Business Analytics Assignment: Case Analysis of Aldi

Question

Task: Write a detailed and well-researched business analytics assignment identifying the challenges encpunter by you selected organisation and provide an analytical solution to overcome the issue.

Answer

Introduction
Aldi is the supermarket chain that is based out in Germany and has more than 10,000 stores in over 20 countries. The organization has the department stores and the online store that provides the customers with a wide range of products. These include groceries, household essentials, alcohols, and likewise. These have enabled the organization to earn revenue of €50 billion (Aldi, 2020).

There is a lot of competition for Aldi in the United States. Kroger is the largest supermarket chain in the USA closely followed by Albertsons. The Wholefoods stores are also present in various parts of the USA providing close competition to Aldi. Tesco PLC, Carrefour, and Metro Group are the strong competitors in the Europe. There are a few key weaknesses that are determined for the Aldi Stores in the USA. The departmental stores have a lot of dependency on the sales volumes and there are low margins. There is poor penetration in the high-income groups and the target audience is limited. There have been issues around inability to determine the customer preferences that are identified in the past with poor employee satisfaction.

Analytical Solutions
There is a lot of competition for Aldi in the market. It is essential for the organization to be able to improve its services and operations at its departmental stores. The use of the analytical solutions shall be done by Aldi for the same.

There are a number of cloud-based analytical solutions that are now available for the multi-chain grocers. A number of vendors, such as Google, Microsoft, Amazon, etc. offer the retail services to the customers(Griva and Pramatari, 2016). For Aldi, the use of Microsoft Azure can be done. One of the most significant aspects for any business organization is the satisfaction of its customers. Aldi has been lacking in gaining an edge in the market as it is not being able to customize its services as per the customer preferences. The use of analytical solutions from Microsoft Azure can enable Aldi to engage the customers so that they may have personalized experiences. The shopping data and behaviour of the customers at the departmental stores can be fed in to the system and can be analysed to determine the patterns associated. The data-driven insights will enable Aldi to improve its revenues and the customer service(Hurwitz, Kaufman and Bowles, 2015). The use of the cloud-based data analytics shall be done by Aldi for improving the overall retail experience at the store. There is an issue of low margins that have been identified for Aldi. The use of the analytical solutions will enable Aldi to exert price and margin management. The benchmark pricing will be enabled at the stores as per the pricing intelligent solutions so that the competitive pricing analysis is done and is implemented. The effective margin management will be possible and the real-time updates on the prices will be done for the stores and the online platforms. The real-time information will also enable Aldi to predict the prices and repricing opportunities associated with the products(Ilijason, 2020).

One of the most significant aspects of store management is the inventory handling and management. The analytical solutions make sure that the inventory is available and visible online so that the store employees can effectively manage the same. The prescriptive analytics can be conducted to generate the alerts. There are also effective inventory forecasts that can be made for the departmental stores. The analytics-based forecasts will enable Aldi to optimize the inventories and regulate the supply chains. The use of the analytical solutions will also promote the experience levels of the supplier groups associated with Aldi. The product promotions and identification of the similar products will also become easier to carry out. The accurate forecasts will assist the vendors to have the timely information shared with them and it will also add to the overall supply chain handling(Ogunmola and Kumar, 2020). The warehouse and waste management processes will also improve for the department stores as the demand and supply cycles will improve.

The predictive and prescriptive analytics that will be conducted and implemented using the cloud-based solutions will provide Aldi with monetary and non-monetary benefits. The effective margin management and pricing will be possible on the basis of the data-driven insights. This will lead to the generation of higher revenues from the departmental stores. Also, the improved retail experience for the customers will have marked improvements in the customer engagement and satisfaction level. Aldi will succeed in expanding its customer base and it will have positive influence on the revenues, market shares, and the reputation of Aldi in the market. The issue around the inability to gain an edge in the market will also be resolved.

The predictive and prescriptive analytics will also assist the department stores of Aldi to streamline the in-store and back-end operations(Sayeed, 2016). The effective inventory and warehouse management will be done. All of these will provide the ability to make sure that the in-store employees are offered with an improved business environment. The engagement and satisfaction levels of the employees will also improve. As a result, the use and application of prescriptive and predictive analysis will have significant improvements and benefits for Aldi.

Requirements for Analytics
Managerial and Human Resources
The implementation of the cloud-based analytical solutions in Aldi will have certain requirements. The first and the foremost will be the need of the human resources to effectively implement these solutions. The cloud vendor will be selected and will be responsible for most of the implementation and post-implementation activities. However, there will be internal resources that will be required to manage and operate these solutions in an effective manner(Speights, Downs and Adi Raz, 2019). The Chief Technical Officer, CTO will be required to be deployed. The CTO will have the knowledge and skills to understand the cloud-based architecture that will be implemented. The CTO will also be responsible in ensuring that the adherence with the information security principles and privacy regulations is done. There will be transition that the organization will make from its existing system to the cloud-based analytical solutions. The transition will be governed and controlled by the CTO.

There will be a dedicated team that would be required to manage the cloud-based analytical solution. The team will comprise of the Project Manager and the technical resources to make sure that the implementation if effective. The involvement and presence of the data analysts will also play a key role(Verma, Malhotra and Singh, 2020). The data that will be fed in the system and the determination of the patterns and trends presented by the system will be monitored and explained by the data analysts.

There will also be support and assistance required from the Aldi top-level management so that the project specific decisions and strategies can be taken and implemented by the organization. The support from the internal and external stakeholders will play an important role.

Adoption and Migration of the System
There is an existing system that is being followed at Aldi and the migration from the existing to the cloud-based system will be necessary. It will be required that the migration from the existing to the new system is done with proper planning and analysis.

There is a specific process that shall be followed so that the adoption and migration is streamlined.

The first and the foremost step that will be required will include the determination of the capabilities. There is a lot that the cloud-based systems and the analytical solutions can offer. However, not all of these will be relevant for the Aldi stores. The organization shall primarily target the margin management & pricing, retail experience, and inventory management as some of the core operations and services. The value generation and the implementation of the system will be easy with the determination of these capabilities(Verma and Singh, 2017).

The alignment of the business and technical goals will also be relevant and must be done. The mapping of the goals shall be carried out and it shall also consider the determination of the security and governance structure.

The analytical solutions and processes shall be lightweight and the migration of the data and services shall then be done in a step by step manner. The use of parallel testing and quality management must be done to make sure that the gaps, if present are fulfilled simultaneously. The involvement of the talented and dedicated resources will also be significant to ensure that the migration and implementation of the cloud is done properly. The combination of all of these steps will enable Aldi to effectively implement the services.

Conclusion
There are significant development and improvements that can be done to the business processes and operations with the aid of technology. The same holds true for Aldi that is currently impacted by the lack of streamlined inventory management and is also facing issues with providing effective customer and employee satisfaction at its departmental stores. The use of analytical solutions at Aldi will enable the organization to have significant improvements in these aspects. It will also make sure that the organization gains an edge in the market.

References
Aldi (2020). ALDI Grocery Stores - Quality Food. Everyday Low Prices. [online] www.aldi.us. Available at: https://www.aldi.us/en/ [Accessed 21 Oct. 2020].

Griva, A. and Pramatari, K. (2016). Framing Customer Shopping Behavior Through Retail Data Analytics. SSRN Electronic Journal.

Hurwitz, J., Kaufman, M. and Bowles, A. (2015). Cognitive computing and big data analytics. Hoboken: John Wiley & Sons.

Ilijason, R. (2020). Beginning Apache Spark using Azure Databricks?: unleashing large cluster analytics in the Cloud. Berkeley, California? Apress.

Ogunmola, G.A. and Kumar, V. (2020). Web Analytics and Online Retail. International Journal of Technoethics, 11(2), pp.18–33.

Petrescu, M. and Krishen, A.S. (2018). Novel retail technologies and marketing analytics. Journal of Marketing Analytics, 6(3), pp.69–71.

Sayeed, Z. (2016). CLOUD ANALYTICS FOR SHORT-TERM LTE METRIC PREDICTION - CAPACITY, CLOUD FRAMEWORK AND PERFORMANCE. Services Transactions on Cloud Computing, 4(4), pp.15–27.

Speights, D.B., Downs, D.M. and Adi Raz (2019). Essentials of modeling and analytics?: retail risk management and asset protection. New York, Ny: Routledge.

Verma, N., Malhotra, D. and Singh, J. (2020). Big data analytics for retail industry using MapReduce-Apriori framework. Journal of Management Analytics, pp.1–19.

Verma, N. and Singh, J. (2017). A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario. Journal of Management Analytics, 4(4), pp.359–392.