Posts Tagged ‘operator engagement’
shopTALK – Episode #1: LED Display Boards
Posted by admin in Machine Truth Blog Thursday, 24 February 2011 15:37 No Comments
QUESTION: How does operator engagement and LED display boards affect an organizations productivity?
ANSWER: One of our customers was clearly able to articulate how operator engagement and LED display boards were able to help their organization achieve record productivity levels. The customer was a discreet job shop: with assembly cells, fabrication cells, and welding cells. They had been working on self directed high performance teams in attempt to balance the throughput to achieve production shift targets. They also incented the employees for production that exceeded the targets of the overall shift, rather than just the individual cells. The difficulty was they were spending a large amount of supervisory and management effort trying to balance the workload and move resources around from one cell to the other to achieve the production targets.
When Shoplogix was implemented the LED display boards were clearly able to articulate, as each of the work centres throughput was displayed on the boards. Individuals were able to see if an area was falling behind and required some assistance and were able to redeploy themselves to achieve the targets of that cell and of course maximize the output of the entire shift. They proceeded to set record production throughput levels upon the first shift after implementing Shoplogix. |
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Michael DedrickMichael Dedrick is the Marketing Campaign Specialist at Shoplogix Inc. |
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Three Myths of Plantnode
Posted by jhyam in Machine Truth Blog Thursday, 14 October 2010 21:17 4 Comments
In my many conversations with manufacturing plant managers, owners and continuous improvement teams, I have often encountered a set of presumptions around our manufacturing performance management software – Plantnode. There were some presumptions I came across often when educating prospects on our solution. I thought I’d take a time out to clear up the most common myths about Plantnode.
Myth #1: Plantnode is similar to an ERP system
Fact: Absolutely not, we are a Real Time Performance Management solution. An ERP’s purpose is to facilitate the flow of information between all business aspects, it takes months to implement and relies on manual plant floor data to be entered into the system.
Our solution Plantnode is what we call “The final frontier to manufacturing excellence,” it fills the gaps between management and shop floor. Plantnode pulls data directly from the machine level giving you 100% machine truth; in other words, it tells you exactly what is happening on your shop floor in real time. Unlike an ERP, it doesn’t take months to implement; we typically have a plant live in a couple of days. Furthermore, our solution encompasses operator engagement, and provides proactive alerts to appropriate plant personal. Plantnode is complementary to an ERP system as the data collected is easily fed into the ERP, in real time, to provide truthful information to the entire organization.
Myth #2: Plantnode is IT centric
Fact: No sir! We are not IT centric at all. We are a plug and play technology that rapidly connects to equipment of any type and monitors performance in real time. How we differ from all other solutions in our space is our rapid time to value and the fact that we can connect to any machine (we don’t need PLCs). All reports are web based so it is non intrusive to your existing systems. Clients are able to pull reports in real time anywhere, where there is an internet connection, whether you’re on vacation sipping a beverage on a sandy beach (via mobile reporting) or in your office you will be able to have a tight grip on things.
Myth #3: Plantnode doesn’t impact the bottom line
Fact: Our clients see a payback on average within 3-6 months. We’ve had clients receive payback as quick as 28 days (http://www.shoplogix.com/ryerson-puts-the-pedal-to-the-metal/). Whether it be packaging giant Grief who realized $80,000 in annual labour savings for their Omaha Multiwall facility (http://www.shoplogix.com/packaging-leader-greif-drives-operational-efficiency-with-automated-performance-management/) or Grande Cheese a smaller cheese producer who accomplished 80% reduction in response time and a payback of 2 months (http://shoplogix-partners.com/casestudies/grande_cheese_company.pdf). Regardless of company size or industry all our clients have one thing in common they all see an increase in production, OEE, and a rapid time to value payback.
In conclusion, we do not replace an ERP system; however we are highly complementary to them. We replace manually imputed data with 100% machine truth. We find between 25-40% discrepancy between manually collected data and Shoplogix Plantnode collected data. We have a plant live typically within a couple days, have a very low IT footprint which also minimizes impact on current projects. Lastly, we offer very dynamic ways to “drive” the solution to identify and establish business benefits as part of an overall justification.
Machine Truth in Context
Posted by gdalleave in Machine Truth Blog Wednesday, 29 September 2010 02:12 No Comments
In a previous article, we discussed what Machine TruthTM is and its importance. Gathering this data automatically is undoubtedly important and a certain amount of work can be done with this data. But it is just machine data. The next step with Machine TruthTM data is to put this data in context by associating the data with additional non-machine data. The non-machine data to use is really dependent upon the business and strategic goals of the organization. A common set of non-machine data to use includes the customer for whom the job is running, the operator running the machine, and the suppliers of materials. Another important item is the production standards used by the organization.
Why is non-machine data important? Consider a purchasing agent that secures a better price for the paper used in the process. By every measure used by this organization, the purchaser has done a good job in saving the company money. However, at the shop floor, it is found that this paper curls a little bit more under the humidity conditions of the plant and leads to the machine jamming which in turn results in more downtime which leads to lower production and increased costs. By associating Machine TruthTM data with supplier information, the loop between purchasing and production can be closed. This data can give the purchaser the ammunition they will need to go back to the supplier to ask for improvements or to change suppliers all together.
Associating the Machine TruthTM data with the labour used to produce the product is important on several accounts. First it provides us with the production per labour hour metric. It may not always be advantageous to increase production by increasing the labour as the increase in costs will eat into the margin. Secondly, associating production with the labour allows the organization to determine which operators are struggling and in which areas in order to allow the organization to target the appropriate training programs to bring all their operators to the required level.
Finally, let’s consider the association of Machine TruthTM data with organizational standards. Among other things, these standards describe how long it should take to set up a machine, the expected run speed, the expected labour hours, and the expected production.
Consider the following diagram. With conventional metrics of uptime and production, Operator B would be considered as having the better day. However, the success of Operator B’s day should really be measured against how well he performed against the standards developed by the organization. If the standards indicate that this operator should have had 70% uptime and produced 700 cases, then he did not have a good day.
Conversely, Operator A had less uptime and produced fewer cases but had to endure more changeovers. If this Operator performed better against the standards provided then he would have had the better day.
The important take away is that Machine TruthTM data by itself may be useful it can only take you so far. It is necessary to put this data into context in order to get the full impact of what this data is telling you.