The concept of digital twins was born from the marriage known as cyber-physical systems. The cyber representation of a product or process was often held digitally within CAD/CAM or PLM systems. These became linked to the physical object through a feedback loop that kept the two in sync.
Digital Twin has moved from the esoteric to mainstream within industrial culture. And digital no longer is consigned to drawing databases, as my recent conversation with Michael Kanellos and Perry Zalvesey of OSIsoft reveals.
They described the process this way, “From devices all the way to buildings and factories, we’re now living in a world where everything is connected. And as these operations become more connected, it’s increasingly important to identify the strongest solution to monitor them. With the introduction of IoT, sensor and even AI technology to industrial operators, there’s been a surge of unfamiliar digital strategies – the latest being digital twins.”
OSIsoft prefers to consider digital twin as a loose term, as it can be either a complete network doppelganger or just a copy of key data streams to narrow in on specific issues. Everyone has their own preference and iteration.
OSIsoft named its digital twin technology the Asset Framework, which allows companies to take a project-by-project approach, creating solutions for each need on a rolling basis.
When one of its customers, DCP Midstream, began deploying OSIsoft’s AF tool it rolled out 12 AF based applications in two months, experiencing a $20-$25 million one-year return.
Application of OSIsoft’s Asset Framework has been strong in the water industry. Zalvesey says that his first work in the area was with modeling processes that were only static models. Today’s digital twins are dynamic. Designers can model the facility and objects within it. Each object has attributes that data are then associated with. Where originally there was a pump object—say we define “Pump 12” and associate data such as temperature and pressure and more. Now with Asset Framework, designers can create a template class “pump” and be able to replicate for as many pumps as a facility contains.
1. Asset Framework is the core digital twin offering. It’s as a relational layer on top of PI that combines all the data streams (temp, pressure, vibration) of an asset into one screen. A lot of people get fancy with the digital twin term but to us it’s a simulation combined with live data.
2. A simple AF template for a pump probably takes a half an hour to build. It can then be replicated ad inifinitum. It’s a drag and drop process. AF is part of PI Server (it was a separate product years ago but combined into it.) Complex ones can take months. Element, a company that OSIsoft helped incubate (and has since culled investment from Kleiner Perkins, GE and others) has built a service called AF accelerator. Basically, they parachute a team of data scientists to study your large assets and then develop automated ways to build AF templates for complete mines or offshore oil platforms. It still takes two months or so but they can streamline a lot of the coding tasks. BP used them.
- DCP. In 2017, the company launched an effort to digitize operations. One of the first steps was using PI to collect the data and use AF to create simple and complex digital twins. DCP has 61 gas plants for instance. Each one has been modeled with AF. Plant managers are show a live feed of current production, idealized production, and the differential in terms of gas produced and revenue. DCP discovered that it could increase production per plant on average $2000-$5000 per day, or millions a year, by giving the plant managers better visibility into current production and market pricing. In year one, it saved $20=$25 million, paying off the entire project (including the cost of building a centralized control center in Colorado and staffing it.) The next year (2018) it saved another $20 million.
- MOL. One of the largest uses of AF. MOL tracks 400,000 data streams and has 21,000+ AF instances based on 300 templates (a single template can be replicated several times.) MOL says that it has added $1 billion EBITDA since 2010 by using its data better. With AF, for instance, they figured out why hydrogen corrosion was exceeding the norm. In some instances, they’ve used advanced analytics—an experiment to see if it could use high sulfur crudes required deep analytics—but most of the time MOL has made its improvements by creating AF templates, studying the phenomena and taking action.
- Colorado Springs. Complete opposite end of big. It’s a small, regional utility.
- Heineken uses AF to model its plants to reduce energy. Aurelian Metals used it to boost gold extraction from ore from 75% to 89%. Michelin saved $4 million because AF let them recover more quickly from a previous outage. Deschutes Brewery meanwhile boosted production by $450K and delayed a plant (per our 2018 meeting.
Moving to sustainable sources of energy to generate electrical power, as Europe has, requires a balancing act. Solar and wind generation provide an imbalance of power since they only operate when proper atmospheric conditions exist—i.e. sunlight or wind. Hydro generation provides a necessary balance, explained Pier-Vittorio Rebba, technology manager power generation for ABB.
But many hydro plants are aging. Management realizes the need to digitalize operations to obtain the best use of Asset Performance Management applications as well as best optimization of plant assets. ABB and its customer Enel Green Power partnered to digitalize operations delivering predictive maintenance solutions that will lower maintenance costs and transform the performance, reliability, and energy efficiency of its hydropower plants throughout Italy.
The three-year contract will enable 33 of Enel Green Power’s hydroelectric plants, comprised of about 100 units, to move from hours-based maintenance to predictive and condition-based maintenance, leveraging the ABB Ability Asset Performance Management solution. With operations in five continents, the Enel Group’s renewable business line, Enel Green Power, is a global leader in the green energy sector, with a managed capacity of more than 43 GW.
“We are privileged to be partnering with Enel Green Power, a digital pioneer, in their move from hours-based to predictive maintenance utilizing ABB Ability technologies for big data, machine learning and advanced analytics,” said Kevin Kosisko, Managing Director, Energy Industries, ABB. “Predictive maintenance and asset performance management must become a key component of plant operators’ strategies to optimize maintenance operations, minimize risk, improve resilience and reduce costs. The results are more competitive electricity rates, in a more sustainable way.”
Collaborating closely since early 2018, the two companies have jointly developed and tested predictive maintenance and advanced solutions (PresAGHO) via a pilot on five Enel plants in Italy and Spain, including Presenzano, a 1,000-megawatt plant near Naples.
The new contract includes digital software solutions and services that will provide analysis of over 190,000 signals and the deployment of about 800 digital asset models, aimed at improving plant operational performance, reducing unplanned failures and enabling more efficient planned maintenance practices through predictive maintenance. The integration is expected to yield savings in fleet maintenance costs and increase plant productivity.
The ABB Ability Collaborative Operations Center for power generation and water will help bring wider benefits of digitalization and engagement, supporting informed decision-making, real-time solutions and cost savings. The center already provides similar digital solutions and advanced applications for more than 700 power plants, water facilities and electric vehicle charging stations globally.
“With personnel retirements resulting in knowledge gaps and more competitive electricity marketplaces, we believe that many power generation customers globally can benefit from this kind of digital transformation around maintenance and operations,” said Mr Kosisko.
I first met Quuppa and saw a demo of its real time locating system at the 2018 Hannover Messe. I have written about it here and here. The company has developed an interesting technology and application.
They wrote about industrial applications picking up, so I asked for an example. Below is a story about defining a problem, sourcing a solution, and then implementing it.
NGK Ceramics is a global specialist in the manufacturing of ceramic substrates used in catalytic converter applications for automotive, truck and off-road vehicles. The US manufacturing facility, located in Mooresville, North Carolina, covers more than 500k square feet with 365 days a year, twenty-four hours a day operations.
The facility was initially designed in 1988 to serve a limited geographical area in the US. However, with the business growing faster than expected and more areas being served by the same production plant, NGK faced a major challenge: how to grow the capacity of the North Carolina industrial plant. Efficiency was clearly the answer. As a first step, ASRS (Automated Storage and Retrieval Systems), together with AGV’s (Automated Guided Vehicles), were introduced to move pallets and materials in the shop floor without human intervention.
Even if this mitigated the problem, it was still not enough to manage high, yet variable, production demands in the long run. As a result, during production peaks, the pallets transporting both raw materials and semi-finished goods were temporarily stored all around the shop floor according to specific procedures. While this addressed the problem of lack of (ASRS) storage space, it introduced a significant new one, the additional time spent finding and moving pallets from one production phase to the next. At least two workers per shift were assigned to this task: just searching for and moving pallets.
In addition to this, at least once a year a complete plant inventory is required to verify all materials stored in the facility, but not yet shipped or sold. During this activity, the entire plant was surveyed, and all pallets were identified and verified against the data registered in the internal ERP system. This activity could take up to one week, with the slow down (if not interruption) of the production activities. Inevitably, any items lost or duplicated created an impact on the bottom line.
To deal with these issues, in 2017 NGK Ceramics decided to explore how solutions based on a Real Time Locating System (RTLS) could help by providing a Digital Twin of the manufacturing plant: the location of every pallet would be tracked continuously and that data would be synchronized with NGK’s MRP systems. This tracking of pallets provides a real-time view of where they are located in the industrial plant, with a number of supporting services to easily and rapidly search them and manage the production cycle.
TRACKING SOLUTION: REQUIREMENTS NGK Ceramics decided to evaluate a number of different scenarios for implementing a RTLS to track the progress of material and semi-finished goods throughout the flow of its manufacturing process. The key requirements to be addressed by the solution were:
• Configurable tracking accuracy: since the industrial plant covers a large area, with different uses of the spaces within the plant (production area vs. stocking areas vs. corridors), the ability to vary the location accuracy of asset tracking was important. In some areas, where the density of pallets is typically high (such as the warehouse) sub-meter accuracy is required in order to easily locate a specific pallet among the many stocked there. On the other hand, a 10 metre accuracy is sufficient in corridors or transit zones, where it is sufficient to track the presence of the pallet in the zone;
• Infrastructure cost: as NGK Ceramics facility is rather large, the number of RTLS antennas required to achieve the desired accuracy was clearly an important variable of the solution to be adopted. This impacted both the cost of the infrastructure as well as the costs related to the cabling (e.g., connectivity and power). Another factor was the cost of the tags to be attached to the pallets. This extended beyond the capital cost to also include the cost of replacing the batteries in the tags.
• Asset search and location functionality : NGK wanted this Digital Twin to be used in a variety of ways, from centralized systems to hand-held devices using a Google maps style red dot metaphor, so how the system was able to process the information and extract actionable knowledge for the final user (the worker in the shop floor) was important. This required addressing issues related to the usability and ergonomics of the system, Machine-2-Machine (M2M) application integration, while delivering on its intended use and the need to facilitate the searching and location of assets.
• Maturity of the solution: an enterprise-ready solution was requested. This refers to the support for active monitoring services of both the platform and the RTLS infrastructure. Any device or software component deployed in the facility needed to be monitored, with notifications sent in case of anomalies in the system. This includes the battery status of the devices/tags used for tracking the pallets.
DIGITISING THE PRODUCTION PROCESS NGK retained the services of Statler Consulting a specialist in the area of beacons and RTLS technologies, and issued a Request for Proposal (RFP) for a solution able to track in real-time the assets in their facility, and to deliver the necessary supporting services for the optimisation and real-time control of their production process. Among the many solutions proposed, ThinkIN was chosen as it proved to be the best match to the requirements identified by NGK. ThinkIN is an innovative IoT platform for real-time tracking, monitoring and control of assets and workforce in industrial environments.
ThinkIN technology is based on Quuppa4 RTLS for the high precision location of assets in the shop floor. Quuppa utilizes a unique combination of Bluetooth Low Energy (BLE) and the Angle of Arrival (AoA) methodology, as well as advanced location algorithms that have been developed over the course of more than 15 years, to calculate highly accurate, real-time indoor positioning, even in the most demanding environments, including inside warehouses and manufacturing facilities. The low-power system is a reliable, highly-customizable, scalable and costefficient solution for providing an accurate “dot on the map.”
ThinkIN platform provides a comprehensive set of services ranging from real-time support (e.g, asset search and location, alerts and geo-fencing, etc.), to Industrial IoT analytics. It also includes a number of tools to support the active monitoring of the infrastructure (both hardware and software) and a comprehensive set of user interfaces to explore the data collected and used to locate assets in real-time in the shop floor. In terms of tracking technology, Quuppa RTLS provided an optimal trade-off in terms of location accuracy, number of antennas required to cover the NGK facility and maturity of technology.
Overall 95 antennas are used to cover the complete NGK facility, with a location accuracy of approximately one meter in the areas of interest and approximately 5 meters in other areas. Different tag form factors were evaluated. Eventually, a custom Bluetooth Low Energy tag with a slim badge form factor was designed and manufactured in order to optimally align with NGK’s existing manufacturing process. The tag ensures 4+ years of life without battery replacement. Pallets, carrying products or semi-finished goods, are identified by means of their Product Travel Ticket (PTT), which includes all the necessary information about the kind of product manufactured, together with information on production stage ( e.g. forming line, firing in kilns, etc.). At the very beginning of the production process, an RTLS TAG is associated with the pallet Travel Ticket through a mobile application running on a scanner.
The application allows the scanning of both the QR code present on the PTT and the QR code on the TAG. This association creates a Digital Twin of the pallet, which is now tracked in real-time throughout its manufacturing process. The pallets can now be easily located through the ThinkIN mobile service. Additionally, plant-level views allow staff to monitor the status of the pallets across the entire facility, maintaining an always up-to-date inventory of all pallets stocked or moving in the facility.
Starting from ThinkIN open APIs, a dedicated mobile interface was created for an optimal utilisation of data over the shop-floor and to facilitate the work of employees in the search and location of pallets with a specific Travel Ticket. Figure 3: Tracking of assets in NGK facility Additional services delivered through the ThinkIN platform enable the quality control of pallets depending on their production stage, with alerts being triggered if the pallet moves into areas not allowed. To prevent this, a specific geo-localised workflow is imposed on the travel path of pallets depending on their production process. Warnings are raised when the specific workflow is not adhered to.
The project started in 2017 with an initial pilot phase, and is now scaling up to the entire production plant with a possible extension in the coming years to other NGK manufacturing sites. NGK is planning to obtain a return on their investment in a 2 year time frame. Today we are in year two and ThinkIN solution is integrated with the production control system adding value to the manufacturing process by making the pallet searching process more effective.
ThinkIN’s platform has allowed NGK to digitize the shop floor by recreating the plant on screens accessible to all workers. Thanks to the data collected by tags and devices, workers can use the interface to find pallets around the manufacturing plant based on the information of the goods transported by the pallets, such as product type, bench number, kill cycle, and other key criteria for the production routing.
The efficiency of the shop floor was significantly increased thanks to ThinkIN for Industry. In the first year, NGK Ceramics reduced the costs of the wasted time searching for pallets and of the time spent doing the annual inventory. Thanks to the new solution, the inventory is constantly up-to-date. Moreover, the accuracy in tracking reduced the risk of accidents caused by the movement of pallets with forklifts in the shop floor searching for the needed pallet. ThinkIN for Industry, therefore, is a location intelligence technology that by capturing data from the shop floor in a digital platform offers the chance to automate the real world in new ways that can enhance and optimise workflows in the shop floor.
I interviewed an interesting CEO of a Swedish start up called Mavenoid. Shahan Lilja is also a co-founder of this company leveraging Artificial Intelligence (AI) in the form of Statistical Machine Learning that tackles maintenance from an entirely different perspective.
(Note: I’m making up for going on vacation rather than going to Hannover by interviewing people I would have seen.)
Every company I’ve talked with for the past many years focuses on predictive or preventive maintenance.
But what happens when things break? A technician still must be sent to the scene in order to trouble shoot the problem. Perhaps the trouble is with a consumer product. The customer calls the company for help troubleshooting. The founders of Mavenoid saw this as an ideal opportunity for a Machine Learning application combined with perhaps a chat bot on the order of Siri or Alexa.
Lilja told me Mavenoid is the world’s leading platform for troubleshooting technical products, from dishwashers and robotic lawn mowers to trucks and cranes. It helps companies organize all of their troubleshooting knowledge, and make it come alive through an intelligent virtual agent. This virtual agent works much like a doctor for technical systems, narrowing down the possible solutions to the problem by asking diagnostic questions, and improves with each interaction. Mavenoid has some of the worlds largest companies as clients, e.g. 7 of the 20 largest manufacturing companies in the Nordics, and has quickly established itself as the future of technical support.
Finally, someone who goes beyond “buzz words” in order to define a strong use case for AI.
Mavenoid technology can be (and in fact is already) embedded within companies’ customer support services. I think this is something you all should definitely check out.
This week is Emerson Global Users Exchange week in San Antonio—with a quick side trip to Houston and a tour of some refineries implementing IoT applications with Hewlett Packard Enterprise. The theme of the week is Digital Transformation just where I reside—at the convergence of OT and IT.
Emerson Automation Solutions (new-ish name for Emerson Process) continues to flesh out its drive to help customers achieve “Top Quartile” performance through Digital Transformation.
It doesn’t just talk digital transformation. The company builds out its offering through product development, services / engineering, and acquisitions. Similar to other major suppliers, it has been making strategic acquisitions rather than taking minor stakes in companies.
Mike Train, Executive President, set the themes and talked about his optimism in the business and industry. Train was recently promoted to COO of Emerson Corporation and introduced Lal Karsanbhai as the new Executive President of Emerson Automation Solutions.
My friends at Putnam Publishing are doing the show daily this year. Flash back to 8 years ago when I was still at Automation World nursing a torn quadraceps muscle doing the show daily in San Antonio. You can see the news from the team here.
Peter Zornio laid out the logic of an “Actionable Roadmap” at a subsequent press conference. The company’s PlantWeb ecosystem continues to grow and develop becoming the key element of Emerson’s Digital Transformation strategy. Below is from the press release.
The Digital Transformation Roadmap includes consulting and implementation services to help companies develop and execute a tailored digital transformation plan to reach Top Quartile performance.
“Our customers have different starting points and levels of maturity when it comes to evaluating and implementing digital transformation strategies,” said Lal Karsanbhai, executive president of Emerson Automation Solutions. “Emerson’s proven digital transformation approach provides the ultimate flexibility while pinpointing the optimum path for each customer, based on their objectives, readiness and overall digital maturity.”
In an Emerson study of industry leaders responsible for digital transformation initiatives, merely 20 percent of respondents said they had a vision, plus a clear and actionable roadmap for digital transformation. Additionally, 90 percent stated that having a clear roadmap was important, very important or extremely important. Absence of a practical roadmap was also cited as the No. 1 barrier for digital transformation projects; cultural adoption and business value round out the top three barriers to progress. While all respondents were actively conducting pilot projects, only 21 percent had moved beyond that stage into new operating standards.
Leveraging customer engagements with successful digital transformation programs, Emerson defined a structured, yet flexible approach to help customers focus on priority areas with a practical roadmap tailored to their business needs and readiness. The goal is to help companies use technology to reach Top Quartile performance, measured by optimized production, improved reliability, enhanced safety and minimized energy usage.
“There is a clear global urgency among executives to harness innovation to improve performance, but many companies feel stalled for lack of a clear path,” Karsanbhai said. “Customers who engage with our operational certainty consultants quickly gain clarity on their best bets for digital transformation and a realistic implementation plan to accelerate time to results.”
Digital Roadmap Combines Technology with Industry Expertise
Emerson’s Digital Transformation Roadmap has two focus areas: business drivers and business enablers. Business drivers look at capabilities and performance relative to industry benchmarks in key areas: production management, reliability and maintenance, safety and security, and/or energy and emissions. The business enabler focus looks at capabilities in organizational effectiveness and systems and data integration. For each, Emerson has identified detailed criteria to measure customer performance along the digital journey – from conventional to best-in-class to the highest level: digitally autonomous operations.
Companies can start the digital transformation journey wherever they are, from starting small in one facility to address key issues, such as pump health or personnel safety mustering; to exploring companywide programs across an entire business driver, such as reliability of critical assets; to driving enterprise-wide adoption of cloud-based technologies and analytics for overall business transformation.
Emerson’s Operational Certainty Consulting Group provides a host of services, from Digital Transformation Jumpstart workshops to deep-dive change management to deployment and adoption of new digitally enabled toolsets. Customers partner with Emerson not only for its consulting expertise, but also to implement its Plantweb™ digital ecosystem, which offers a robust software, data analytics, and product technology and services portfolio to solve real-world problems while improving plant performance.
Emerson’s proven capability is bolstered by a global implementation team that includes more than 80 solutions architects and analytics integration engineers, backed by a project and service engineering workforce that exceeds 8,400. Important foundations for digital transformation have been established with producers around the world. For example, Emerson has collaborated with customers to deploy more than 37,000 wireless network installations and over 175 integrated reliability platforms and applications, to name a few.
Who buys enterprise software applications, how and why? I ran across this article by a contact of mine, Gabriel Gheorghiu, Founder and principal analyst at Questions Consulting, with a background in business management and 15 years experience in enterprise software. I thought it would be most useful. I’m not an ERP analyst, but I have some background and training on the financial side of things. I think this analysis fits with other large-scale software acquisition projects, though, including MES/MOM, analytics, asset performance, and the like.
This will summarize some interesting points. I highly recommend reading the whole thing.
Before we begin, my brief take on enterprise software applications. How many of you have been involved with an SAP acquisition and roll out? How many happy people were there? Same with Oracle or any other ERP, CRM, MES, APM, etc. application. Why did using Microsoft Excel seem to go better?
Well, the big applications all force you to change all your business processes to fit their template. You build Excel to fit what you’re doing. It’s just not powerful enough to do everything, right?
Gheorghiu conducted interviews with 225 companies who were all looking for enterprise resource planning (ERP). The goal of this survey was simple – listen and learn from what these companies had to say about their individual decision-making strategies. We all agree that this is not a simple task. But we also agree that selecting the best ERP software is a critical factor for business success.
Here is why the research phase of this process is considered to be so vital:
- It has the greatest impact on all the subsequent phases and consequently, your final decision.
- Research begins at home – in other words, the first step is to determine your company’s specific and unique needs.
- Once your company has thought through and determined its software requirement, then and only then does the process to evaluate vendors and their offerings begin. This can be a very challenging step because many companies are not equipped with the time, knowledge, or tools to perform this step.
Buyer Profiles: Who’s Looking for ERP and Why?
One problem for analysis is that many are not doing business in just one industry. The breakdown of companies in our business sample, by industry, was as follows: manufacturing (47%), distribution (18%), services (12%), construction (4%), retail (3%), utilities (3%), government (3%), healthcare (3%), and other (10%). However, to complicate matters a little, 20% of manufacturers also manage distribution and some distributors include light manufacturing in their operations, like assembly.
“Companies looking to invest in business software may very well be addressing this additional challenge – looking for a comprehensive package that integrates all aspects of a business. ERP software systems are powerful and comprehensive but are not necessarily known for their agility and ability to accommodate many disparate functions.”
Gheorghiu identifies as a strong influencer consumerization, which changes focus from organization-oriented offerings to end-user focused products. “This was a highly significant turning point in the IT marketplace. By developing new technologies and models that originate in the consumer space rather than in the enterprise sector, software producers opened up the market to a flood of small and medium-sized businesses looking for more cost effective, and less complicated solutions to run their businesses.”
The consumerization of software (as noted above) has precipitated the move by many companies away from enterprise IT towards more streamlined and user friendly consumer-oriented technology. This change is equally relevant for ERP software and manufacturing companies have participated in this very significant development, albeit more cautiously and slowly than SMBs.
Most industries follow a “purposeful implementation” strategy, managing software adoption as a series of “sprints in a well-planned program” rather than insisting on the “all or nothing” approach.
For example, a small company looking to invest in software might decide to begin with an accounting system which can be used alongside point solutions and spreadsheets. As companies grow and their transactions become more complex, they may find that they have also outgrown their initial software selections.
The chart below provides a visual analysis of the mix of software that is currently utilized by our business sample:
Some relevant comments we extracted from our survey included:
- The CEO of a small services company mentioned that he was “tired of the hodgepodge of systems”
- A manufacturer considered their current arrangement to be “very siloed.” Reconciling the inventory balance is a “constant battle.”
Buyer Behavior: How are Companies Approaching ERP Selection?
The selection process is most successful when companies adhere to some basic selection rules: involve as many direct stakeholders as possible and keep business priorities and strategies firmly in mind when making the final decision.
A software change can trigger a vast administrative upheaval within the company. It is important to carefully analyze the business case for the change and whether it supports the level of disruption as well as the implementation time and spending that will be required. Even if the change may be entirely justified, a well thought out analysis is well worth the time and effort.
The Vendors in the Spotlight
According to our survey results, the chart below identifies the vendors under consideration by the companies surveyed. A majority of companies (53%) were not, for the moment, looking at specific vendors. However 47% of respondents had narrowed their search to specific vendors.
Who’s Involved in this Decision Selection Process?
Our sample results indicate that the people in charge of the selection process are distributed as follows: employees in the finance and accounting departments (23%), IT department employees (23%). The other important categories were independent consultants helping companies with the selection process (17%), operations managers (17%) and presidents or CEOs (12%). It is worthwhile mentioning that project managers and business analysts only made up 5% of the total.
By far, the most effective method of choosing a software is to employ a collaborative system whereby the actual stakeholders of that system (the end-users) have a direct voice in the decision outcome. As the front-line users of the system, their insight and knowledge is very valuable. Their input along with all the other stakeholders input will produce the best possible outcome of this process.
An ERP system is a major business investment and is best handled with the appropriate amount of time and diligence given to the process.
The advent of cloud computing has indeed radically changed the landscape for deployment of business software. According to a recent press release by Gartner, “by 2020, a Corporate “No-Cloud” policy will be as rare as a “No-Internet” policy is today”. In other words, cloud deployment will become the default by 2020.
Our survey results, in fact, support Gartner’s analysis. Ninety-five percent of companies responded that they were open to a cloud deployment model, while just over 50% were willing to also consider on premises ERP. Of this latter group of respondents, 65% of them were manufacturers and distributors. This makes sense of course, given that these industries made significant investments in hardware and IT personnel and may not be as ready or as willing to move to the cloud model.
As for the preference for cloud computing (as demonstrated by our responses), we argue that it reflects the very strong tendency in the market to opt for simpler, more streamlined and less expensive computing solutions. As more information and assurances of security and stability by cloud providers enter the marketplace, more and more businesses will be convinced that the many benefits of the cloud outweigh some of their remaining concerns. Gartner’s prediction that cloud will increasingly be the default option for software deployment looks to be right on course.
An important consideration for companies embarking on an ERP software selection process – the average lifespan of an ERP system is approximately 5 to 10 years. If we consider important factors like the investment of capital, time, and loss of productivity that the selection and replacement of an ERP system requires, perhaps all companies would be more willing to invest the necessary effort in this process.