Aldi Forecasting

1 January 2017

The time series forecasting method is part of the quantitative forecasting method in which the analysis of historical data; usually measured within successive intervals or over successive periods is used. The time series forecasting method makes use of assumptions of past patterns observable within data, data points from which data is derived from for the forecasting. Casual forecasting utilizes either known or perceived relationships between the factors of forecast and either internal or external factors.

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The peculiarity of the German market when it comes to getting information for forecasting purposes for Aldi therefore calls for a combination of the three types of forecasting techniques in order to effectively and efficiently meets the customers’ requirements (forecasting, n. d). This is necessitated by the peculiarity of the market where not too much information is available from an academic angle. The Delphi method as well as market research within the qualitative research method would be useful when utilizing panels of experts, test markets and surveys to gather required information that can be put together to aid in forecasting.

Being a new market in which the American retail store has no previous experience in, the questions asked would be administered anonymously. This is an expensive and time consuming exercise, but in comparison to the amount of investment Aldi is to put into the market, would be a worthwhile avenue to consider. Using asset of observable elements within the time-series forecasting technique would involve the analysis of historical data and subsequently use assumptions that would be derived from any observable patterns in the past.

The use of moving averages, exponential smoothing, mathematical models, and the box-Jenkins methods are part of the time series technique which could be used to utilize observable elements and trends from historical data gathered. Since historical data is being relied upon to forecast the future, the use of mathematical formulas would serve its due purpose of attempting to get an accurate outcome and best-fit result in the forecasts within the German market. Worth noting would be to take note of the componenets within the time series technique that would be most useful for analytical purposes.

The use of averages, trends (historical), seasonal influences, cyclical movements and random errors would be used to utilize long-terms and short-term variables for forecasting. Combining the time-series and qualitative forecasting method with the casual forecasting method in which the use of variables with similar characteristics would serve for the best forecasting result. Casual forecasting would also utilize the use of regression formulas, economic models, input-output models and simulation modeling so as to figure out the relating variables from within and also externally to the retail chain’s success.

Improving Aldi’s supply chain management Based on the issues previously identified, Strategic Level The most dominant issues seem that aldi does not sufficiently exploit the potentials of collaborative suppliers’ relationship. By taking a more collaborative approach, major improvement could be made. One way is by embracing the concept of “Collaborative Planning, Forecasting and Replenishment” (CPFR) which have been developed and successfully employed by leading food retailers. It foresees that data is shared and discussed actively between retailers and suppliers, e. g. y producing joint forecast on annual production volumes, also considering foreseeable flunctuations. With a better understanding of the mutual dependencies, the planning basisi could be improve and complexity reduced. On the short term planning basis, making aviable sales data collected in-store 9from the scanner-equipped cash registers) to suppliers in real time allows suppliers to produce more accuratelty to the actual demand, and thus reducing cost for buffers and excess inventory (Trebilcock 006). Of course, Aldi will have to receive a certain share of these benefits.

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