Friday, April 8, 2022

Productivity Drivers - Productivity Analysis - Productivity Science - Productivity Engineering - Productivity Management



Productivity Drivers - Productivity Analysis - Productivity Science - Productivity Engineering - Productivity Management


Productivity drivers provide direction to productivity engineering and productivity management project activities.

Productivity analysis identifies productivity drivers or determinants of productivity.

Productivity science is the systematic collection of theories related to productivity improvement and productivity drivers.

Analyze - Identify - Engineer.

Analysis is comparing current conditions of a process with standards or best practices. Identifying the gaps and possibilities of improvement is the next step. Then detailed engineering to implement the concepts selected follows.

2019



Find new productivity drivers from welding operations data. 


WeldCloud - Esab

WeldCloud is an online management system that connects welding power supplies to a software platform that manages data to be analyzed for maximum productivity.

Every time a welder completes a pass, a trove of data is infused in the weld. That data has the power to inform future decisions, improve productivity, and provide you with the information necessary to understand how the weld was made.

Data for Productivity in Welding

As a productivity tracking platform using 3G combined with WI-FI and Ethernet, WeldCloud can plug into practically any in-house software system. WeldCloud is a secure, locked-down system that ensures your data is totally confidential.

WeldCloud offers:

Traceability. The platform can trace back to welds that have already been created and provided the details on how and when they were created.
Two-way communications. The platform can actually push settings to machines, such as a new weld parameter combo, while the machines can send data back to the platform.
Easy set up. Be up and running with WeldCloud in a couple of hours and trained in just a few days.
Alert management. WeldCloud automatically pushes out alerts when machines have issues, as needed and scheduled by the user.
Simple integration.  WeldCloud has solutions that can work with existing machines and get you up and running - quickly.
User-friendly interface. WeldCloud's responsive design makes using the platform easy from wherever you are, whether that's at home on a desktop, at work on a laptop, or on the move on mobile or tablet devices.
100% scalable. Put WeldCloud to the test by adding it on one or two machines and adding it onto others over time. You'll also benefit from new features and functionality that are constantly being developed and updated on the platform.

Operations Managers.
The people in this department are constantly looking for opportunities to increase productivity while maintaining a high-quality output. WeldCloud makes that possible by equipping them with data to find new productivity drivers.

Welding Engineers.
Welders' main focus is to determine the best possible welding process for a given application and test it. Quite a bit of data is required to make that initial decision, then it has to be tested on one machine. Once proven, WeldCloud can send the process to the cloud and - when it's ready - push it out to all the machines in the shop, saving a tremendous amount of time.

http://www.esabna.com/us/en/weldcloud/index.cfm



OECD MultiProd Project: The micro drivers of aggregate productivity


The MultiProd project studies productivity patterns and investes the extent to which different policy frameworks can shape firm productivity. It examines the way resources are allocated to more productive firms.
Drawing on the experience of the OECD ‘DynEmp’ (Dynamics of Employment) project, which provided harmonised micro-aggregated data to analyse employment dynamics, the MultiProd project provides cross-country harmonised micro-aggregated data of paramount importance for understanding productivity performances.
http://www.oecd.org/sti/ind/multiprod.htm

2015


Productivity Drivers  

Productivity Accounting, Emili Grifell-Tatjé, C. A. Knox Lovell


Cambridge University Press, 26-Jan-2015 - Business & Economics - 408 pages
(Pages 22 to 32)
https://books.google.co.in/books?id=Xz3WBQAAQBAJ

Productivity Drivers Internal


1. Quality of Management: General
2. Quality of Management: Human Effort Engineering and Human Resource Management.
3. Quality of Management: Allocation of Resources
4. Quality of Management: Adoption of New Technology
5. Quality of Management: Product Range and Diversification
6. Quality of Management: Cost Reduction and Waste Elimination
7. Quality of Labor
8. Quality of Capital ( and Intermediate Goods in a Gross Output Context)
9. Quality of Outputs of Services

Productivity Drivers External


1. Institutions
2. Ownership
3.The competitive environment
4. Regulation, Deregulation and Regulatory Structure
5. Structural Reform and Liberalization
6. Demographics
7. Geography
8. Public Infrastructure and Research & Development


The authors say the list is only indicative and not comprehensive. They are intended to emphasize that productivity is controllable either by firm management, or by the aggregate economy's helmsmen.


Process Parameters of Abrasive Waterjet Machining


Hydraulic Parameters: Waterjet nozzle diameter, supply pressure
Abrasive Parameters: Abrasive material, abrasive size, abrasive flow rate
Mixing Parameters: Mixing tube dimensions, nozzle material,
Cutting Parameters: Traverse rate, standoff distance,impingement angle, depth of cut, material to be cut

Expected performance criteria in AWJM: Cut quality, Material removal rate, Kerf topography, Kerf geometry, Cutting time

In a research paper the following parameters were condired as important to achieve above performance criteria.

1. Pressure inside pumping system
2. Abrasive material grain size
3. Angle of impact
4. Target material properties
5. Focusing tube diameter
6. Nozzle speed
7. Stand-off distance
8. Abrasive mass flow rate

Source: "Identification of Process Parameter of Abrasive Waterjet Machining for Performance Improvement - An AHP Approach," Industrial Engineering Journal, June, 2015, Vol. VII Issue No. 6
Authors: P.P. Badgujar, M.G.  Rathi, S.D. Kalpande




2014

Die Casting - Process Parameters


Processing factors that can affect process performance are:

1. Melting temperature of the cast metal
2. Injection pressure
3. Plunger speed
4. Cooling time

Source: Mahesh N. Adke and Shrikant V. Karanjkar, "Optimization of die-casting process parameters to identify optimized level for cycle time using Taguchi method," International Journal of Innovations in Engineering and Technology (IJIET), Vol 4, Issue 4, December 2014

2012

Productivity Drivers - Additive Manufacturing


Build time is sum of non-sintering operations and sintering operations.

Non-sintering operation time is the sum of the time for moving the platform elevator down one step and the time to deposit a new material layer. Total non-sintering operation time is time for each layer, multiplied by number layers used.

The time for sintering operation for one layer is a function of area of the layer, beam scan speed and  scan spacing .

Source: p. 144 of Phd Thesis "Multi Objective Optimization in Additive Manufacturing" by Giovanni Strano, May 2012
University of Exeter



2008

A Productivity Driver in Laser Cutting


Improvement in beam delivery optics is a productivity driver in laser cutting process since the 1970s. Improved coatings and thicker lenses etc. have facilitated  high-pressure applications and ,  have helped drive the quality and productivity of today's laser cutting machines. Fabricators enjoyed benefits  of the enhanced beam quality, reduced maintenance, increased power, and improved mode stability, all stemming from years of research and development invested in laser beam delivery optics.

https://www.thefabricator.com/article/lasercutting/laser-optics-special-delivery

Adaptive adjustable radius optics

2007

Susanna Mancinelli and Massimilliano Mazzanti, "SME Performance, Innovation and Networking," Fondazione Eni Enrico Mattei, Nova Di Lavoro 50.2007

Section 3.1 Main Productivity Drivers

Structural firm feature: Size
Expenditure per employee on R&D


2006
Prof. Dominique Foray, "Enriching the indicator base for the economics of knowledge," Blue Sky II Conference, Statistics Canada, Ottawa.

Knowledge management and other organizational complements play a crucial role in explaining the recent surge in productivity in OECD countries. (p.6)

ud. 8.4.2022
pub. 25.1.2019

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