Aug 19 2020
Last Monday, SESA Systems invited me to give a webinar on my books and posted the video on Youtube:
#manufacturingbooks, #lean assembly, #leanlogistics, #workingwithmachines, #manufacturingsystemsanalysis, #introductiontomanufacturing
Aug 3 2020
“Many schemes, ranging from simple to complex, using process behavior charts with Covid data have been tried. But regardless of their complexity, they all come up against the fact that epidemiological data do not represent a steady-state system where we need to discover if assignable causes are present. Process behavior charts simply ask the wrong questions here. When dealing with data from a dynamic system where the causes are well understood, the data will create a running record that can be interpreted at face value. The long-term changes will be sufficiently clear so that further data analysis becomes moot.
So, while specialists may use epidemiological models, when it comes to data analysis by nonspecialists we do not need more analysis, but less. We need to draw the graphs that let the data speak for themselves, and then get out of the way. As always, the best analysis is the simplest analysis that provides the needed insight.”
Sourced : Quality Digest
Michel Baudin‘s comments: Don Wheeler is correct that process behavior charts are not a fit for data about the pandemic. Non-specialists, however, cannot ignore epidemiological models for several reasons:
That's why I took a stab at learning and sharing about them in a few recent posts:
#covid19, #coronavirus, #epidemiology, #pandemic
Jul 29 2020
The BOM Rap recommended restricting the centrally managed part of the Bill Of Materials (BOM) of an assembly plant to the Gozinto (“goes-into”) structure of the items. Building on this, Part II used a small toy example to introduce the Vàzsonyi procedure as a tool usable on a laptop computer to extract useful information from BOMs for use in assembly improvement projects, together with graphic visualization tools. Here, we apply these tools on the larger, more realistic BOM, of the Legotractors from our Leanix games.
Jul 9 2020
The BOM Rap recommended restricting the centrally managed part of the Bill Of Materials (BOM) of an assembly plant to the Gozinto (“goes-into”) structure of the items — that is, triplets with an item ID, the ID of an item it goes into, and the quantity used, together with an item list carrying units of measure.
This is the common core to all uses of the BOM, to which engineers, production planners, or accountants can attach additional data for their own purposes.
As discussed in The BOM Rap, BOMs are usually kept in ERP systems that only support their uses in their transaction menus, and production engineers often need to do more.
May 28 2020
In today’s New York Times, N. Popovitch and M. Sanger-Katz wrote an article about how The World Is Still Far From Herd Immunity for Coronavirus, in which they treat herd immunity as if it were a characteristic of the disease only, achieved when 60% of the population has antibodies.
The US CDC website defines herd immunity it as “a situation in which a sufficient proportion of a population is immune to an infectious disease (through vaccination and/or prior illness) to make its spread from person to person unlikely.”
The CDC makes it clear is that the “sufficient proportion” depends on both the disease and the population in which it spreads. In other words, for a given disease herd immunity varies with the herd. The same proportion of immune individuals will not achieve it in populations with different lifestyles. It is higher if they commute in crowded buses to work shoulder-to-shoulder on assembly lines; lower, if they move in individual cars and work in private offices.
The CDC’s definition fails to say what they mean by unlikely. To reopen factories without making them COVID-19 hot spots, we need the workforce to have herd immunity. It means that its members must be unlikely to infect each other, not that 60% of them must have immunity.
Two of the charts from my previous post on this subject can clarify the issues. The first one shows the generic pattern of an epidemic over time in a population, in the classic SIR model.
The key parameter often mentioned today by people like Angela Merkel is R_{0} , pronounced “are-nought,” which can be interpreted as the expected number of people an infected person would transmit the disease to while infectious in a population where no one else is infected. In a population of size N The number of infected people peaks when the number S of susceptible individuals drops enough to have R_{0} = N/S . Some authors call the ratio r = 1- S/N of recovered people to the entire population at the peak of the epidemic the “herd immunity threshold.” Past this point, the epidemic ebbs, but infection can still be likely.
At the right side of the curve, where the number of infected people drops to 0 , the limit r_{\infty} of r varies between 0% and 100% depending on R_{0} . r_{\infty} describes the proportion of the population with acquired immunity that is necessary to confer herd immunity on the entire population. You don’t have to say how unlikely transmission is.
r_{\infty} is a final score directly observable only when the epidemic is over. R_{0} , on the other hand, can be estimated early on, albeit with wide margins of error. With a model of the epidemic, r_{\infty} can then be inferred from R_{0} . The second chart plots s_{\infty} = 1- r_{\infty} as a function of R_{0} in the basic SIR model, with the ranges of R_{0} estimates published for the seasonal flu and COVID-1.
All the practices introduced into a factory to prevent contagion at work lower the R_{0} of the disease within the workforce while working, which lowers both the herd immunity threshold and the level of actual immunity required to achieve herd immunity in the long run, and this is quantifiable.
Of course, outside of work, the employees of the factory are within society at large. They are subject to its contagion dynamics. The main problem of today, however, is factories turning into epidemic hot spots.
#herdimmunity, #covid19, #factoryreopening, #factoryhotspots
Sep 10 2020
Series Of Events In Manufacturing
Factories are controlled environments, designed to put out consistent products in volumes according to a plan. Controls, however, are never perfect, and managers respond to series of events of both internal and external origin.
An event is an instantaneous state change, with a timestamp but no duration. An operation on a manufacturing shop floor is not an event but its start and its completion are. Machine failures and quality problem reports from customers are all events. Customer orders and truck arrivals are also events.
For rare events, you measure times between occurrences and mark each occurrence on a timeline. For frequent events, you count occurrences per unit of time and plot these numbers over time. On individual machines, you record the times between failures. For incoming online orders for a product, you count how many you receive each hour or each day. Every 200,000 years, compass needles reverse directions. That is a rare event, recorded in basalt oozing from the mid-Atlantic ridge as it cools down, as on a magnetic tape. (See Paleomagnetism.)
Once a year, an American TV viewer tuning in to the Academy Awards is a frequent event. For this, we care how many millions do it, but not how much time elapses between two tune-ins.
Responding to series of events is central to many businesses. Stores respond to customers coming in, airlines to passengers with reservations showing up to fly — or not, maintenance crews to machine failures, social networks to subscriber activities,… The challenges posed by the randomness of the arrival and service processes has given rise to queuing theory and commonly used results like Little’s Law about lead times, throughput and inventory in steady-state, or Kingman’s Rule about the way lead times explode when systems saturate.
We are concerned here with a different and simpler topic: monitoring series of events to detect changes in their rates of occurrence and tell fluctuations apart from shifts with assignable causes. If the arrival rate of quality problem reports from the field suddenly doubles, a sophisticated analysis is not needed. If it increases by 10%, the conclusion is not so obvious.
Continue reading…
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By Michel Baudin • Data science • 0 • Tags: Alarm Generation, Equipment failure, Queueing, Series of Events, Time Series, uip