What does a cement plant of the future look like? (Part 2)

In the first part of this two-part series, we explored what defines a cognitive manufacturing plant.

This blog will look at a specific use case -a cement manufacturer – to get a sense of exactly how this would work.

Cement-making requires precision and little room for error

Cement manufacturing has three key process steps:

1) Limestone from the quarry is crushed and appropriate raw material, like iron oxide, silica oxide, aluminum oxide, etc is mixed and ground to get the raw meal.

2) This raw meal is passed through the cement kiln at very high temperatures to produce clinker.

3) The cement mill grinds the clinkers to an appropriate heat to produce cement.

Each step is critical but the final step is where the magic happens.

Grinding to perfection

In the final step, the cement mill grinds the clinkers using horizontal ball mills. The ball mill has two rotating chambers with ceramic balls that grind the clinkers.  The clinkers are fed from one side.   The mill is heated at various temperatures along the separator.  The temperature evaporates the water in the clinker and initiates various chemical reactions within the chambers. The size of the ball, speed of rotation and duration of grinding will impact how fine the cement is.

While higher speeds may give better grinding, the centrifugal force on the balls can impact the grinding capability beyond a certain speed.  Similarly, the longer the grinding duration, the more fineness it will have, but more energy will be consumed in its creation. This excess energy could exceed quality requirements and lower yield.   Optimization between yield, quality and cost is a fine balancing act which is necessary to maximize return on invested capital (ROIC).

How can a cognitive plant help?

A cognitive cement plant uses advanced cognitive computing to predict variability across key metrics. We base these metrics, including throughput, quality and energy consumption, on  data that we obtain from the processes and machines.  When the predictions are out of range, advanced algorithms are used to prescribe operating parameters that could optimize the production KPIs.

Using these parameters enable the variability in plant performance to be minimized.  Thus a cognitive cement plant is run at optimal performance irrespective of raw material or environmental variants. This can save millions of dollars in energy cost and throughput every year.

IBM Plant Advisor recommends ways to reduce energy costs

Grinding cement requires a great deal of energy.  As the fineness of the cement increases, it needs more energy to grind it.  But every cement quality band has a certain fineness requirement.  In one example mill, we consider the fineness and the energy consumed for eleven months for a cement mill. In the first five months, we found that this plant has been producing cement quality more than what is necessary to produce.  As a result, this plant is consuming at an average 15% more energy than what is necessary.

Later, the plant implemented IBM Plant Advisor for this process.  As we covered in part one, Plant Advisor is trained with the historic data and the system predicts the fineness and energy for a given situation. From there, it starts recommending set points for two of the manipulated variables; rotating speed of the separator and the flow rate for the clinker feed. The operators accept the recommendation and make changes to the control parameter set points.

The net result; the fineness is under the acceptable band and they reduce energy consumption by 12-15% by implementing the recommendations given. Plant Advisor also predicts the future value of these variables and plots them.  The plant manager monitors these KPIs and their predicted values to be proactive.

Cementing the benefits of a cognitive plant

Moving to a cognitive plant can yield up to several million in cost savings per year in energy savings and higher return in terms of increased throughput. In addition to that, the Plant Advisor solution can provide the following improvements:

  • Helps to retain expert operator skills and leverage them with less experienced users. This keeps the knowledge within the company.
  • The purpose-built machine learning pipelines enable a quick start and early time to value.
  • Offered as a solution-as-a-service requiring minimum on-premise infrastructure, it reduces capital investment and accelerates return on investment.

Going beyond the cement manufacturing plant

Beyond cement, cognitive manufacturing has potential in many areas, such as in steel production, mining, power plants, pulp and paper production and metal smelting.

McKinsey and Company estimates net saving of $ 1M per year for large processes and $ 50-100K per year for midsize processes.  For a chemical company with 150 plants, that could mean $ 50-500M savings per year.

Where to find additional information:

Ready to take the next step on your cognitive plant journey?

Learn more about IBM’s IoT for manufacturing solutions.

View a demo of IBM Plant Advisor.

Visit the IBM Marketplace to learn more about IBM Plant Advisor.


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Save money by getting rid of the space you don’t use

When it comes to space in a building, there are three key types. There are spaces that make your business money, such as labs or offices. There are spaces that cost you money, like a cafeteria. But, the most egregious waste of space is that which sits unused. It is both an expense and an opportunity cost for your business. So what is the best way to optimize your facilities to keep unused space to a minimum?

The nature of facilities management

Sitting here at TRIMax this week, it’s hard not to put the topics of conversation into context. As our speakers talk about space utilization and creating buildings that understand our movements, you start to wonder how that is put into practice and what that experience is like. Imagine a workplace that understands how frequently you come into the office or where you spend most of your day.

The very nature of facilities management (FM) revolves around understanding occupancy trends and asset performance. Understanding these trends enables improved utilization of the space you have and can improve financial performance. Consider the savings for your bottom line if you could eliminate the unused space that you pay for. Rather than just estimating by walking around the building, you could have data that supports those hypotheses and eliminates the guesswork.

Understanding your space occupancy is more complex than just walking around the building. – Jennifer Wickwire, Teradyne

Facilities management is as complex as it is fun

There are four key inputs that drive the management of your workplaces and buildings. These are people, places,  processes, and technology. To align these four drivers, it is key to drive upon principles of engineering, architecture, planning, accounting, finance, management, and behavioral science. It’s no small order!

We often attack FM from three angles of responsibility: strategic, tactical and operational.

Strategic: this includes acquisition and disposal of properties, environmental considerations, long range planning and capital forecasting

Tactical: at a more granular level, this is the upkeep and maintenance of the building, space allocations, employee move management, and safety policies.

Operational: At this level, it’s all about day-to-day operations of the facilities, including utility management, emergency response, and budgeting.

For those most interested in space management, the tactical responsibilities are extremely important.

Teradyne re-invents space allocation for their buildings by tailoring the workplaces to the worker

Teradyne, a designer and manufacturer of automatic testing equipment for the semi-conductor industry, has 70 locations with 4900 employees. Headquartered in Boston, or the “center of the universe” as our speaker liked to fondly refer to it as, they were looking for a better way to optimize their spaces.

Not too long ago (1998), they were managing and analyzing space allocations straight from CAD drawings. This was both time-intensive and prone to inaccuracy.

“There was really no accountability within our allocation process.” said Jennifer Wickwire, Facilities Manager/Architect at Teradyne. “Our CAD files didn’t relate to any of our database records. There was a lot of guesswork involved.” And it was always a cumbersome and manual task.

Fast-forward to today

From a $ 20,000 investment in 1998 grew a much more efficient and cost-effective operation.  Each year they send out a view of allocations and managers can update their allocations online in the portal. There’s no walking around and making best guesses. They can accurately determine where their people are and how the space is being used.

Four things to consider when implementing an IWMS initiative

Through their years of experience with implementing TRIRIGA in their facilities, Teradyne had four key considerations to think about when kicking off a similar initiative.

1) Start up time: Kickstart within 3-6 months and use a beta site to get the kinks out.

2) Cost: determine based on scope of work. You will likely need to invest in services.

3) Personnel: craft a solid team with strong technical skills. Trust and mutual respect are very important!

4) Ongoing Maintenance: there is a lifetime commitment to keeping your data fresh!

Learn more and let us know what you learned at TRIMax!

To learn more about IBM TRIRIGA, visit the IBM Marketplace.

And don’t forget to follow the event on Twitter using #TRIMax2017 or via the Watson IoT channel: @IBMIoT

Read some of the other posts featuring TRIMax activities!

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Energy providers: connecting for operational efficiencies

“How do you reduce operational costs and increase consumer satisfaction?” asks DTE Energy’s Gary Gauthier. He posed this age-old business quandary with the authoritative tone that suggests he may already have the answer. Electricity companies, of course, can never simply starve operations. Because blackouts, angry headlines, and business disruptions would result.

But DTE Energy has applied IBM analytics and its own connected equipment intelligence to predict failure in its electricity distribution assets, such as its 1.2 million utility poles, millions of switches, and untold miles of wire. For instance, if a certain type of transformer is known to fail after a certain number of years, DTE Energy can head off trouble by replacing it. The predicted operational savings has the company looking for additional ways to apply IoT insights. “Putting these technologies in service in the real world has allowed us to derive value faster,” says Gauthier, the company’s manager of operational technologies.

Getting the jump on IoT deployment

In comparison to other industries, energy may have the jump on IoT deployment. Utility poles are conveniently spaced to gather data; smart meters are widespread. But until now, such “nodes” have been underutilized as little more than elaborate customer billing systems. Now providers such as DTE Energy are adding a cognitive and data analytics layer. This allows them to discover what Gauthier calls “the trouble behind the trouble.” When outages occur and crews head to the field, technicians can consult smart-meter-based intelligence. That allows them to discern who may still be without electricity despite a repair, saving costly return trips. To ensure long-term continuity, DTE Energy brings its experts to analyze IBM Watson intelligence. Then, they can estimate the life expectancy of various components in field assets, helping to improve maintenance efficiencies.

But what about the weather … ?

For another company in the energy space, adding real-time weather insight to IoT capability is leading to big productivity gains. Swiss robotics maker ABB expects buyers of its wind turbine equipment to see a 15 percent increase in productivity over the next few years. The company manufactures connected generators, motors, power automation systems and other turbine parts. Customers can harness a Watson-powered dynamic control platform. This brings together regional power use, real-time network feedback, and metrology forecasting. “With B2B customers, we’re seeing a need for more cognitive input and dynamic relationships,” says Doug Voda, Global Segment Leader for Smart Grid. He also noted that utilities need to make nearly instantaneous decisions to meet customer demand—or risk disruption. “They need information faster.”

Some of the operational tools energy companies have adopted—keeping watch on the grid we all rely on—include industrial IoT dashboards such as IBM’s Maximo. This allows them to extract insights from assets; Blockchain for the IoT for tracking interactions with partners; and the metrology insights from The Weather Company. IBM’s forecasting unit is a leader in real-time data and prediction nationally. That out-front position is partly thanks to the barometric pressure readings it ingests from the more than 100 million Weather Channel app users around the globe.

Creating a better plan

Looking beyond operational improvements of individual energy players, one can see an even bigger win for the environment. Electricity generation in the U.S. has always been something of a guessing game when matching production to demand, leading to inefficiencies. By harnessing IoT sensor data and artificial intelligence, providers are getting better at being able to predict and plan for demand spikes. That eliminates wasteful “spinning reserves” and other big capital expenses. “The idea is to fine tune our grid,” says Gauthier, “that way, energy generation goes where it’s needed.”

Three steps to boost energy operations through connection

  • Install a company-wide dashboard for industrial operations so that you can view the health of assets across the enterprise.
  • Set up blockchain tracking and verification across partners and vendors to ensure the security and reliability along the supply chain.
  • Integrate real-world intelligence, such as value-added weather predictions to the other data streams that inform your insights.

IBM and Watson IoT have the solutions for today’s energy and utility providers. That’s because data is the key to adapt to constant change. Visit our site to learn how to analyze your data better and predict performance and risk to deliver safe, reliable, affordable, and sustainable energy to your customers.


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How to build a better bed: IoT and AI at the zoo

What do queues, zoos, and machine learning have in common? Watson IoT, of course! Marwell Zoo is building better beds for their animals with IoT and machine learning. Using Watson IoT Platform, the park’s keepers are working in tandem with IBM, designing a better way to reduce energy consumption. All while creating a more comfortable environment for the animals.

Harnessing machine learning to reduce costs and improve living conditions

Can machine learning be used to create better conditions for animals at Marwell Zoo? The question first emerged after Andy Stanford-Clark, CTO for IBM UK & Ireland, presented an introduction to Internet of Things at a UK Chamber of Commerce conference in February 2017.

Marwell Zoo is dedicated to the conservation of wildlife and other natural resources. The zoo aims to conserve species and their habitats, locally and globally, and advocates environmental and social responsibility in support of these goals. For these reasons, the Marwell sustainability team have embarked on a quest to improve the sleeping conditions for the animals. They want to provide the animals with more comfortable housing, while reducing energy consumption and cutting heating costs – without compromising habitat quality.

Marwell Hall and animals, courtesy of Marwell Wildlife.

Marwell Hall and animals, courtesy of Marwell Wildlife. 

Using technology to optimize the park’s heating system

Currently, Marwell Zoo uses infrared heaters installed above the bedding areas of many of their animal houses. On cold nights, the keepers turn the heaters on, leaving them on throughout the night. Typically, these 2 and 3kW heaters cost about £13p (roughly $ 0.17) per hour to run. When you add that up over the course of the year, the heating load jumps considerably from 100 kilowatts in the summer to over 300 kilowatts in the winter. Recognizing an opportunity to reduce energy consumption and expenses, Marwell staff are looking for an IoT solution using sensors. They want to detect whether an animal is present in the bedding area, then trigger the heating to be turned on or off, accordingly.

Aligning sleeping patterns using sensor data

The first task the team face is identifying the right type of sensor, They need one that collects the most useful data to help adapt a heating system to an individual animal’s sleeping patterns. The sensor needs to be able to detect when an animal is present in a bedding area, and then use that to decide when to turn the heating on. Conversely, when the animal moves away from the bedding area, the heating needs to be switched off after a few minutes. The end goal is to ensure the heating is turned on when the animal is sleeping within its enclosure. Heating an empty bed wastes energy and incurs unnecessary expense.

Previous attempts with infrared sensors unsuccessful

It’s not the first time the zoo attempted to solve this problem using sensors. The team tried using passive infrared sensors. Those are the type used in a burglar alarm system, that sit in the corner of a room, triggering when movement is detected. The problem with using motion detection, however, is not unlike what an office worker might experience when they sit still for too long in one place. The lights will turn off because the sensor stops detecting movement. Flashback to the mad arm-waving from your desk. The same situation occurs when an animal is sleeping in its bedding area. After falling asleep, the animal becomes motionless, the heaters would turn off – and the animal left exposed to the cold while sleeping within its enclosure.

“A-ha, I may well have a solution for you!”

Several weeks prior to the serendipitous meeting between Andy and Duncan East, the Sustainability Manager at Marwell Zoo, Andy installed a system at the Watson IoT headquarters in Munich to monitor congestion at the coffee bar. Using two thermal imaging sensors, each taking temperature readings at eight different spots, the sensor records sixteen temperature points from the area where people queue for drinks. By reflecting the data from the thermal sensors onto an LED display in the offices on the floor above the coffee bar, Andy can display how busy the coffee bar is, based on how many hot spots the sensors detect.

Given the popularity of the coffee bar, it’s not surprising to learn that the Munich office staff find this to be a welcome addition to the many IoT applications in the building. At a glance, any employee can clearly see how many of the LED lights are dancing around – indicating whether the coffee bar is busy. If too busy, staff might forgo their coffee break at that peak time – opting to leave their desk after the crush so they needn’t wait as long.

A lateral move from coffee queues to zoos

It is this recent project in Munich that Andy latched on to when Marwell Zoo contacted him. To make a portable version of the coffee queue monitoring system, Andy installed one of the thermal sensors, connected to an ESP-8266 Arduino-compatible microprocessor, in a small 3D-printed plastic box. This was connected to a Raspberry Pi using a micro-USB cable, and he used the 8×8 grid of LEDs on a SenseHAT add-on board on the Pi as a display. A Node-RED application running on the Raspberry Pi displays the 16 temperatures readings being detected by the thermal sensor as a heat map, updating once a second.

Prototype construction (ingredients):

  • Raspberry Pi
  • SenseHAT
  • 3D printed box
  • Omron D6T thermal sensor
  • Wemos ESP-8266
  • USB battery pack

Using the same principles from the Munich coffee bar, Andy points his portable prototype at an animal from outside the enclosure. Some of the LEDs on the Raspberry Pi display go from blue to red when an animal’s thermal footprint is detected. This indicates the sensor has registered that the temperature of the animal is hotter than the background.

Spurred on by the success of the modified coffee-bar prototype, the team brainstorms on how they can adapt this concept to suit the environment at the zoo. They agree that the thermal sensor should be mounted above the animals’ bedding, pointing downwards to get a 4×4 grid view. Essentially, it’s a square shape looking down on the animals’ sleeping quarters.

Practical considerations and design constraints

The thermal sensor has an effective range of two to three meters.The team estimates the height of a convenient wooden cross-beam to be about three meters. That’s high enough to avoid any disturbances from the animals, space to house the essential components, and easy access to essential services: power and WiFi. Other practical considerations include how to make the system sufficiently water-resistant. Whether it’s animals, or keepers cleaning out enclosures, splash proofing the electronics is important.

Setting up a trial period to gather data

Once the team accounts for these design-constraints, they turn their attention to connecting the system to a heater. Understandably, the team at Marwell Zoo doesn’t want to override the heating system until the innovative approach is proven effective. They suggest a trial period to gather data.

To test the system initially, Andy uses an infra-red camera, which is available for the Raspberry Pi, and a ring of infra-red LEDs to illuminate the scene with light that is not visible to humans (or nyalas). Using IBM Watson IoT Platform, Andy sends the 16 temperature readings from the sensor, over WiFi, up to the Watson IoT Platform once a second. The sensor data is then analyzed using a Node-RED application in the IBM Cloud. The first version of the analysis application uses a simple statistical method to see if there are hot-spots relative to the average background temperature.

Images from the Marwell Zoo IoT sensor test shows nyalas enjoying the evening.

Images from the nyala bedding trial phase, courtesy of Andy Stanford-Clark @andysc

How to train a neural net

The team is planning to train a neural net classifier based on the data that is being gathered to improve the animal recognition. Essentially, it’s a model that can decide whether the animal is there or not, based on the temperature readings. If the animal is just wandering past and not actually sitting down, then the heater should not be turned on. The algorithm uses a 30-second time-window and a range of likely percentages. For instance, anything higher than 75 percent of the readings would have to detect the animal’s presence before the heater is triggered to be turned on. When the system is confident an animal is present, it sends a control message to Watson IoT Platform saying, “Turn the heater on.” When it decides there are no longer any animals in the bedding area, it sends another message to turn it off.

For the trial period, instead of turning the actual heater on, the Raspberry Pi with its infra-red camera, takes photos to compile a repository of images which fall into three categories – On, Off, and Control. Every photo taken is sent over MQTT to the IBM Cloud, and placed into one of three folders in a file system:

  • On – a folder that contains images taken when the system would have turned the heater on. Photos in this folder should all show animals;
  • Off – a folder that includes photos taken when we would turn the heater off. None of the photos in this folder should have animals in them;
  • Control – a folder that contains photos taken at 30-minute intervals 24 hours a day to provide context and background knowledge of what’s occurring all the time.

From neural nets to Nyalas

Once the system is installed, it immediately starts taking photos in the nyala house. Nyalas are little deer-like creatures. Sometimes there is one animal in an image, and sometimes, six to ten. Despite the size of the animals and the low resolution of the thermal sensor, with some minor tweaks to the algorithm to make allowances for the number of “bright spots” in the sensor data, the team’s concept and prototype design appears to be working correctly. The photos being taken are getting routed to the right directories – ON, OFF and CONTROL.

To ensure the design concept is working, Andy monitors the system in real-time. He reviews the photos, watching what the Raspberry Pi is displaying based on what the sensors are detecting, reviewing how the algorithm is performing against the images. This is to ensure that the decisions the team’s algorithm is making – to turn the heater on or off at the right time – are correct based on the “ground truth” data. In this case, it’s the photos. This is where the control photos come in handy. They help the team to refine and tweak the algorithm.

Poppet, one of the nyalas at the Marwell Zoo, will sleep more comfortably and sustainably, thanks to Watson IoT

Poppet, one of the adorable nyalas at the Marwell Zoo.

Taking advantage of the Cloud

By using relatively inexpensive off-the-shelf components, and the flexibility and convenience of Watson IoT Platform and IBM Cloud, it was easy to build the prototype system. This enabled the team to test their concept.

A deliberate decision was made to keep the application logic very simple on the devices installed at the zoo: the thermal sensor and the Raspberry Pi camera. All the analytics were implemented in the Cloud. This gave the team the ability to tinker with the parameters for thresholds of whether the animals are present or not, try out new algorithms, and train and test the neural net model – all within the IBM Cloud. This avoids having to return to the zoo, schedule time with the keepers, to update the software on the devices in the enclosures.

Using the Cloud enables the team to make updates on the fly. In this case, it makes sense to keep the edge technology very simple. The clever and complicated stuff – the models, the algorithms, the logic, the image repository – might not work first-time. Keeping it all in the Cloud offers the flexibility to tweak things at any time.

Progressing to the next stage

So far, the Marwell Zoo team is extremely happy with the initial results of the project. They have access to the photos so they, too, can monitor the decisions being made based on the visual data in the photos. When the Marwell Zoo is confident the system is making the right decisions, they will proceed to the next stage. They will add a controller, another device connected to the Watson IoT Platform, to control a mains relay that will turn the heater on and off.

In the winter months, the team plans to incorporate data from an on-site weather station in the park. They will look at the ambient temperature at the zoo to decide if it’s cold enough to turn the heaters on during the day– based on the local weather conditions. Just because there are animals present, doesn’t necessarily mean the heaters needs to be on. On warmer, sunnier days, there might be no need to activate the heaters.

A scalable, sharable, and portable prototype

If the initial trial in the nyala enclosure works successfully, the Marwell Zoo team is hoping to roll the solution out to include many more of the animal houses at the zoo – possibly up to 40 enclosures. Marwell Zoo has been trail-blazing this novel solution. And if it is successful, there are other zoos interested in the outcome of the trial.

The long tail

Thinking through the long tail of the solution, there are other possible uses where these relatively inexpensive thermal sensors could be used. They could not only improve the habitats of the animals but also improve the experience for zoo visitors. For example, what if there was a way when animals were detected in their enclosure, to convey that information to visitors? Not only would the customer experience be better, the animals’ living conditions would be more natural. They could be viewed in a way that was not disruptive to their natural cycles of sleeping and waking.

Moving away from animals, what about other environments where thermal sensors could be used to detect a physical presence?Remote bus stops or train platforms could become more welcoming. Lighting and heating could be triggered if a thermal mass was evident. There are many other applications where detecting the presence or absence of people (and whether there are “a few” or “lots” of them) could trigger an action. Blackgang Chine theme park on the Isle of Wight is actively looking at their use for controlling interactive exhibits in innovative ways.

Learn more

In many ways, and for many types of creatures, the Internet of Things opens up new avenues for comfort, caring and cost savings. To learn more about Watson IoT, please visit: ibm.com/IoT




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How to equip your TRIRIGA chariot with fierce stallions

There’s an old metaphor originating from Plato that compares the soul to that of a chariot with a pair of winged horses. Chariots of the gods were equipped with two good horses, while us mere mortals were given one good horse and one bad, unruly horse (depicting the soul). Due to this imbalance, we would always face hardships. But those of us who could put that unruliness to use could potentially rise high enough to hang with the gods.

What does this have to do with TRIRIGA and hanging out at TRIMax this week? Not a whole lot, I just like to throw out fascinating metaphors. However, if you’re looking to power up your facilities ‘chariot’ with the fiercest of good stallions, and corral the unruliness of unused data,  you may want to read on.

The five fierce stallions of 2017

When it comes to fierce stallions, why have two when you can have 5? And when you’re talking about investments around TRIRIGA, the more the merrier.  Here are the top 5 stallions driving the chariot of your facilities management efforts this year:

1) New Lease Accounting standards drive compliance domination

With the new FASB and IASB standards published in 2016 and taking effect as early as January 2019, organizations with both real estate and equipment leases face new challenges. These include the need for stricter data management, updated reporting systems, improved technical compliance, and potential organizational changes.

To address these early on, it is important for organizations to implement a solution with features supporting worldwide compliance, handling of the complete lease portfolio from new adoption through impairment and termination, and financial reporting using quantitative and qualitative data from leases. TRIRIGA does all this and more with the latest release. Most notably, the new journal entry configuration framework and predefined journal entry templates accelerate an organization’s effort to implement TRIRIGA.

2) Analytics help you understand the performance of your chariot

Existing reporting and analytics capabilities often require involvement of IT resources when new requirements surface. TRIRIGA users need self-service options that allow for quick, accurate ad-hoc analysis.

Watson Analytics helps just about anyone – mortals and gods alike – quickly discover patterns and meanings in their data – all on their own. Guided data exploration, automated predictive analytics and cognitive capabilities such as natural language dialogue allow users to interact with data conversationally, and with reason and purpose, to get answers in ways they can easily understand.

3) Cloud(s) – not just for mythical gods

If you are already a user of TRIRIGA (or if you are considering which delivery model makes sense for your business), have you considered a move to the cloud? According to Zeus, it’s light and airy there, and the temperature is moderate. In addition to that, it provides the flexibility of a monthly subscription with the ability to easily upgrade to the newest platforms and applications as they become available.

4) Mobility enables the business to soar from anywhere

We know that mobility is key to how employees engage with each other and their surroundings in the workplace. Enabling mobile access increases engagement of employees seeking facilities services. It also improves TRIRIGA user involvement, productivity,  and data quality. New mobile apps include a unified workplace services portal, a service request app to submit work requests, a reservation app to request meeting spaces, and a “move me” app.

Example of mobile app for booking rooms.

5) User Experience drives herculean engagement

Whether your employees are driving their cars to work or riding their own stallions in, it’s important that their experience in the workplace is productive and engaging. With new perceptive apps in the latest release of TRIRIGA, the realm of possibilities is herculean.  These new apps are built on top of a new UI.  Built with a user-centric design they also have the ability to automatically adapt to run on a number of different types of devices, from mobile phones to desktop screens.

 Where will our chariot go in 2018?

There are some exciting adventures ahead in the coming months. The latest outlook for the remainder of the year and into 2018 includes ongoing focus on these five areas. In addition, development will continue around cloud offerings working closely together with Wipro, TRIRIGA’s new development partner.

There will also be increased focus around capital planning and projects.  This creates both new functionality and leverages existing core capabilities of the current solution. We believe that an offering in this area differentiates TRIRIGA from the competition. At the same time it offers significant value to customers – current and future.

Learn more and let us know if you are at TRIMax!

To learn more about IBM TRIRIGA, visit the IBM Marketplace.

And don’t forget to follow the event on Twitter using #TRIMax or via the Watson IoT channel: @IBMIoT

Read some of the other posts featuring TRIMax activities!

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