" value-add services" " Insight is critical" "glean better data from multiple touch points"
"highest possible level of visibility and intelligence"
"a key reason for that shifting dynamic" " the agility needed to maintain a competitive edge" "operations that leverage technology to its fullest"
I challange you to re-write that article in English. Once you remove all the Dilbert-style management buzzwords theres not a lot of information left. It's little more than "collect data, and use it to make your production line more efficient".
I think the editors of DN should have required the author to clean in up a bit, too.
While I agree in general with what is presented, I have seen "systems" create problems as well, especially when the system is trusted over all other (human, from the manufacturing floor) input.
An example that I observed several years ago was a plant that manufactures a catalog of small, relatively inexpensive widgets. They make a small number of high volume widgets, and quite a number of others in various quantities, down to perhaps 12 dozen per year of certain models.
The plant went through the typical process of just-in-time, lean manufacturing, and computer driven workflow, etc. A big emphasis was put on reducing changeover times for equipment, and making it quicker to produce parts in small lots. Once this capability was achieved, the workflow system started injecting small lots into the production schedules in any way that made sense to it (the computer).
Because of the nature of the manufacturing process for the widgets in question, some quantity of parts are lost to machine setups, and destructive testing requirements. So, every time a particular model needs to be manufactured, regardless of lot size, there will be some loss to the setup and testing processes. The workflow system didn't seem to take these factors into consideration.
The end result was that instead of scheduling and manufacturing some profitable number of pieces, the system would kick out orders of perhaps 12 pieces or less of a product that would sell at a rate of 12 per month, year after year.
The common sense approach would be to produce 12 months worth of the parts, which may produce 4 or 5 scrap parts, instead of producing 12 per month, every month, and producing the same 4 or 5 (sometimes more) scrap parts each month.
The end result was that the company focused on their high volume parts and sent all of the "losers" to Mexico. I would contend that most of the losers would be winners if they used some common sense in scheduling, instead of completely trusting the computerized system.
Naperlou, in general I agree with all of your statements. In the back of my mind though, I see echos of Robert McNamara applying similar methods to the soldiers in Vietnam, turning lives (and deaths) into numbers and efficiency factors.
Some time ago I was given a tour of a building controls factory in the Chicago area. As you walked on to the production floor there was a monitor with the status of each production cell displayed. Of course, this information was provided by the MRP system. Tying this type of information to external market data and making it available to the whole value chain can, as the article points out, make the whole enterprise more efficient. It also helps management plan better. This is definately an aspect of making US manufacturing more efficient, but more imortantly, more responsive.
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
In a bid to boost the viability of lithium-based electric car batteries, a team at Lawrence Berkeley National Laboratory has developed a chemistry that could possibly double an EV’s driving range while cutting its battery cost in half.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.