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Asset-intensive organizations that rely on the continued performance of their physical assets, such as big manufacturers, multisite organizations and large utilities, need to make sure their enterprise asset management, or EAM, systems work well with their ERP systems.
Both ERP and EAM software serve distinct, specific purposes that add value to an organization. Each system complements the other by doing what it does best in its respective field, Smith added. ERP systems are better at managing financial assets while EAM systems do a better job of managing physical assets. Bringing EAM software and ERP software together with an effective integration strategy enables them to do what they do best.
“Although there are modules in an ERP system that you could use to manage your physical assets, the functionality just isn’t there. The focus of the software company is not around assets, it’s around financials,” Smith said. “What we say is that you can have the best of both worlds.”
A company can have an ERP system to manage its finances; issue checks, roll costs up to the general ledger and manage cash, receivables and payables, but it should have a separate system that is entirely dedicated to its physical assets, which means EAM software, according to Smith.
Smith said he tells his clients they need both an EAM system and an ERP system, and they need to talk to each other.
“But that’s where it gets a little sticky — making sure that they’re communicating with each other in the correct manner is critical,” he said.
Historically, integrating EAM and ERP systems has been complex and expensive. Different types of databases, table structures, upgrade issues and system constraints have added costs and complexity to getting EAM software and ERP systems in sync and communicating, Smith said.
This is the main reason that some organizations have chosen ERPover EAM when it comes to asset management.
ERP systems normally manage the organization’s financials. When using EAM software, a portion of those financials related to asset management activities, e.g., maintenance, repair and operations, materials management, and purchasing, are initiated and tracked in the EAM system. To ensure that costs are correctly allocated so vendors are paid and cost information is passed to the ERP system, the two systems must be integrated, Smith said.
Instrumentation is coming – 2018 promises the IoT-ification of a lot of existing technology, plus edge computing, improved analytics and even some security improvements, if we’re reading these tea-leaves correctly.
IoT has been one of the biggest phenomena in technology for years, but 2018 is the year that it begins to really shake up the rank-and-file of enterprise users, according to Christian Renaud, director of 451 Research’s IoT practice.
There’s a new level of sophistication coming to the way companies approach the analytical end of the IoT phenomenon, Renaud said. Businesses store roughly half of the data they capture, and analyze about half of what’s stored.
“So why did I pay all that money for all those damn sensors if I’m not going to do anything with all the data that they capture?” he saud. “I think the people that have deployed are getting a lot better about what data [they’re] capturing, is important and what data is not.”
That’s set to change as businesses recognize the importance of processing all the data they’re taking in. The exploratory/discovery phase is over, and more widespread deployment is on the way.
How that new focus on analytics looks on the ground will vary by industry. In the retail sector, for instance, companies are tying point-of-sale systems into all their other databases, which are multiplying because of IoT. This lets them correlate PoS data with numbers of people who enter the store, what areas of the store they visited, demographics and tying that into conversion rate.
Kilton Hopkins is an entrepreneur and the IoT program director at Northeastern University’s Silicon Valley outpost. He said that the measurement trend has been on the rise for some time now, and that falling hardware prices are part of the equation.
“For every year of IoT that goes by, we see an increase in measurement. With continual price decreases in sensor and microcontroller hardware, it becomes more cost effective to gather more data over time,” he said. “Before we can do any analysis and make any improvements to our business operations, we need to have data. So if there is something that still needs to be measured, maybe this is the year?”
Edge computing, in the context of IoT, is the idea that you can actually do some of the computational work required by a system close to the endpoints instead of in a cloud or a data center. The intent is to minimize latency, which, according to Renaud, means that it’s going to be a hot trend in certain kinds of industrial IoT application.
“You’ve got these IT vendors who’ve been convincing people to go to the cloud for the last few decades, and they went out and said ‘OK, we’re going to bring the cloud to IoT land’ and the IoT people said, ‘The hell you are, because I’ve got ultra-low latency applications that can’t take a 200ms lap time through AWS,’” he said.
The hottest trend is the use of dedicated edge boxes to connect brownfield devices or to do some form of low-latency analytics or control-plane applications.
“The people that are in the driver’s seat for buying IoT solutions are generally not the IT people,” said Renaud. “They’re generally the guy who owns the factory, the guy who runs the fleet.”
Over the last few years, many people—myself included—have been touting the Internet of Things (IoT) as a driving force behind digital transformation.
But is IoT by itself truly that transformational?
Well, I would argue that it is not.
IoT focuses mainly on securely connecting devices that generate data. It is a key element of disruption and change, but it needs to partner with other technologies such as artificial intelligence (AI), blockchain and fog computing to create billions—some say trillions—of dollars in value and transform industries.
Let’s take a closer look at these cross-technology relationships:
IoT and AI have a remarkably synergistic relationship. AI, especially machine learning, provides intelligence—the ability to evaluate options, learn from experience and make smart decisions. IoT, like the body, provides the ability to sense and act. IoT delivers both the data AI needs, and the physical means to act on AI’s decisions.
The convergence of AI and IoT is creating countless new opportunities. For example, remote healthcare monitoring offers in-home diagnostics, analyzing a 24×7 stream of data and providing insights for care. In manufacturing, predictive analytics give production managers the intelligence to evaluate the trade-offs between building a new plant, for example, or buying extra capacity as needed. And preventive maintenance systems use IoT data plus AI to predict and prevent equipment problems before they happen.
Now, let’s add blockchain technology to the mix. Blockchain allows a secure exchange of value between entities in distributed networks. Having a trusted means of transferring and tracking assets, capabilities or transactions online enables a completely new class of IoT applications and can help address one of the biggest barriers to IoT adoption—data security. For example, an energy company is looking at blockchain to manage the interactions between solar panels and the power grid. And automakers are considering the technology to authenticate the interactions between connected vehicles and roadside infrastructure.
Since blockchain creates a tamper-proof record of transactions, it can also trace and authenticate the source of goods throughout production and distribution, preventing counterfeit components from being introduced and isolating the sources of quality issues.
Cloud technology has sparked a data processing revolution, but it is often inadequate to address real-time demands of new bandwidth-intensive applications. The first generation of cloud focused on batch processing of large amounts of data such as seismic surveys, or non-time-sensitive IoT use cases such as basic connected vending machines.
But now consider an offshore oilrig. Its thousands of sensors generate one to two terabytes of data per day, which would take days to transmit to the cloud using a satellite connection. Enter distributed cloud capabilities—or fog computing. By extending the existing cloud architectures to the oilrig itself, fog computing enables real-time data to be processed and analyzed locally based on policies coming from the cloud with only exceptions and alerts sent over the satellite link.
While each of these technologies enables new IoT applications and accelerates adoption, their impact is multiplied when we bring them together. Let’s look at autonomous drones and autonomous vehicles as examples.
IoT turns drones into high-value tools when combined with AI, fog computingand blockchain. Such autonomous drones can work longer and more efficiently than piloted drones. They can choose the most efficient flight path automatically, and change it on the fly to avoid bad weather, trees or power lines. They can even operate in dark, obstacle-filled environments beyond the reach of the Internet and GPS. Such drones are ready for mission-critical applications — whether that means an inspection of a gas pipeline or secure package delivery in New York City. The U.S. military is experimenting with using “swarms” of drones that communicate with each other in the air and collaborate to devise the best way to accomplish their collective mission.
Radar technology is used in many apps, including Warby Parker, Via, SeatGeek, Chick-fil-A, Raise. See videos how the technology was used by Via & by Raise. App developers can use our iOS and Android toolkits (software development kits, or “SDKs”) to add these capabilities to their apps in just a few lines of code. Building these capabilities from scratch can take weeks or months, but integrating Radar takes only a few hours.
Taking an example of the retail industry, unstoppable disruptive forces can cause reversals that are hard to believe. Since 2000, retail sector has seen many reversals. In 2016, Walmart reported its first annual sales decline since 1980, underlining the firm challenges it faces competing against Amazon. Target stores are merging with Kmart in bid to boost struggling chain; In 2000, Kmart was the third-largest US retailer, with $36 billion in sales; by 2014, its annual revenues declined by two-thirds while Amazon’s annual sales grew to $89 billion from about $2.8 billion over the same period. Only a 15-year-old company Alibaba, the market leader in China’s booming e-commerce business, now values more than $25 billion.
The next 14 to 15 years, will be even more disruptive and Radar based IoT solution is an undisputed force in this space.
Radar strongly believes that location is the future of mobile. We’ve had smartphones for over 10 years, but most apps are not location-aware in the ways that we describe above. Moreover, many companies in the location space are ad tech companies, like Foursquare and Factual. Radar wants to change this. Their priority is to help developers build great location-aware product experiences, and to collect and store location data in a privacy-sensitive way.