Skip to 0 minutes and 12 seconds So now that you’ve explored what type of analysis you might like to use for each stage of the supply chain we are going to focus on the data that is required for each analysis. Most people would simply go to a store, pick up a bottle and that’s it but there’s a lot more than meets the eye when it comes to purchasing a pint of milk. Lets take a moment to consider the data points involved with the procurement of such a commodity as milk. If we follow the chain back from the fridge to the source we can identify a number of data sources that feed into the supply chain.
Skip to 0 minutes and 45 seconds House level consumption, local supply through your neighbourhood shop, regional supply through supermarkets, deliveries to individuals, local shops and supermarket by distribution centres, deliveries from bottling plants, collections from farms, production by farms and lets not forget those external data sources we might want access to. What type of data would be of interest to each of these data sources? Typical data point questions that would need to be addressed from a consumer point of view for instance are how much milk do they consume? What time of day do your customers, on average, consume or buy milk? Is the milk safe? Are they lactose intolerant? And even do cows really contribute to global warming?
Skip to 1 minute and 36 seconds Once you have determined what analysis each step of the supply chain might need and looked at what data these analyses require you are ready to start gathering this data and designing your decision making process.
AI technologies and the supply chain: data and data sources
What type of data do you need for your analysis? Where and how do we get it?
In this video, we explore what data is required to undertake analysis for each step in the supply chain to take milk from ‘grass to glass’.
Think about a decision that you make (almost) every day. What data do you use to make the decision: is it internal or external? Is it easily available? Is there any additional data that you might like to have but is either hard to find or not available at the time you make the decision?
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