitability of the company. In addition, the raw data that is stored in the internal database of most companies often require substantial cleaning and transformation even before any insight is able to be derived from it.
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For JnJ, the raw data that includes information such as the raw material cost and the overhead cost are being extracted from their internal SAP system. Given that this extraction is done on an ad-hoc basis, much effort and time is required to clean the data so as to be able to generate the required reports. Furthermore, due to the inconsistency in the understanding of data and the different users in different geographical region, it results in the quality issues and inconsistency. As such, this tedious and error-prone methodology of extracting quality information in order to derive insights from it could potentially compromise the accuracy of the report generated. Therefore, there is a need to substantially clean and understand the data even before any insights can be derived from the dataset.
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For this practicum, Python was the primary language used in the processing and transformation of the original raw dataset in order to meet the requirements of the commerce side. The reason for choosing Python as the primary language in our data processing is because of the ease in the language usage and the wide extensive library packages for transformation (e.g. Pandas). Nevertheless, other languages such as R which is well-regarded for its built-in statistical and analytical tools was also considered. However, the final decision to use Python was also due to the team familiarity with the language.
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In any company that delivers a physical product or solution, supply chain has always been a huge challenge in the operations of the organisation. There are many established companies in the market that operate the supply chain in silos and treat it as another cost center without clearly identifying what constitutes the cost in the entire supply chain ecosystem. Our motivation therefore is to not only determine the various costs that are inherent in the client’s supply chain ecosystem. We are keen to illustrate and automate the entire data analysis process for the client in a sustainable and scalable manner.  
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Cost optimization of the supply chain is a challenge for many companies, as the whole process of supply chain is a complex network. Between each point on this network, there are different cost components for different transactions. Currently, XXX’s analysis only focuses on separate parts of this whole supply chain network and look at them in silos, preventing them from seeing the whole picture.  
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As such, our motivation is to use different analysis methods and visualization tools to map the end-to-end business process. Given the complex structure of the company’s supply chain network, we are keen to explore ways to map the flow from the plant to the final distribution centre. Through this, we are hoping that we would be able to discover what are the cost implications for different parts of the supply chain and how to potentially improve this whole network.
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With our background in Operations Management and Information Systems, we are eager to work on this project as it allows us to apply what we have learned to a real world business problem. In addition, this project requires us to constantly learn on the job as we do not possess actual knowledge of supply chain analytics or some of the tools required in carrying out the analysis. As daunting as this may be, it will be interesting to discover whether our learnings, analysis and application has a positive effect on a huge multinational organisation like our client.
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The aim of our project is to help XXX's supply chain department, to identify any analytics trends and patterns in their current budget spending when they purchase raw materials of baby oil. We will identify any descriptive analytics patterns which helps to save on their budget. At the same time, based on the patterns, predictive analytics will also be conducted in this project. XXX will be able to foresee any inflation in the price of the raw materials from this project.
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The main aim of our project is to help XXX’s supply chain team explore any trends and patterns in their current supply chain spending when they produce their adult wash product. Through this trends and patterns identified, it is hoped that we will be able to ultimately help them improve their end-to-end supply chain understanding as well as automate their data extraction for future supply chain analysis. Based on the patterns identified, a dashboard reporting system will also be developed simultaneously in this project to provide a visual interface for their future usage. The objectives of this project are: <br/>
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The objectives of this project are: <br/>
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1. To summarise the information of the materials prices based on different criteria and condition type
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1.      To summarise the information of the raw materials prices based on different criteria such as volume, flavour, percentage of ingredient and etc <br/>
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2. To clearly map the process of the supply chain from the internal manufacturer to the final distribution centre with the transaction cost incurred at each point
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2.      To determine the most cost-efficient budget plan based on the criteria <br/>
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3. Build a data automation process that can help to clean and transform the raw data required for visualisation
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3.      To identify the clustering factors and determine their effects on each other <br/>
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4. To identify the clustering factors and determine their effects on each other  
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4.      To build a dashboard reporting system which helps with cleaning the data and doing data exploration in the future <br/>
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5. Establish a dashboard reporting system which helps with data exploration and visualisation of the supply chain flow in the future
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1. Deere & Company (brand name John Deere) is well known for the manufacture and supply of machinery used in agriculture, construction, and forestry, as well as diesel engines and lawn care equipment. Deere and company has a complex product range, which includes a mix of heavy machinery for the consumer market and industrial equipment which is made to order. Retail activity is extremely seasonal, with the majority of sales made between March and July.  The company undertook a supply chain network redesign program, resulting in the commissioning of intermediate “merge centers” and optimization of cross-dock terminal locations. Deere & Company also began consolidating shipments and using break-bulk terminals during the seasonal peak. The company also increased its use of third party logistics providers and effectively created a network which could be tactically optimized at any given point in time. Deere & Company’s supply chain cost management achievements included inventory reduction of $1 billion, a significant reduction in customer delivery lead times (from ten days to five or less) and annual transportation cost savings of around 5%. <br/>
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To fully understand the importance and use of analytics tools in driving a supply chain network, preliminary assessment into data driven supply chain network was explored.
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The use of data to understand the supply chain flow is not new in the industry and much has been done in leverage on Big Data Analytics (BDA) to do so. According to Arunachalam, Kumar and Kawalek (2017), the entire process of transforming raw data into useful insights until recently has been known as Business Intelligence and is used interchangeably with many terms such as Big Data Analytics or Business Analytics. However, it is the use of the data to help understand the supply chain network and derive insights that make the data truly useful. In order to truly leverage on the power of the data, companies have to drive the data-centric culture into the business decision making process (Arunachalam et. al, 2017).
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In our literature review, the use of Python in automating the data cleaning process and driving the supply chain efficiencies are lesser in comparison to other research studies. Nevertheless, the usage of visualisation in deriving insights and analysis has been heavily focused on. For instance, leveraging on Python, the use of Sankey diagram as a tool for visualisation has proven useful for multidimensional data.
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Therefore, given the complexity of the dataset, we aim to design the entire process through substantial secondary research on forums and liaising with the Information Technology team from JnJ.
  
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2. During 2007 and 2008, Starbucks leadership began to have serious doubts about the company’s ability to supply its 16,700 outlets. As in most commercial sectors at that time, sales were falling. At the same time though, supply chain costs rose by more than $75 million. Their challenges include: Fewer than 50% of outlet deliveries were arriving on time. A number of poor outsourcing decisions had led to excessive 3PL expenses. The supply chain had, (like those of many global organizations) evolved, rather than grown by design, and had hence become unnecessarily complex. To organize its supply chain better, Starbucks divided all its supply chain functions into four key groups, known as “plan” “make” and “deliver”. It also opened a new production facility, bringing the total number of U.S. plants to four. Next, the company set about terminating partnerships with all but its most effective 3PLs. The remaining partners were then managed via a weekly scorecard system, which was aligned with renewed service level agreements.By the time Starbucks’ supply chain transformation program was completed, the company had made savings of more than $500 million over the course of 2009 and 2010.
 
 
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Latest revision as of 19:43, 3 December 2017


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PROJECT OVERVIEW

 

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DOCUMENTATION

 

 



Introduction


The supply chain network forms the backbone in the movement of any goods or services for any organisation with a tangible product. The ability to accurately identify the cost incurred at each stage of the network could potentially create substantial benefits in terms of cost savings and efficiencies for the company. Given that most organisations always regard the supply chain network as a cost centre, not much has been done in fully leveraging on the power of analytics to derive insights that is able to contribute to the profitability of the company. In addition, the raw data that is stored in the internal database of most companies often require substantial cleaning and transformation even before any insight is able to be derived from it.

For JnJ, the raw data that includes information such as the raw material cost and the overhead cost are being extracted from their internal SAP system. Given that this extraction is done on an ad-hoc basis, much effort and time is required to clean the data so as to be able to generate the required reports. Furthermore, due to the inconsistency in the understanding of data and the different users in different geographical region, it results in the quality issues and inconsistency. As such, this tedious and error-prone methodology of extracting quality information in order to derive insights from it could potentially compromise the accuracy of the report generated. Therefore, there is a need to substantially clean and understand the data even before any insights can be derived from the dataset.

For this practicum, Python was the primary language used in the processing and transformation of the original raw dataset in order to meet the requirements of the commerce side. The reason for choosing Python as the primary language in our data processing is because of the ease in the language usage and the wide extensive library packages for transformation (e.g. Pandas). Nevertheless, other languages such as R which is well-regarded for its built-in statistical and analytical tools was also considered. However, the final decision to use Python was also due to the team familiarity with the language.



Motivation


Cost optimization of the supply chain is a challenge for many companies, as the whole process of supply chain is a complex network. Between each point on this network, there are different cost components for different transactions. Currently, XXX’s analysis only focuses on separate parts of this whole supply chain network and look at them in silos, preventing them from seeing the whole picture. As such, our motivation is to use different analysis methods and visualization tools to map the end-to-end business process. Given the complex structure of the company’s supply chain network, we are keen to explore ways to map the flow from the plant to the final distribution centre. Through this, we are hoping that we would be able to discover what are the cost implications for different parts of the supply chain and how to potentially improve this whole network.


Objectives


The main aim of our project is to help XXX’s supply chain team explore any trends and patterns in their current supply chain spending when they produce their adult wash product. Through this trends and patterns identified, it is hoped that we will be able to ultimately help them improve their end-to-end supply chain understanding as well as automate their data extraction for future supply chain analysis. Based on the patterns identified, a dashboard reporting system will also be developed simultaneously in this project to provide a visual interface for their future usage. The objectives of this project are:
1. To summarise the information of the materials prices based on different criteria and condition type 2. To clearly map the process of the supply chain from the internal manufacturer to the final distribution centre with the transaction cost incurred at each point 3. Build a data automation process that can help to clean and transform the raw data required for visualisation 4. To identify the clustering factors and determine their effects on each other 5. Establish a dashboard reporting system which helps with data exploration and visualisation of the supply chain flow in the future


Literature Research


To fully understand the importance and use of analytics tools in driving a supply chain network, preliminary assessment into data driven supply chain network was explored.

The use of data to understand the supply chain flow is not new in the industry and much has been done in leverage on Big Data Analytics (BDA) to do so. According to Arunachalam, Kumar and Kawalek (2017), the entire process of transforming raw data into useful insights until recently has been known as Business Intelligence and is used interchangeably with many terms such as Big Data Analytics or Business Analytics. However, it is the use of the data to help understand the supply chain network and derive insights that make the data truly useful. In order to truly leverage on the power of the data, companies have to drive the data-centric culture into the business decision making process (Arunachalam et. al, 2017).

In our literature review, the use of Python in automating the data cleaning process and driving the supply chain efficiencies are lesser in comparison to other research studies. Nevertheless, the usage of visualisation in deriving insights and analysis has been heavily focused on. For instance, leveraging on Python, the use of Sankey diagram as a tool for visualisation has proven useful for multidimensional data.

Therefore, given the complexity of the dataset, we aim to design the entire process through substantial secondary research on forums and liaising with the Information Technology team from JnJ.


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