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The basics of managing a collaborative supply chain (Part 5 of 5)

In this the last of a series of  five posts on collaborative food chains, I’ll sum up by asking whether the Scottish pig industry achieved a ROI (return on their investment) in an information system that collects data across the whole supply chain.

Was this just an annoying additional set of ‘paperwork’, or does the system supply information that allows players up-and-down the food chain to work out why variations occur and what can be done about them?

Improvements depended on knowledge of the entire supply chain

The Scottish pig industry described two examples of vitamin supplements on the farm helping to control quality control at the abattoir (by reducing ‘drip loss’) and in the shop by slowing discolouring (which you and I don’t like when we buy meat).

These examples show that a business cannot be dependent only on information collected within their own business. They need information on businesses on either side of them in the chain. The pig industry provides that in 4 quarterly reports.

Improvements depend upon us experimenting systematically to find the causes of unexplained variations

These reports are obviously ‘after the event’. They are not part of the day-to-day management of operations which generate forward momentum. They are an additional diagnostic system to help us understand ‘unexplained variation’.

We have the information systems now to run experiments.  For example, I can ask, if I add Silenium and Vitamin E, will the colour of the meat hold up all the way to the 2nd or 3rd day of display in the store?  Perfecting our craft becomes a matter of understanding consequences along the line.

3 simple lessons for managing collaborative supply chains in other industries

To draw out lessons from the Scottish pig industry for other industries:

  • Collect data across the whole food chain so people at the beginning can help solve variations later in the food chain.
  • Remember this is a diagnostic loop.  It provides data after the event.  It is does not tell us what to do when.  That is management.  But used correctly, and an extra diagnostic loop helps us understand what is important and what is not.
  • Don’t think quantity and control.  Think variations and unexplained variance.  We want to understand what is happening so we can bring good food across the system from farm to plate.

Does the new system help provide better food at a good price?

Well, I hope so because to be well-fed, I need farmers to be making a fair living and I also want farmers to know when I am walking past their food in the shop and not buying it.

A free market system of letting the incompetent go broke is naïve.  Of course we learn some things by chance but in a system as complicated as a modern food chain, we also need a sophisticated feedback system so that everyone who is really into what they do, can do a better job – with data, proper analysis, and well thought-out experiments to understand events beyond our immediate control yet affecting us and being affected by us in small part.

I hope these five posts have helped explain why collaborative supply chains are a critical part of business in a developed economy.  The Scottish pig industry is a good example, down-to-earth, close-to-home and relatively easy to imagine why we collect and share information at industry level.

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