Benjamin Fritzsch

Graduation:
M.Sc.
Function:
Project engineer
Practice Areas:
Assembly planning, simulation of material flow, factory planning
Phone:
+49 (0)511 279 76-451
E-Mail:
fritzsch@iph-hannover.de
vCard:
vCard
Xing:
https://www.xing.com/profile/Benjamin_Fritzsch2

Publications

Material transports in production facilities are subject to constant change. Production batch sizes are becoming smaller and a trend towards batch size 1 is becoming apparent. For this reason, a new research project is examining drones respectively unmanned aerial vehicles (UAV) as so far rarely used transport technology for possible applications in intralogistics. Barriers to use UAV and risks during its use are categorized and evaluated. In the following, requirements for the operating environment and the technical equipment itself are derived from this. Overall, it is investigated to what extent UAV can be integrated into the operational infrastructure of companies in certain use cases and whether the resulting change in logistics performance justifies its use cost-effectively.

drones, material transport, requirements, economic efficiency

Combining standard time series models with search query data can be helpful in predicting sales. We include the search volume of company as well as product-related keywords provided by Google Trends as new predictors in models to forecast sales on a product level. Using weekly data from January 2015 to December 2016 of two products of the audio company Sennheiser we find evidence that using Google Trends data can enhance the prediction performance of conventional models.

Google econometrics, forecasting, search query data

The forecast of sales volumes represents a challenge for the production planning. Above all, sales forecasts that are difficult to predict, such as those caused by promotions, are obstructive. Often, additional information from macroeconomic indexes is not topical, the level of detail of products to be forecast too low and the forecast expenditure too high. Aim of a research project therefore is to develop a model based on search engine data to forecast sales volumes at product level. By the use of complementary application of search engine data to the sales forecast is expected that the forecast mistake can be reduced compared with conventional forecast models upon product level. In general it should be clarified whether and in which extend the logistical efficiency of an enterprise can be improved by search engine data based forecast of sales volumes in the production planning.

production planning, sales forecast, search engine data, forecast model

Research projects