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The basics of managing a collaborative supply chain (Part 4 of 5)

This is a series of posts using the Scottish pig industry to explain collaborative supply chains.

In post 1, I described the problem of complex supply chains.  Feedback gets lost.  Or to use an example, if I don’t like the bacon on my plate, the farmer does not get to hear about it.

In post 2, I explained that in years gone by we thought food had to be cheap or expensive and there was no in-between. Toyota showed in the car industry that there is an in-between when we move from cheap high-volume to agile, just-in-time supply systems by working closely with our suppliers. Computers make it easier to work collaboratively across a whole sector.

In post 3, I briefly described the diagnostic system that runs in addition to the management system.  Information is sent out every quarter that allows everyone to see the whole supply chain and to work out where variations in quality are happening. I ended that post by staying that a management system will tell us what is explained variance and what is unexplained variance.

Explained variance allows us to act; we have to think about unexplained variance

Simply when we understand the cause of a ‘blip’, we can take action, confidently.  When we see variations that don’t have a known cause, then we have unexplained variance and we have to stop and think.  So what are our choices?

What can we do about unexplained variance?

Unexplained variance means one of three things:

  • We need to do more analysis to see if any of the factors we had thought to be important, and have been dutifully recording, indeed account for dips in quality.
  • Maybe there is no answer, at least for now, and we are going to have to plan for variations in quality (more wastage).
  • Or we can investigate further and collect data on new factors to see if they explain variations as they happen, not only in our own business, but further along the line.

 

Unexplained variance might have its cause several steps removed in the supply chain

You might think that everyone does this already. They do – with the data they have.  But by working together across the whole food chain, the Scottish pig industry is able to help farmers see if there is something they can do on the farm that will help manage variability much further along.

  • To take a simple example where the farmer’s action brings a clear and immediate benefit to the farmer – giving a pig Vitamin C shortly before it is sent to the abattoir reduces the drip-effect, i.e., maintains the weight of the meat and gives the farmer a better price per carcass
  • To take another example that benefits the whole industry and gives the farmer a better price eventually because average prices are higher – giving a pig Selenium and Vitamin E slows down the discolouring of meat, meaning it looks a heap nicer in the supermarket and I as a consumer are willing to keep it in my mix of groceries.

When we can match data on what is happening in our business with data on what is happening in businesses up-and-down the chain, we might find new solutions to unwanted variations.

Once we know what to do and what to look for, future variations done to these causes, become of course explained variance – which is good, we know what to do now.

But is this science good business?  Is there a ROI on a collaborative supply chain?

In the next post, let’s ask whether the Scottish pig industry got a ROI (return on their investment).

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