Group 9 Assignment - SCM [PDF]

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Group Assignment ALG Energy case study Subject: Supply Chain Management Lecturer: PhD. Nguyen Hoang Dung

GROUP 9 STT 1 2 3 4 5

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Họ và tên Dương Thùy Dương Bùi Cẩm Hường Lê Thị Ngọc Lan Nguyễn Minh Thư Nguyễn Phương Trúc

MSSV K184080992 K184081000 K184081002 K184081033 K184081045

1) Why is it difficult to align supply and demand at AGL Energy? There are many reasons why it is difficult to align supply and demand at AGL Energy.  Customer information management Its systems are specially developed to meet the needs of diverse business units, one power generation and supplier and customer care side. Information is entered and stored in different ways across many different customers and payment platforms. And the messy arrangement of data and analysis by many customers makes required information difficult to check and analyze.  The capabilities to analyze data Employees cannot analyze system data to benefit the company. Employee motivation to adapt to the new data flow is a matter of discussion. A lot of work for employees ranges from rarely or never analyzing available data, to constantly maneuvering their movements/actions in response to incoming data streams. There will be a lot of pressure on the AGL employee system to be able to analyze such informational and stratified data.  Uncertainty The third factor is uncertainty from both sides: supply and demand. Not excluded from the mainstream, uncertainty appears at AGL in many shapes and, of course, neither side benefits from uncertainty. A trading company that relies on its ability to actually predict short- and long-term needs. Profit maximization relies on the ability to minimize excess or shortage energy. In short, it is based on the predicted ability to match supply with demand.In short, there is uncertainty about the prediction of supply with demand. Uncertainty also comes from Customers who have become increasingly energy conscious and feel strongly that they need to do something about rising energy bills.  Customer segmentation Traditional customer segmentation based on current energy use and demographics that have been the mainstay in the energy industry will likely

provide limited understanding about what the customer of the future might look like. 2) What data challenges are facing the upstream (merchant) and downstream (retail) parts of the business? In your answer, consider the core data required to manage the supply chain (e.g., customers, usage, generation, prices) and the advantages and disadvantages of data consolidation (merchant and retail) onto a single IT system/platform.  Data challenges that the upstream (merchant) and downstream (retail) parts of the business are facing:  Firstly, both the upstream and downstream are facing uncertainty in the demand forecast which is always a major problem. Challenges in developing core competencies in data governance and control are equally important.  Secondly, the challenges each part has to face is: + The upstream (merchant) challenges:Electricity shortfall can result in a situation in which an energy company has to buy additional electricity from the short term wholesale market at uncertain and volatile spot and contract prices. Significant volatility in wholesale prices due to severe weather conditions, or a breakdown in energy production or transmission can easily blow away any profits for a month. Wholesale supply prices can range from $25 to over $10,000 a megawatt hour at different times of the year. And its also affect to the retail's price The possession of capabilities to analyze data in an efficient and effective manner BUT the IT systems have grown in a haphazard way as the company acquired new companies and their customers. At any one time, there were 12

to 15 different customer billing platforms. The challenge arises when information is entered and stored in different ways across different payment platforms and customers. + The downstream (retail) challenges: At AGL the firm relies heavily upon the ability to retain its customer base. The customer base provides the platform to make upstream investments in gas and electricity generation, BUT in the retail business, the rate of customer churn is high (26%). It creates strong pressures for innovation and continuous improvement. Uncertainty from the customer. Customers have become increasingly energy conscious and feel strongly that they need to do something about rising energy bills. Traditional customer segmentation based on current energy use and demographics that have been the mainstay in the energy industry will likely provide limited understanding about what the customer of the future might look like. The vertical integration strategy makes the risks not disproportionately allocated:

A downstream retailer lacks upstream capacity then becomes

vulnerable and subject to wholesale price variability.  The advantages and disadvantages of data consolidation (merchant and retail) onto a single IT system/platform.  Advantages:+ Reduction in the high number of system outages+ Reduction in the number of back office personnel helps reduce cost+ A consistent organization-wide database for all customer-related data and delivery of a single-instance billing platform+ Create a platform that could collect, analyze, and use large data sets coming in from both retail and merchant.+ Better position AGL for the challenges of an uncertain future.+ Co-create

value with customers and improve industry performance.+ Meet AGL’s strategic goals of expanding the range of services to customers.  Disadvantages: + The need to develop a core capability around governance and control of data+ The need for appropriate governance controls to ensure data integrity, supported by a data-driven culture throughout the supply chain.+ Unless employees are willing to incorporate data into what they do and think critically about the decisions being made on the database, there is a huge cost. 3) Why might it be difficult to move supply chain work practices to a data analytics strategy? There are five reasons why it might be difficult to move supply chain work practices to a data analytics strategy:  Characteristics

of

the

market

and

product

enhances

high

competitiveness.The commodity market, in which any businesses offer nondifferentiated products, intangible components, and is sold primarily on the basis of price. The core components of a commodity are well known, mostly stable, and widely shared amongst competing firms. Each firm has its own core competencies, especially, as aforementioned statements, the energy industry is a commodity market and data analytics strategy is not only the further goal of AGL Energy in extending and prevailing business, but also the universal objectives of any energy firm. From this point, several quick actions need to be taken to guarantee its first mover’s benefits. Nevertheless, AGL is not the one that specializes in data analytics, even in the past, it has to make a collaboration through acquiring an external data analytic company, so the lack of experiences and practical knowledge will exert a burden.  The cost of managing, controlling and analysing data.“We have really given a lot of energy in recent times to smart meter analytics.” (Anthony Fowler, Group General Manager Merchant Energy). The Executive Chief in AGL has

admitted that they had invested a large amount of energy in flowing these data. Owen is aware that investment in analytics can be a huge sunk cost unless employees are willing to incorporate the data into what they do and think critically about the decisions that are being made on the basis of data. This means that besides the expense of data transportation, there is labor cost because the IT workforce is originally shortage and requires a high salary.  Problems from customers themselves.As we acknowledged from the case, smart meters deliver all the information about how much gas and electricity a customer uses and how much it costs. Based on it, utility bills (electricity and gas) are calculated, unfortunately, he/she can take advantage of and cheat on it to minimally decrease his/her payment. Besides, customers might not be willing to reveal their personal consumption (although AGL makes sure that personal information regarding to name, address, bank account,... will not be taken). Along with those, high customer churn rates make the data system messy  Unified data management systemAGL Energy has grown by acquisition. Its systems have been developed separately to meet the needs of diverse business units one generates and delivers power and the other looks after customers. So, information was entered and stored in different ways in many different customer and billing platforms. At any one time, there were 12 to 15 different customer billing platforms, the distribution business was not well connected and costs were increasing. The haphazard arrangement of data and fragmented infrastructure meant that the information technology (IT) systems at AGL were buckling under the pressure to meet staff and customer expectations. The IT systems have grown in a haphazard way as the company acquired new companies and their customers.  Demand forecast is affected by weather and time The demand forecast bias would be larger when affected by another forecast (weather forecast). In addition to medium and long term demand forecasts, AGL also has short term forecasts such as daily or half an hour. This makes data analysis complicated and costly

If you were in Owen’s position, how would you go about delivering the tools needed to manage a network of supply and demand? Building a data analytics capability is the one point of focus that AGL is trying to build. That’s why I think Owen should focus on developing “Big Data” infrastructure because it always has some advantages to be the first mover. AGL should Enhance mid- and long-term forecasts to create a well-balanced match between supply and demand at all times for the company to make the best decision The forecasting energy demand based on weather and other historical data more often will reduce pressure on the analysis skills within the business units. The company should invest into not only appropriate governance controls to ensure data integrity but also data analytics capabilities and corresponding IT systems.