Case-Study SCM [PDF]

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CASE STUDY Question 1) It is difficult to align supply and demand at AGL energy because no one in the company can analyze the data in accuracy that they spent millions of dollars on. A lot of data is available, but if no one can use it in AGL’s favor, what good does it do? On the finance perspective, information like this helps in creating forward contracts on electricity. It also helps on setting competitive prices. With good forecasting, energy can be available at cheaper rates than their competitors. Solving this problem requires a careful matching of supply and production capacity with demand as well as the conditions that go along with producing the power to be available. Aligning supply and demand at AGL energy can be quite difficult due to employee motivation to adapt to the new data system. It is a lot of work for employees to go from rarely or never analyzing available data, to constantly maneuver his/her actions to react to the new incoming data streams. Reacting to demands in different sectors to managing the price from area to area to supply consumers. Analyzing how much each action is costing you gives you a better idea of what to charge. With consumption data so readily available, the volatility in supply should be little to none. With such high price volatility, customer service needs to be another issue. Using data from each consumer can help you determine the given demand for each consumer.

Question 2) Data challenges are facing the upstream (merchant) and downstream (retail) parts of the business AGL Energy depends seriously on data and IT maintain day to day business activities throughout the supply chain operation. For example, in both upstream operations and downstream retail, the business units are deeply dependent on access to data such as energy consumption through electricity meters, weather forecasts,... In addition, the ownership of capabilities to analyze data efficiently and effectively is significantly equal. However, the IT systems at AGL have become unorganized and messy than ever before because of the acquisition process. At any point in time, there are 12 to 15 different customer billing platforms operating at the same time. This leads to the disconnection of the distribution business and increasing the costs as a result incredibly. Most of IT-related services   are suffering. consider the core data required to manage the supply chain (e.g., customers, usage, generation, prices) The energy industry supply chain involves a series of steps involved to get a product or service to the customer. The steps begin with the exploration and mining of natural resources and finish with the transportation, residential, commercial and industrial consumption. Source of Supply is Natural resources (oil, gas, coal, uranium, solar, water and wind) and the transmission of those resources. The distribution network and the retail markets significantly downstream customer demand. Retailers sell the power to the end consumers and issue invoices to collect payments. After that, the energy industry supply chain ends. the advantages and disadvantages of data consolidation (merchant and retail) onto a single IT system/platform Expected advantages are decreasing in the elevated number of system downtimes and decreasing the number of supporting offices, a reliable and trusted whole-organization database for all customerrelated information, as well as a release of a single-instance payment system. This transformation program is helpful and essential because of meeting AGL’s strategic goals and objectives of intensifying the array of services to its customer.

Question 3) It is difficult to move supply chain work practices to a data analytics strategy because of 2 reasons:  Developing a data analytic will be challenging because, in the past, the company has failed at making sense out of the vast amount of raw data that comes into its data management systems. None of the company’s employees can be able to use this raw data effectively and skillfully to benefit the company although it spent an enormous amount of money on this data. Therefore,

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it becomes useless data because no one can transfer it into valuable and meaningful information. It is a huge sunk cost while the company invests a lot of money in analytics, employees are not willing to integrate the data into what they think and do in their daily works. They are used to working without this raw data so they will encounter difficulties in practicing with the fresh incoming data streams.  In other words, if the employees are not motivated to adapt to the new data stream adjustments then it would be difficult to move supply chain work practices to a data analytics strategy. In other words, if the employees are not motivated to adapt to the new data stream adjustments then it would be difficult to move supply chain work practices to a data analytics strategy.