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Digital Manufacturing: The role of Big Data in the Future of Manufacturing

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Mohamed Zaki and Professor Philip Shapira, University of Cambridge and University of Manchester

Published in: Business
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Digital Manufacturing: The role of Big Data in the Future of Manufacturing

  1. 1. Dr Mohamed Zaki, Dr Babis Theodoulidis, Prof Philip Shapira Digital Manufacturing: The Role of Big Data in the Future of Manufacturing
  2. 2. • RdM Concept • Framework and Case Studies • Exercise Outline
  3. 3. • It is a transformation to empower customer interaction, the facilitation of ad hoc supply chains, the recapture and redeployment of valuable materials and resources, the optimisation of manufacturing processes and informing new opportunities for the user-driven design of customised goods and services to be manufactured and assembled, both small-scale and on-demand. RdM
  4. 4. Value Capture How can big data impact redistributed manufacturing in the consumer goods industry?
  5. 5. Manufacturing strategy: Price is key (food, drink & personal care) Price Differentiation Cost Quality Delivery Flexibility Examples: Company F1 (dairy products), PepsiCo, Coca-Cola, Nestle Conclusion: Already distributed networks motivated by price and product nature -> leverage existing distributed manufacturing network - Plan supply and forecast demand (e.g. Mondelez collaborative shipping) - Fast customer insights to serve products for regional needs (e.g. Dannon Greek yoghurt in USA) - Ensure quality: food safety (e.g. Nestle), consistent quality (e.g. Coca-Cola) - Optimize and measure marketing (e.g. Mondelez “mPulse Lab”) Data source Data activity Customer feedback POS data Social media Quality data Manufacturing data Production planning Distributed (country-continent level)
  6. 6. Manufacturing strategy: Price and Differentiation is key (fashion) Conclusion: Already distributed networks motivated by price and product nature -> leverage existing distributed manufacturing network Price Differentiation Cost Quality Delivery Flexibility Examples: Zara, Company C1, (Metersbonwe) - Customer insights for newest fashion from social media and other channels - Daily reporting of best-selling rank from each store (Zara) - Market segmentation closest to customer needs - Plan supply and forecast demand (e.g. Metersbonwe had reportedly problems with high inventory; 80% of shops franchise -> bad POS data) Data source Data activity Customer feedback POS data Social media Production planning production mix 70% local 30% in Asia
  7. 7. • Groups of four/five, 30 mins… • Choose a product from furnishings, appliance, auto parts • Identify how you might “redistribute” the product using the proposed framework… • Be prepared to pitch your product when we get back (10 minutes pitch)… • Think about: • Time and a cost- how to reduce the production and delivery cycle time (e.g. cars, furniture could take from 1 to 3 months) • How to customize products based on customers’ profile and taste ( e.g. intelligent applicances) Exercise

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