Monte Carlo Simulation

 Good intro piece to a method we used for many purposes in the enterprise.   Even creating usable models for key processes that were used for decades.  Consider its similarities to Digital Twins.  

 Monte Carlo Simulation

Darío Weitz   in Towards Data Science, Engineer, Ms. Sc., Former Associate Professor at Ing. en Sistemas de Información, Fac. Reg. Rosario, Univ. Tecnológica Nacional, Argentina. Data Viz Consultant.

Part 1: The News Vendor Problem

In the first article of this series, we defined simulation as a numerical technique consisting of building a mathematical and/or logical model of the system under study and then experimenting with it, collecting data that allows us to obtain an estimator to help solve a complex decision problem.

In the same article, we defined a model as a simplified but valid representation of a real process or system, intending to gain some understanding of its behavior.

We also made a classification of models, distinguishing in particular between continuous models, those in which their behavior (state variables) changes continuously over time, and discrete models, those in which the state variables only change at separate points in time. Another important classification involves static models, those that are a representation of the system at a particular time, and dynamic models, those that evolve over time.

Related to the above classification there are three different types of simulations: continuous event simulation, discrete event simulation, and Monte Carlo simulation.

Principles and concepts about Discrete Event Simulation (DES) were provided in the previously indicated series. We coded several examples with SimPy, an object-oriented, process-based, discrete-event simulation framework based on pure Python. In future articles, we will develop concepts and principles related to continuous event simulation.

In this article (and probably in a couple of others) we are dealing with Monte Carlo Simulations.

Monte Carlo Methods

Monte Carlo Methods (MCM) is a collection of numerical methods for the solution of mathematical problems, where the use of random samples differentiates them from equivalent methods.

The term was coined by the Greek-American physicist Nicholas Metropolis when he was working at Los Alamos National Laboratory with John von Neumann and Stanislaw Ulam in the development of the first atomic bomb. The term gets its origin from the famous casino located in the Principality of Monaco.

The conceptual idea of the MCM consists in the estimation of certain quantities through repeated sampling from models represented in a computer. Two classes of mathematical problems are usually solved with these techniques: integration and optimization.

Concerning the contents described in this series of articles, when we refer to Monte Carlo Simulation models we are talking about static, discrete, stochastic models trying to solve an optimization problem.

From a methodological point of view, a Monte Carlo simulation is a sampling experiment whose aim is to estimate the distribution of a quantity of interest that depends on one or more stochastic input variables. We are particularly interested in calculating point estimates and confidence intervals for that quantities. Inevitably, our estimator will have a sampling error and our first task will be to determine the number of replications to improve the degree of certainty in the value of the estimator.  .... ' 

Database of World Management Practices

This came to my attention.   Could have been useful, depending on how precise the practices were detailed, in past modeling efforts.   This effort under way for 18 years! Now if we could have working process models of each organization type to work from.   Likely much harder.  But what if we could build up from basic models?  Forecast of future practices mentioned also of interest. Note offering measurement as well.  Has to be good data here. 

The World Management Survey at 18: Lessons and the Way Forward   by Daniela Scur, Raffaella Sadun, John Van Reenen, Renata Lemos, and Nicholas Bloom  in HBSWK

With a dataset of 13,000 firms and 4,000 schools and hospitals spanning more than 35 countries, the World Management Survey provides a systematic measure of management practices used in organizations. This paper gives an overview of lessons learned and a management policy toolkit for policymakers.

Author Abstract

Understanding how differences in management “best practices” affect organizational outcomes has been a focus of both theoretical and empirical work in the fields of management, sociology, economics and public policy. The World Management Survey (WMS) project was born almost two decades ago with the main goal of developing a new systematic measure of management practices being used in organizations. The WMS has contributed to a body of knowledge around how managerial structures, not just managerial talent, relates to organizational performance. Over 18 years of research, a set of consistent patterns have emerged and spurred new questions. We will present a brief overview of what we have learned in terms of measuring and understanding management practices and condense the implications of these findings for policy. We end with an outline of what we see as the path forward for both research and policy implications of this research program. ... '

  Paper: https://www.nber.org/papers/w28524

Uses of Process Mining

Had heard the term 'Process Mining' relatively little used lately.    But as I mentioned in recent posts, it helps you to understand how existing process and related decisions are done today.   Once that is understood, you can use forms of business process modeling to improve them.   And AI or other forms of analytics, to look for patterns that can be improved.    Very natural progression. so I can understand IBM's interest, given their considerable background in the BPM space.   We used their systems there.

German process mining startup Celonis teams up with IBM and Red Hat   By Douglas Busvine in Reuters

BERLIN (Reuters) - Celonis, a fast-growing German process mining software startup, has struck a strategic partnership with IBM to help companies make the most of the digital transformation that many are undergoing at speed.

IBM’s Global Business Services consulting arm will weave the Celonis Execution Management System into its offering, adding the ability to analyse data thrown off by processes like supply-chain management, finance or procurement to identify weaknesses and recommend fixes.

Celonis will also shift its software stack to IBM’s Red Hat OpenShift platform, which enables companies to operate in an open ‘hybrid’ setting that can include public or private cloud data centres, on-premise servers and mainframe computers.

That represents a big step for the Munich startup, which last raised funds here from investors at a valuation of $2.5 billion in 2019 and counts Coca-Cola, Siemens, Uber and Vodafone as clients.   ... " 

RPA Examined

See also aspects of business process modeling for extensions to the idea.  And likely combination with broader methods of ML and AI

Some thoughts on RPA: By Maria Macaraig  October 8, 2019    in Datafloq

Robotic process automation or RPA is the technology that makes it possible to program software and empower machines to mimic human actions, replicate human motion, and perform human functions automatically, and repetitively. 

RPA is powering waves of transformative change in manufacturing industries, defense, aerospace, business, and healthcare. The intelligent software and its visible application, the robot, don’t rest, is error-free, and is more productive and profitable than a human.   

Robotic process automation is not only changing the way we work; the technology is quietly revolutionizing the future. It should be useful to review the developments that set the stage for RPAs dominance so that we gain a better appreciation of the role and significance of RPA.

Kansas software experts at Tricension explain the history and role of RPA in enabling companies to enhance quality and improve productivity by streamlining business processes. 

The 1950s and 60s: Emergence of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP)

Foundational research conducted by eminent American computer scientists led by John McCarthy and Arthur Samuel paves the way for imbuing computers with the capability to think and respond like humans. 

AI, ML, computer engineering, and linguistic sciences were combining resources to deliver a single toolkit that could boost robotic process automation. 

Artificial Intelligence was focusing on creating smart machines that could mimic human intelligence (using logic, and reasoning) to solve complex issues that were beyond the range of the human brain. 

Machine Learning, developing as an offshoot of AI, was coding software algorithms that could gather and analyze big data. We designed algorithms that could “think” and “learn” on their own without being expressly programmed to do so.

Advancements in Natural Language Processing enabled us to bridge the gap between computers and natural human language. Artificial Intelligence technology-enabled computers to analyze large volumes of natural language and accurately comprehend the meaning and intent behind human-oriented spoken and written commands.   ... " 

ReThinking the Global Supply Chain

Yes, do a better job of risk analyses.

Is It Time to Rethink Globalized Supply Chains?

The COVID-19 pandemic should be a wake-up call for managers and prompt them to consider actions that will improve their resilience to future shocks.

By Willy Shih  in SloanReview

The COVID-19 contagion has had a major impact on Chinese manufacturers, and because of the central role many Chinese companies play in the supply chains of other companies, the impact is being felt around the world. The disruption is particularly acute in the electronics and auto industries, but it is also affecting pharmaceuticals, metals, and a wide range of consumer and industrial products, including surgical gowns and masks.

How did we end up with such complex interdependency in our supply chains — and what should managers be thinking about once we get through this?  ... 

Improving Performance Engineering with Machine Learning

Had never heard this specifically positioned this way, nice idea.   Needs some more detail to explain, how it has been done, but a great start.   We did a form of this with business process modelling, and the integration with machine learning to determine how elements of the process performed.   Not sure if this is quite the same thing.  Like the anomaly reference.

Machine Learning: How it Improves Performance Engineering in DSC    Posted by Ryan Williamson  

Enterprise software, as well as other kinds, remains a complicated endeavor, thus necessitating the use of modern means to gauge, analyze, and adapt their performance. And one of the most popular technologies in the performance engineering market right now is machine learning. Since it has demonstrated an unparalleled ability to not only help foresee performance issues and fix them. When used in the right manner — this combination can also help performance engineering teams to steer clear of any issues at all completely. It is because machine learning comes equipped with the ability to interpret and analyze data in real-time, thus delivering valuable insights about the system’s performance.

However, if you are going to truly leverage machine learning’s abilities in the context of performance engineering, it is first essential to understand the basics. Through this article, let’s discuss the kind of performance anomalies one can encounter.

Point anomalies: This is when there is only one data point that is distinct from the entire set.
Contextual anomalies: In this scenario, the anomaly is contextual, i.e., exists only in a particular context.
Collective anomalies: This refers to a data set that exhibits signs of an anomaly.  ....  " 

A Look at the Booming RPA Market

Very detailed piece, below the introduction.  Probably the most common question I get asked:  What is it, how is it different than AI?  People should also ask: How is it different from Business Process Modeling?  The analysis linked to below show how it is booming, even in difficult times. Note the highlighted key points below. 

Robotic process automation battle for a bigger prize: automation everywhere

Analysis by Dave Vellante in SiliconAngle

Robotic process automation solutions remain one of the most attractive investments for business technology buyers — this despite our overall 2020 tech spending forecasts, which remain at the depressed levels of -4% to -5% for the year.

Relative to previous surveys, we do see some softness in traditional RPA strongholds such as large financials, insurance and giant public and privates. But RPA relative to other technology investments remains at the top as the sector with the highest spending momentum – neck and neck with that of machine learning and artificial intelligence, and ahead of containers and even cloud computing.

In this week’s Wikibon CUBE Insights, powered by ETR, we want to update you on the latest RPA trends and share fresh ETR survey data with our communities.

Key points in this segment

Despite our tepid spending forecast for the year, demand for RPA software continues to grow at a 60% to 70% clip. Remember, RPA mimics human computer interactions using software scripts or robots that execute human tasks in a runtime assembly of discrete steps. The practice first became popular for back-office functions – mostly as unattended bots.

The pandemic appears to be accelerating front-office adoption and that’s creating a bit of a schism between front- and back-office processes, strategies and implementations. Digital transformation initiatives, in many ways, create the connective tissue between the front and back of the house. We see that connection as a linchpin of digital efforts.   ... "

Misconceptions about the Use of RPA

NIcely put, nontechnical piece.  In particular I liked the comments about its difference between BPM modeling models.   All users considering its use should follow up on these modeling ideas:

Robotic Process Automation: 6 common misconceptions
By Daniel Schmidt of Kofax

What false expectations are raised using RPA in companies?

The advantage of Robotic Process Automation (RPA) is that it automates repetitive, remedial tasks and frees employees to work on higher value tasks. But many companies believe RPA will enable them to automate even the most complex Business Process Management (BPM) activities, although there are much more suitable solutions available. The following overview shows which other misconceptions companies frequently use to counter RPA solutions.  .. " 

Systems of Insight, Analytics in Context

Precisely what I have been suggesting for some time. The results of analytics need to be plugged into business need.    As the Computerworld Article states it:  " .... Businesses want to use data to understand customers, but they can’t do that without harnessing insights and consistently turning data into effective action   ... " .  In order to do this you need to know where the insight plugs in.  Which means it helps to know how your business operates to begin with,  to understand its effect.  Not always as simple as it may seem.    One approach is to understand your business with a process model.  That is rare in business today, and often rejected as requiring too much effort.   The insight should be understood in process context.    Taking this further, the logic in the process model can also be modeled, leading to a cognitive model.

Embedding Machine Learning into RPA Process

Something we did, but with BPM models and process flow.   Makes sense because you can better understand the context involved.  Process models, even simple visualizations, can help sell the model, get useful data, and promote the contextual design and value.  Rules are understandable, but algorithms are usually not to decision makers.

Small ML is the next big leap in RPA
Instead of doing big ML projects, embed ML into your day-to-day RPA work and be amazed.   By Eljas Linna in TowardsDataScience

The boom in robotic process automation (RPA) over the past few years has made it pretty clear that business processes in nearly every industry have an endless amount of bottlenecks to be resolved and efficiency improvements to be gained. Years before the full surge of RPA, McKinsey already estimated the annual impact of knowledge work automation to be around $6 trillion in 2025.

Having followed the evolution of RPA from python scripts towards generalized platforms, I’ve witnessed quite a transformation. The tools and libraries available in RPA have improved over time, each iteration widening the variety of processes that can be automated and improving the overall automation rates further. I believe the addition of machine learning (ML) in the everyday toolbox of RPA developers is the next huge leap in the scope and effectiveness of process automation. And I am not alone. But there’s a catch. It will look very different from what all the hype would lead you to believe.

Why even care about machine learning?
Imagine RPA without if-else logic or variables. You could only automate simple and completely static click-through processes. As we gradually add in some variables and logic, we can start automating more complex and impactful processes. The more complex the process you want to automate, the more logic rules you need to add, and the more edge cases you need to consider. The burden on the RPA developer’s rule system grows exponentially. See where I’m going with this?  ... " 

AI in Banking

Useful survey of AI uses in Banking..Notable use of BPM.

Barclays Innovating in use of AI in Banking

Barclays Bank is emerging as an innovator in the use of AI in financial services. The UK bank, ranked 20th on the S&P Global’s list of the top 100 banks, works with suppliers of AI products and services more than it develops AI applications in house, according to a recent account from  emerj.

Here are three AI initiatives underway at Barclays and the industry partners working on each one:

Risk Modeling with Simudyne, employs predictive analytics to assess loan risk
Voice Recognition for Authentication, with Nuance, aims to apply verification and authentication using voice recognition;

Business Process Automation with IBM, a project to automate debit card deactivation, and analyze customer feedback.

AI and Aerospace

Model based systems engineering is along time approach in this space. we did much of i in the enterprise.  AI/machine learning can be used to focus subtasks with broader process or resource implications. 

Modeling and simulation: Achieve next-level results with AI
Artificial intelligence-based approaches are key to realizing model-based systems engineering benefits.

Aerospace executives can now optimize manufacturing processes by leveraging artificial intelligence (AI) with high-performance computing (HPC) technologies and the digital thread. A digital thread follows the lifecycle of a product from design inception through engineering and product lifecycle management, to manufacturing instructions, supply chain management, and through to service events. You'll be able to enhance the aerospace design process to protect budgets, avoid static production rates, and nudge your business ahead of competitors.

Even better, as aerospace design becomes more complex, AI can help keep your business ahead of the innovation curve.... '

Reflections and Projections about IBM Watson

An nicely done internal look at Watson.   As I have mentioned here before, Watson may not have lived up to some of the early expectations, but it remains a serious contender for serious, practical AI  applications.  And in particular because they already have capabilities like BPM which can be used to model and link to real business process.  Early on talked to them about that.  They seemed enthusiastic about the idea, but I have not seen any development since.  They have external collaboration with RPA vendors, which is a good thing also.

IBM Watson: Reflections and Projections  By Rob Thomas, IBM

AI has gone through many cycles since we first coined the term “machine learning” in 1959. Our latest resurgence began in 2011 when we put Watson on national television to play Jeopardy! against humans. This became a cornerstone event, demonstrating that we had something unique. And we saw early success, putting Watson to work on projects with clients. This created even more excitement. That excitement led to more opportunity. At this stage, we have a large product organization, separate dedicated research organization, and an entire health organization all leveraging and building on this technology.

So, what is Watson? This is the question I’ve been asked the most since IBM combined its Data and AI software units earlier this year. ......

By Rob Thomas
Author of ‘The End of Tech Companies’ & ‘Big Data Revolution’  amzn.to/2uVu84R. Leading Data and AI @IBM. Robdthomas@gmail

Real World RPA

I think RPA, or other process understanding or improvement  methods should always be considered when you are planning AI.     We at least sketched a flow of what we were working on, and there is so much more you can do today.   You need to understand what you are doing, considering, risking.  Some good thoughts here.

RPA In The Real World: Driving Marketing, Analytics, Productivity and Security
As we continue to move forward in digital transformation, an increasing number of companies are discovering the promise of robotic process automation (RPA). In a nutshell, RPA allows companies to gain efficiencies and (hopefully) save money by automating routine tasks. RPA is what I’d call the low-hanging fruit of artificial intelligence. It’s governed by structured input. Its processes are mundane and rule-based. It doesn’t require the deep, complex system or infrastructure integration that other more substantial AI requires. Best yet, it frees up your employees to work on higher-value projects, rather than repetitive day-to-day tasks. And for that reason, it’s become a hot commodity. Forrester says RPA will be a $1.5 billion business by 2020. This spending is a boon for vendors like UiPath, Automation Anywhere and Blue Prism that are at the forefront of this product offering.

But rather than more "philosophical" use cases for RPA, let's look at how businesses are using RPA in the real-world right now—and how your organization may benefit as well. .... " 

Appian and Business Process

Happened to look at Appian which we used in the enterprise.

We used them for BPM (Business process management)  And we talked to them first around 2007.  Had some relationship to CACI, which does process simulation and optimization for the DOD as well.

Appian now does RPA (Robotic Process Automation), using 'Blue Prism',  an 'AI' type method.      See:  https://www.appian.com/platform/robotic-process-automation-rpa/ 

Read their Acquisition Management Overview:  https://www.appian.com/industries/public-sector/acquisition-management/

Gartner Publishes first Magic Quadrant for RPA

Have followed RPA since it emerged, as a way to install logic in process to efficiently automate in-context tasks.   Should be used in combination with AI and Big Data analytics to transparently improve process.  Not sure if that is common, but should be.  Reminds me of the work we did with Prolog based knowledge systems.

Gartner publishes first Magic Quadrant for Robotic Process Automation market  By Mike Wheatley in SiliconAngle

Gartner Inc. this week published its first Magic Quadrant for the robotic process automation software market, shining a light on the leading players and key trends in the rapidly growing technology segment.

RPA relates to the use of software with artificial intelligence and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform. These tasks can include queries, calculations and maintenance of records and transactions. RPA software relies on robots that can mimic a human worker by logging into an application, entering data or calculating and completing tasks and then logging out once the task is done.

The technology is believed to save companies huge amounts of time and money, so it’s not much of a surprise to see Gartner estimating the market for this type of software will reach $2.4 billion a year by 2024, from just $850 million today. Gartner also said RPA is the fastest-growing subsegment of enterprise software it tracks, with an annual growth rate of 63% in 2018.   .... "  (Quadrant at Link) 

Qubic for Process Anyone?

Am looking to do an application that will insert 'smart contract' type specifications to observed business process, focused by business process models. Anyone used IOTA's Qubic?   Qubic is still in development I know, but any early impressions or tests would be interesting.   Connect with me via my Linkedin contact.

" .... What is Qubic?
In short: Qubic began life as as an initialism-turned-acronym, QBC, which stands for quorum-based computation.

Quorum (distributed_computing) on wikipedia

Specifically, Qubic is a protocol that specifies IOTA's solution for quorum-based computations, including such constructs as oracle machines, outsourced computations, and smart contracts. Qubic provides general-purpose, cloud- or fog-based, permissionless, multiprocessing capabilities on the Tangle. In the long term, Qubic will allow people to leverage world-wide unused computing capacity for a myriad of computational needs, all while helping to secure the IOTA Tangle: an IOTA-based world supercomputer.

More generally, a qubic is what we call a packaged quorum-based computation that occurs according to the Qubic protocol. Below are some examples of different types of qubics - while they are distinguished here for clarity, they are all nevertheless variations on a single, general-purpose concept: the quorum-based computation, or qubic.  .... " 

Google has an AI Cloud Platform. Lets link it with BPM

Quite some detail for making AI applications work with the cloud in this new production factory for AI in the Cloud.  I like the idea of standardizing such learning projects and installed solutions. I would also like to see this kind of work linked with business process models like BPM.

AI Platform

Create your AI applications once, then run them easily on both GCP and on-premises.

Take your machine learning projects to production

AI Platform makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, AI Platform’s integrated tool chain helps you build and run your own machine learning applications.

AI Platform supports Kubeflow, Google’s open-source platform, which lets you build portable ML pipelines that you can run on-premises or on Google Cloud without significant code changes. And you’ll have access to cutting-edge Google AI technology like TensorFlow, TPUs, and TFX tools as you deploy your AI applications to production.  ... "

A testimonial they provide:

" ... In retail, it’s important to provide customers with easy access to alternative products or recommended add-ons. We train our own machine learning models with TensorFlow on Google Cloud ML, and we automate the periodic retraining of these models with Kubeflow Pipelines. Together with AI Hub, useful for sharing models between data scientists, we can now iterate faster on our models, and automatically deploy them to staging and production. ... '    Lucas Ngoo, co-founder, CTO, Carousell

See also: https://techcrunch.com/2019/04/10/google-expands-its-ai-services/

Difference Between BPM and RPA?

We did both, starting with years of BPM (Business Process Modeling)  ....  found the RPA (Robotic Process Automation)  ideas more focused to specific improvement while BPM is much more often connected to detailed semantic data and process modeling, now often aka 'Knowledge Graphs', which means a much greater investment in really understanding how your data is used and maintained.   RPA can and is more often casually used.

Robotic process automation (RPA) and business process management (BPM) are both valuable solutions that focus on helping companies improve operational performance by optimizing their business processes, so it's not surprising that the two are often confused.  However, while RPA and BPM definitions can overlap in some areas, their core functionalities are completely different.   In fact, many companies that are turning to a BPM approach to transform their business operations are finding that including RPA software in the mix delivers a far superior case for process improvement – resulting in greater profitability for the company's bottom line.

So what are the key differences between BPM and RPA and which is the best option for your business? We’ve compiled a list of the main differences to help you make the right decision: .... '

Business Process Modeling and Simulation

Been re-visiting a book I used long ago.   I only have the first edition, but its still useful as an overview of the methods.    Also a good intro to simulation.   Not enough about topics like RPA to actually use the modeling results.  Have not used later versions of ExtendSim.  Too expensive in the second edition, but what else is new on textbooks.

Business Process Modeling, Simulation and Design 2nd Edition
by Manuel Laguna  (Author), Johan Marklund 

Most textbooks on business process management focus on either the nuts and bolts of computer simulation or the managerial aspects of business processes. Covering both technical and managerial aspects of business process management, Business Process Modeling, Simulation and Design, Second Edition presents the tools to design effective business processes and the management techniques to operate them efficiently.

New to the Second Edition

Three completely revised chapters that incorporate ExtendSim 8
An introduction to simulation

A chapter on business process analytics

Developed from the authors’ many years of teaching process design and simulation courses, the text provides students with a thorough understanding of numerous analytical tools that can be used to model, analyze, design, manage, and improve business processes. It covers a wide range of approaches, including discrete event simulation, graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, analytical queuing methods, and data mining. Unlike other operations management books, this one emphasizes user-friendly simulation software as well as business processes, rather than only manufacturing processes or general operations management problems.

Taking an analytical modeling approach to process design, this book illustrates the power of simulation modeling as a vehicle for analyzing and designing business processes. It teaches how to apply process simulation and discusses the managerial implications of redesigning processes. The ExtendSim software is available online and ancillaries are available for instructors. ... "