What is Needed to Get Started with Augmented Analytics?
Thinkers360
May 01, 2020
An obstacle to more rapid progress is the lack of relevant skills in many procurement organizations. While augmented analytics reduces the need to know how the technology works, what is missing is access to the technology and an appreciation of why it should be used, i.e. the benefits it can bring, such as identifying supply market trends ahead of the competition.
Gartner estimates that on average, only 35% of people in organizations have access to analytics and BI tools. “Despite the compelling benefits of augmented analytics, which can accelerate adoption well beyond this limited level, efforts to incorporate augmented analytics encounter resistance,” writes Gartner, and this is the case for a number of reasons, including the lack of data literacy in the broader user population, distrust, concerns about job security etc. The outlook is slightly rosier in procurement: in our Digital Procurement Survey 50% of respondents rated their awareness of digitalization as “up to date” or “excellent”. But there is some way to go.
That said, we believe the “How Augmented Analytics Will Transform Your Organization: A Gartner Trend Insight Report Gartner Trend Insight Report” gives some excellent advice on gearing up for the next wave of augmented analytics, concluding that “Early adopters of augmented analytics have the potential to realize more strategic and differentiating business benefits from their analytics investments than those who wait until these technologies are widely adopted.” That is certainly already proving to be the case in procurement. The is certainly worth your consideration.
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Tags: AI, Open Innovation, Predictive Analytics
What about Smart Cities?
Thinkers360
April 25, 2020
According to the National Foundation Research of Singapore's Prime minister office, “Virtual Singapore” is already in progress. This project will be capable of visualizing landscapes against ongoing and future renovation projects.
This may be done by positioning the sensors at the right place in the city to get the most relevant information such as bus stops, traffic lights, bridges, parking etc.
By powering the smart cities with interconnected sensors, we will enable the optimal use of the available assets by detecting where is the nearest available parking, hence reducing the traffic light, saving time and fuel and so participating in developing the garden cities to make our lives healthier, more liveable, and more relaxed than today.
Another example could be a better monitoring of the transport flows and pedestrian movement patterns or analyze which buildings is more suitable for the installation of solar panels based on the potential for solar energy production.
Many possibilities are offered in terms of comfort, but also in their wake, a multitude of ethical issues and identity, especially regarding data confidentiality should be considered.
Smart Cities, Business Success, Increase Confidence: This is the promise of Digital Twin.
In conclusion, there is no doubt that we are moving to a new world, a world where business itself is going to be forced to change by a new generation. This is a great opportunity for all of us to rethink the way we do business.
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Tags: AI, Open Innovation, Procurement
How To Tackle The RPA Security Issues
Thinkers360
April 23, 2020
With the beautiful stories about RPA, we must not be ignorant of the risks associated with deploying robots or automating business operations. The increase in cyber-attacks, RPA introduced new forms of threat that can be leveraged by hackers to access unauthorized systems and exploit vulnerabilities.
Routine procedures such as transfer of files, processing of orders, payroll management, payment verification, etc can be automated and to do this the automation platform requires access to confidential information of staff, customers, vendors, etc. Credit card numbers, financial information, Social Security numbers, etc could be misused by software robots or robotic workflow developers.
These security risk issues exist in the two forms below:
a) Data Security Issues: This has to do with improper use of confidential personal and corporate data. Implementing companies must work to ensure the full privacy and security of data used by machines.
b) Access Security Issues: This relates to unauthorized users' possibility of accessing and using confidential data dealt with by bots. Access to such data must be prevented from employees and hackers.
Here are some ways to tackle RPA security issues that should be around: Integrity, Confidentiality, Traceability and Control:
(i) Governance & Product Security: It is important to build an RPA security strategy with clear policies that should be compliant with the company’s security policies. Besides, it is crucial to perform security architecture risk analysis of chosen RPA solutions and include security scanning tools as part of the bot creation/maintenance process.
(ii) Prevention of Unauthorised Access: Everyone on an RPA team must not have free access to the data; there must be an assignment of roles (i.e Bot Creator, Tester, Controller, Runner). Assigning roles to RPA members ensures the curtailing of too many accesses to sensitive data and thereby keeping fraudulent activities under check. Password or login credentials must be used by all users and processes to have access to the credential vault. Like this, only specific authorized users may get to the sensitive data area.
(iii) Active Directory Integration: This assists in assigning roles by centralizing team credentials for easy management. Active directory integration creates a control center for login credentials. With this, activities of robotic processes can be tracked
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Tags: AI, Open Innovation, Predictive Analytics
What is the Digital Twin?
Thinkers360
April 20, 2020
On Aug. 14, Morandi Bridge in Genoa, Italy, collapsed killing more than 40 people as dozens of vehicles and tons of concrete and steel plunged onto city streets below in a disaster that prompted an emergency review of Italy’s aging infrastructure. What if we could have prevented that disaster before even taking off?
20 April 2010 the Deepwater Horizon drilling rig in the Gulf of Mexico collapsed causing the largest marine oil spill in the history of the petroleum industry known as BP oil spill/leak. What if we could have fixed the drilling rig before even it broke down?
Can machines by time repair itself? Learn from its own mistakes?
Yes, it can, by Digital Twin!
So, what is the Digital Twin?
Digital Twin refers to a digital representation showing in real-time the operation and life cycle of physical assets, processes, people, places, systems, and devices. It can be used for detecting the root cause of issues and improve productivity.
It’s done by hooking smart sensors on an object be it a machine, aircraft or a building to gather data about its real-time status and working condition through internet.
Thanks to the Internet of Things (IoT) connection, it’s possible to retrieve and sends real-time information for analysis, offering many opportunities to enhance product performance or plan the activities of an entire production line.
In short, a Digital Twin is built on the connection of data between the real physical product and the virtual product.
In conclusion, there is no doubt that we are moving to a new world, a world where the business itself is going to be forced to change by a new generation. This is a great opportunity for all of us to rethink the way we do business.
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Tags: AI, Open Innovation, Predictive Analytics
The RPA pitfalls
Thinkers360
April 17, 2020
Is Robotic Process Automation (RPA) the solution for any digital transformation issue? Is it the perfect solution? We may think Yes based on the market hype. The truth is that RPA is not always the quick fix for every process automation.
As for every project, it’s key to plan carefully the RPA solution and focus on both process, technology and maintenance to avoid magnifying existing problems. In other words, if there are errors in the process, the bots will continue to replicate those errors indefinitely.
My advice to my customers is that associating automation with a “set it and forget it” can’t be a reality. There are many chances that an RPA project fails if companies are not aware of the RPA pitfalls from the very beginning.
Don’t implement RPA if your process:
- is very complex and/or changes frequently
- involves higher-level cognitive tasks (in the next article we will talk about the hype around Cognitive RPA)
- is not optimized
- is with insignificant business impact and is not cost-effective
Don’t implement RPA if you don’t,
- have a clear strategy
- set-up KPI to measure the automation's success
- have the leadership buy-in and support from the key functions
- plan the maintenance's needs and cost
Before starting any RPA activity, be ready to have the right checklist to define whether RPA is ideal for your operations, and where it will work best. You should start with the use cases that make sense, can optimize the existing process, increase employee and customer satisfaction.
In case you are a robot reading this article, please feel free to like and share it with your other bots’ friends mining the web !
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Tags: AI, Mobility, Open Innovation
Nine Benefits of Robotic Process Automation (RPA)
Thinkers360
April 15, 2020
Whether it is a small, medium or large organization, all of them are aiming to automate its business processes in a way that it adds value to that manual strategizing. Robotic Process Automation eliminates dull tasks, freeing workers to focus on higher-value work. While also reducing human error and staffing cost by eliminates jobs and creating new ones.
Any business hoping to compete with others today must embrace robots! Few benefits of RPA are stated below:
1) Improve Accuracy and Boost Productivity: While humans work on the productivity of 60% with some margin of errors, our dear RPA work on an error-free productivity level of 100%!
2) Reduce Compliance Issues: Once you set the requirement, it continues to work by following the rules.
3) Optimal Cost Savings: With RPA deployed, your cost will continue to drop. You save the cost of hiring, training, overtime payment and so on.
4) Improved Flexibility: With RPA implementation, a business needed only to deploy virtual workers that imitate human employees to map up the change. With few lines of software code change, the bots continue working.
5) Speed: Companies that implemented RPA attest to the increase in productivity due to the speed of operation. RPAs work tirelessly round the clock (i.e., 24 hours), whereas human works an average of 8 hours or so per day and would still need breaks, vacations, leaves, etc. An RPA never sleeps, never takes a break, never goes on vacation and costs relatively lower than an employee.
6) Seamless Set-Up: Setting-up the bot does not take much, no need for any expensive installation or system overhaul.
7) Fast Detection of Fraud: Robots can detect fraud faster than humans. They can easily do background checks and complex fraud investigations.
9) Time-Saving: Robots work faster and on multiple tasks at a time compared to humans; therefore, they save a lot of time and in return money.
In case you are a robot reading this blog, please feel free to like and share it with your other bots’ friends mining the web !
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Tags: AI, Future of Work, Procurement
What is Autonomous Procurement?
Thinkers360
April 10, 2020
Let’s start by looking at what an autonomous procurement system means. At JAGGAER, our own definition of a truly autonomous procurement solution is one that will not only have cognitive capabilities embedded throughout the platform, but will build on those capabilities to automate entire sourcing and procurement processes without any buyer interference whatsoever when the opportunity arises.
That final piece is key; “when the opportunity arises.” The goal of autonomous procurement is not to eliminate the human element of sourcing and procurement. Instead, it is to remove unnecessary tasks that take up time and resources, and enable team members to think and operate at a strategic level. This is not just a good idea – it’s a really good idea. Here’s why:
We’ve only scratched the surface on Autonomous Procurement
Many procurement suite providers already offer functionality that can loosely be defined as “augmented,” but this is only the second of four steps on the journey to autonomy. “Smart” technologies like vendor-managed inventory tools are a strong start, but current technology can go well beyond rules-based systems.
Instead, modern technology exists in the third step toward autonomous procurement: Intelligent Procurement. Intelligent systems should already be learning from past purchases, user preferences, and experiences, not just responding to direct stimuli based on rules and decision trees. Tools like vendor-managed inventory can remove the ordering step when inventories hit predetermined levels, but this kind of rules-based programming is not autonomous.
Autonomous procurement will deliver on its promises
Artificial intelligence (AI) is, in many ways, still in its infancy. And yet even now, AI capabilities already go beyond their human counterparts in some areas. For example, a 2018 study pitted 20 barred attorneys against the LawGeex Artificial Intelligence solution. The results were staggering, with the attorneys catching 85% of mistakes on average and the AI system spotting 94%. Moreover, it took the attorneys 92 minutes, while the computer did the task in 26 seconds. AI is not just the future – it’s already here.
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Tags: AI, Future of Work, Procurement
Data Lake Vs. Data Warehouse: How They Compare?
Thinkers360
April 09, 2020
What Is A Data Lake?
Data Lake holds data in an unstructured way — no hierarchy or organization among individual data. It basically holds raw data. Data Lake accepts and keeps different forms of data from various sources. It supports all data types until when the need for them arises. Since Data lake holds several types of data, with time some of the data may never be used. The lake then gets messy and unmanageable, it then degenerates into data swamp.
To make it easy to understand, think of Data Lake as a natural lake. Water from every source streams into the lake to fill it. The lake contains different organisms and materials; fishes, tadpoles, frogs, water plants, bacteria, algae, mud, sand, etc. Now take the water and contents of the lake as Data in different forms.
The lake takes just about anything into it (unstructured or raw data). Anyone can dive into it, take samples, fish and probably swim. Also, since the lake can be treacherous only professionals (data scientists) can easily swim or fish in it but open to everyone. With that simple analogy, you should understand the concept called data lake now.
What Is A Data Warehouse?
Data Warehouse stores data in an organized and processed manner. Everything is archived and ordered in a defined way. During the development of a data warehouse, so much goes into it. The sources of data are analyzed and understand the business process. Data to include and exclude is predetermined from inception. Data to be loaded in the warehouse must have predetermined use before loading them into it.
For a better understanding of the Data Warehouse concept, we shall look at Data Warehouse as a swimming pool or fish pond. The pool or pond is clean and well structured. Only clean water (structured or process data) is allowed into the pool or pond. The water is treated with chemicals, the pool is clean regularly and kept tidy.
Just anyone cannot just dive into a swimming pool, you must be authorized to do so. In the case of a fish pond, the fishes or organisms (data) inside it have their purpose predetermined from set-up. This explanation should give you a better understanding of the data warehouse concept.
Data Lake Vs. Data Warehouse: How They Compare
Data Lake and Data Warehouse differ in so many ways and much more different than they are alike. The only similarity between the two concepts is the high-level purpose of data storage.
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Tags: Future of Work, Mobility, Procurement
The One Piece of Information you need before implementing AI
Thinkers360
April 08, 2020
What is the right way to implement AI?
The real Artificial Intelligence (AI) transformation starts by asking the right questions and building the habit of transformation amongst the staff because one who isn’t eager to learn or move forward will stay behind.
At the moment when you think to leverage AI in your company, you should at the same time:
- Think about an implementation strategy to not lose money.
- So, once the AI strategy is defined, the problems to be solved are clear, you need to educate your teams and enable transformation internally by clarifying the targets to be achieved.
- Engage your internal team
Involving your employees and teams is important. It has to come from the top down. If you don’t have a CEO and top managers who understand the importance of AI in your context, it isn’t going to work.
- Get clean and integrated data
It’s all about the data. It is important to work on the data sources and identify how you can get clean and structured data to start. AI without data may lead to a big failure. AI with the wrong training data will certainly lead to incorrect recommendations and maybe bad decisions.
Once all of this is done, I suggest doing a proof of concept and well define measures of success.
What are the wrong ways?
There are many failure factors when implementing AI such as:
- Don’t start AI because you want it. AI is not right for everything
- No AI strategy: understand what you are doing and Why you are doing it.
- You don’t have clean, structured, relevant and enough data
- Without support from leadership, your AI transformation might not succeed
- Don’t start alone. Partnership and market expertise are important
- Without KPI to measure the success
In a word, it’s important to not jump on an AI implementation project blindly.
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Tags: AI, Mobility, Procurement
FROM “A SOFTWARE AS A SERVICE” TO “A CONVERSATION AS A PLATFORM”.
Thinkers360
April 07, 2020
Quoting John McCarthy, he classified AI as the “The science and engineering of making intelligent machines, especially intelligent computer programs”.
Invariably, Artificial Intelligence (AI) has detailed itself as an actively developing machine, evolving through every era of change. For a long time now, it seems that man has been able to create a super-brilliant machine, usually robots – to be able to do what humans can do with ease. Predominantly, these machines have even proven to be able to do more, as they are unequivocally capable of processing huge information within a short time. In the same vein, they are still able to fill the void of agility and dexterity that humans cannot adequately fill in to.
One of the simplest methodologies toward achieving a remarkable user experience is by presenting an impeccable, flawless and deliberate design, which bolsters more on interaction experience for every device. Also, the visual simplicity makes the interfaces quite simple to communicate with. The design however doesn’t have to be excessively entangled to work unusually, thereby causing clients upbeat.
The idea depends on more conversational connections as opposed to tapping on buttons and icons on every product. Thus, allowing diversity on the Procurement software itself in a chat window within the external Procurement application.
Likewise, to be conversational, the interaction would be with the machine virtual assistance, usually a chatbot. This bot would be smart enough to comprehend what is being said and reply with significant bits of knowledge and accurately programmed information.
Cognitive Intelligence is tied in with making the quotidian experiences more intelligent, by implanting prescient intelligence into our regular applications. We should consider it thusly; since the modern insurgency, people have made tools to amplify human capacities. Of course, you can get crosswise over Europe in a horse carriage; but with an airplane, you can access the whole of Europe within the shortest time possible, and comfortably too. Simply put, AI the very tool that has been created to encourage us and our clients to get things done quicker and easier as well.
AI, is simply present to help us reorganize and rethink the procurement procedures to focus more on the most vital undertakings progressively and adjust the progressions.
With it, we’ll be able to changeround the business choices taken by our clients with moment experiences from anyplace.
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Tags: Future of Work, Predictive Analytics, Procurement
What Does Augmented Analytics Mean for Procurement?
Thinkers360
April 06, 2020
It’s OK, we understand. Here we are throwing another buzzword at you. But bear with us because it is not really the words that matter, but the thinking and the technology behind the words. And most importantly, what augmented analytics can do for you.
Let’s step back first.
Analytics has been with us for some time – more than a couple of decades – though until recently it was generally called business intelligence (BI). But it is worth reminding ourselves of what it means, and what made it possible.
Analytics is a very broad term and we can get a better understanding of what it means by viewing it in four layers, each of which answers a successively more difficult question:
- What happened? (Descriptive Analytics, formerly called Business Intelligence)
- Why did it happen? (Diagnostic Analytics)
- What might happen? (Predictive Analytics, also referred to as Augmented Analytics)
- What should I do about it? (Prescriptive Analytics, also referred to as Augmented Analytics)
Augmented Analytics was made possible thanks to the use of machine learning techniques such as neural networks and multiple regression analysis and mathematical approaches such as decision tree analysis Bayesian inference modeling. These found their earliest commercial applications in areas such as customer profiling, market segmentation, and credit scoring as well as risk analysis (e.g. to determine the likelihood that a major client will default on a bank loan).
Further technological developments have changed that to give us what Gartner defines as augmented analytics, which “… uses machine learning and artificial intelligence techniques to transform how analytics content is developed, consumed and shared. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.”
This is a journey, and we are still only at the early stages. Nevertheless, by embedding intelligence into all the key applications from source to pay, algorithms can set certain actions in motion based on the collected data, which trigger recommendations for the user in the system.
What is Needed to Get Started?
An obstacle to more rapid progress is the lack of relevant skills in many procurement organizations. While augmented analytics reduces the need to know how the technology works, what is missing is access to the technology and an appreciation of why it should be used, i.e. the benefits it can bring, such as identifying supply market trends ahead of the competition.
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Tags: Future of Work, Predictive Analytics, Procurement
Why Aren’t We Using the Fusion of Digital Twin and Procurement To Generate Power Yet?
Thinkers360
April 05, 2020
In this blog, we are going to see how Digital Twin can make procurement better and provide some use cases.
Digital twin has been applied in different fields such as aerospace for aircraft real-time monitoring, diagnosis and prognosis, maintenance, etc. Some enterprises (e.g., Siemens, Dassault, PTC) also use digital twin to support their services for customers.
How can this be used in procurement?
1) Save time and much more
Nowadays, the Spend Management solutions play a crucial role for the companies that are investing billions of dollars in implementation Digital Twin.
The editors and consulting firms have a big opportunity to collaborate with these companies as the Intelligent procurement and Digital Twin are two innovations that, by converging, would enable the next generation of the digital transformation in procurement.
A recent study by ARC Advisory Group reveals that the global process industry loses up to $20 billion (almost 5% of annual production) due to unscheduled downtime.
Thanks to Digital Twin, you can realize enormous savings by reducing the testing process and take advantage of the virtual training system. In this way, companies can reduce the systems failures and better plan the purchases based on predictive maintenance requests. According to a Microsoft Blog, Thyssenkrupp is one of the industry leaders using this new class of digital twin to transform many of their services businesses. Last year the company announced how, by using the predictive maintenance capabilities of Azure IoT, they could predict potential problems with elevators, which allow them to make time-saving interventions and quicker fixes for customers. Service technicians can visualize potential causes of issues even before they go to a job site, work hands-free while on the job, and engage with remote experts who can virtually walk them through solutions to fix potentially very complex problems. Work that used to take one to two hours is now taking less than 20 minutes.
2) The Big Procurement Transformation: Artificial Intelligence + Digital Twin
The e-procurement tools can get more and more powerful thanks to the Artificial Intelligence (AI) techniques combined with Digital Twin. Buyers can run forecasts and “what if scenarios” with various modeling algorithms and high number of variables that we get from connected sensors embedded in the products.
You can read more here: https://www.linkedin.com/pulse/when-digital-twin-meets-procurement-amenallah-reghimi/
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Tags: Procurement, Predictive Analytics, Future of Work
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