Tag: food supply chain

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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This is the third post in series on collaborative supply management.  The posts are based on a case study of the pig industry which I’ve rewritten to bring out how we manage collaborative supply chains.

In post 1, I described the supply chain for pork products simply and pointed out that feedback gets lost in complex supply chains and does not reach people who need it.

In post 2, I pointed out that Toyota showed that it is not necessary to choose between cost-driven high volume businesses and high margin niche luxury businesses if we are able to manage our supply chains to deliver ‘just-in-time’.  Becoming nimble becomes easier with modern computers.

In this post, I’ll move from telling you about the general problem to what the Scottish pig industry did to manage their collaborative supply chain.

The Scottish pig industry already had an efficient system of delivering pigs and pork to market

The Scottish pig industry got together to do what they do well even better.  The farmers, the butchers, the shop-keepers and yes, the farm and meat inspectors run the ‘forward system’.  In psychological parlance, they track – they pay attention, they coordinate and they get everything done in an intricate and complicated dance.

And they added a diagnostic system which feeds information on the whole system back to individual players

The Scottish pig industry, working together, then added a ‘diagnostic system’ which collects information and feeds it back to everyone in the food chain every three months.

An example of a collaborative supply chain

Let’s take an example of how it works.

The farmer has records of how much food was given to a particularly pig, what supplements it gobbled up, how often it was ill and what medicines it was given.

By monitoring food throughout the chain, farmers can now learn what happens to a pig after it leaves them and they can find the condition of the meat when it lands on our plates (or rather leaves the supermarket in our trolley).

Let’s imagine that sometimes the pork I eat is fantastic and sometimes it makes me regret my purchase.  The information system in Scotland lets farmers know that this variation is happening.

The information system can also analyse the variation to see whether it the fluctuations are triggered at a particularly farm, a slaughter-house, a shop or transport system.

Once we have seen the numbers, then we can begin to understand ‘unexplained variance’

Moreover, the information separates out explained and unexplained variance.

Explained variance means, in plain English, that we know what caused something.  If we know what caused a blip, then we know what to do and we do it with confidence knowing that when we do the necessary, quality will go up and will be seen to go up. The consumer will be happy again.  Bravo. Simple. We can get it done.

I’ll explain in the next post what we mean by unexplained variance.

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Our food comes a long way from a farm

Let’s imagine a farm and on this farm, the farmer keeps pigs.  He, or maybe she, feeds them and waters them and breeds them and fattens them.  And when the pigs are big and fat, the farmer puts them on a truck and sends them off to the abattoir.  At the abattoir, the pigs are slaughtered, and sold as carcasses to butcheries and supermarkets who butcher the meat and package it into smaller quantities for us to take home and cook.

But if we don’t like it, it is difficult to let the farmer know

You and I, the shoppers at the supermarket, know what we want.  We want a meat that cooks well, looks good, smells good, tastes good, and feels good.

And when we don’t get what we want. . .well, exactly how is that communicated back to the farmer?

And are we sure that the problem was with the farm and not elsewhere in the complicated food chain?

And indeed how would the farmer know that it was something to do with his (or her) farming that created the undesirable quality.  Possibly the problem is elsewhere in the food chain… the transporting of the live pig or the handling of the pork at the butchery. . . to pick only two possible points.

A series of five posts to understand how we benefit from the collaborative management of a complicated supply chain

In this series of five posts, I have rewritten a case study of the pig industry in Scotland to help people who are interested in collaborative supply chain management understand how we  organize the collaboration and the key role of computers, data, analysis and experiments.

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