The food industry has a unique position when it comes to reduce, reuse and recycle. The industry focuses around the organic and deals with animate material. When I first started in food, it was at a canning company in Australia. As a student who had previously worked in the plastics industry, it amazed me how so much product could be eeked out of fruit. AND, even more so, that any unused or wasted food, could go right back into the food chain.
The Shepparton Preserving Company (SPC, now SPC-Ardmona), used the parts of the apples and pears that did not go into the cans, as the basis for juice. Through enzymes and separation technology, they were able to get an incredible product. The peel, seeds, etc that weren't used, went to pig feed.
Last week, PepsiCo UK announced that it plans to make potato chip bags from ... potatoes! How is that for redirecting a waste stream! (see foodproductiondaily.com news article) The question will be does the customer find it more acceptable than the SunChip compostable bag.
Have you looked to see how your waste streams can be reduced, reused or recycled? And I don't just mean plastic and metal!
Monday, October 18, 2010
Monday, October 4, 2010
Why vs What If
When children are growing up, they go through a stage when they believe adults have all the answers - unlike when they are a teen and believe parents know nothing! For many children the favourite question is "why?" Two of my children though were "What if..." children.
"Why?" is a fantastic question to get to the root cause of a problem. It is also a great tool to understand how a process or machine works. When it is answered by one word, "because", then it can also lead to the perfect place to start improvements.
"What if" is also an excellent tool for problem solving, but it excels when it comes time to implement solutions. Success in implementation is often dependant on the ability to mistake-proof or contingency plan. This is the ability to consider "what if" situations and determine the impact if the imagined scenario occurs and what the impact could be and what needs to be done to minimize the negative impact.
The "what if" scenarios my kids created often ended up with me saying either "we'd all be in big trouble" (aliens blowing up the Earth) or "we'd all be really happy" (instead of shooting bullets, all guns shot bubble gum). Some "what ifs" are highly improbably but they are worth some discussion because they free up a team to think more creatively but realistically about what can happen.
Are you a "why" or a "what if" person? Does your team have members of both camps? What would you do if there were no restrictions on your business and why aren't you already trying to be like that?
"Why?" is a fantastic question to get to the root cause of a problem. It is also a great tool to understand how a process or machine works. When it is answered by one word, "because", then it can also lead to the perfect place to start improvements.
"What if" is also an excellent tool for problem solving, but it excels when it comes time to implement solutions. Success in implementation is often dependant on the ability to mistake-proof or contingency plan. This is the ability to consider "what if" situations and determine the impact if the imagined scenario occurs and what the impact could be and what needs to be done to minimize the negative impact.
The "what if" scenarios my kids created often ended up with me saying either "we'd all be in big trouble" (aliens blowing up the Earth) or "we'd all be really happy" (instead of shooting bullets, all guns shot bubble gum). Some "what ifs" are highly improbably but they are worth some discussion because they free up a team to think more creatively but realistically about what can happen.
Are you a "why" or a "what if" person? Does your team have members of both camps? What would you do if there were no restrictions on your business and why aren't you already trying to be like that?
Friday, September 24, 2010
Manufacturing, Process Optimization and Product Lifecycle Management
This week I took advantage of some free webinars being offered about the food industry. I was really intrigued by the Product Lifecycle Management for the Food Industry given by Greg Nutter of Lascom Solutions, through the Prepared Foods webinars.
Why was I intrigued? Well, some complaints you hear in manufacturing facilities are; you can never find the information you need (ie Sales won't give it to you) or that the other business departments don't understand. Or you may discover that the same product is made a different way at another facility. Or people are using different standards somewhere else! Or, that no one kept track of the way the process was set up when R&D or Sales did a last minute trial run.
Imagine being able to have all this information available easily and set up so that changes in spot initiate notices wherever else it may have an impact. Imagine being able to get your product to market faster because information is centralized. Wouldn't it be great to be able to see why things are being done the way they are? To be able to identify ways to improve and cut costs.
Check out the presentation on Product Lifecycle Management. The concept would fill many people's wishlists.
As this will only be archived for a limited time, send me an email if you would like a copy of the podcast: gemma@edgeofcontrol.ca
Why was I intrigued? Well, some complaints you hear in manufacturing facilities are; you can never find the information you need (ie Sales won't give it to you) or that the other business departments don't understand. Or you may discover that the same product is made a different way at another facility. Or people are using different standards somewhere else! Or, that no one kept track of the way the process was set up when R&D or Sales did a last minute trial run.
Imagine being able to have all this information available easily and set up so that changes in spot initiate notices wherever else it may have an impact. Imagine being able to get your product to market faster because information is centralized. Wouldn't it be great to be able to see why things are being done the way they are? To be able to identify ways to improve and cut costs.
Check out the presentation on Product Lifecycle Management. The concept would fill many people's wishlists.
As this will only be archived for a limited time, send me an email if you would like a copy of the podcast: gemma@edgeofcontrol.ca
Tuesday, September 21, 2010
Don't Touch That Dial - Part 2
Last time I discussed accurate data and how that impacts our reacting to our process outputs. This week we will look at interpreting the story those outputs tell us. Without the right story and context, manufacturing optimization will not occur effectively.
A lot of the time the data comes to us in numbers. Some people can read numbers very well presented in tabular form. Me, I prefer a picture. The best way to see that picture is in a graph. On the Y-axis, we typically put the outputs of our process – kgs produced, hrs uptime, % efficiency etc. On the X-axis is our inputs with time being the most common. If you are trying to decide whether to do an input or time, ask yourself, which changes? If you always have a setting constant, then you don’t want it on your X-axis. Time can be days, hours, minutes, shifts or even batches or lots.
Charts can tell you a lot but only if you have done them correctly. We will focus at this time on run charts, spec charts and control charts. Below are 3 graphs with the identical production information – but each tells a different story.
Figure 1 is a run chart. The shift production rates are plotted against the day and shift. That’s it. Is it good or bad? Who knows. The production rate varies from shift to shift and it looks like some were worse than others but who knows if it was bad?
Figure 1: Run Chart
Figure 2: Spec Chart
The next chart has the production goal laid over top. We will call this a spec chart because often it is not the production goals but the specification limits the customer wants. Now we have some context and can see that some of the production was great, well over the goal but some was bad, well below. But can we expect our process to perform more consistently, with less variation?
Figure 3: Control Chart
Figure 3 removes the production goal and puts what the mean (average) production rate is for the whole time period. It also introduces an Upper Control Limit (UCL) and a Lower Control Limit (LCL). According to those limits, there is so much variation in the process that the probability of having a day with an unplanned production of 0 KLiters is as likely as a day with 150 KLiters of production. Therefore, we should look at our process, not for one-off things to change (ie train Operator B to do CIP properly) but for improvements that go across all shifts, all the time.
Figure 4: Control Chart for Fine Tuned Process (with Production Goal)
Say we managed to fine tune the process and we ended up with Figure 4. Wow, lot less variation but, without any special events occurring, we would never make the production goal. One shift though something happened (special event – ie power outage) and the production really fell off. Another shift though, production was phenomenal, exceeding the control limits AND the Production Goal. Again, this is a special event, so we SHOULD be able to find out what happened differently and see how we can adjust the process to be like that all the time.
Context is critical when looking at data. And that context has to reflect the process itself and its capabilites and not just the goals of the business. The goals will put emphasis, pressure and criticality on the process, but if it cannot fundamentally perform that way, you have to do a complete rethink on what you want.
To put it another way; If you put a bowl of cookies on an 8 ft wall and tell your 6yr old to jump and grab one, they will try again and again and will get closer and closer to it but will never reach it. That is, until they get a ladder, get a taller friend or start throwing things to knock the bowl off. Does your data tell you it is time to get a ladder?
A lot of the time the data comes to us in numbers. Some people can read numbers very well presented in tabular form. Me, I prefer a picture. The best way to see that picture is in a graph. On the Y-axis, we typically put the outputs of our process – kgs produced, hrs uptime, % efficiency etc. On the X-axis is our inputs with time being the most common. If you are trying to decide whether to do an input or time, ask yourself, which changes? If you always have a setting constant, then you don’t want it on your X-axis. Time can be days, hours, minutes, shifts or even batches or lots.
Charts can tell you a lot but only if you have done them correctly. We will focus at this time on run charts, spec charts and control charts. Below are 3 graphs with the identical production information – but each tells a different story.
Figure 1 is a run chart. The shift production rates are plotted against the day and shift. That’s it. Is it good or bad? Who knows. The production rate varies from shift to shift and it looks like some were worse than others but who knows if it was bad?
The next chart has the production goal laid over top. We will call this a spec chart because often it is not the production goals but the specification limits the customer wants. Now we have some context and can see that some of the production was great, well over the goal but some was bad, well below. But can we expect our process to perform more consistently, with less variation?
Figure 3 removes the production goal and puts what the mean (average) production rate is for the whole time period. It also introduces an Upper Control Limit (UCL) and a Lower Control Limit (LCL). According to those limits, there is so much variation in the process that the probability of having a day with an unplanned production of 0 KLiters is as likely as a day with 150 KLiters of production. Therefore, we should look at our process, not for one-off things to change (ie train Operator B to do CIP properly) but for improvements that go across all shifts, all the time.
Say we managed to fine tune the process and we ended up with Figure 4. Wow, lot less variation but, without any special events occurring, we would never make the production goal. One shift though something happened (special event – ie power outage) and the production really fell off. Another shift though, production was phenomenal, exceeding the control limits AND the Production Goal. Again, this is a special event, so we SHOULD be able to find out what happened differently and see how we can adjust the process to be like that all the time.
Context is critical when looking at data. And that context has to reflect the process itself and its capabilites and not just the goals of the business. The goals will put emphasis, pressure and criticality on the process, but if it cannot fundamentally perform that way, you have to do a complete rethink on what you want.
To put it another way; If you put a bowl of cookies on an 8 ft wall and tell your 6yr old to jump and grab one, they will try again and again and will get closer and closer to it but will never reach it. That is, until they get a ladder, get a taller friend or start throwing things to knock the bowl off. Does your data tell you it is time to get a ladder?
Subscribe to:
Posts (Atom)
Posts
