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By Milan Shetti, CEO Rocket Software

In today’s volatile markets, agile and adaptable business operations have become a necessity to keep up with constantly evolving customer and industry demands. To remain resilient to change and deliver innovative experiences and offerings fast, organizations have introduced DevOps testing into their infrastructures. DevOps environments give development teams the flexibility and structure needed to drive productivity and implement early and often “shift left” testing to ensure application optimization.

While DevOps testing ecosystems require cloud technology, DevOps modernization software has allowed businesses that utilize mainframe infrastructure to successfully implement DevOps testing processes into their multi-code environments. However, introducing DevOps to mainframe infrastructure can be nearly impossible for companies that do not adequately standardize and automate testing processes before implementation.

The problem with unstructured manual testing processes

The benefits of DevOps testing revolve around increased speed and flexibility. In order to reach the full potential of these benefits and ensure a successful DevOps adoption, organizations should work to unify testing operations and eliminate any threats to productivity long before implementation begins. 

While it is important to equip developers with tools they are comfortable with, businesses working within multi-code environments must shift away from processes that require multiple vendors or lack integration. Operations that force development teams to jump from software to software to perform tasks create a complicated testing environment that can slow processes and create a disconnect between teams and departments. 

Manual testing also creates barriers to optimizing DevOps. While manual processes will still play an essential role in Quality Assurance (QA) testing, the potential for human error and the tedious, time-consuming tasks that come with manual testing make it impossible to create the speed and accuracy required for DevOps testing. And, if your testing is done using a specific developer script, you’re likely not capturing key metrics to improve your software development lifecycle, such as how the code changes the database. DevOps and true “shift left” testing environments demand structure and flexibility throughout operations that can only be achieved through standardization and automation.

Elevating testing with standardized and automated processes

To ensure successful DevOps implementation, businesses must start with an entire audit of their current operations and value stream — which is all the activities required to turn a customer request or need into a product or service. In doing so, teams can determine which software or processes create disconnects or slow operations and where automation can be integrated to enhance speed and accuracy.

Opting for vendors that offer user-friendly, code-agnostic and highly comprehensive DevOps platforms enable teams to create a central point of visibility, reporting and collaboration for processes. This standardized approach eliminates silos between teams, minimizes onboarding and allows teams a common means to rapidly commit, document and test changes to code and applications. Integrating systems and operations into a unified DevOps environment allows development and QA teams to track and schedule testing times between departments effortlessly.

From there, development teams should look to automate as many testing processes as possible. Leveraging automation in testing allows teams to implement automatic, continuous testing that eliminates human error and ensures all bugs are squashed before production. Teams can create multiple test environments and processes like unit testing, integration testing and regression testing. Standardization allows multi-code testing to be done with greater predictability and by different people — reducing the reliance on a few gifted developers and creating a more stable production phase.

Development teams can also create knowledge bases of automated testing templates to quickly pull and use or adjust to fit new and evolving testing needs. And, by leveraging automated DevOps tools, teams can configure software with controls that automatically test and vet any new coding introduced into the environment to quickly identify and address any bugs in the code or changes to the application.

The future of the mainframe and DevOps testing

A recent Rocket survey of over 500 U.S. IT professional businesses showed that the mainframe is here to stay, with more than half of the companies (56%) stating the mainframe still makes up the majority of its IT infrastructure due to its security and reliability. Thanks to highly integrative and intuitive DevOps modernization software, multi-code environments can reap the benefits of increased productivity and enhanced innovation through continuous “shift left” testing methods.

Just as the mainframe continues to modernize, so too does DevOps modernization software. Future DevOps testing software looks to leverage Artificial Intelligence (AI) and Machine Learning (ML) technology to further strengthen and streamline testing environments. Organizations like Rocket Software are working to develop technologies that use AI to study testing processes to help teams identify where testing is required and what needs to be tested more accurately. ML software will be used to track relationships in testing environments to identify patterns that help teams predict future testing needs and take a more proactive approach.

As agility and speed become more important in today’s digital market, the ability of teams working within multi-code environments to implement DevOps testing into operations will become a greater necessity. Businesses that standardize processes and utilize automation throughout testing will set their teams up for success. By creating structured and flexible DevOps testing environments, teams will enhance innovation and increase speed to market to help their business pull ahead and stay ahead of the competition.

To learn more about Rocket Software’s DevOps tools and solutions, visit the Rocket DevOps product page.

Software Development

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NetSuite is adding a host of new features and applications to its cloud-based NetSuite ERP suite, in an effort to enhance its automation capabilities and compete with midmarket rivals such as Epicor, IFS, Microsoft Dynamics, Infor, and Zoho in multiple domains including HR, supply chain, banking, finance, and sales.

The new capabilities were announced Wednesday at the company’s annual SuiteWorld conference in Las Vegas.

NetSuite and other ERP software providers have been focusing on automation as CIOs and other C-suite leaders look to navigate challenges such as labor shortages and supply chain issues caused by geopolitical crises and the pandemic, said R Wang, principal analyst at Constellation Research.

Enterprises want to cut down the number of employee hours spent on certain processes, and so are asking for automation, Wang said, adding that automation frees up resources to focus on strategic areas and can cut down errors in repetitive tasks.

NetSuite differentiates itself from its corporate parent, Oracle, by focusing on customers in the midmarket segment that may not be big or complex enough to require, or may not be ready to implement, many separate applications.    

“The addition of the new tools feeds directly into the expansion of the suite strategy as most NetSuite customers prefer to keep all software solutions inside NetSuite,” Wang said.

NetSuite uses the SaaS model, with customers paying a subscription fee based on number of users.  NetSuite ERP is a suite of applications that work together, reside on a common database, and are designed to automate core enterprise business processes. The ERP suite is  available on Oracle Cloud Infrastructure; companies need various tools and connectors to run the system on infrastructure from other cloud providers.

New tools automate routine HR tasks

In an effort to automate some human resources tasks such as wage calculations and attendance tracking, NetSuite has added a workforce management software suite to its HR application, SuitePeople. The software includes tools that can help schedule shifts, calculate labor and operational metrics, and record employee engagement.

A new visual scheduling tool, according to NetSuite, will allow companies to eliminate the use of standalone scheduling applications or spreadsheets.  It also enables teams to use a combination of forecasts, employee schedule templates, labor costs, and labor deployment models to build a staffing plan.

In order to automatically track attendance, the management suite of tools comes with a SuitePeople Time Clock that gives employees various capabilities, available via mobile devices, to record time and attendance. Time Clock comes with options for photo capture and biometric fingerprint verification that eliminate the risk of employees logging in and out of work on behalf of other employees, NetSuite said.

In addition, SuitePeople’s now can also automate wage calculations, as data from other tools such as Time Clock and scheduler can flow directly into the payroll tool for processing.

The SuitePeople Workforce Management suite is presently available in the US, Canada, Australia and New Zealand.  Information on timing of the rollout in other geographies was not immediately available.

Mobile app enhances warehouse operations

NetSuite has also introduced a new mobile application, NetSuite Ship Central, as part of its warehouse management system (WMS) software suite. WMS software is typically architected to optimize warehouse tasks such as manpower allocation and inventory control.

The mobile application, which can also be installed on a kiosk device, is expected to minimize shipping costs and transit times, NetSuite said.

NetSuite Ship Central is being sold now in the US and will be available worldwide in November, the company said.

Automation speeds accounts payable

Looking to help enterprises process bills and pay vendors faster from within NetSuite’s ERP platform, the company has introduced a new tool, Accounts Payable (AP) Automation, inside its SuiteBanking application.

The tool can capture vendor bills using machine learning-based object detection and optical character recognition (OCR), the company said, adding that it comes with a bill-matching and approvals feature to avoid overpayment or fraudulent or duplicate payments.

Automated bill matching ensures vendor bills are two- or three-way matched with the associated purchase orders to ensure details such as unit pricing, quantity, and totals are accurate, NetSuite said.

The new tool comes with a vendor payment automation feature in partnership with HSBC Bank, the company added. This partnership allows enterprises to access payment options such as checks or virtual credit cards, among others.

AP Automation also offers payment reconciliation, designed to improve the accuracy of accounting with the help of a rules engine that matches and reconciles virtual credit card charges while flagging discrepancies for further review by accounting staff. 

“Having core account aspects within the ERP gives the user the opportunity to incorporate all the other data flows within the organization into their accounts payable planning and accounts payable strategy. Using one window to see the entire spend workflow and ERP data saves time and reduces errors,” said Kevin Permenter, research director at IDC.

The software is currently available in the US, and Oracle is offering free implementation, no charges for bill scanning, plus a 50% discount on the subscription fee to its first 1,000 customers for AP Automation.

CPQ tool streamlines sales process

NetSuite also introduced a new add-on sales application, NetSuite Configure, Price and Quote (CPQ). The software, according to the company, will help enterprise sales teams configure, price and quote complex products accurately and reliably, directly from with the NetSuite ERP.

While the guided selling feature is designed to allow enterprises to find the exact products and services needed from thousands of SKUs by providing an e-commerce-like catalog experience and filtering tools, the configurator feature allows enterprises to save time spent on reworking orders by applying customizable rules that ensure every configuration, across product and service features, is correct.

The add-on tool, which is priced separately from the core NetSuite ERP system, is currently being offered only in the North America region and includes features such as a proposal generator, bill of materials calculator and e-commerce integration.

ERP Systems

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Since the outset of the pandemic, organizations have been increasingly launching initiatives aimed at automating business processes, turning to technologies such as robotic process automation (RPA) in efforts to reduce costs, speed up tasks, and improve accuracy of core business operations.

Some leading organizations, however, are not stopping there. Seeking to push their automation agendas forward, they are embracing a move toward broader “intelligent automation” (IA), a strategy that weaves capabilities such as artificial intelligence (AI) and machine learning (ML) into standard RPA to enhance its functionality.

In addition to RPA, AI, and ML, intelligent automation strategies can also incorporate a mix of technologies such as natural language processing, chatbots, and others that complement each other, says Lakshmanan Chidambaram, president of Americas strategic verticals at global IT consulting firm Tech Mahindra.

“These technologies together allow us to automate business processes to a larger extent, when compared to simple RPA automations,” Chidambaram says.

As RPA adoption matures, it appears likely that IA will also gain traction within enterprises seeking to improve automation outcomes. An August 2022 report from Gartner projects global RPA software spending to reach $2.9 billion this year, up 20% from 2021. The worldwide RPA software market is expected to continue experiencing double-digit growth in 2023, according to the research firm.

Vendors are rapidly evolving their RPA offerings into broader automation platfors with embedded capabilities for hyperautomation — Gartner’s term for IA. As a starting point toward hyperautomation, organizations will increase their spending on RPA software because they still have many repetitive, manual work tasks. Automating these could free up employees’ time to focus on more strategic work, the firm says.

Here is a look at how leading organizations are bringing intelligence to their automation strategies — and advice for CIOs seeking to do the same.

Embracing intelligent automation

Technology services provider Insight Enterprises is one such company embracing IA, leveraging a variety of automation technologies to support its business processes.

“Our RPA team focuses on internal optimization of highly manual back-office processes and a few client-facing reporting activities,” says Sumana Nallapati, CIO. “The team’s two primary towers currently focus on operations and finance, with a goal to provide a thoughtful approach to tackling manual processes, reducing costs, increasing productivity, and smoothing out error-prone processes.”

The firm is using Automation Anywhere’s RPA platform, which combines the basic RPA functionality with the ML and analysis capabilities of automatic process discovery and process analytics, as well as cognitive technologies such as computer vision, natural language processing, and fuzzy logic.

The initial drivers for deploying RPA at Insight were to optimize operations and enhance critical back-office functions. “We sought to standardize the organization’s critical processes while working to scale and increase productivity across our entire business,” Nallapati says.

Insight understood early on, however, that automation was vital for growth in many business areas, Nallapati says, so it focused on two primary use cases when launching its RPA initiative in 2018: deal registration and sales order entry.

Since RPA was new to the company, Insight began slowly in those two areas to work through the challenges of implementing an RPA strategy and infrastructure, Nallapati says. “Once the teams could show success in standing up an environment and automating more minor activities, we looked to scale the focus of automation,” she says. “Now, we’re working to transform how we maximize intelligent automation’s tactical, strategic, and competitive advantage.”

Insight is expanding its use of automation and IA globally. “As part of our integrated technology roadmap, we have a focused stream on transforming our organization into an ‘automation first’ culture,” Nallapati says. “The goal for this stream is to go from being focused solely on less valuable, individual automation to creating a center for enablement of strategically focused intelligent automation.”

For an organization to maximize the benefits of IA, the effort should be tied to a larger strategic initiative and be strongly linked to process transformation, Nallapati says.

“In the short term, our team is focused on a formal approach that identifies areas of our business that are heavy in manual, resource-intensive processes,” Nallapati says. “Ultimately, our vision is to run a self-service, scalable automation model that allows for teammate-driven development and maintenance of bots/automation.”

Growing the IA strategy

Cloud software provider Freshworks is also deploying IA, as part of a multi-year digital transformation effort, with a focus so far mainly in the human resources department.

For example, the company is using the AI capabilities of its own IT service management software (ITSM) combined with the RPA capabilities of Automation Anywhere’s platform to enhance the onboarding process and give new employees access to the tools they need on their first day of work.

With IA, the company has streamlined areas such as invoice handling, employee onboarding and offboarding, and customer order processing, says Prasad Ramakrishnan, Freshworks CIO.

“This automation has allowed our IT team members to get time back by skipping mundane repetitive tasks, and focus on what they were hired to do,” Ramakrishnan says. “Using AI and RPA technology, all of our employees can find the information they need in a streamlined and efficient manner.”

The need to increase automation of business processes “increased drastically” when all of Freshworks’ employees began working remotely in 2020, Ramakrishnan says.

“When it came to onboarding, different stakeholders needed to come together to achieve a successful day-one experience for our new hires,” he says. “With all stakeholders moving to a remote-first working model, [IA] enabled us to create role sets and streamline approval processes, thus reducing the long cycle times. Once the team started, there was no limit to our imagination on things we could identify and optimize using IA.”

Freshworks has set up task forces across the business to continuously identify new opportunities to implement IA. “We see an increased need to intelligently automate the various tasks that employees perform,” Ramakrisnan says.

Kickstarting a smooth IA journey

Adopting and expanding intelligent automation can be challenging because it involves a number of components and impacts multiple processes. Experts suggest several practices to ensure a smooth move.

One is to understand that automation is a journey, not a destination. “It’s never ‘good enough’; there are always more opportunities to uncover and tackle,” Nallapati says.

A good way to find and exploit those opportunities is to create an “automation first” culture, Nallapati says. “Much of the automation journey is rooted in change management,” she says. “Working with leaders to understand what automation can do, walking teams through the change, and teaching stakeholders how to work with and manage a ‘digital worker’ is critical to the program’s success.”

Automation isn’t a one-and-done endeavor, Nallapati says. “Much like the traditional employee, digital workers need coaching, occasional support, and the best tools and processes to make their work easier and more productive,” she says.

Another good practice is to set bold goals then work iteratively in small, decisive steps toward the big goal, Nallapati says. “Do not try to boil the ocean with automation; you must stay methodical and focused on the outcome you are trying to achieve,” she says. “Show value quickly. Break down a larger goal into smaller pieces that allow you to realize value more rapidly and tie it to a specific, measurable outcome and return on the investment.”

IT-business alignment and collaboration are key

Companies also need to optimize business processes to increase the effectiveness of automation, Nallapati says. “Working together in a partnership, the business unit and the automation teams can leverage their expertise to refine the best approach and way forward to optimize the efficiency of the bot/automation,” she says.

Technology leaders should make sure to get business leaders and users involved in the IA process, Ramakrishnan says. “Educate them about the possibilities and collaborate with them in joint problem-solving sessions,” he says.

One recent example at Freshworks was a joint effort between the automation team and the billing department. “With a large number of customers and a large number of invoices to process every day, any small savings through automation goes a long way in increasing productivity, accuracy, and improving employee and end customer satisfaction,” Ramakrishnan says.

Similar to the type of hackathons that are common in IT organizations today, Ramakrishnan says, “we partnered with the business to have a business-side hackathon/ideathon. We educated the key users from the billing team on the possibilities of automation, and then they were encouraged to come back with ideas on automation.”

IT and billing then jointly reviewed the suggestions for feasibility, prioritized them, and came up with an implementation plan, Ramakrishnan says.

It’s also smart to avoid jumping into IA, and automation in general, without a good reason.

“Never build a solution looking for a problem,” Ramakrishnan says. “Be sure to identify the problem first, and confirm if there’s a way to solve it already before creating more havoc for employees with a new application to deploy, implement, and learn,” he says.

Artificial Intelligence, Business Process Management, Machine Learning, Robotic Process Automation

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Understanding the student lifecycle isn’t easy. With more higher education institutions attempting to embrace digital learning, there is a growing need for visibility throughout the student journey. By gathering data across every student, faculty and alumni touchpoint, institutions can optimise each stage of the admission and onboarding process. 

The appetite for insights among higher education institutions is such that the global big data analytics in education market is expected to grow from a value of $18.02 billion in 2022 to reach $36.12 billion by 2027.

Unfortunately, many institutions remain reliant on legacy solutions with siloed data, which introduces lots of ad hoc manual tasks that slow the process of attracting and nurturing prospects. 

Automation will play a key role in enabling providers to implement a data-first approach – and better support prospects and recruitment faculties to ensure the student lifecycle runs as smoothly as possible. 

The problem with legacy tools in higher education 

Most higher education institutions today rely on legacy middleware they are familiar with, but that fails to offer visibility over the student lifecycle. These solutions make it difficult to access student records, accommodation, financial data and third party or cloud platforms. 

Data is also isolated and siloed in on-premises solutions, making it difficult to generate insights and optimise the student experience. 

In order to generate concrete insights, data needs to be collected at the edge of the network and across campus to feed into a centralised analytics solution. There it can be processed to develop insights into how to improve operations over the long-term.

How Boomi addresses these challenges 

The answer for these organisations is to undergo digital transformation by migrating datasets to the cloud. Ultimately, this will generate concrete insights to enhance the experience for students and faculties. 

While this transition is already underway, with 54.3% of higher education institutions reporting they were cloud-based in 2021, there are many that still need to migrate to the cloud. 

Integration platform as a service (iPaaS) solutions like the Boomi AtomSphere Platform can help enable this transition by unifying application data to ensure insights are accessible throughout the environment via a single cloud platform. 

Essentially, Boomi offers organisations the ability to connect data from a variety of sources, helping with the process of migrating data to the cloud and connecting data sources wherever they may be.  

Connecting data allows decision makers to generate the insights needed to make faster admission decisions – such as streamlining the onboarding experience for prospects and recruitment faculties. 

The easy way to move to the cloud 

Boomi has emerged as a key provider in enabling higher education institutions to move to the cloud. Boomi supports Amazon Web Services (AWS) data migration and application modernisation to link data, systems, applications, processes, and people together as part of a cohesive ecosystem. 

This approach enables higher education institutions to leverage a growing number of services through AWS, simplify data pipelines and improve transparency for decision makers. 

Ultimately, by providing decision makers with access to high quality data, institutions will not only increase the quality of the student experience but become more cost efficient by maximising retention.

To find out more about Boomi click here.

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Education and Training Software, Education Industry

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To help meet the needs of enterprises that are looking to navigate uncertain economic conditions while complying with new data regulations, ServiceNow has released the next iteration of its Now workflow automation platform, dubbed Tokyo, with new features that focus on easing supply chain complexities and optimizing asset and human resource (HR) management.

Tokyo’s release comes just months after the company released the previous version of the Now platform, named San Diego, that focused on personalization and automation of work experiences.

The new release, according to the company, is geared more toward chief financial officers and chief operating officers who are looking for a return on their IT investment.

Simplifying the supply base

The release comes with a new feature, dubbed Supplier Lifecycle Management (SLM), that can read names and other data of suppliers from emails and spreadsheets and move them into a new window inside the Now platform.

Automatically moving these supplier contacts and information, according to ServiceNow, helps enterprises reduce operating cost and allows the supply chain team to focus on creating a more resilient supplier base.

The SLM also offers a supplier-facing interface that can be used to launch queries for the enterprise.

For users in the enterprise itself, Tokyo includes a new tool, dubbed Enterprise Asset Management (EAM), designed to automatically track and help manage the full lifecycle of physical business assets, from planning to retirement, for industries such as healthcare, financial services, retail, manufacturing, and the public sector.

The EAM tool can enhance companies’ strategic planning capabilies as it allows easy visibility into the enterprise asset estate, the company said, adding that EAM can alo help to optimize inventory levels in order to generate maximum efficiency from existing assets.

Automating HR issue resolution

ServiceNow’s Tokyo release also offers features that focus on simplifying human resource management.

One new feature, Issue Auto Resolution for Human Resources (ITSM), is designed to help HR teams manage issues brought up by company staff by applying natural language understanding to analyze employee requests and deliver content through the same channels used by employee. These channels can be Microsoft Teams, SMS or email, the company said, adding that ITSM understands and routes any request to a specific HR representatives in case of pressing matters.

Another feature, dubbed Manager Hub, is focused on employee retention. The feature, which can be accessed via the Employee Center (desktop or mobile), provides a single window for managers within an enterprise to map employee milestones and review them.

The Manager Hub can be used by an enterprise to deliver personalized training to all managers within an enterprise, Service Now said.

Security and Sustainability

The Tokyo iteration of Now also comes with added sustainability-planning and security features.

The new release offers a feature named Vault, designed to secure business-critical ServiceNow applications by using controls such as flexible key management and data anonymization. It also allows enterprises to export their ServiceNow system and application logs at scale and in near real-time, the company said.

Another tool in Tokyo’s arsenal is the Admin Center, which allows system administrators to discover, install and configure ServiceNow tools or features through a self-service interface. Admin Center, according to the company, can take advantage of new Adoption Blueprint features, which in turn can recommend applications to administrators based on criteria such as instance maturity and application entitlements.

In order to help enterprises plan and manage their sustainability goals, the Now platform’s Tokyo release comes with an environmental, social, and governance (ESG) management tool.

The tool, according to the company, can track performance towards goals, collect and validate data for audits and create reports that aligns with major ESG reporting frameworks.

Human Resources, Supply Chain Management Software

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Without a doubt, one of the key drivers of the Fourth Industrial Revolution is Robotic Process Automation (RPA). Organizations worldwide have increasingly leveraged RPA technology and are now adopting multi-vendor strategies for a multitude of enterprise automation tools, beyond RPA. From recent conversations with the VOCAL Council, I estimate that almost 40% of the nearly 40,000 customers using RPA are deploying a multi-vendor strategy.

RPA tools have evolved from simple bots that automate single, micro tasks or activities to more complex end-to-end, unattended solutions that can automate entire processes and deliver unprecedented benefits. However, automation management is the automation that automation vendors forgot.

At the heart of RPA is the orchestrator. While orchestrators have improved and some have moved to become cloud-based, several have not been rearchitected. As a result, the design debt built over the years (with a focus on selling bots vs. managing them) has limited orchestrators to basic operational bot metrics.

The high cost of orchestrators prevents organizations from fully capitalizing on the current limited benefits, and integration challenges make it even harder to incorporate multi-vendor orchestrators into the tech stack. The swivel chair approach (input data from one system to another) that automation is supposed to eliminate is back, with precious resources swiveling to manage multiple automation vendors, extract metrics only to populate Excel and PowerPoints, meticulously and manually maintain bot schedules and reschedules, and painfully pray that bot failure may be detected early.

Lack of support, missing automation management capabilities, and inadequate/missing self-recovery are just a few of the serious challenges in managing automations.

Automation vs. orchestration: Same pod, different peas?

The easiest way to understand the automation vs. orchestration divide is in terms of “one” vs. “many”. Automation is often about automating a single repetitive task or activity within a process to run on its own and with minimal (or no) human intervention. For example, you could set up an RPA bot to automatically create IT service tickets, another to launch a web server, and a third to change a line of code in JSON files. Each of these bots would continually execute its specific task until you stop it. Repetition is the name of the game. Intelligence and logic – not so much.

Orchestration, on the other hand, is about automating multiple tasks to work seamlessly together as part of a larger workflow. The effort could involve multiple environments, devices, services, and people. And that’s why it is much more complex than a single bot for a simple task.

This complexity makes it vital to understand the many steps involved during orchestration and how these steps intersect. It also requires seamless coordination to prevent bottlenecks and ensure that enterprises can successfully derive the expected benefits from the effort, whether it’s process optimization, error-free output, accelerated innovation, improved employee experiences, or faster time-to-market.

“Process automation is not as simple as automating every task within that process, there is also a layer of orchestration and task interdependency where most judgement calls and other complexities live. Automating and managing that layer is the toughest part of the journey.”

Max Ioffe, Global Intelligent Automation Leader, WESCO Distribution

Optimization vs. orchestration

There are network optimization tools that came following the shortcomings with network orchestrators. Cloud optimization followed a similar path.  One can integrate orchestration and scheduling capability to create dynamic schedules, create task queues, initiate workflows, and monitor and track executions to self-recovering and predictive maintenance. You can also incorporate triggering events and advanced logic to automate multiple tasks within a process and ensure timely, efficient and consistent execution.

Over time, these tools can confer benefits like reduced IT costs, higher-quality output, and limited process downtime and going a bit further even provide self-sustainability. Automation promises to remove the mundane, yet automation management is laden with the manual and mundane.

With automation optimization, your precious resources don’t have to worry about mundane activities like managing, scheduling, or restarting bots. The automation optimization tool steps in and boosts your license, utilization and infrastructure efficiency while driving higher employee engagement. 

“For automation to succeed, having & sustaining optimal utilization, is key. Automation optimization tools that integrate with multiple vendors and offer a single pane of glass to manage automations will lead to a better Total Cost of Ownership and perhaps even increased RPA adoption.”

Akash Choudhary, Director Enterprise Architecture, ServiceNow

Sound without music

Benefits notwithstanding, just orchestration – which is almost always part of the overall RPA solution package – has some limitations. For one, many of these solutions are so complex and prohibitively expensive that companies with small IT budgets and teams struggle just to purchase the solution, much less leverage its benefits.

Costs do vary, depending on the instances you want to deploy and can be a TCO barrier[1]. Support is often an afterthought, which impacts the quality and timeliness of automation maintenance, change requests and incident management. The longer the response time to classify, handle and resolve incidents, the larger the amount of disruption and potentially losses for customers. Qualified support people who understand the architecture of orchestrators are limited and hence often customers face more annoying sound than music, with limited or no recommendations on how to apply appropriate solutions.

Some orchestrators don’t monitor all incidents or provide a holistic view of incidents, which is key for incident monitoring, investigations, and root cause analyses. While it is possible to automate these aspects, many orchestrators don’t provide “automation management” capabilities. Further, they don’t provide a single, centralized “incident box” that can help organizations keep track of all their automation initiatives. Under this scenario, determining the lifecycle metrics of an automation program is almost impossible.

Many customers seek a self-recovery/self-healing capability and sometimes want the orchestrator to simply restart a service that stops or becomes unresponsive. At other times, they need higher-level orchestrated workflows to automatically start up a new virtual machine (VM), check that its services start correctly, update the DNS put it into the load balancer, and even shift services to a different data center. In today’s automation environment, if a Virtual Desktop Interface (VDI) fails, it cannot automatically resolve the underlying problem, much less reboot on its own in the fastest possible time. Additionally, due to this limitation, the enterprise cannot monitor CPU or memory usage or self-clone machines to dynamically scale up capabilities to match peak requirements or scale down (“shutdown mode”) when requirements are low.

Organizations must custom-build an inventory management system for the orchestrator, which can be an arduous and resource-intensive effort. An automation management solution with a built-in or integrated inventory management tool increases the visibility into the automation ecosystem. It provides opportunities to streamline systems maintenance, improve automation efficiency, enhance output quality (lower errors), and reduce downtime.

“You need to reduce costs, streamline, and improve visibility of your RPA bots to scale your automation program. An automation optimization tool to monitor and control bots and derive tangible business, value and automation lifecycle metrics is must.”

Amol Rajamane, Global Digital Automation Leader, DuPont

The rise of automation optimization solutions

In an ideal world, automation workloads – whatever their heritage – should be able to move seamlessly between and be shared among, automation providers, wherever the optimal combination of performance, functionality cost, security, resilience etc. can be found – while avoiding the dreaded “vendor lock-in.” What if your automation can meet your demand without demanding more from you? Automation hopping, at the risk of introducing yet another term in a busy highway of terminology in automation, is not a far reality. Consumption pricing will pave the highway.

Automation optimization is a category that brings together end-to-end automation orchestration and management capabilities to cut cost, increase performance, and measure business impact – across multiple automation tools.

Several niche solutions tend to focus just on the control aspect of automation management, ie addressing portions of the orchestration limitations described above. Automation executives are often looking for a holistic, cost-efficient solution. CFOs are seeking a holistic, cost-efficient solution – an integrated, all-around athlete vs. buying multiple brands of shoes to potentially become an athlete.

An end-to-end automation optimization solution strings together the automation lifecycle: idea generation and classification, document gathering (discovery), building the bot, controlling the bots with dynamic scheduling, license/bots/utilization optimization with dynamic caching and AI-powered bot failure prediction, and deriving key value metrics. This approach provides the necessary operational (bot) metrics, value (KPI) metrics, and lifecycle (idea to value) metrics to determine and communicate the automation value to your organization.

Automation optimization solutions address the shortcomings of existing orchestrators but also offer the ultimate outcome challenging and shaping the automation industry today: improving adoption and scale.

A few solutions have emerged in recent years that deliver all these advantages, address the limitations of older platforms, and allow organizations to easily add or remove bots to match their evolving automation needs.

“The cost of bot license, infrastructure and automation management creates an significant dent in the technology budget. To drive customer centricity, automation technology vendors are better served helping their customers improve their existing license, infrastructure, scheduling utilization rather than pushing more bots. The rise of automation optimization solutions is evidence that there is a major gap in the market.”

Ankit Thakkar, Automation & Finance Digitization Leader, Thermofisher

The future of orchestration and optimization solutions

As automation and orchestration technologies develop, many more cutting-edge solutions will emerge for different enterprise use cases. The best automation optimization solutions will allow organizations to capture all the benefits of large-scale automation at the lowest possible TCO. A solution, for example, like Turbotic (disclosure: I sit on Turbotic’s Board) speeds up bot development by allocating development tasks and approval steps appropriately and systematically. It also clearly shows the entire automation opportunity, all the way from ideation to implementation in a single flow. Further, the solution automatically creates an automation business case, boosts bottom-up pipeline generation, and supports value tracking and monitoring to bring greater transparency into the automation ecosystem.

The best automation optimization solutions also provide visually compelling dashboards and real-time metrics to measure these benefits and assess the true value of the automation initiative across the enterprise. In addition, they will effortlessly integrate with existing systems to minimize disruptions and deliver all the advantages promised by enterprise-wide automation.

Advanced orchestrator solutions enable organizations to implement hyperautomation with support for cutting-edge technologies like AI, ML, and cognitive NLP. These “predictive” orchestrators leverage data and learning to better orchestrate all automation using improved predictions and decision support. These capabilities enable enterprises to optimize their automation licenses and resources in order to optimize costs, throughput, compliance, and ROI, and eliminate the chances of costly SLA breaches.

Today’s companies are operating in a highly challenging business landscape with the great resignation, quiet quitting, lack of qualified automation talent, and more. Using orchestration alone, with its current limitations, is not enough.

Orchestrate or optimize? I say both.

[1] Orchestration is often an upfront cost and impacts ROI until one has a critical mass of process automations and bots in production.

Robotic Process Automation

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As organizations brace for challenging economic conditions, they will need to be strategic and flexible on where they spend their resources to maintain business resilience. Proactive intelligence and automation tools will be essential as organizations enter “survival mode,” focusing on sustaining growth and efficiency. More importantly, organizations should ensure that even with a limited workforce and tightened budgets, the value and services they deliver to customers aren’t impacted.

However, monitoring and maintaining the myriad of infrastructure and application platforms that support business services is difficult when only using traditional methods. Investing in a solution that automatically and securely collects, aggregates, and analyzes data can enable teams with proactive intelligence to help organizations achieve quick time to value and be more productive.

With proactive intelligence, businesses can get ahead of potential issues and reduce both downtime and time to resolution so teams can focus on key priorities that maintain critical operations. This has a critical impact on businesses: one hour of IT downtime can often exceed one million to over five million dollars for mid-size and enterprise companies according to ITIC’s 2021 Hourly Cost of Downtime Survey. In addition, strategic investments in automation can help teams proactively identify and prevent problems while increasing security, reliability, and productivity. Rather than spending time firefighting, teams can focus on tasks that bring value to the business.

Automated Issue Avoidance

Between the move to the cloud, remote work, and the accelerated adoption of new technologies – IT complexity continues to grow with workforce attention already spread thin. Solutions that enable proactive intelligence services can help reduce pressure on IT teams by helping identify the problematic issues that cause downtime. Through AI/ML, more quickly through automated collection and analysis of product usage data. These capabilities provide a more effective mechanism for identifying potential problems, guiding how to remediate, and ultimately avoiding challenging service requests.

A large part of the support process today is dedicated to identifying the problem and determining its underlying cause. Without proactive support tools, companies are leaving value on the table. Expecting the unexpected in your IT environment means your business is solving problems that are broken – not just symptoms of problems – and avoiding issues before they occur.

Automate Common Workflows with APIs

APIs (Application Programming Interfaces) can be a powerful tool in automating common support workflows. APIs are a highly technical yet important aspect of a business’s underlying IT infrastructure – they are integral to bridging systems and enabling seamless transfer of information and connectivity. APIs enable different systems, applications, and platforms to connect and share data with one another and perform varied types of functions. Without APIs, enterprise tools and their benefits could become siloed – resulting in a reduced bottom line.

As organizations scale their environments, APIs are key to improving the developer experience as they facilitate collaboration and reusability. A better developer experience means better DevSecOps productivity which translates into immediate business value. Creating a software development culture that optimizes the developer experience allows teams to invest less time in internal processes and more time in creating something valuable for their customers. By automating common tasks and eliminating manual intervention, APIs can help organizations foster better developer productivity while significantly reducing costs.

Improved Productivity

The process of identifying a problem, determining its root cause, and troubleshooting can be time-consuming, and requiring the customer administrator to communicate and contextualize information for every support request logged further adds to this time. Proactive intelligence capabilities can help arm customers with holistic visibility into their environment fostering a faster, smarter, and easier way to maintain a healthy and productive environment. Intelligence tools like VMware Skyline can help to empower teams with the insights to solve issues on their own, and enable those organizations to move from reactive, fire-fighting mode to a proactive, predictive, and prescriptive posture.

When enterprises have tools that empower proactive responses and automate issue resolution, teams can increase productivity and dedicate more time to other business priorities. In addition, by improving overall security posture and environmental health, businesses can realize performance improvements to translate to greater operational efficiencies.

Succeeding in today’s business environment requires innovative approaches that lead to greater business operational agility. Break/Fix support is not enough anymore to monitor and support the extensive infrastructures enterprises have today that can span on-premises, remote sites, and the cloud.

Proactive intelligence and machine learning tools allow organizations to embrace an automated approach for troubleshooting, pinpointing root/cause analysis, guiding remediation and when needed an improved support experience that translates to more productivity for teams and better visibility into systems.

To learn more, visit us here.

Build Automation, IT Leadership

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A modern, agile IT infrastructure has become the critical enabler for success, allowing organizations to unlock the potential of new technologies such as AI, analytics, and automation. Yet modernization journeys are often bumpy; IT leaders must overcome barriers such as resistance to change, management complexity, high costs, and talent shortages.

Those successful in their modernization endeavors can expect significant business gains. In Ampol’s case, the transport fuels provider enjoyed enhanced operational efficiency, business agility, and maximized service uptimes.

A vision for transformation, hampered by legacy

Ampol had a clear goal: intelligent operations for improved service reliability, increased agility, and reduced cost. To achieve this, Ampol created a vision centered on “uplifting and modernizing existing cloud environment and practices,” according to Lindsay Hoare, Ampol’s Head of Technology.

This meant having enterprise-wide visibility and environment transparency for real-time updates, modernizing its environment management capabilities with cloud-based and cloud-ready tools, building the right capabilities and skillsets for the cloud, redesigning the current infrastructure into a cloud-first one, and leveraging automation for enhanced operations.  

While Ampol had most workloads in the cloud, it is still highly dependent on its data center. This meant added complexity to infrastructure networking and management, which in turn drove up maintenance and management costs. The need for human intervention across the environment further increased the risk of error and resultant downtime. Its ambition to enable automation across the entire enterprise, at that point in time, felt unattainable as it lacked the technical expertise and capabilities to do so.

Realizing its ambitions with the right partner

Ampol knew it was not able to modernize its enterprise and bridge the ambition gap alone. It then turned to Accenture. “We needed a partner with a cloud mindset, one that could cover the technological breadth at which Ampol operates,” said Hoare. “Hence why we turned to Accenture, with whom we’ve built a strong partnership that has spanned over a decade.”

Accenture has been helping Ampol in its digital transformation journey across many aspects of its IT operations and as such has a deep understanding of Ampol’s automation ambitions.

“We brought to the table our AIOps capability that leverages automation, analytics, and AI for intelligent operations. Through our ongoing work with Ampol, we were able to accelerate cloud adoption alongside automation implementation, reducing implementation and deployment time,” said Duncan Eadie, Accenture’s Managing Director of Cloud, Infra, and Engineering for AAPAC.

Reaping business benefits through intelligent operations

Through its collaboration with Accenture, Ampol was able to realize its vision for intelligent operations which then translates to business benefits.

Visualization and monitoring

Ampol can now quickly pinpoint incidents to reduce the time to resolve. Recently, a device failure impacted Ampol’s retail network and service stations, but a map-based visualization of the network allowed engineers to identify the device and switch over to the secondary within the hour: an 85% improvement in downtime reduction.

Self-healing capabilities

Intelligent operations not only detect failures but also attempt to resolve them independently and create incidents for human intervention only when basic resolution is unsuccessful. As a result, Ampol’s network incidents have been reduced by 40% while business-impacting retail incidents are down by half.

Automating mundane tasks

Automation now regularly takes care of mundane and routine tasks such as patching, updates, virtual machine builds, and software installs. This frees up employees’ time that is otherwise spent on maintenance, enabling them to innovate and add real business value through working on more strategic assignments and business growth.

Future-proofing

As Ampol focuses on the global energy transition, it is investing in new energy solutions in a highly dynamic environment. A cloud-first infrastructure removes complexity, increases the levels of abstraction, and offers greater leverage of platform services, enabling agility and responsiveness. The right architecture and security zoning facilitate critical business-led experimentation and innovation to ensure Ampol continues to place at the front of the pack.

As IT infrastructure becomes a critical enabler across industries, organizations are compelled to embrace modernization. While significant roadblocks exist, a clear vision and the right partner can help overcome challenges and unlock the potential of the cloud, AI and analytics, and automation, to be a true game-changer.

“This is a long journey,” says Hoare, “we’ve been at it for years now… It needs drive and tenacity. But when you get there, you’ll be in a great place.”

Learn more about getting started with a modern infrastructure here.

Cloud Management, Digital Transformation

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April 5, 2022

Source: Jon M. Quigley | Value Transformation LLC

There is no silver bullet.

Cost of Manufacturing

Businesses are constantly under cost pressures, this includes manufacturing.  Material and labor cost can be a significant cost in the product.  We use value engineering techniques to ensure that material costs are as low as we can while still meeting the performance and quality objectives of the customer and the organization.  Besides the labor costs, automation can improve quality.  I shake when I hear people use the words always and never.  People are not that repeatable; automation can lead to a very repeatable outcome if done appropriately.

Automation is usually not an all or nothing proposition.  It is possible to automate some of our manufacturing line and still require people in support of the manufacturing line.  The level of investment to automate balanced against the level of human talent available to keep the manufacturing line working.  Up front investment of capital, juxtaposed against hourly rate to work the line, and both often exist. That is where things can become difficult.  We may believe that automation is the savior for reducing the associated labor costs, but unless the line is fully automated, we will still have people working the line.  This can present some challenges.

Modern lines are not only automated, but can also perform the measurements, not just on some of the parts, but all the parts and automatically.  This reduces the cognitive and physical load upon the team members. This is perhaps a good thing, but does not help when it comes to engaging the team.  I have heard managers say just this, and is one of the reasons for writing.

Team Engagement

Have you ever been part of a team? I do not mean what most refer to as team, but those rare occurrences when we are thoroughly engaged and more importantly, so too our team mates.  If you have had these rare moments, you understand the power of team engagement.  A business makes progress not through technology, but through the creativity and talent of the team members.  It takes these things to determine the best approach to automation.  No problem here.  However, what happens to the level of engagement for those remaining on the manufacturing line?  I have been interested in employee engagement for years now, a good place to view employee engagement over the years, is the Gallup Workplace Engagement.  I have been following this poll for many years, and some of this is quite scary.  Their poll breaks down into the three categories below[1]. 

Engaged – work with passion and emotionally attached to organization. Generating new ideas and consistent performance to move the organization forward.
Not engaged – put their time in, but no passion or energy into their work.
Actively disengaged – unhappy and resentful, they spread negativity within the organization.

Lack of engagement limits the available creativity and talent; this is not the way to continuous improvement or competitive advantage. 

Team members do not have the same demand for their attention on the manufacturing line as those lines that are largely manual.  This reduction of focus on the manufacturing line should be a good thing in that it provides time for our team to explore other improvements and creative ideas.  Yet in discussions with managers of manufacturing lines we find that keeping the team engaged to be difficult.  Reduced stress due to a high degree of focus on the line, should make it possible to improve the organization at large.  It is in everybody’s best interest to find ways to engage our team members beyond the focus of the automated manufacturing line.

What can we do?

It is not in our organizations best interest and longevity to have unmotivated talent.  The level of automation of the manufacturing line may place limits the amount of team engagement maintaining a manufacturing line.  If the line is not extensively automated, we may be able to improve it through the creativity and talent of our team, engagement.  If we can improve the line, we can use a host of techniques and develop a culture of continuous improvement constant learning.  If the line automation is significant but not entirely, we can focus the talent supporting the line on other areas of the organization.

Operations improvement (logistics)
Process improvement
Quality improvement
Value Stream Mapping
Value Engineering (or other product / process cost improvement)

The approach need not be any of these, or any other formal regiment. The point is to find a motivating objective, and develop an environment that will not only allow, but value team exploration and learning.  In fact, there are good arguments for moderating the formalism, making easier for the team to explore alternatives.  If increased engagement can be obtained through some home-grown approach that will be the best solution.

Conclusion

Competition does not end because we have automated our manufacturing line.  True competitors are constantly striving to improving.  Developing a line that balances the capital investment along with the labor burden at time t=0 (start of manufacturing) does not necessarily mean the end improvements.  This is a fact to which suppliers to the automotive Original Equipment Manufacturing (OEMs) are all too aware.  From experience, OEMs go to their tier 1 suppliers and “request” cost reductions annually.  Also from experience, these decreases in sale price are often contractual obligations.  Even when the production runs are 10 or more years long with little or no additional investments post launch.  Continuous improvement does not end when we have automated the line.  Creativity does not come from automation, but from our team and continuous exploration.  The on-going concern is best served not through an over reliance on automation, but the talent, creativity, constant learning, and engagement of the team members.  Not apathy.

Jon M. Quigley PMP (204278) CTFL is a principal and founding member of Value Transformation, a product development (from idea to product retirement) and cost improvement organization established in 2009. Jon has an Engineering Degree from the University of North Carolina at Charlotte, and two master’s Degrees from the City University of Seattle as well as two globally recognized certifications. Jon has more than thirty years of product development and manufacturing experience, ranging from embedded hardware and software through verification and process and project management. Jon has won awards such as the Volvo-3P Technical Award in 2005 going on to win the 2006 Volvo Technology Award.

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