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Buchgeschichte
(2019)
This dissertation investigates different applications of data analytics in supply chain planning. In the last years, data analytics became more important, because of the increase of computational power and the larger availability of data. Data analytics is used in various domains to improve operations performance, increase customer satisfaction and revenues. However, both the research and the application of data analytics in supply chain management is still lacking behind other industries. We1 analyze the potential of data analytics in the field of supply chain planning in three exemplary fields: demand forecasting, partial defection prediction and price discrimination. In addition, we demonstrate how to deal with three common challenges in the field of data analytics: the manual effort for method selection and hyperparameter tuning, the difficult interpretability of machine learning methods and the risks associated with data collection through randomized experiments. In the first paper, we develop a method selection approach in the field of intermittent demand prediction. Our model combines high predictive performance with automation and calculation efficiency. Unlike common practice, the prediction method gets automatically chosen for each data set without any manual selection. Our results are stable across three different data sets that come from different sources but all contain intermittent demand time series. We showcase the impact of the proposed forecasting approach with a warehouse operation simulation. We thereby prove the financial benefit with empirical data. In the second paper, we deal with partial defection prediction in a business-tobusiness environment in the logistics industry. The predictions must combine predictive performance with interpretability and profit maximization. Our model uses a large variety of customer-based and time-series-based features to predict the probability of partial defection for each customer. We use a data permutation approach to make the best performing, black-box models interpretable. Furthermore, we use a profit assessment to identify the method that leads to the highest revenue through successful retention actions. In the third paper, we study price sensitivity prediction. We do not use any randomized experiments, because the risk of loosing customers through such experiments is too high. Thereby, we address the challenge of data availability
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Faber and Spinler (2019a,b,c).
In this competitive world, a company has to keep evolving – either by expansion and/or by diversification. Particularly looking at the sporting industry, the former nowadays is a necessity. With a market size of USD 60bn, the global sporting goods market is growing rapidly. Every company in the industry has to be agile to avoid being disrupted. The acquisition of Amer Sports by Anta Sports reflects this line of thought. Founded three decades ago as a low-cost manufacturer for global brands, Anta is now aiming to rival Adidas and Nike with its planned USD 6.3bn takeover.
Deal Logic BB&T / SunTrust
(2019)
Two regional retail banks merging to become the 6th largest bank in the US sounds very much like inorganic growth. After all, that is how Chemical Bank, Manufacturer's Hanover Trust Company, Chase Manhattan Bank and J.P. Morgan became one of the largest banks in the world today. But an aspect which is more relevant today than ever is inorganic growth enabling organic growth. Scalability applies to technology on the revenue side as well as on the cost side and gives larger banks a crucial competitive advantage. The strategic option of consolidation seems to be something which was long overdue, considering the dynamic market environment.
Deal Logic Careem / Uber
(2019)
Confirmed in March 2019, Uber plans to acquire its Middle Eastern rival Careem in a deal worth USD 3.1bn. The transaction value is expected to be a record for a Middle Eastern tech startup exit and among the highest globally for ride-hailing mergers and acquisitions. As part of this deal, which is expected to close in early 2020, Uber will acquire Careem’s mobility, delivery and payments business across the greater Middle Eastern region, which includes operations in Egypt, Jordan, Pakistan, Saudi Arabia and the UAE. After pulling out of major markets like China and selling its business in Southeast Asia to Grab last March, Uber has been seeking new avenues of growth.
Fiserv (NASDAQ: FISV) and First Data Corporation (NYSE: FDC) coannounced on Jan 16th an unanimous merger agreement under which Fiserv would acquire First Data in a pure-stock transaction. The merger would combine two well-established Fintech companies into one giant. For each share of First Data, a fixed exchange ratio of 0.303 Fiserv shares is agreed, for a total equity value of USD 22bn. After the close of the transaction, Fiserv shareholders will own 57.5% of the new combined company, and First Data shareholders will own 42.5% on a fully diluted basis. The pure stock transaction is intended to be tax-free to First Data shareholders. The transaction is slated to close in the second half of the year, subject to shareholder and regulatory approval.