Zusammenfassung
Das Thema der Digitalisierung der Supply Chain stand seit 2020 unter dem Eindruck der Chip-Krise. Der politische Faktor wird in Kap. 2 diskutiert. Die Covid-19 Pandemie als Auslöser von logistischen Fehlentscheidungen hat Schwächen in den traditionellen Fertigungs- und Supply Chain Methoden wie das Versagen von Just-In-Time, Lean Management und Forecasting Methoden aufgedeckt. Die Theorien dazu werden im Detail erläutert. Die Vernachlässigung des Data Management ist ein weiterer Faktor. Use Cases für den Einsatz von Machine Learning und Deep Learning Anwendungen werden vorgestellt und die Herausforderungen an Logistik und Supply Chain Management werden diskutiert.
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Literatur
Barrett E (2021) Taiwan’s drought is exposing just how much water chipmakers like TSMC use (and reuse), Fortune Media IP Limited. https://fortune.com/2021/06/12/chip-shortage-taiwan-drought-tsmc-water-usage/. Zugegriffen am 19.06.2022
Beer A (2014) Der Bullwhip-Effekt in einem komplexen Produktionsnetzwerk, Springer
Bradbury J (2018) Muda Mura Muri, Kaizen Institute Blog. https://www.kaizen.com/blog/post/2018/05/09/muda-mura-muri.html. Zugegriffen am 20.11.2019
Chen F et al. (2000) The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect, Naval Research logistics, 47. Jg., Nr. 4, S. 269–286
Clark D (2022) Intel to Invest at Least $20 Billion in New Chip Factories in Ohio, The New York Times. https://www.nytimes.com/2022/01/21/technology/intel-chip-factories-ohio.html. Zugegriffen am 19.06.2022
Clark D & Santariano A (2022) Intel to Invest at Least $19 Billion for New Chips Plant in Germany, The New York Times. https://www.nytimes.com/2022/03/15/technology/intel-factory-germany.html. Zugegriffen am 19.06.2022
Cornwell S (2021) COVID-19’s Forecasting Failure, and What We’ve Learned, SupplyChainBrain. https://www.supplychainbrain.com/articles/32602-covid-19s-forecasting-failure-and-what-weve-learned. Zugegriffen am 30.06.2022
DecisionCraft (2010) Choosing the Right Forecasting Technique, DecisionCraft Inc. http://www.decisioncraft.com/dmdirect/forecastingtechnique.htm. Zugegriffen am 29.10.2019
Dejonckheere J et al. (2003) Measuring and avoiding the Bullwhip Effect: a controlling theoretic approach”, European Journal of Operational Research, Vol. 147 Issue 3, pp. 567–590.
FitchRatings (2021) Taiwan Drought Highlights Water Stress as Growing Environmental Risk, FitchRatings, Inc. https://www.fitchratings.com/research/corporate-finance/taiwan-drought-highlights-water-stress-as-growing-environmental-risk-04-05-2021. Zugegriffen am 19.06.2022
Glass S (2018) AI and the Evolution of Demand Forecasting, Aera Technology. https://medium.com/@Aera_Technology/ai-and-the-evolution-of-demand-forecasting-147dd4e783aa. Zugegriffen am 30.06.2022
Goodman S und Chokshi N (2021) How the World Ran Out of Everything, The New York Times. https://www.nytimes.com/2021/06/01/business/coronavirus-global-shortages.html. Zugegriffen am 30.06.2022
Gronwald K (2020) Integrierte Business-Informationssysteme, Springer
Hadwick A (2020) The end of just-in-time? Reuters Events. https://www.reutersevents.com/supplychain/supply-chain/end-just-time. Zugegriffen am 30.06.2022
Interesting Engineering (2021) 5 reasons why the world is running out of chips, YouTube. https://www.youtube.com/watch?v=SREbl7Mpw2g. Zugegriffen am 10.06.2022
Jones A (2021) Semiconductor market might reach overcapacity by 2023, IDC suggests, Industry Europe. https://industryeurope.com/sectors/technology-innovation/semiconductor-market-might-reach-overcapacity-by-2023/. Zugegriffen am 19.06.2022
Lee H et al. (1997) Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science; Apr 1997; 43; 4; ABI/INFORM Global pg. 546.
maruti (2022) 9 Ways Machine Learning Can Transform Supply Chain Management, Maruti techlabs. https://marutitech.com/machine-learning-in-supply-chain/#What_is_Machine_Learning. Zugegriffen am 30.06.2022
Ohno T (1988) Toyota production system: beyond large-scale production, Portland, Oregon: Productivity Press
Schrage M (2020) Data, Not Digitalization, Transforms the Post-Pandemic Supply Chain. MITSloan Management Review. https://sloanreview.mit.edu/article/data-not-digitalization-transforms-the-post-pandemic-supply-chain/. (Zugegriffen: 05.03.2022)
Sharwood S (2021) China’s biggest chipmaker to build colossal chip factory, The Register. https://www.theregister.com/2021/09/06/smic_new_fab/. Zugegriffen am 19.06.2022
Sterman J (1989) Modelling Managerial Behaviour: Misperceptions of feedback in dynamic decisions making experiment, Management Science, 35(3), pp. 321–339.
Wang X und Disney M (2015) The bullwhip effect: Progress, trends and directions, European Journal of Operational Research 250 (2016) 691–701. https://www.elsevier.com/locate/ejor. Zugegriffen am 20.10.2019
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Gronwald, KD. (2023). Digitalisierung der globalen Supply Chain. In: Globale Kommunikation und Kollaboration. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-39099-0_3
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