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RECENT METHODS FOR OPTIMIZATION OF PLASTIC INJECTION MOLDING PROCESS –A RETROSPECTIVE AND LITERATURE REVIEW
International Journal of Engineering Science and Technology
Vol. 2(9), 2010, 4540-4554
P.K. Bharti
Assistant professor, Mechanical Engineering Department, Integral
University, Lucknow, 226023, India
M. I. Khan
Prof. and Head, Mechanical Engineering Department, Integral University, Lucknow, 226023, India
Harbinder Singh
Professor and Director, Bundel khand Institute of Engineering and Technology, Jhansi, India
Abstract:
Injection molding has been a challenging process for many manufacturers and researchers to produce products
meeting requirements at the lowest cost. Faced with global competition in injection molding industry, using the trialand-error approach to determine the process parameters for injection molding is no longer good enough. Factors that
affect the quality of a molded part can be classified into four categories: part design, mold design, machine
performance and processing conditions. The part and mold design are assumed as established and fixed. During
production, quality characteristics may deviate due to drifting or shifting of processing conditions caused by
machine wear, environmental change or operator fatigue.
Determining optimal process parameter settings critically influences productivity, quality, and cost of production in
the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or
Taguchi’s parameter design method to determine optimal process parameter settings for PIM. However, these
methods are unsuitable in present PIM because of the increasing complexity of product design and the requirement
of multi-response quality characteristics.
This article aims to review the recent research in designing and determining process parameters of injection
molding. A number of research works based on various approaches have been performed in the domain of the
parameter setting for injection molding. These approaches, including mathematical models, Taguchi method,
Artificial Neural Networks (ANN),Fuzzy logic, Case Based Reasoning (CBR), Genetic Algorithms (GA), Finite
Element Method(FEM),Non Linear Modeling, Response Surface Methodology, Linear Regression Analysis ,Grey
Rational Analysis and Principle Component Analysis (PCA) are described in this article. The strength and the
weakness of individual approaches are discussed. It is then followed by conclusions and discussions of the potential
research in determining process parameters for injection molding
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Milacron’s servo injection molding machines offers energy efficiency, reliability, precision, and product versatility. The reduced energy use reduces the heat load on the factory and reduces maintenance and operational costs over the life of the machine.
Note: It is important to read the books by Boothroyd to understand the full method of DFMA. The DFMA method is to be combined with Value Analysis and Engineering to do product industrial engineering. In the note only attempt is made to make readers aware of issues raised and solutions proposed by DFMA method.
8. Design for Injection Molding 339
8.1 Introduction
8.2 Injection Molding Materials
8.3 The Molding Cycle
8.4 Injection Molding Systems
8.5 Injection Molds 346
8.6 Molding Machine Size 351
8.7 Molding Cycle Time
8.8 Mold Cost Estimation 359
8.9 Mold Cost Point System 367
8.10 Estimation of the Optimum Number of Cavities 369
8.11 Design Example 372
8.12 Insert Molding 374
8.13 Design Guidelines 375
8.14 Assembly Techniques 376
References 379
Design for Injection Molding
Injection molding technology is a method of processing predominantly used for thermoplastic polymers. It consists of heating thermoplastic material until it melts, then forcing this melted plastic into a steel mold, where it cools and solidifies. The increasingly sophisticated use of injection molding is one of the principal tools in the battle to produce elegant product structures with reduced part counts.
Injection molding technology is a method of processing predominantly used for thermoplastic polymers. It consists of heating thermoplastic material until it melts, then forcing this melted plastic into a steel mold, where it cools and solidifies. The increasingly sophisticated use of injection molding is one of the principal tools in the battle to produce elegant product structures with reduced part counts.
The most common types of molds used in industry today are (1) two-plate molds, (2) three-plate molds, (3) side-action molds, and (4) unscrewing molds.
MOLD COST POINT SYSTEM
The main cost drivers are given. For each cost driver, there are associated graphs or tables to be referred to for determination of the appropriate number of points. The mold manufacturing cost is determined by equating each point to one hour of mold manufacture.
(i) Projected Area of Part (cm2)
Equations provide points for the size effect on manufacturing cost plus points for an appropriate ejection system,
(ii) Geometric Complexity
—identify complexity ratings for inner and outer surfaces according to the procedure.
—Apply Eq. to determine the appropriate point score
(iii) Side-Pulls
—identify number of holes or apertures requiring separate side-pulls (side cores) in the molding operation.
— Allow 65 points for each side-pull.
(iv) Internal Lifters — Identify number of internal depressions or undercuts requiring separate internal core lifters. — Allow 150 points for each lifter.
(v) Unscrewing Devices — Identify number of screw threads that would require an unscrewing device. — Allow 250 points for each unscrewing device.
(vi) Surface Finish/Appearance — Refer to Table identify the appropriate percentage value for the required appearance category. — Apply the percentage value to the sum of the points determined for (i) and (ii) to obtain the appropriate point score related to part finish and appearance.
(vii) Tolerance Level — Refer to Table to identify the appropriate percentage value for the required tolerance category. — Apply the percentage value to the geometrical complexity points determined for (ii) to obtain the appropriate point score related to part tolerance.
(viii) Texture — If portions of the molded part surface require standard texture patterns, such as checkered, leather grain, etc., then add 5% of the point scores from (i) and (ii).
(ix) Parting Plane — Determine the category of parting plane from Table and note the value of the parting plane factor, to obtain the point score from Equation . To determine the cost to manufacture a single cavity and matching core(s) the total point score is multiplied by the appropriate average hourly rate for tool manufacture.
DESIGN GUIDELINES
Several have published design manuals or handbooks need to be consulted for designing injection molding components. Information can be obtained from them on the design of ribbed structures, gears, bearings, spring elements, etc. Du Pont, G.E. Plastics Division, or Mobay Corporation and other plastics manufacturers provide design information associated with their engineering thermoplastics.
Generally accepted design guidelines are listed below.
1. Design the main wall of uniform thickness with adequate tapers or draft for easy release from the mold. This will minimize part distortion by facilitating even cooling throughout the part.
2. Choose the material and the main wall thickness for minimum cost. Note that a more expensive material with greater strength or stiffness may often be the best choice. The thinner wall this choice allows will reduce material volume to offset the material cost increase. More important, the thinner wall will significantly reduce cycle time and hence processing cost.
3. Design the thickness of all projections from the main wall with a preferred value of one-half of the main wall thickness and do not exceed two-thirds of the main wall thickness. This will minimize cooling problems at the junction between the projection and main wall, where the section is necessarily thicker.
4. If possible, align projections in the direction of molding or at right angles to the molding direction lying on the parting plane. This will eliminate the need for mold mechanisms.
5. Avoid depressions on the inner surfaces of the part, which would require moving core pins to be built inside the main core. The mechanisms to produce these movements (referred to in mold making as lifters) are very expensive to build and maintain. Through holes on the side surfaces, instead of internal depressions, can always be produced with less expensive side-pulls.
6. If possible, design external screw threads so that they lie in the molding plane. Alternatively, use a rounded or rolled-type thread profile which can be stripped from the cavity or core without rotating. In the latter case, polymer suppliers should be consulted for material choice and appropriate thread profile and depth.
In addition to these general rules, design books should be consulted for design tips and innovative design ideas.
REFERENCES
1. Dewhurst, P., and Boothroyd, G., Design for Assembly in Action, Assembly Eng., January 1987.
2. Rosato, D.V (Ed.), Injection Molding Handbook, Van Nonstrand Reinhold, New York, 1986.
4. MacDermott, C.P., Selecting Thermoplastics for Engineering Applications, Marcel Dekker, New York, 1984.
5. Bernhardt, E.G. (ed.), Computer-Aided Engineering for Injection Molding, Hanser Publishers, Munich, 1983.
6. Design Handbook for Dupont Engineering Polymers, E.I. du Pont de Nemours and Co. Inc., 1986.
7. Farrell, R.E., Injection Molding Thermoplastics, Modern Plastics Encyclopedia, 1985-86, pp. 252-270.
8. Khullar, P., A Computer-Aided Mold Design System for Injection Molding of Plastics, Ph.D. Dissertation, Cornell University, 1981.
9. Gordon Jr., B.E., Design and Development of a Computer Aided Processing System with Application to Injection Molding of Plastics, Ph.D. Thesis, Worcester Polytechnic Institute, Worcester, MA, November 1976.
11. Ballman, P., and Shusman, R., Easy Way to Calculate Injection Molding Set-Up Time, Modern Plastics, McGraw-Hill, New York, 1959.
13. Dewhurst, P., and Kuppurajan, K., Optimum Processing Conditions for Injection
Molding, Report No. 12, Product Design for Manufacture Series, University of Rhode Island, Kingston, February 1987.
14. Schuster, A., Injection Mold Tooling, Society of Plastic Engineers Seminar, New York, September 30-October 1, 1987.
15. Sors, L., Bardocz, L., and Radnoti, I., Plastic Molds and Dies, Van Nostrand Reinhold, New York, 1981.
16. Archer, D., Economic Model of Injection Molding, M.S. Thesis, University of Rhode Island, Kingston, 1988.
18. Reinbacker, W.R., A Computer Approach to Mold Quotations, PACTEC V, 5th Pacific Technical Conference, Los Angeles, February 1980.
Designing Plastics Parts for Assembly - The pioneering development of the IBM ProPrinter.
Designing Plastics Parts for Assembly, Preface 1st Edition, by Dr. Peter Dewhurst, Department of Industrial and Manufacturing Engineering,
University of Rhode Island, November, 1993
https://www.ets-corp.com/lectures/dppa/p1th.htm
Process Design of Injection Molding System for Umbrella Handle Based on Moldflow
ICIIP 2019: Proceedings of the 2019 4th International Conference on Intelligent Information ProcessingNovember 2019 Pages 146–150https://doi.org/10.1145/3378065.3378093
https://dl.acm.org/doi/10.1145/3378065.3378093
Injection molding design: 10 critical considerations for designing high-quality molded parts, part one
Protolabs injection molding facility in Plymouth, Minn. has been inducted into the World Economic Forum’s Global Lighthouse Network, recognizing our industry leading efforts to implement Fourth Industrial Revolution (4IR) technologies.
The digital manufacturer becomes one of 10 U.S. companies so far honored by the World Economic Forum for industry advancements through manufacturing technology
Protolabs’ injection molding facility was recognized for its transformation as a prototype provider to now a full production provider through the implementation of 4IR technologies connecting its e-commerce experience to the shop floor. Its end-to-end connection—termed the digital thread—enables the technology-driven manufacturer to provide production lead times in as fast as one day, instead of two to three months with traditional manufacturers.
The 4IR technologies recognized by the World Economic Forum include Protolabs’ interactive e-commerce quoting system with automated design for manufacturability (DFM) analysis, its automated mold design and toolpathing programs, and its digital process controls and inspections. The prevailing theme with those technologies is the overall reduction in manual processes—both for Protolabs and its customer—due to strategic implementation of automation. That automation then drives significant customer value like product innovation, speed to market, supply chain risk reduction, and a multitude of other benefits.
By implementing manufacturing automation and Industrial IoT technologies like this, Protolabs is able to unlock new levels of sustainability and efficiency for itself and its customers
About Protolabs
Protolabs is the world’s leading provider of digital manufacturing services. The e-commerce-based company offers injection molding, CNC machining, 3D printing, and sheet metal fabrication to product developers, engineers, and supply chain teams across the globe. Protolabs serves customers using in-house production capabilities that bring unprecedented speed in tandem with Hubs, a Protolabs Company, which serves customers through its network of premium manufacturing partners. Together, they help companies bring new ideas to market with the fastest and most comprehensive digital manufacturing service in the world.
Protolabs Automated Design Analysis & Consultative Design Service for Injection Moulding
Already well known in the industry for our automated design for manufacturing analysis via our digital quoting platform, we have now launched an injection moulding consultative design service, to help you find the best manufacturable solution.
Our automated design analysis takes your CAD and typically within a couple of hours provides feedback on whether your design is manufacturable. It also helps identify any particularly challenging features and provides suggestions on how to improve manufacturability. You can submit your part for analysis and quote as many times as you want, at no cost.
The new complementary consultative design service allows you to take advantage of our engineers' experience in injection moulding, to help you optimise your solution. Whilst our quoting platform will check your design is possible, you may also need the critical thinking of an experienced person to find the best solution, whether it be support with a particularly complex geometry, trying to find a solution for a text requirement, or even help in cost cutting ideas to ensure your part is within budget.
One of IndustryWeek's four Best Plants 2022 winners
Protolabs Injection Molding Facility
Plymouth, Minnesota
Employees: 450
Square Footage, Manufacturing: 140,000
Primary Products: Injection molding
Start-up date: 2014
Achievements: Automated digital twin, Recognition as a WEF Global Lighthouse Network plant, 16.4% reduction in scrap and rework costs within past 3 years, digital process control reduced parts non-conformance by 45%, increased large injection molding capacity by more than 50% in the past 3 years.
Digital thread: Essentially, the term refers to digitizing every aspect of product development and manufacturing —from design through production—to enable full access to every relevant piece of data associated with the product for tracking, quality assurance, troubleshooting and maintenance.
Injection Moulding (or Molding to use the US spelling), along with extrusion ranks as one of the prime processes for producing plastics articles. It is a fast process and is used to produce large numbers of identical items from high precision engineering components to disposable consumer goods. https://www.bpf.co.uk/plastipedia/processes/injection_moulding.aspx
One hour video
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Electronica Plastic Machines
Futura 60 Injection Molding Machine
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Source. https://pec.ac.in/node/12718
Punjab Engineering College Chandigarh
The electronica machines has a sales & service office in Hiranandani Estate, Arcadia.
Google Analytics is a good example of big data analytics and its utility. Google analytics provides descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics.
It is big data because data of millions of websites is captured by the analytics application and it has to be processed in almost similar way. So number of computers using big data techniques are used to record and process data and create the dash boards that the user sees for his web site or web sites.
What differentiates “big data” from the term “data”?
Big data includes data sets that can’t be analyzed by the common traditional data analysis tools Big data refers to a high volume of data from a variety of sources. This required methods different from the ones used in conventional database systems for decision making. Big data enables processing of a large volume of real-world data and generating information.
Big Data was defined by the 3V model (Volume, Velocity, and Variety) in a study by Laney (2001b). Volume refers to the amount of available data; Velocity refers to the timeliness of the data; and Variety refers to the diversity of the data types, including unstructured, semi-structured, and structured data sets.
Two other important Vs have been added to the definition of big data. Value is added by Idc-Vesset et al. ( 2012). Value refers to the profit gained by analyzing a huge volume of data and that is why big data analysis is done. Veracity refers to the uncertainty and imprecision in the real world data (Schroeck et al., 2012). Wamba et al., (2015) integrated and introduced the 5V big data framework.
Big data analysis is a process that transforms terabytes of data into a small amount of high-value information. A big data system has five consecutive phases: data generation (in the environment), data acquisition at the source and transmission to central devices), data storage, and data analytics and information communication to decision makers.
Big data applications in a business
In the early days of electronic data processing, businesses used their own data to make decisions. But now, new technologies give businesses access to various brand-new types of datasets. The usage of social networking is booming at a quick pace, and a huge volume of consumer data is being provided to businesses through point of sales terminals at retail outlets . Big data has shown its useful applications in decision areas of business.
Data plays a vital role in developing today’s operational systems. Big data can be used to increase business competitiveness in real time. A large volume of data is generated every minute. Big data can be used for continual improvement. The data can be subjected to descriptive analytics. Then advanced analytics such as predictive analytics, automated algorithms, and real-time data analysis can be used.
Various techniques such as data mining, machine learning, neural networks, pattern recognition, visualization, etc. are used to extract valuable information out of big data Cloud storage and computing are used to store, develop, and deploy big data in business processes.
Data management costs have decreased. In 2019, storing a terabyte of data using relational traditional databases could cost over $20000 for a company (Sonra, 2015), but storing the same amount of data could cost just $1000-$2000 using big data technology such as a Hadoop cluster (StatSlice, 2013). Hadoop gained popularity because of its low price and capacity for data storage.
Not much research was published on big data before 2010.
What are the different categories of big data analytics that are used in supply chain management?
What are the factors that affect the attractiveness of using big data analytics in supply chain management studies?
What supply chain management research topics are studied more often by big data analytics?
What are the hurdles and advantages of using big data analytics in supply chain management research? What must be done in the future?
Coca-Cola Bottler Digitizes Manufacturing Processes with AWS
by Justin Honaman | on 13 JUL 2021
CCI is modernizing its manufacturing facility by creating a digital plant replica—a digital twin—in the cloud. It hopes to unlock value with advanced analytics, artificial intelligence (AI), and real-time asset monitoring. In fact, CCI has produced a repeatable playbook so other Coca-Cola bottlers can deploy the same digital manufacturing solution in their facilities.
If you are on the premium version of Google Analytics, you can use Big Query, a big data engine provided by Google, to sift through Google Analytics. Many companies on the premium version of GA have billions of rows of data in GA. You need Google’s big data tools.
Smarter insights to improve your marketing decisions and get better ROI
14 October 2020
To help you get better ROI from your marketing for the long term, we're creating a new, more intelligent Google Analytics that builds on the foundation of the App + Web property we introduced in beta last year. It has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms. It’s privacy-centric by design, so you can rely on Analytics even as industry changes like restrictions on cookies and identifiers create gaps in your data. The new Google Analytics will give you the essential insights you need to be ready for what’s next.
Manufacturing applies resources such as machines, tools, and labor and converts raw materials into useful products. The manufacturing industry contains a huge volume of data created by sensors, electronic devices, and digital machines in factories (Zhong et al., 2015).
Manufacturing plants collect data using different channels such as manufacturing processes, supply chain management systems, and tracking the products sold. Using big data can help to develop new products based on customer needs. Moreover, manufacturers have the opportunity to better plan out their supply chain with a more accurate demand forecast. Managers believe that using big data can help diagnose defective products, improve process quality, and better plan supply chains (Nedelcu, 2013).
Many of the logistics processes in manufacturing plants (storage, retrieval from the storage and transport) are now performed using radio-frequency identification (RFID) tags, which allows real-time tracking of the products. Using data analysis on the shop floor enables the system to efficiently implement real-time manufacturing instructions, planning, and scheduling based on the material delivery time and the real-time information coming from the manufacturing processes. Analyzing the big data can help the plant manager to better plan space limitations regarding material flow and warehousing operations.
There are a lot of process, personnel, and departments data generated during a product’s life cycle. The nine stages of a product’s life cycle were introduced by Tao et al. (2018): product concept, design, raw material purchase, manufacturing, transportation, sale, utilization, after-sale service, and recycle/disposal. In each stage, a lot of data is generated, and by collecting this data for all products, we can have a dataset with big data characteristics.
Five areas of big data application in manufacturing are (Benhenni, 2017):
1. using data to forecast a complex process’s output;
2. using data to capture that which is difficult to measure under regular conditions,
3. developing algorithms which can more accurately control the quality and safety of the final product;
4. using image metrology to reduce the amount of human supervision required; and finally,
5. determining the optimal time periods for doing predictive maintenance.
The continued growth of the devices connected to Internet of Things will increase the amount of data available to manufacturing companies. It has been forecasted that by 2025, about 175 trillion gigabytes of data will be available, and the manufacturing industry will be the second-fastest-growing sector for data generation, after the healthcare industry (Reinsel et al., 2018). Data mining has been used frequently in manufacturing decision making problems (Hanumanthappa & Sarakutty, 2011). Research into big data applications in manufacturing still needs to be carried out in a big way.
There are several different areas of manufacturing in which big data analysis was used, including new product development (Niebel et al., 2019; Zhan et al., 2018), smart manufacturing (O’Donovan et al., 2015), cloud-based manufacturing (Kumar et al., 2016), process improvement (Gupta et al., 2020), predictive manufacturing (Lee et al., 2013), and redistributed manufacturing (Zaki et al., 2019).
Belhadi et al., (2019) studied the major contributions of big data analytics in manufacturing systems by examining several case studies.
Important areas of manufacturing in which big data analysis is reported are described below.
Operations improvement
A number of studies show that big data analytics can improve the entire operational performance in manufacturing systems.
Yadegaridehkordi et al., (2018) developed a hybrid approach to study the effect of the adoption of big data analytics on manufacturing companies’ performance. Popovič et al., (2018) showed that big data analytics’ capability, along with organizational readiness and certain design factors, could enhance a business’s performance. In another study, Guo et al. (2017) applied data visualization and machine learning algorithms to better inform the operations manager of the product’s market situation. Some other applications of big data analytics in manufacturing systems are covered in Dutta & Bose (2015).
(Huang et al., 2019) developed a theoretical approach to demonstrate the application of big data analytics in the area of production safety management.
Sustainability
Xu et al. (2019) showed how using the available big data on used products can increase the efficiency of remanufacturing systems and save more resources. Dubey et al. (2016) performed a survey 405 senior managers to develop a framework that could use big data to determine the most important factors for maintaining a sustainable manufacturing system. Lowering service costs, increasing the level of trust between stakeholders, respecting customers’ privacy, and increasing data-sharing security are among the benefits that big data analytics may bring to sustainable manufacturing systems (Rehman et al., 2016).
The application of big data analytics in Bosch Car Multimedia’s (Braga-Portugal) organization (Santos et al., 2017) reviews the challenges of collecting, integrating, storing and processing the data in a manufacturing environment. It also shows the potential opportunity that is created for sustainable innovations in a future manufacturing environment by big data analysis.
Mani et al. (2017), showed that applying big data analytics in order to mitigate the supply chain’s social risk can help improve social and economic sustainability.
Smart Manufacturing and Agile Manufacturing
Big data analytics can be used in smart manufacturing to solve shop floor problems at speed. Big data analytics has been proven to be a valuable tool for manufacturers to help them develop strategies, share data, design predictive models, and connect factories in order to control processes (Kusiak, 2017). A study by Bumblauskas et al. (2017a) found big data applied to designing a smart maintenance decision support system, improved an asset’s lifecycle. Liu et al. (2019) used big data analytics for routing order pickup and delivery as well as assigning orders to laundry terminals in smart laundry service enterprises. Big data applications in strategy development and agile manufacturing have also been studied by Opresnik & Taisch (2015), Waller & Fawcett (2013), Guha & Kumar (2018), and Gunasekaran et al. (2018).
Ren et al. (2019) reviewed the available research in big data applications that support sustainable smart manufacturing. Big data applications to facilitate agility in a manufacturing system, the capability to better deal with unpredictable events, and turn these events into benefits were also noted (Swafford et al., 2008).
The Complete Business Process Handbook: Body of Knowledge from Process Modeling to BPM, Volume 1
Mark von Rosing, Henrik von Scheel, August-Wilhelm Scheer
Morgan Kaufmann, 06-Dec-2014 - Business & Economics - 776 pages
The Complete Business Process Handbook is the most comprehensive body of knowledge on business processes with revealing new research. Written as a practical guide for Executives, Practitioners, Managers and Students by the authorities that have shaped the way we think and work with process today. It stands out as a masterpiece, being part of the BPM bachelor and master degree curriculum at universities around the world, with revealing academic research and insight from the leaders in the market.
This book provides everything you need to know about the processes and frameworks, methods, and approaches to implement BPM. Through real-world examples, best practices, LEADing practices and advice from experts, readers will understand how BPM works and how to best use it to their advantage. Cases from industry leaders and innovators show how early adopters of LEADing Practices improved their businesses by using BPM technology and methodology. As the first of three volumes, this book represents the most comprehensive body of knowledge published on business process. Following closely behind, the second volume uniquely bridges theory with how BPM is applied today with the most extensive information on extended BPM. The third volume will explore award winning real-life examples of leading business process practices and how it can be replaced to your advantage.
Learn what Business Process is and how to get started
Comprehensive historical process evolution
In-depth look at the Process Anatomy, Semantics and Ontology
Find out how to link Strategy to Operation with value driven BPM
Uncover how to establish a way of Thinking, Working, Modelling and Implementation
Explore comprehensive Frameworks, Methods and Approaches
How to build BPM competencies and establish a Center of Excellence
Discover how to apply Social BPM, Sustainable and Evidence based BPM
Learn how Value & Performance Measurement and Management
Learn how to roll-out and deploy process
Explore how to enable Process Owners, Roles and Knowledge Workers
Discover how to Process and Application Modelling
Uncover Process Lifecycle, Maturity, Alignment and Continuous Improvement
Practical continuous improvement with the way of Governance
Future BPM trends that will affect business
Explore the BPM Body of Knowledge
Financial system industrial engineering is the study of resource use in various accounting and finance mobilization, allocation and payment and repayment activities with a view to increasing the efficiency or eliminating the waste wherever possible. While the finance activities are designed to account for financial resources and procure and use them, variety of others resources namely, manpower, data processing equipment, information and others are used in these activities. The use of resources in finance activities is carefully investigated by the industrial engineering to identify and remove waste. Industrial engineering succeeded in reducing the cost of many processes designed in the first iteration by the managers up to 50% and hence it is a very important activity in systems design or systems engineering.
Famous example of industrial engineering, is Henry Ford's production system redesign, that reduced the price of the automobile by half. Taylor reduced cost of many manufacturing activities. Gilbreth and Harrigton Emerson also achieved similar cost reduction in construction activity and rail road operations.
1. Principles of Motion Economy
2. Motion Study
3. Workstation Design
4. Application of Ergonomics and Biomechanics
5. Fatigue Studies
6. Productivity/Safety/Comfort Device Design
7. Standardization of Methods
8. Operator training
9. Incentive Systems
10. Job Evaluation
11. Learning effect capture
12. Work Measurement
EFFICIENCY IMPROVEMENT TECHNIQUES OF INDUSTRIAL ENGINEERING
1. Process Analysis
2. Operation Analysis
3. Layout Efficiency Analysis
4. Value engineering
5. Statistical quality control
6. Statistical inventory control and ABC Classification Based Inventory Sytems
7. Six sigma
8. Operations research
9. Variety reduction
10. Standardization
11. Incentive schemes
12. Waste reduction or elimination
13. Activity based management
14. Business process improvement
15. Fatigue analysis and reduction
16. Engineering economy analysis
17. Learning effect capture and continuous improvement (Kaizen, Quality circles and suggestion schemes)
18. Standard costing
Applications in Finance Function
Manpower planning in Finance departments
Cash flow models
Furniture selection for the department
Office processes improvement studies
Together, people and AI are reinventing business processes from the ground up.
Book by Accenture Consultants: Daugherty, Paul R. and Wilson, H.J., Human + Machine: Reimagining
Work in the Age of AI. Boston: Harvard Business Review Press, 2018.
SMART MACHINES ARE REINVENTING HOW WORK IS DONE
Smart machines are helping some companies achieve amazing results in some business processes.
The machines and men in combination as agents of process change are unlocking entirely new roles and new ways for humans and machines to work together. This is the third era of process change. It is already delivering profound results, across industries and for the economy as a whole.
Accenture consultants Daugherty, Paul R. and Wilson, H.J., surveyed more than 1,075 process professionals from large companies that use artificial intelligence technologies in at least one business process.
Some 88 percent of organizations using machine learning have seen at least a 200 percent improvement in KPIs in enterprise processes.
Many companies are employing some degree of automation using smart machines but, only 9% are using the full force of AI
To move from process automation to reinvention, a small group of leaders are doing three tasks
simultaneously:
• Reimagining processes from scratch
• Unlocking the full potential for human and machine interaction
• Capturing the exponential power of dark data
As a result of this approach, 31 percent of their reimagined processes are generating 10x KPI improvements. For those not applying all three, only 15 percent of the processes are generating such KPI improvements. In other words, the probability of achieving 10x improvement doubles all three tasks are applied. These are three overlapping lenses through which processes are observed and redesigned.
Roles for Humans and Smart Machines in Process Design and Operation.
1. Humans train AI for performance.
Human trainers help to improve an algorithm’s performance through activities like data-cleaning and data- and image-labelling. At the more sophisticated, humans teach algorithms how to mimic human behaviors to improve the AI’s social, emotional (and even natural language) intelligence.
2. Humans make AI explainable.
By using experimental analytical techniques on AI data models, humans can explain why algorithms make the decisions they do, such as passing over an employee for a promotion, halting a manufacturing process or targeting a subset of customers with online ads. Large enterprises that deploy advanced AI systems should consider hiring employees who can explain the inner workings of complex algorithms to non-technical professionals.
3. Humans make AI sustainable.
Sustainers make sure AI systems operate as they’ve been designed to do. If any unintended consequences occur, they ensure that the enterprise responds accordingly. However, only one-third of the companies surveyed have a high degree of confidence in the fairness and auditability of their AI system. And less than half have the same level of confidence in the safety of these systems. Clearly there are some fundamental issues to be addressed. This is where sustainers play a crucial role.
4. Machines augment with powerful insight.
Smart machines amplify human capabilities with powerful data-driven insights. They do this by sifting through real-time process data to enhance workers’ judgment and creativity to match, recommend and spot patterns, with interactions usually taking place through a PC or tablet screen.
5. Machines give User Interface (UI) personality.
The fifth type of alliance enables personality-based interactions between humans and machines through voice and natural language. AI based machine acts as an adviser, whether that involves analyzing customer feedback, inferring causalities between events (drugs and patient outcomes, for instance) or processing financial-trading operations.
In every case, the AI augments a worker’s capability either by freeing them for higher-level, more engaging tasks or helping them to work faster—maybe even both. Interactive alliances are reimagining business processes in many white-collar professions.
For example, maintenance work and field training are both being updated by AI agents incorporated into augmented-reality glasses (where visual information or workload instructions are overlaid on workers’ fields of view). One of the most advanced examples is GE’s digital twin implementation. Here, the AI agent’s advise based real-time data simulations of industrial machines. Imagine a maintenance process in a power plant. The worker gets reports from the digital twin, which reports damage to a turbine rotor. Through a conversation between human and machine, the AI can describe how the turbine has been running over the past six months, how the damage has escalated during that period, and predict the lifespan impact if no repairs are carried out. Because the worker is wearing an AI headset, the computer will also show the human exactly where the damage is, and suggest next-best actions for resolving the issue in real time.
6. Machines provide physical aid.
The human-machine alliance improves people’s ability to work. It also means they’re less likely to become fatigued or suffer injury. Suddenly, factory jobs aren’t just for workers in their physical prime, but can be designed for people of all ages and abilities. This alliance extends careers and retains expertise for a longer period.
PREPARING FOR THE THIRD ERA OF PROCESS CHANGE
Henry Ford's assembly line started the first era of process design. It changed manufacturing forever. Companies have been using technology to revolutionize their business processes.
The business-process-reengineering (BPR) movement of the 1990s identified and promoted business process redesign harnessing the power of desktop computing to create greater efficiencies. This effort of second era of process change has provided many rewards.
Now the third era of process change has started. Processes can be designed by incorporating smart machines for business processes. We can term them as smart business process machines.
Steamlined Process Improvement
H. James Harrington
McGraw Hill - July 2011
Harrington's process improvement method cuts costs by 60% while improving quality 100%
One of the best-known innovators in the field, Harrington worked in process improvement at IBM for 40 years and was a quality advisor for Ernst & Young for 10 years; he is the author of Business Process Improvement
Shows operations managers how to improve processes by using simulation modeling, risk analysis, innovation techniques, Lean, process variation, and organizational change management http://www.mcgraw-hill.com.au/html/9780071768634.html
Chapter two of the book
What is Streamlined Process Improvement?
A chapter by Harrington - McGraw Hill Book http://www.mhprofessional.com/downloads/products/0071768637/0071768637_harrington_streamlined-process-improvement_ch_2.pdf
Business Process Improvement Toolbox
Bjorn Andersen
ASQ Quality Press, 01-Jan-2007 - Business & Economics - 296 pages
This best-seller is fully revised and updated! Its goal is still to give readers practical insight into how they can create a coherent business process improvement system. The author works from the premise that consistently working on improving various aspects of how things are done, large and small, is the key to success for any organization. The first half presents an overall business process improvement model, with the ensuing chapters dealing with topics of understanding and modeling your current business processes, using performance measurement in improvement work, creating a business process improvement roadmap, and organizing for improvement work. The second half of the book presents the overall toolbox, followed by one chapter for each phase of the overall improvement model. For each of these phases, a selection of suitable tools is presented with background, steps to use them, and an example of their use. The final two chapters contain two more extensive case studies illustrating the use of the full methodology. And finally, a number of templates can be found at the very end of the book, templates that support most of the tools presented. This book is suitable for employees and managers at any organizational level in any type of industry, including service, manufacturing, and the public sector. It should also be useful as a textbook for students in courses relating to quality management and continuous improvement.
Business Process Improvement Workbook
H.James Harrington et. al.
McGraw Hill, 1997
Aydinli, O.F, Brinkkemper, S, and Ravesteyn, P. “”Business Process Improvement in Organizational Design of e-Government Services.” Electronic Journal of e-Government Volume 7 Issue 2 2009, (pp. 123 - 134), available online at www.ejeg.com
Literature review
Title:A structured evaluation of business process improvement approaches
Author(s):Gregor Zellner (Department of Management Information Systems, University of Regensburg, Regensburg, Germany)
Gregor Zellner, "A structured evaluation of business process improvement approaches", Emerald 17, (2011)
Abstract:
Purpose – The purpose of this paper is to provide a structured overview of so-called business process improvement (BPI) approaches and their contribution to the actual act of improving. Even though a lot is said about BPI, there is still a lack of supporting the act of improving the process. Most approaches concentrate on what needs to be done before and after the improvement act, but the act of improving itself still seems to be a black box.
Design/methodology/approach – This paper is mainly based on a review of literature that deals with the term “Business Process Improvement”. The analysis of the literature is supported by qualitative content analysis. The structure of the evaluation follows the mandatory elements of a method (MEM).
Findings – A lot of literature and consulting approaches deal with the restructuring and improvement of business processes.
Originality/value – The paper is valuable for academics and practitioners because the impact of BPI on organizational performance is high. Its originality is in the structured evaluation of so-called BPI approaches according to the MEM, which so far no one has investigated. http://www.emeraldinsight.com/case_studies.htm/case_studies.htm?articleid=1921892&show=html
A Model-driven and Service-oriented framework
for the business process improvement
Andrea Delgado, Francisco Ruiz, Ignacio García-Rodríguez de Guzmán,Mario Piattini
Journal of Systems Integration 2010/3 http://si-journal.org/index.php/JSI/article/viewFile/55/41
Role of process knowledge in business process improvement methodology: a case study
Ravi Seethamraju, Olivera Marjanovic, (2009) "Role of process knowledge in business process improvement methodology: a case study", Business Process Management Journal, Vol. 15 Iss: 6, pp.920 - 936
Business-oriented process improvement: practices and experiences
at Thales Naval The Netherlands (TNNL)
Jos J.M. Trienekens, Rob J. Kusters, Ben Rendering, Kees Stoklac
Information and Software Technology 47 (2005) 67–79
ANALYSIS OF TECHNIQUES FOR BUSINESS PROCESS IMPROVEMENT
Griesberger, Philipp, University of Regensburg, Universitätsstraße 31, 93040 Regensburg,
Germany,
Leist, Susanne, University of Regensburg, Universitätsstraße 31, 93040 Regensburg,
Germany,
Zellner, Gregor, University of Regensburg, Universitätsstraße 31, 93040 Regensburg,
Germany,
Abstract
This paper is about identifying and analyzing techniques that can be used in a business process improvement (BPI) situation. To determine suitable techniques, the term BPI is defined and criteria are
derived to analyze the usability of the selected techniques. Over 300 techniques from various improvement methods were reduced to those techniques which can be applied to help improve business
processes and, in particular, to support the act of improvement. Identifying these techniques is helpful,
because in most cases the act of improvement is a black box that lacks guidelines or structured procedures. In our research we developed an evaluation scheme to analyze the usability of BPI techniques
and give hints about how to select a suitable technique for a certain improvement situation
Effectiveness First and Efficiency Next in the design steps. Industrial engineering focuses on efficiency improvement.
Organizations have to be effective and efficient simultaneously in all tasks in their working. Processes have to be effective and efficient simultaneously. Operations have to be effective and efficient simultaneously. - Narayana Rao
Three Major Channels of Process Improvement.
1. Process Redesign by Process Planning Team.
2. Process Improvement Study by Industrial Engineering Team.
3. Continuous #Improvement by Involving Shop Floor Employees and All Employees.
Continuous Improvement - Employee Participation Principle of Industrial Engineering
Together, people and AI are reinventing business processes from the ground up.
Book by Accenture Consultants: Daugherty, Paul R. and Wilson, H.J., Human + Machine: Reimagining Work in the Age of AI. Boston: Harvard Business Review Press, 2018.
SMART MACHINES ARE REINVENTING HOW WORK IS DONE
Smart machines are helping some companies achieve amazing results in some business processes. It is already delivering profound results, across industries and for the economy as a whole.
Accenture consultants Daugherty, Paul R. and Wilson, H.J., surveyed more than 1,075 process professionals from large companies that use artificial intelligence technologies in at least one business process. Some 88 percent of organizations using machine learning have seen at least a 200 percent improvement in KPIs in enterprise processes.
Business Process Efficiency Improvement or Engineering
Business Process Efficiency Improvement or Engineering must involve process re-design to obtain processes that achieve the same (functional) goals, while increasing efficiency of the process (decreasing the cost of the process)
Maxine Attong - COD Business Process Improvement Manual, (Page 146)
Process Measurements
Three sets of measures are used to gauge the process.
1. Process efficiency - measures the time that activities take to covert inputs to outputs.
2.Output effectiveness - measures how well the output meets the design requirements.
3. Output effectiveness and customer satisfaction - measures how well output meets customers' needs.
Process Efficiency Measures
Ideally, one measures identifies the minimum possible resources to be consumed during the process. Actual resource consumption is quantified and assessed against set standards as a variance, variation or deviation. The results lead to the control (managerial actions) of people, materials, methods, environment and the way each resource or factor interacts with the other. Resource consumption is an easy measure since it is tangible. Standards are set based on the experience or scientific investigations (Scientific Management).
Example - Accounts Payable Process Efficiency Measures
Inputs - Purchase invoices received per month.
Time - The cycle time and the basic work time taken for an invoice to be processes and for the vendor to receive payment.
People - Payroll cost, Level of training or skilled labor used in the process
Equipment - Utilization and cost
Output - The number of accurate payments generated per month and reasons for delays
Detailed description of measurements made is available in the book.
Purpose of Efficiency Measures
Efficiency measures are used to drive decision making around improving the process. Each measurement tells the story about the process. The process owner, process designer, and efficiency engineer (industrial engineer) have to know the causes before changes can be made to improve the process.
Efficiency Analysis of Inputs
Efficiency Analysis of Time
Efficiency Analysis of People
Details given in the book.
Identifying Drivers of Inefficiency in Business Processes
Using Business Process Re-engineering to Increase Process Efficiency of E-Catalogue
Distribution System
Zulkhairi Md Dahalin and Siti Fatimah Yusof IBIMA Business Review
http://www.ibimapublishing.com/journals/IBIMABR/ibimabr.html
Vol. 2012 (2012), Article ID 731793, 8 pages http://www.ibimapublishing.com/journals/IBIMABR/2012/731793/731793.pdf
A More Comprehensive Approach to Enhancing Business Process Efficiency
Seung-Hyun Rhee 1, Nam Wook Cho 2, and Hyerim Bae 3
1 Department of Industrial Engineering, Seoul National University, 151-742, Seoul, Republic of Korea
2 Department of Industrial and Systems Engineering,
Seoul National University of Technology, 139-743, Seoul, Republic of Korea
3 Department of Industrial Engineering, Pusan National University, 635-709, Busan, Republic of Korea
Abstract. Whereas Business Process Management (BPM) systematically guides employee participation in business processes, there has been little support, use or development of user-friendly functions to improve the efficiency of those processes. To enhance business process efficiency, it is necessary to provide automatic rational task allocation and work-item importance prioritization, so that task performers no longer need to be concerned with process performance. In the context of BPM, two different perspectives, the Process Engine Perspective (PEP) and the Task Performer Perspective (TPP), are considered. Accordingly, we developed a comprehensive method that considers those two perspectives, in combination rather than separately.
Frank B. Gilbreth - Industrial Engineering Achievements: Motion Study - Productivity Science of Human Effort - Principles of Motion Economy - Human Effort Productivity Engineering - Process Charts - IE Measurement Devices - Productivity Management (Productivity Training).
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Original Films of Motions made by Frank and Lillian Gilbreth
https://www.youtube.com/watch?v=9fQJfap7SAQ
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Frank Gilbreth accepted the recommendation of F.W. Taylor to develop science of work. Gilbreth provided the basic foundation for the science of human effort or work. Based on that foundation, he developed principles of motion economy and motion study to reduce the number of motions and time taken to do them.
Frameworks for Productivity Science of Machine Effort and Human Effort.
Frank Gilbreth developed motion study in detail and thus made contribution to making human effort engineering a rigorous science based engineering subject.
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7th July is birthday of Frank Gilbreth.
We celebrate the day as Knowledge Day of Industrial Engineering
Frank B. Gilbreth, the engineer who conceived the "Motion Study" Principles (techniques for manual productivity improvement) once visited a British-Japanese Exposition. There a demonstration of polishing shoes was being held to help the sales of Japanese shoe polish.
Casually walking and talking with his friend, Gilbreth stopped to view the shoe polish wrapping demonstration. Gilbreth watched for a few moments, then simply said, "They are really skilled, but they could produce more." He timed the fastest girl and without hesitation, ascended the platform. He found she was being paid on a piecework basis and said, "I’m going to tell you how to earn more money, but you must follow my instructions." He changed the location of her supplies and showed her how to wrap and set aside more efficiently. He timed her again after several cycles. When he rejoined his friend he said, "When she gets the hang of it she’ll be making twice her former earnings."
That is an example of the applied results of using Gilbreth’s Motion Study Principles. Industrial Engineers used these guiding rules throughout the United States. Gilbreth said if his Motion Study Principles had not been previously applied to any manual work, by their application the productivity would be doubled or more.
In 1885, Gilbreth started out as an apprentice bricklayer. On his second day of work, with a Master Journeyman to train him, he noticed different methods of bricklaying. Undoubtedly in jest, he was informed there were three techniques: one, for just a regular day, the second was to hurry up to finish a wall, and the third, just to stretch out the job to fill the day. His question led him to think there should be one efficient and approved method, "The One Best Way."
Motion Study was first developed when it was applied to the world’s oldest trade --- bricklaying. The traditional method, even after 6,000 years, involved unnecessary stooping, walking and reaching. The time-consuming, tiring part of the job had been stooping 125 times per hour for brick and 125 times for mortar. By using Gilbreth’s method, a man could lay more bricks, standing normally, and return home after a full day’s work not nearly as tired.
Application of the Gilbreth system of motion analysis reduced the motions per brick from 18 to 5 and increased the number of bricks laid per hour from 125 to 350.
Following Gilbreth’s outstanding success in bricklaying and construction, he then pursued broad research into diversified manufacturing operations. He created an entirely new technique on how to improve industrial efficiency, while at the same time significantly improving working conditions for the worker.
His work took a firm hold in engineering and economic societies as well as with our country’s industrial companies.
His Motion Study Principles affected all management. It created a different type of engineer: The Industrial Engineer, concerned with improving manual work (Human Effort Industrial Engineering). Gilbreth was a pioneer of American history.
From 1910 to 1924, he promoted his system as a consultant and a teacher. He died in 1924. (Society of Industrial Engineers, published number of items about him in its 1924 bulletin. Available on Hathitrust.org). His wife, Mrs. Lillian M. Gilbreth, educated in psychology and with an insight into the fundamentals of labor management, had been his partner.
Mrs. Gilbreth, who had been of great assistance with the running of the Gilbreth Consulting Firm, took over and carried the full load, all by herself. She taught Motion Study at Purdue University, consulted and ran the company, along with being a wonderful mother to 12 children, all college educated.
In the late 1940’s, James S. Perkins, an Industrial Engineer, on a research assignment for the Western Electric Company, was at the University of Iowa, where he met Mrs. Gilbreth, who was a speaker at the Industrial Engineering Conference there. She visited with him and reviewed his research. Gilbreth’s film studies, research and conclusions, preserved by James Perkins extend into many diverse areas:
Motion and Fatigue Study
Skill Study
Plant Layout and Material Handling
Inventory Control
Production Control
Business Procedures
Safety Methods
Developing Occupations for the Handicapped
Athletic Training and Skills
Military Training
Surgical Operations
Gilbreth developed the route model technique to improve the flow of materials in manufacturing operations. When he first developed it, Gilbreth said that several of his engineering friends, at an engineering meeting, laughed themselves to death, but that it was quickly accepted by Plant Managers. He found that by its use, the layout distance was often cut by 75% and product processing time was reduced substantially. Further, plant productivity was usually increased by 15 to 25%.
In 1968, the American Society of Mechanical Engineers decided to honor the achievements of Frank B. Gilbreth, (on his 100th anniversary) at their Annual Meeting at the Waldorf Astoria Hotel. The sound films prepared by Perkins were shown for the first time at the Annual Meeting of the ASME honoring Frank B. Gilbreth.
Gilbreth’s cyclegraph technique, to learn about skill, was one of his significant contributions. He demonstrates this technique in the film and also shows the three-dimensional model he made from the pictures of a drilling operation. He said, "The expert uses the motion model for learning the existing motion path and the possible lines for improvement. An efficient and skillful motion has smoothness, grace, strong marks of habit, decision, lack of hesitation and is not fatiguing."
The film includes motion pictures of a baseball game between the Giants and the Phillies, taken at the Polo Grounds on May 31, 1913. One of the observations Gilbreth made after analyzing these pictures was that after the ball left the pitcher’s hand, it took about 1-1/2 seconds before it could be relayed to second base by the catcher. The dash to steal second base, with an eight foot lead, required a speed faster than the world’s record for the 100-yard dash.
In Gilbreth’s film studies of surgical operations, he observed that the doctors took more time searching for instruments than in performing the operation itself. He worked with doctors and came up with a technique which is still being used today. When the doctor was ready for a new instrument, he simply extended his hand, palm up, to the nurse and called for the instrument he wanted. By this means, he was able to keep his eyes focused upon the open incision, thereby significantly reducing operating time, so critical to both patient and doctor. The film shows doctors, nurses and technicians prepare a patient and the removal of a large tumor.
Frank and Lillian Gilbreth: Critical Evaluations in Business and Management, Volume 1 Michael C. Wood, John Cunningham Wood Taylor & Francis, 2003 - Industrial engineering - 376 pages
