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Lagging behind – the risks of NOT using AI in manufacturing And the advantages of using it

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Recent changes in global consumption habits and demand, along with growing trends of digitalization and advanced technologies have all led the manufacturing industry to embrace the inevitable disruption of what we call today ‘industry 4.0.’


With customer demand constantly changing, manufacturers had to redesign their workflows in order to support shorter production runs to ultimately increase their brand’s sales and improve customer service and experience. In this increasingly competitive landscape, manufacturers cannot afford being lagged behind with legacy systems and outdated operational workflows. It’s clear that in order to keep their competitive advantage, they need to boost operational management in speed, scale, and simplicity.


As a consequence, traditional workflows that have dominated the manufacturing sector for decades are now giving way to advanced technologies; in fact, without the implementation of automation, IoT, cloud computing, and other innovative technologies, manufacturers risk falling behind their competitors.


Implementing advanced technologies is not enough

However, implementing these innovative technologies across the manufacturing supply chain is not enough; as clearly shown by IDC, only 30% of manufacturers investing in transforming the digital operations of their business will reach their full potential, and the main reason for that is that technologies cannot manage themselves.


The amount of data produced by digitalized processes and connected machinery across the manufacturing supply chain is unprecedented, and in most cases is not run and managed by one platform. According to a Gartner survey, 75% of manufacturers indicate that multiple data-sources constitute as a main hurdle to enterprise-integration, which in turn, is a hurdle to leveraging that data for real business value and operational success.


Turning the problem into the solution

Harnessing the great amount of data gathered by the connected manufacturing supply chain with its intelligent machinery has become one of the greatest challenges for manufacturers, and recent developments in artificial intelligence have proved to be the practical, immediate cure.


Based on machine-learning algorithms, artificial intelligence systems are the only ones able to normalize vast amounts of aggregated data, analyze it automatically, and identify behaviors to detect anomalies and alert in real-time. It is the only viable tool for manufacturers to gain control over their data in order to use it for better decision making.


The benefits of artificial intelligence real-time monitoring in manufacturing are easily visible when considering the risks of not implementing this technology –


Here are some of the highest risks of not using AI in manufacturing:

  • Lack of inventory traceability

When there’s no real-time view of current inventory, there’s no real certainty of meeting customer demand, which in turn leads to stockouts or wasteful production surplus.


AI real-time monitoring enables inventory traceability to continuously adapt production capacity to market demand.


  • Inefficient processes and errors due to unreliable manual processes

Human monitoring is prone to error by default, which can lead to inconsistent quality assurance, or wasted staff time on manual machinery checks and paper records.


AI leaves maintenance monitoring to automated anomaly detection and real-time alerts, thus enabling staff to focus on higher value tasks.


  • Failure to comply with regulations

Growing regulations on product safety or disposal management, though a blessing for consumers, are a heavy burden for manufacturers, especially in highly regulated industries such as the medical sector, the chemical or electronic manufacturing.


AI monitoring in real-time provides visibility into global supply chains to ensure regulatory compliance.


  • Longer equipment downtimes

Relying on manual machinery checks can lead to higher equipment failures due to human error and slow response times.


AI-based preventive maintenance not only alerts immediately on technical issues, but also predicts them before they occur and indicates their root cause, thus minimizing downtime, lowering repair and operating costs, and ensuring a safer working environment for the staff.


  • Lack of executive visibility into plant-floor processes

When executives are disconnected from the processes on the plant floor, they cannot have real control over performance and problem-solving.


AI-based dashboards and ongoing analysis translate processes from the plant floor to C-level executives, thus tightening the connection between executives and the processes on the ground.


  • Inaccurate business intelligence

Using BI solutions to report on past events no longer meets the needs of decision-making executives, and does not leverage the real-time data coming from across the manufacturing supply chain. Though once a valuable reporting tool, BI cannot predict future trends, report on real-time events or automatically identify root causes. It requires expensive staff time and even then produces complex reports that require additional executive time to decipher.


AI makes BI-based management solutions irrelevant, thanks to its ability to show the big picture along with meticulous drill-down, alerting on anomalies in real-time and providing predictive analytics.


Missing out on AI today might cost you your competitive edge tomorrow

Missing out on the opportunity to harness available AI solutions could cost not only in efficiency and expenses, but in the manufacturer’s ability to stay in the game. What started as a machine-learning algorithm now became the greatest competitive advantage in manufacturing, as it harnesses the real power of data, allowing faster response times and driving greater efficiency to maximize high-quality production.


How can AI real-time alerts optimize operational performance to boost production capacity in your factory?

Iris TsidonLagging behind – the risks of NOT using AI in manufacturing And the advantages of using it
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Interview with Stuart Appley, CBRE Managing Director, GWS Digital & Technology

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We caught up with Stuart Appley to discuss the current challenges in the commercial real estate space and how new technologies, such as AI and IOT, are revolutionizing this market.

Q: What are the main challenges faced by commercial real estate companies these days?

Commercial real estate is an industry that has been a laggard in its adoption of technology and digital approaches, and it’s always been a relationship driven industry, so there are a lot of challenges.  Investors are very skeptical as they’ve been hugely successful, and they don’t all feel that they need to change. As a result, vendors have not historically been as innovative as in other industries, so there are a lot of inefficiencies inherent in the legacy real estate technology offerings.

However, we are starting to see a lot of startups coming into the market over the last few years due to the huge amount of investment capital flowing in, and the recognition from outsiders that the disruption potential is very large. These new startups are changing how we think about space, with the space as a service offering as just one example, but they’re also bringing new digital approaches into the industry with a big focus on the user experience and open integrations. All that investment money coming into the industry is actually bringing in some great innovations, while forcing the legacy vendors to re-evaluate how they serve the real estate industry.

Q: You mentioned inefficiencies. Where in the market do you see them? Who are the industry players that need to take the next step?

Operations is where you see most inefficiency today. Because it is very easy to start a commercial real estate company, it doesn’t take much to manage one building. But there are a lot of inefficiencies when you begin to scale. How do these investment and management companies move from one building to many? More broadly, the tenant/employee experience is also very disconnected today. The digital consumer mindset where you can order anything from your app, and the ability to interact with your physical surroundings, have both been missing. So, how do we fulfill our tenants’ and employee needs on a day to day basis? How are we being proactive into what those tenants and employees need and the operations of the physical aspects of the building?

Additionally, if you take the day to day lifecycle of an asset, from the sourcing of a building, to transacting, maintaining, leasing and then servicing the tenant, for many companies it’s not a holistic process, but rather a very fragmented process with many inefficiencies in how it’s done today.

Q: Can you describe some of the operational challenges and give examples?

From the building perspective, it is challenging to ensure you’re fully leveraging the assets in a way that you can proactively fix equipment in order to avoid day to day issues that cost more in the long run. Companies are also not very proactive in reacting to tenants’ needs and understanding where tenants experience pains and have the most issues. Companies don’t always have accurate and real time information about what’s happening with the tenants or the building itself, and when they do, they don’t know how to use that data. How do you know what types of space tenants or employees like to use? Are they utilizing the space today in the way you thought they were? These are important operational and utilization questions that are coming up daily.

We, as employees or consumers, are used to the digital ways of dealing with applications in our experience, and when we go to the office we expect an experience as good as at home. There is a lot of opportunity to improve how we interact with the physical building, but owners are challenged in meeting those new experiences.

From an employer perspective, the same issues translate into how you fight for talent. How do you ensure your employees are happy? How can they get to their work in the most productive and efficient manner, while also ensuring employees benefit from digital technologies as the blending of work and home increases? Dealing with these questions on a day to day perspective is operationally challenging.

From the investor perspective, getting a pulse on what makes some assets perform better, while ensuring they have a proper handle on their buildings revenue, debt and capital is critical. What’s challenging is when the data is on spreadsheets and not at their fingertips. Having data at your fingertips, and the ability to understand and leverage all that data for better business outcomes is still a challenge for many operators today.

Q: Looking into the near future, how do you see technology as a solution to these operational challenges?

As mentioned before, most companies don’t have a good grasp of the data that is even in the organization. There is a lot of gold there. There is a wealth of information from the service requests that come in, the insights from the tenants’ leasing patterns, and the insightful perspectives on which buildings are being bought and sold. That data is there, but it is locked up in silos, and where it’s not, correlating and leveraging the data to improve decision making is lacking

Making sense of your data is where Artificial Intelligence can help.  AI can provide insights and real time information based on that data. If you can predict in advance what assets will fail before others, you’ll be more efficient in your capital spending. If you can optimize the routes your technicians make, you’ll satisfy your customers quicker, while reducing the amount of wasteful drive time between buildings.

With AI, we have the ability to truly augment the way we work in very transformative ways. I see it automating the manual and redundant tasks, performing tasks faster and better than we can today, helping us make better decisions, and automating the decision process to make quicker and more insightful decisions.

Q: Do you see companies starting to utilize their data?

IOT and smart buildings have been talked about for many years, so it is not a new topic, but it is starting to finally change. With IOT, we are finally starting to make progress with the cost of sensors coming down, the increasing use of edge computing and the ability to store the data locally, and the availability of open API’s and more open integration capabilities.

Data is starting to open up from their silos, but it’s still early and many companies don’t know where to start or the best approach. For those that are getting their hands on more data, the quality of it is an issue and many companies have a long way to go in having accurate and reliable data.

Q: Looking into the future, how do you see this industry in 10 years from now?

I think the space as service trend will have a continued impact on the industry. Many owners are trying to create their own flexible space offerings, and it will be interesting to see how this plays out, particularly when we see a downturn.  Autonomous vehicles will have a great impact in how real estate is used, particularly parking lots, but also from where people choose to live. Will they feel more comfortable with longer, but easier commutes? AI and IOT will be very significant and they will be leveraged much more than they are today. I’m excited to think about how we’ll finally utilize AI in ways that are truly impactful in how we work and how we interact with customers.

I think we will have more augmented reality use cases and collaboration among building engineers and FM technicians, who can leverage augmented reality to fix problems remotely or get information about assets digitally.

Iris TsidonInterview with Stuart Appley, CBRE Managing Director, GWS Digital & Technology
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Forget ‘tomorrow’ or the ‘next decade’ – Personalized AI notifications boost teams’ operational performance today!

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Let’s start with the ‘bad news’: we know today that 75% of teams fail to meet core business objectives around a few significant parameters, including staying on schedule, meeting customer expectations and aligning with company goals.

This statistics is particularly surprising in light of the great promise IoT and Big Data have both presented to operational success. With the emergence of data coming from both on-premise and cloud infrastructure, it was expected that operational teams would have all the information they need to improve their performance and drive higher revenue.

However, things haven’t exactly turned out that way, for various reasons

The Big Data and IoT revolution still hold great promise to aggregate and harness the power of data for operational improvement, but all the data in the world could never be useful if it’s not actionable. That’s exactly why operational teams often get the data but are unable to apply it properly to achieve their goals.

Here are just a few of the common reasons why teams fail to harness the data:

  • Difficulty in prioritizing tasks
  • Too much information coming from too many sources
  • Too many notifications on irrelevant issues

Inaccessible data

In other words, just like analysts experience data overload without an adequate Artificial Intelligence mechanism to help segment, prioritize, and analyze it – so do operational teams. Without a smart mechanism to help team member and managers to detect relevant data from noise, identify and alert on issues in real-time, and predict future problems – they won’t be able to improve their performance and reach the higher bar set for them ever since Big Data has entered the equation.

Leveraging AI to personalize the data for each member of the team

The biggest advantage of AI for operational teams is its ability to correlate and prioritize the multiple events coming from various sources at once, while studying the data behavior to detect anomalies, alert in real-time and identify the root causes for the issues found. All of these capabilities can be used to boost teams’ operational excellence through personalized notifications:

  • Prevent software or equipment downtime

When team members are notified on unusual equipment or software behavior in real-time, they can operate to fix the problem or order replacement before the damage is severe, and at the same time improve the operational lifespan of the product, while preventing or minimizing costly downtimes.

  • Improve customer service

Personal AI notifications also help keep customer satisfaction and retention rates high: by simplifying workflows and reducing administrative “noise,” personal notifications on customer concerns or inquiries allow teams to focus on the relationship with the customers, thus improving service and increasing satisfaction rates.

Meet even higher business objectives

In other words, when the data is visualized and personalized for each individual in the team, they can proactively access and use it to fix issues in real-time, prevent future problems, and offer better servicing for the customers. This allows team members and their managers to focus on core human-tasks that increase growth such as goal-setting, decision-making, and personal communication, rather than investing in continually putting out fires.

By identifying trends that the human eye cannot possibly trace, personalized AI notifications bring out the best in employees, improving not only their performance but also their motivation and self-driven success. And that, in turn, helps teams meet their business goals, and set their performance bars even higher.


Want to know how personalized AI notifications work? Take a look >

Iris TsidonForget ‘tomorrow’ or the ‘next decade’ – Personalized AI notifications boost teams’ operational performance today!
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Get Ready to Hear this More Often – AI is Today’s Biggest Competitive Advantage in the Container Shipping Industry

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From losses to gains – this is the golden age for carriers

“Way behind other industries, the container shipping industry has a lot to catch up on when it comes to digitalization, IT system’s modernization, automation, and smart technologies,” says Eyal Ben Amram, Chief Information Officer at ZIM, one of the world’s top 10 leading container carriers.

Once reluctant to adopt new technologies, the global container shipping industry is now opening its gates to Artificial Intelligence and other advanced technologies in the hope of reducing operational costs and improving well-deserved customer experience. With more than 80% of the world’s goods being carried by ocean shipping (estimated at $4 trillion yearly), this seems like a blessed, necessary change.

Eyal Ben-Amram, ZIM’s CIO, has been leading the implementation and adoption of advanced technologies across the company’s tens of branches all over the world. After lagging behind other industries’ leap into digitalization and smart technologies, it seems now is the golden time for the global container shipping industry, but what does this journey really entail, and how much business value can be derived from AI and other advanced technologies? Ben-Amram thinks business benefits are already in place, and this, he believes, is only the beginning.

But in order to understand how the practical application of AI, ML, IoT, and other smart technologies impact the business bottom line, we need to start with today’s most significant challenges in the industry –

Challenges in Global Container Shipping Industry Today

For nearly a decade now, the carrier industry is suffering losses, mainly due to its inherent structural capacity surplus: carriers around the world have acquired bigger ships in the purpose of achieving better operational efficiency per container. However, while global demand for shipping goods is constantly on the rise, it’s still not fast enough to catch up with the excessive supply in the industry. This process had naturally induced lower prices, which in turn had forced a few of the biggest global carriers to either merge with others or simply go bankrupt.

But that’s not all; as mentioned above, since the global container shipping industry has lagged behind technologically for too many years, its operational workflow is lacking accuracy; poor logistics efficiency, no end-to-end visibility in the supply chain, and wasteful expenses.

“Predictive analytics, Big Data, or optimization models were a long way from entering the container shipping industry. It is only just recently that these digital platforms and smart technologies have started drizzling into the carriers’ world,” says Ben-Amram.

In this reality, it’s clear that technology-driven management by transformative technologies such as AI, ML, IoT, and blockchain is vital to reduce expenses, increase efficiency, and improve carriers’ ability to deliver

Leveraging AI and smart technologies to gain the competitive edge

Though not one of the biggest carriers in the world, ZIM is one of the top 10 leading carriers, and it specializes in niche markets. As such, it is vital for the company to develop its own competitive advantage in order to stay in the game. “We wanted to start leveraging smart technologies in our core systems to offer our customers the same kind of fast, interactive, and easy-to-use service they are used to getting in other industries.”

“Smart technologies, and Artificial Intelligence in particular, are the users’ eyes and ears. Management and the entire team should be able to monitor the company’s performance across different sections to gain real-time visibility and view the big picture while also be able to drill down, identify ongoing problems, and find root causes to fix them as quickly as possible.” This was all difficult to achieve with the capabilities of BI tools and traditional industry KPI, which were only capable of reporting past events.

“But the real difference lies in AI’s ability to provide anomalies management through real-time alerts,” says Ben-Amram. “In a standard KPI system, you need to analyze the reports, identify the anomalies, and decide where you want to drill down to find the problem. But with the Machine Learning-based AI solution that we are now using with Okapi, you don’t have to look for the anomalies; they are given to you on a silver platter via personalized push notifications.”

This carries significant business benefits. Here’s how –

  • Customer service

A major part of improving customer service and user experience lies in giving customers the freedom to get the information they need independently, without having to wait in line or talk to representatives. With AI push notifications, customers can be notified on their mobile on anything from schedule and ETA changes to the temperature of their refrigerated cargo.

  • Operational excellence

The data gathered and analyzed through AI and ML algorithms allows carriers to optimize their travel times and better plan the resources needed for moving cargo. A good example here is how predictive analytics can assist in the infamous repositioning trips of empty containers costing carriers hundreds of millions of dollars a year: machine learning algorithms were already shown to more accurately predict future chassis demand. This results not only in lower repositioning costs, but also in reduced environmental impact by the elimination of unnecessary chassis repositioning trips.

But AI is also about enabling better response time in case of an ongoing event: in the world of container shipping, the chances for unexpected events such as unpredicted weather changes or employee strikes are extremely high. That’s why the ability to react in real-time to minimize the damage is critical. This is where AI can be a vital tool, enabling a quick recovery by providing the right solutions on time, such as finding alternative routes, alerting on scheduling changes,

  • Cost reduction

Costs are also reduced as a consequence of improved equipment maintenance: AI notifications can alert on anomalies in smart containers in real-time, thus enabling faster response times and preventing both equipment break-downs and damaged goods.

  • Revenue increase

When supply and demand are better predicted, and prices are tailored to meet the market’s current demand, cash flow is better managed and revenues can be optimized. Improved customer service, on the one hand, faster time-to-resolution on the other, and real-time visibility to fix anomalies on time, all enable better planning of travel times and resources, ultimately resulting in reduced costs and higher revenues.

Best practices for quick implementation and high adoption rates across the organization

So how can the industry take that leap to smart technologies implementation with its legacy systems and outdated KPI tools? Ben-Amram thinks that the more the solution is simple and intuitive to use, the easier it’ll be for the employees to adopt it in their daily tasks. But even more importantly, he says, it has to come from the top down: “It all starts with management; once they start using the insights AI solutions are giving them on a daily basis, the rest of the organization will soon follow.”

Navigating the ship 

AI capabilities and other advanced technologies provide executives with the ability to predict where their business is going by identifying trends and gaining visibility in real-time. Ben-Amram has already begun to see real business value from the smart solutions he had integrated across ZIM’s systems, and considers AI and other advanced technologies as today’s biggest competitive advantage in this field.

Read more on the practical advantages of AI for operational excellence



Iris TsidonGet Ready to Hear this More Often – AI is Today’s Biggest Competitive Advantage in the Container Shipping Industry
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5 Practical AI Applications in Commercial Real Estate with Real Business Value

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5 Practical AI Applications in Commercial Real Estate with Real Business Value

No longer a futuristic notion – AI can be used right here and now to maximize profits

The potential benefits of implementing Artificial Intelligence (AI) in almost any industry are well talked about. The idea of having machines to process information like humans do and produce much better outcomes has been considered a futuristic, though tangible prospect up until not so long ago. However, today that notion is closer to reality than ever before, and in some industries, AI is already presenting practical capabilities that deliver real business value – here and now.

One of these industries is Commercial Real Estate, where AI applications are already saving costs, driving better decision-making, boosting asset longevity and improving operational excellence. All of these measurable business benefits can be achieved by applying AI along the commercial real estate ecosystem, in 5 key areas:

#1 Tenant Satisfaction – Boosting Retention, Avoiding Turnover

As in every other sector, here too it is far easier and less costly to keep an existing customer than attract a new one. That’s why tenant satisfaction is so paramount to a property’s prosperity, and it’s also why so many resources and teams are operating to provide the tenants with timely quality service.

However, this becomes an increasingly challenging task when managing multi-campus enterprises with hundreds and sometimes thousands of tenants. This is exactly where AI comes in: by monitoring aggregated data from the building’s existing performance system controls, AI can identify recurring errors, anomalies in response times, or trends in work orders to allow property managers to fix problems faster and gain better insight on the root causes (poor maintenance practices, equipment wear-down, et cetera).

And as opposed to common perception, AI doesn’t only work on smart buildings with sensor-based automated systems, but rather integrates with existing legacy systems, making these capabilities available to practically any property.

#2 Lower Maintenance Costs and Improved Equipment Longevity

Maintenance costs in commercial real estate can skyrocket; from staff to equipment, expenses are high. But the real problem in building maintenance goes deeper than that – the whole value chain is broken: while one department is responsible for preventive upkeep of the equipment, another is responsible for actual repairs in case of equipment break-down, and a different one is responsible for dealing with ongoing fixes – with no one management workflow to link all these together.

This disruptive linkage between the various stages of maintenance is bad for business for the simple reason that the quality of the equipment undeniably affects preventive measures, which in turn have a substantial effect on ongoing troubleshooting. AI aligns all of these into one linked value-chain of predictive analytics: an ongoing equipment monitoring that solicits proactive maintenance activities before a breakdown even occurs, thus substantially reducing costs.

#3 Better Resource Management and Allocation

Traditionally, resource allocation was done based on experience; from the number of electricians needed for one building’s upkeep to how many operation managers are needed to monitor the tenants’ work orders – without analyzing whether it’s too little or too much. Using AI in resource management to analyze the relationship between the workload and the team capacity, provides accurate conclusions on how much staff and resources are needed to handle each project.

#4 Closing the Gap between Service Procurement and Tenant Retention

Providing superior vendor service for the property’s tenants is a top priority, as already established above. However, more often than not, service levels are dropping without proper reporting, since the data on slow response times and pending work orders is lost in the overwhelming data clutter of multiple suppliers across multiple properties. Leveraging AI in these processes enables vendor-specific segmentation in order to identify trends in service levels across different vendors and alert on any anomalies in service.

#5 Predict Cash Flow Deficiencies based on Suspicious Trends in Account Payables

Finding cash flow deficiencies in real-time is already too late. The careful tracking of the company’s cash flow is especially essential in commercial real estate, since any deficiencies can dramatically affect the next acquisition. And while dubbing managers can monitor payment on their ERP systems, they cannot be expected to predict changes in customers’ payment behaviors, track recurring payment delays, or constantly worry about the implications on the company’s cash flow. However, AI algorithms can be used here to identify trends and anomalies in payment patterns, and alert in advance on any behaviors that might affect the company’s cash flow.

Maximizing Property Profits Starts at Leveraging AI Across the Asset’s Ecosystem

Overall, the quality and success of managing the operational and financial aspects of commercial real estate depend on the articulate juggling between tenant satisfaction and retention, timely maintenance, cost control, and quality of service providers. Taking these aspects to the next level requires optimizing the relationship between these links in the value chain, and AI is doing precisely that, thus enabling executives to make better decisions and balance between maximizing profits and keeping maintenance rates low.

How did one giant Canadian commercial real estate enterprise manage to boost its operational performance through the roof by implementing AI-based personal notifications? Read here >

Iris Tsidon5 Practical AI Applications in Commercial Real Estate with Real Business Value
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Turning IoT into Measurable Business Value

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AI Paving the Way to Operational Excellence

There’s no argument that the Internet of Things (IoT) has introduced a real gospel to practically every industry. Promising a future of autonomous devices and sensors seamlessly connected to the enterprise’s IT systems to improve business results and operational excellence, it seems IoT is the new gold rush, and everybody is looking to get a piece.

However, as new waves of technology often do, this IoT revolution carries challenges and hurdles that must be handled wisely in order to achieve the real potential of IoT. At the very front of these challenges stand the biggest of them all- the massive flood of data generated from IoT devices aims to overwhelm existing and traditional data storage and analysis platforms, to the point where all that data is no more than a clutter of 1s and 0s.

The IoT problem

Everybody talks about Big Data. But the sheer magnitude of IoT generated data is beyond anything anyone could have imagined, processing massive amounts of information on a daily basis. This overwhelming overload of data has to be stored, segmented, managed, and analyzed to produce any kind of value to the enterprise.

But that’s not where the problem ends; this data flood is not just coming from one source but is continually streaming from multiple sources at once, all with different protocols, types, and frequencies, just flowing-in in masses.

In this reality, it’s clear that traditional data management and Business Intelligence (BI) systems cannot be expected to process, analyze and produce valuable insights from that endless stream of data, and here’s why –

Why can’t BI tools and traditional data management systems handle the IoT data flood?

The reason is simple – BI systems are inherently designed to produce descriptive analysis and reporting on events that have already taken place. But in the IoT reality, these capabilities are deemed irrelevant because of the volume and speed in which new information constantly keeps coming, immediately making any old data outdated.

In fact, using BI tools to handle IoT generated data will never realize IoT’s true potential because it could never work in real-time. Take manufacturers for example: they want their IoT data to help them improve waste removal, refine their supply chain, cut procurement and execution costs, create more efficient inventory, etc. In order to achieve those goals, they can’t rely solely on monitoring rule-based numeric thresholds- they need real-time actionable insights and accurate operational predictions.

Just like Gartner warn in their report, the flood of unstructured IoT data is expected to overwhelm existing data management solutions, and the lack of new information capabilities adopted especially to handle IoT data is estimated to result in a 25% failure to deploy IoT applications.

In other words, for IoT to realize its full potential, all that data from smart sensors and devices needs to be analyzed in real-time to automatically identify patterns and detect anomalies, to result in actionable business insights and operational excellence. Advanced, intelligent information processing and analytics in the age of industrial IoT has to become a core competency – without it, IoT applications will fail.

From descriptive reporting to predictive analytics

Recent advances in Artificial Intelligence (AI), specifically Machine Learning (ML), present new capabilities to overcome the information silos and overload caused by Big Data and IoT. By collecting disparate data from the multiple sources (including smart sensors as well as IT systems such as CRM and ERP) and processing it intelligently regardless of its type or frequency, machine learning algorithms can automatically sift through it and detect anomalies in real-time, thus predicting outcomes.

Using AI and IoT to boost operational excellence

Optimizing asset maintenance, reliability and efficiency is just the tip of what AI capabilities can do with IoT data; using these AI technologies, enterprises can finally leverage IoT data to realize real business value quickly with actionable insights. Predictive analytics by self-learning systems provides companies with the ability to make the right decisions in order to run the equipment, design their processes to avoid damage, maximize profitability, and ultimately drive business growth with:

  • High profitability
  • Faster time-to-market
  • Improved production and throughput
  • Optimized business processes
  • Improved operational efficiencies
  • High-level services

This predictive approach to IoT data analysis predicts not only what is going to happen, but when it is going to happen, and what we can do about it. It anticipates a problem enough time ahead to create an opportunity to fix it before the damage is even done.

By intelligently combining three vital elements: existing operating needs, equipment capabilities, and business goals – an AI solution with real-time notifications gives enterprises the competitive edge they need to drive growth by boosting operational excellence.

And so, using AI to handle the IoT data flood is not merely another operational metric – but a strategic priority.

For more information on how advanced AI can boost your operational excellence contact us.

Iris TsidonTurning IoT into Measurable Business Value
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New AI-advisor guides your entire team to achieve operational excellence

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Today, organizations invest a lot of resources in providing their managers with tools that will help them make informed decisions. However, merely providing managers with such tools is not enough to achieve operational excellence. Using these tools, managers can know that their department did not reach its goals, however without alerts on changes in real time and a clear knowledge of what exactly caused the failure to meet the goal, they will find it difficult to provide their employees with practical guidance to improve it. For the employee, questions like “what is the root cause of the lack of performance?”, “should I change my priorities?”, “should I change the work method that I am using?”, “should use other tools/materials?”, and many other questions remain unanswered.  

AI-Powered Personal Advisor for Operational Excellence

Imagine if you could have an ultra-smart advisor with superpowers that constantly analyzes all your organization’s data, understand your organization’s goals and come up with real-time insights and recommendations to meet these goals. Now, imagine that you could have this super advisor sitting next to every user in your organization, constantly providing recommendations on what to do next. Sounds like science fiction, right? Well, for Okapi’s clients this has become a reality.

Okapi puts the power of Artificial intelligence (AI) technology in your employees’ hands. Okapi’s platform collects and analyzes data from your entire organization, all your IT (Information Technology) and OT (Operations Technology) systems, etc. All this data is then analyzed using over 4,000 best practice metrics and processed by Okapi’s four AI engines:

  • Sensitivity analysis engine – based on the organization’s goals, this engine focuses on the main operational metrics driving the required outcomes.
  • Anomaly detection engine – real-time alerts on changes from existing patterns.
  • Cause & effect engine – root cause analysis for automatic detection of underlying problems.
  • Predictive engine – forecasting, preventive actions, and effective planning.

The result of this analysis is presented on Okapi’s mobile or web-based interface as personalized real-time notifications. These alerts are essentially the advisor’s recommendations to the specific user on what to do next. For example, the notification below alerts the plant’s shift manager that Okapi’s Anomaly Detection Engine found an increase in the setup time in two specific machines. This insight is based on analysis of data coming from the machines’ sensors and the ERP system.


Providing employees and managers with the tools to achieve the organization’s goals

A property management organization, which manages hundreds of properties around the world, can decide to increase customer satisfaction. But what does it mean for the specific manager of one of its properties?

The system understands the connection between the organizational goal to the departmental goals. For example, the customer satisfaction goal is translated to the maintenance manager’s specific goal is to decrease the number of tenants’ repeated requests. Okapi will find insights into the organization’s data and will constantly produce recommendations that will help the user reach this goal. It may, for instance, find that repeat request for air conditioning on a specific floor was 20% higher than usual. The manager can then use this information to proactively take preventive actions.

Finding insights across multiple systems and throughout the organization

Okapi analyzes the data across the entire organization to find valuable insights. For example, in a manufacturing facility, the organization’s goal was to improve quality, at the department level, this goal was translated into “Increasing the number of QC approved products after the first inspection”. There are many factors that can influence the number of QC approved products, but Okapi found that in another department, lowering the furnace temperature by 2 degrees for specific raw material, increased the number of QC approved products after the first inspection. Okapi used this insight to recommend the performance of the same action in the second department.

Notifications in real-time keep everyone up-to-date

Artificial Intelligence is used to automatically locate, visualize and narrate important findings. AI is creating a world where organizations don’t need to struggle with charts or complex reports anymore.
Everyone, no matter their level of analytical skill, can easily get the answers they need from their data.

This is a new revolution in the way organizations are managed and employees perform. By using this technology, you are essentially putting the world’s most powerful tool in the hands of your employees. It’s like a “magic pill” that allows your employees to be smarter and be able to constantly leverage all the organization’s data to reach operational excellence and achieve your organization’s goals.

Iris TsidonNew AI-advisor guides your entire team to achieve operational excellence
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Okapi’s Spotlight on Data: Interview with Jennifer Sertl

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This month, we’re featuring author, thought leader, and Okapi partner Jennifer Sertl in our latest installment of the Okapi Spotlight on Data Q&A series.

Jennifer Sertl is an author, thought leader, and the founder/president of Agility3R, focusing on strengthening the strategic and critical thinking skills of companies to make their leaders more resilient, responsive, and reflective. She is the author of the book, Strategy, Leadership and the Soul: Resilience, Responsiveness and Reflection for a Global Economy which she co-wrote with Okapi Advisory Board member Koby Huberman.

We talked to Jennifer about the many challenges surrounding organizational data & strategy and her approach to this.

Okapi: As someone dedicated to strategic critical thinking, how well do you think companies are handling the challenges they face around the data they collect and how they distribute & deliver it?

JS: I think Clay Shirky said it best: The issue today is not information overload, it’s filter failure.
I believe that companies have yet to sufficiently codify their strategies to enable them to truly leverage information that they’re collecting. The result? Many businesses attempt to accommodate everyone without a clear strategic intent. But if companies create a strategic filter to streamline the data, it can be more easily leveraged to reinforce certain themes. This allows the disseminated information to be used to increase productivity, employee cohesion, and ultimately optimize brand strategy. This strategic filter reveals the company’s level of innovation, know-how, and collaboration.
Many companies are preoccupied with just “getting the data” and using machine learning to do so. But are they actually executing a strategy and going to market using this data? Many are not there yet.
Okapi: What’s your approach to this challenge?
JS: There’s a business simulation game I run called Interplay. It allows teams to choose out of six strategies: synergy, know-how, velocity, collaboration, cost, and delivery. What we see, (e.g., in a company of 3000 employees with a 12-person executive team), is that a small subset of people each select a different subset of strategies. That means that even at the executive level, there an absence of alignment of priorities. And that impacts execution, for example: If cost is at the epicenter, there’s one way to execute. If it’s delivery, there’s another. So marketing and data can be acquired, but the core issue is getting a consensus around what we care about the most as an organization—strategically.
Okapi: So how do we change the mindset?

JS: If we think of every employee as a strategic agent, we know that every choice they make can bring us closer or further away from brand alignment. So it’s not just what they do, but how they execute that impacts the cumulative outcomes of an organization. By changing how we think, we can hierarchize our choices, rank them in terms of our organization’s priorities, and communicate that at the customer interface level to impact perception and outcomes. It gives us a behavioral blueprint. And it has allowed companies a way to ground themselves, sharpen their skills, and be more productive.

Okapi: That’s what our customers are saying.

JS: That’s what I love about Okapi. With the dashboard in your solution, you’re creating grounded criteria to generate visibility and feedback. Our behavior models are aligned. It just blows my mind that we actually have to strategically convince people that they need to be doing this.

Okapi: Why do you think that is?

JS: I think that understandably, people are reluctant to give all that information away. It can terrify people that everything is so transparent. Company leaders must understand that it’s their job to create clarity and a framework via which information can be leveraged as quickly as possible to win. That’s when the magic happens.

Want to make some operational and organizational magic for your business? Contact us for a demo today. Okapi is a custom data filter, a personal virtual assistant and an integrated, web-and-mobile-enabled dashboard—designed to help you reach your business goals.

Iris TsidonOkapi’s Spotlight on Data: Interview with Jennifer Sertl
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Identifying Your Main Challenges

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Over many years of working with organizations, we have encountered numerous challenges and misses that have repeated themselves. These are the main mistakes which you should look out for in your organization. By heading them off at the pass, you can prevent them all together and move forward with your strategy:
People are not connected enough to the larger business needs; rather, they are motivated by professional considerations, without seeing the prices we pay in the commercial aspects.

Lack of progress:
The tasks truly important for the growth of the business are not progressing. People here work very hard and are very devoted to their work, however, the assignments we need to perform in order to grow the business are not given priority

Unable to change to stay competitive:
In a competitive market, you need the ability to adapt your management infrastructure to change. However, this process as to happen quickly and efficiently. Many organizations do not succeed in changing courses in time to keep up with their competition.

Data is to complicated to understand easily:
To receive a picture of the state of the company, you should not need to dig through intricate Excel reports. Complicated reports and their preparation of consumes lots of valuable time. Creating a system to enabling the receipt of a timely, readily available picture on a current basis will add a great deal of value.

No coherent management plan:
There are many people who think that systematic management is not important, or they don’t use one because setting it up and following a plan is not one of their strong points and they pay too heavy a price for it. They get too involved in facilitating transactions, leading business development, and creating solutions to immediate problems in the company. Managers need to learn how to delegate responsibility for their own current management so that others can work towards the shared objectives we have defined.

To download the full “Six Steps to Operational Excellence” FREE E-Book click here:

Iris TsidonIdentifying Your Main Challenges
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Okapi’s Spotlight on Data Series: Q&A with Dr. Cindy Gordon

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In the latest installment of Okapi’s Spotlight on Data series, we talked to Dr. Cindy Gordon, CEO and Founder of Saleschoice, a predictive and prescriptive sales analytics cloud company. Cindy is a leading expert and recognized thought leader on the subjects of Big Data, SaaS, business innovation, early-stage software commercialization & sales business practices. She is the author of 15 books.

Okapi: What’s your take on the relationship between the data that companies collect & disseminate internally and sales performance?

CG: Data is a huge asset to any organization; a sacred resource, if you will. But it’s also a liability—and we have to treat it that way. Firstly, organizations need to lead by example from the top. A CRO should be using tools that they expect their sales reps to use to promote harmonization amongst the processes.
The second point is that we must reward good behavior when it comes to data. That means laying out very clearly—in every job description—responsibilities around data quality and cleanliness. Too often, information and knowledge are hoarded in other repositories for self-serving reasons, namely sales commissions. But if we can tie awards and recognition to KPIs and data responsibilities, we can actually improve sales performance across the organization.

Okapi: How is this best achieved?

CG: The key is mandatory fields: If you make fields mandatory and tie the completion of these fields to rewards, you get results. The goal is to not allow the salesperson to advance to the next sales gate unless all of the fields have been populated. That’s the only way to manage the gate. Right now, the mistake that companies are making is having too many open fields that are not mandatory. If it’s not mandatory, chances are it won’t be filled in. And when you tie these inputting tasks to commission, (i.e., withholding commission until the data is accurate and complete), we start to see the data coming in.
It comes down to this: if you measure it, you will succeed and if you don’t you won’t. And in order to measure it, you need consequences.

Okapi: Who is doing this well?

CG: In terms of pipeline management and sales forecasting and supply; advancing those opportunities and those target accounts and demonstrating closure to drive top-line profitable growth? There are many companies that do this well.
Take Intel, for example. They have gone through a long journey where they had 30-50 different workflows or instances of Salesforce running in North America. They consolidated everything into one unified workflow across all the different product and solution sets, giving them a 360-degree view of every product cycle, every segment in the lifecycle, etc. They know how many proposals are at the introductory phase, how many of them are in negotiation, etc.—across the whole product solution services portfolio. That’s smart thinking. They care about data quality and controls because of who Intel is and what they stand for.

Okapi: What about Artificial Intelligence? How does it fit in?

CG: When we leverage AI, we are predicting the future. And to do that, we need to rely on good quality data in order to achieve accurate sales forecasts. But science aside, as long as we’ve got 40 to 50 percent, we can at least get a baseline if there’s enough volume of data. And at the end of the day, it’s not about the workarounds. It’s about a clear vision and understanding.
The companies that have really anchored their operating processes with good control systems will be able to take advantage AI more skillfully than others. Take the manufacturing sector, for example. They have worked so hard for so long and may be in a better position because of their dedication to ISO standards and the quality improvement cycle. The hi-tech industries—despite their success—are always a little bit more cowboy-ish in this area and their processes aren’t always as tight. They are driven more by innovation than operations.

Data is one of the most significant resources in our economy and our future. And if we ever hope to get to a true AI layer, we need the best quality, reliable data. It’s like running on a dirt road vs. a bullet train.

Want to ride the bullet train? Contact Okapi for a demo today. Okapi is a custom data filter, a personal virtual assistant and an integrated, web-and-mobile-enabled dashboard-designed to help you reach your sales and operational goals.

Iris TsidonOkapi’s Spotlight on Data Series: Q&A with Dr. Cindy Gordon
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