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Life Sciences Real Estate in the Time of COVID-19

life sciences

Life Sciences Real Estate in the Time of COVID-19

Increased funding plus employees that need an office makes the Life Sciences real estate sector resilient in a global pandemic.

The life sciences industry has become one of the most talked-about sectors as the entire world races to find a vaccine for COVID-19. In the first six months of 2020, investors have spent more than $16 billion on life sciences, while the National Institutes of Health (NIH) continues to increase its grants. In 1994, NIH gave out $11 billion in grants, and by the end of 2019, that number jumped to $39.1 billion – fueled by COVID-19-related therapeutics, antibody tests, and vaccines. Additionally, the aging U.S. population needing life-sustaining care, wellness-conscious millennials, and a prescription drug market on track to reach $1 trillion by 2022 has also played a part.

In an effort to continue research, development, and production, life sciences companies, owners, and operators of laboratories and office space are fast-tracking the use of current and new technologies.

The Importance of Technology

Over the years, technology has improved the R&D landscape of life sciences by significantly reducing costs. Connections between tech and biotech are creating more targeted drug development, replacing the previous time-consuming theories. Nowadays, interaction simulations can be run at the click of a button, and clinical trials can be done quicker and cheaper through technology efficiencies. Artificial Intelligence (AI) has become so valuable in finding links in the ever-growing global data resources. Also, it has created more platforms and business opportunities for biotech companies to utilize.

These days, many work-from-home policies are hard to apply to the work done in labs. So, life sciences companies have relied on scheduling and remote communication tech to coordinate calendars for on-site employees to conduct activities that cannot be done at home. Calendar tools with features that allow all employees access to real-time scheduling software have also become more widespread. And some companies have even sped up the integration of cloud-based platforms into ongoing research. This movement toward remote research tools has been inspired by the pandemic, allowing researchers to analyze data from home and focus during their time in the lab.

Market Applications

The pandemic has forced pharmaceutical companies to confront new challenges to traditional methods when conducting clinical trials. Many life sciences companies have had to ramp up fast, integrate virtual engagement into their clinical trial protocols, all while using telehealth technologies to connect with trial participants more widely than ever before.

In fact, in March 2020, the U.S. Food and Drug Administration issued guidance bolstering clinical trial sponsors to “evaluate whether alternative methods for safety assessments (phone contact, virtual visit, alternative location for the assessment…) could be implemented if necessary.  Some industry experts this transition to more tech-focused engagement would have taken many more years without the momentum ignited by the pandemic.

Additionally, COVID-19 has had a monumental impact on how technology is used in today’s drug development and drug applications. Many life sciences companies are increasing the use of AI in the search for a vaccine and identifying existing drugs that may be repurposed for therapeutic solutions. AI can make data collection and analysis so much more efficient in clinical trials and can be used to synthesize data too fast to determine drug candidates’ safety and efficacy.

Who’s Investing?

Today, with a significant focus on health and wellness, life science companies expand with large investments from financial and corporate venture capital groups. As a result, investment capital is surging into the life science market. The U.S. is the leader for investment by a lot, with China right behind it, having had some large investment rounds. While the life science market is healthy, other industries are in distress. As we all know, retail is in trouble, and corporate offices are struggling, especially in the wake of COVID-19. With the increased telecommuting, the future of the office sector is uncertain. So, life sciences have become a focus in the real estate industry, making it attractive to investors looking for an opportunity.

There is a lot of VC money being invested in life sciences, so these companies are well-capitalized. This sector has traditionally weathered economic challenges well. Think tech crash of the early 2000s and the Great Recession as examples. Since life science companies like to invest in their premises and stay long term, rents are higher, making life sciences a really attractive investment opportunity right now. Rents are continuing to increase, with sustained growth in most areas, and that growth has been consistent over time, making for a smart investment.

Key Issues to Watch For

To manage the impact of the COVID-19 pandemic, owners and managers of properties that house life sciences offices, manufacturing, and laboratory space have been able to apply many of the pandemic-related solutions that they have used elsewhere. Given that many labs are typically single-tenant buildings, landlords can cater to unique concerns. However, life sciences tenants can be less experienced than others, presenting landlords and property managers with an opportunity to add value by providing tenants with advice on the solutions they have seen work effectively across the buildings they own and manage, such as sanitization and touchless technologies.

Long term, some see the pandemic and corresponding focus on the design and repositioning of spaces for tenants as a continuing driver toward developing healthy buildings. The users of life science office and lab space are more than likely to be some of the most highly-educated consumers of real estate in any market. For them, the management of space in a sustainable way has become an expectation instead of a plus. Moving forward, competitive advantage will be the integration of health and wellness facilities and technologies as we enter the post-COVID-19 “new normal.”

Where Do We Go From Here?

Looking into the future, as the life sciences boom continues in the real estate industry, owners and operators must be aware of how they and their tenants can harness the right technology to address the obstacles from the pandemic. To successfully market spaces to biotech, pharmaceutical, and medical device companies, real estate developers must be mindful of this challenge, as their users are likely to be more tech-savvy than the average real estate consumer. On the other hand, owners of older spaces hoping to reposition them as an office or lab space must convince potential tenants to integrate innovative technologies as effectively as developers of new modern spaces.

As real estate owners, investors, and operators move into the post-COVID-19 world with a focus on life sciences, they will need to demonstrate to the market that they have a keen understanding of current issues and solutions applicable to life sciences tenants and how the right technology can solve them.

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Louis Lehot is the founder of L2 Counsel. Louis is a corporate, securities, and M & A lawyer, and he helps his clients, whether they be public or private companies, financial sponsors, venture capitalists, investors or investment banks, in forming, financing, governing, buying and selling companies. He is formerly the co-managing partner of DLA Piper’s Silicon Valley office and co-chair of its leading venture capital and emerging growth company team. 

L2 Counsel, P.C. is an elite boutique law firm based in Silicon Valley designed to serve entrepreneurs, innovative companies and investors with sound legal strategies and solutions. 

expenses

Expenses and Company Culture in the New Normal

A clearly documented corporate expense policy should eliminate any confusion about what employees can and cannot submit for reimbursement. In the context of the current pandemic, travel and meetings have substantially decreased, while other categories that support employees who are working from home (such as home internet and home office equipment) have increased. This shift gives companies the opportunity to create expense programs that go well beyond travel and expense, and shape the broader company culture moving forward.

Traditionally, companies have thought of their expense program as “travel and entertainment” programs, and indeed that was the bulk – but not all – of employee expenses. However, if your expense policy only focuses on how the company can save as much money as possible, it’s not enough. A corporate expense policy is an effective way to communicate how employers value their employees’ time and happiness.

When designed with a clear direction in mind, your expense policy can strengthen your organization’s values and avoid unnecessary anxiety and mistrust, resulting in higher employee job satisfaction and productivity.


The spectrum of expense policy enforcement

Expense policies at organizations can range from very strict to very lax. An overly-strict expense policy may require manager approval on each expense and refuse reimbursement on anything out of policy, no matter how trivial the dollar amount.

On the other hand, some expense policies are extremely lax. Netflix, the streaming giant, is an example of an expense policy written in a high-trust environment that reflects the company culture. Their company expense policy is only five words, “Act in Netflix’s best interests.” They expect their employees to spend the company money thoughtfully as if it were their own. After implementing this policy, Netflix found it actually saved money on employee expenses. Employees spent company money extremely carefully because of Netflix’s high-performance environment. Also, by letting employees book their own travel without using travel agencies, they found better deals on flights and hotels.

Although this worked well for Netflix, depending on your company culture, an unclear expense policy may result in bad behavior. Palantir, a Silicon Valley decacorn valued at $20B, came under scrutiny after reports of engineers expensing lavish meals at the office, including lobster tails and sashimi, dubbed by media outlets as “Palantir Entitlement Syndrome.” Under Armour was criticized for “being run like a frat house” after it was revealed that executives regularly expensed strip club visits, gambling, and limousines.


Design the expense policy that’s right for your company culture

A carefully crafted expense policy can help reinforce company values and commitment to employees, giving companies a competitive advantage. For example, Starbucks offers 100% tuition coverage for its employees, promising to reimburse any out-of-pocket tuition costs its employees accrue at the end of the semester. Genentech, the San Francisco-based biotechnology company, offers a range of perks ranging from tuition assistance to counseling and legal advice. Other companies reward their employees for spending money wisely – for example, if an employee usually selects the lowest airfare cost, they’ll be rewarded with a free upgrade on a future flight.

Expense policies can also be critical for attracting and retaining talent. LinkedIn, headquartered in highly competitive Silicon Valley, has very generous benefits, including education reimbursement, donation matching, student loan repayments, house cleaning, and personal trainers.

Employee perks in the “new normal”

These types of perks are even more critical to employee happiness in today’s environment, where the majority of office employees are working remotely for the foreseeable future. Google has already announced that it will allow employees to work from home through June 2021. Some tech companies such as Twitter and Square have announced that their employees can work from home permanently if they choose to.

This huge change in the way we work has forced companies to rethink company perks. There’s been a dramatic shift due to the pandemic, and most previous company policies are irrelevant now that employees are working from home. As an organization, how do you make sure your policy is resilient to the changing climate?

Companies that usually bolstered morale with happy hours and catered lunches now need to rethink the needs of their employees at home. Some companies are offering food delivery services to their employees via services like GrubHub and DoorDash to replace the catered meals in the office. Facebook gave a $1,000 stipend to each employee to use at their discretion. Slack is offering childcare reimbursement to employees with children, who are now juggling working full-time with their kids at home. Many companies are allowing their employees to expense keyboards, monitors, desks, chairs, and office equipment to build their home offices. Salesforce is giving an extra six weeks of paid vacation for employees with children, to acknowledge the struggle of having to work from home full time while also caring for their children.

Another important consideration is tracking these new types of expenses. With artificial intelligence solutions, companies have better visibility into where employees are spending. Is there a sudden, unexplained spike in Starbucks or food delivery expenses that doesn’t reflect your policy? AI can give you near real-time and up-to-date information on T&E trends so you can make accurate, timely decisions and update your policy where needed.

Conclusion

Setting clear expectations and guidelines around expenses is critical for fostering a healthy company culture. Expense policies around what can and cannot be expensed is reflective of company culture as a whole. Building an atmosphere of trust, transparency, and efficiency around expense reports helps contribute to a similar atmosphere throughout your organization. To learn more, check out our webinar or download our whitepaper.

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Anant Kale is Co-Founder and CEO of AppZen the leader in AI software for finance teams to automate manual finance processes, reduce expenditures, and gain real-time insights into their business spend trends.

Temperature sensor

Temperature Sensor Market is Projected to Reach USD 9 Billion by 2026

According to a recent study from market research firm Global Market Insights, The temperature sensor market is projected to garner noteworthy gains on account of surging application across diverse electronic appliances. The product is widely used in microwaves, air conditioners, battery chargers, and refrigerators. Since the introduction of AI and IoT, consumer electronics firms have started to combine these technologies with temperature sensors to improve their product’s efficiency and usefulness by gathering real-time temperature data.

Temperature sensors are witnessing massive traction across the oil & gas sector as they play a vital part in operations like extraction, production, refining, and distribution of oil, gases, and petrochemicals. This has compelled temperature sensor manufacturers to launch new product lineups. Citing an instance, earlier in October 2018, Crowcon released a high-temperature sensor that detects the levels of H2S across oil & gas applications in the Middle East.

According to a study conducted by Global Market Insights, Inc., the temperature sensor market could cross USD 9 billion by 2026.

To meet the high market demand, temperature sensor manufacturers are working towards developing novel technologies to meet the changing demands of the consumers as well as to achieve a competitive edge. For example, in 2018, Crowcon introduced a high- temperature sensor that allows the detection of H2S in the oil and gas sector in the Middle East.

Rising instances of digitalization in the manufacturing industry across Germany is one of the major factors supporting regional industrial development. Companies present in the region are working towards using advanced automation processes to enhance their manufacturing and improve competitiveness against other countries such as China. Consistent industrial advancement in Europe will bolster temperature sensor requirements.

Over the years, the automotive industry has registered notable gains due to the growing demand for different types of vehicles as a result of the improved lifestyle of people in both developed as well as developing regions. Thermistor contact sensors in the engine control units as well as exhausts are widely used in vehicles.

These sensors offer high-temperature sensing capacities with a cost-effective, robust design system. The recent emergence of e-vehicles due to the need to control environment degradation has supported the demand for temperature sensors all the more.

The prevailing coronavirus pandemic has magnified the demand for temperature sensor market due to the increasing need to regularly check the body temperature of the patients. Non-contact temperature sensors in particular have gained enormous growth opportunities in the healthcare industry owing to the growing use of infrared technology in temperature monitoring systems used towards the diagnosis of COVID-19.

Many medical device companies are working towards developing contact-less, reliable temperature checking machines. For instance, in August 2020, CORE- a venture of greenTEG AG- a Switzerland based engineering firm developed a wearable device that constantly measures the core body temperature on the go. A small temperature sensor market, about the size of 1.5 dominoes can be mounted in various ways.

Key Companies covered in the temperature sensor market are ABLIC Inc, Amphenol Advanced Sensors, ams AG, Analog Devices, Inc, Dwyer Instruments, Inc, Emerson Electric Co, Hans Turck GmbH & Co. KG, Honeywell International Inc, ifm electronic gmbh, Kongsberg Maritime, Littelfuse, Inc, Maxim Integrated, Microchip Technology Inc, Murata Manufacturing Co., Ltd, NXP Semiconductors, OMEGA Engineering, On Semiconductor, Pyromation, ROHM CO., LTD, Sensata Technologies, Inc, Sensirion AG Switzerland, Siemens, STMicroelectronics, TE Connectivity, Texas Instruments Incorporated, TOREX SEMICONDUCTOR LTD, Vishay.

Source: https://www.gminsights.com/pressrelease/temperature-sensors-market

cloud

5 Strategies to Reduce Cloud Cost

After initial migration to the cloud, companies often discover that their infrastructure costs are surprisingly high. No matter how good the initial planning and cost estimation process was, the final costs almost always come in above expectations.

On-demand provisioning of cloud resources can be used to save money, but initially, it contributes to increased infrastructure usage due to the ease and speed at which the resources can be provisioned. But companies shouldn’t be discouraged by that. And infrastructure teams shouldn’t use it as a reason to tighten security policies or take flexibility back from the engineering teams. There are ways to achieve both high flexibility and low cost but it requires experience, the right tooling, and small changes to the development process and company culture.

In this article, we present five strategies that we use to help companies reduce their cloud costs and effectively plan for cloud migration.

Lightweight CICD

In one of our recent articles we discussed how companies can migrate to microservices but often forget to refactor the release process. The monolithic release process can lead to bloated integration environments. Unfortunately, after being starved for test environments in the data center, teams often overcompensate when migrating to the cloud by provisioning too many environments. The ease with which it can be done in the cloud makes the situation even worse.

Unfortunately, a high number of non-production environments don’t even help with increasing speed to market. Instead, it can lead to a longer and more brittle release process, even if all parts of the process are automated.

If you notice that your non-production infrastructure costs are getting high, you may be able to reduce your total cloud costs by implementing a lightweight continuous delivery process. To implement it, the key changes would include:

-Shifting testing to the level of individual microservices or applications in isolation. If designed right, the majority of defects can be found at the service-level testing. Proper implementation of stubs and test data would ensure high test coverage.

-Reducing the number of integration testing environments, including functional integration, performance integration, user acceptance, and staging.

-Embracing service mesh and smart routing between applications and microservices. The service mesh can allow multiple logical “environments” to safely exist within the perimeter of production environments and allows testing of services in the “dark launch” mode directly in production.

-Onboarding modern continuous delivery tooling such as Harness.io to streamline the CICD pipeline, implement safe dark launches in the production environment, and enable controlled and monitored canary releases.

See our previous article that goes into more detail on the subject.

Application modernization: containers, serverless, and cloud-native stack

The lift and shift strategy of cloud migration is becoming less and less popular but only a few companies choose to do deep application modernization and migrate their workloads to containers or serverless computing. Deploying applications directly on VMs is a viable approach, which can align with immutable infrastructure, infrastructure-as-code, and lightweight CICD requirements. For some applications, including many stateful components, it is the only reliable choice. However, VM-based deployment brings infrastructure overheads.

Resource (memory, CPU) overhead of container clusters may be less for 30% or more due to denser packing, larger machines and asynchronous workload scavenging unused capacity.

Containers improve resource (memory, CPU) utilization for approximately 30% compared to VM-based workloads because of denser packing and larger machines. Asynchronous jobs further improve efficiency by scavenging unused capacity.

The good news is that container platforms have matured significantly over the last few years. Most cloud providers support Kubernetes as a service with Amazon EKS, Google GKE, and Azure AKS. With only rare exceptions of sine packaged legacy applications or non-standard technology stacks, the Kubernetes-based platform can support most application workloads and satisfy enterprise requirements.

Whether to host stateful components such as databases, caches, and message queues in containers is still open for choice but even migrating stateless applications will reduce infrastructure costs. In case stateful components are not hosted in container platforms, cloud services such as Amazon RDS, Amazon DynamoDB, Amazon Kinesis, Google Cloud SQL, Google Spanner, Google Pub/Sub, Azure SQL, Azure CosmosDB, and many others can be used. We have recently published an article comparing a subset of cloud databases and EDWs.

More advanced modernization can include migration to serverless deployments with Amazon Lambdas, Google Cloud Functions, or Azure Functions. Modern cloud container runtimes like Google Cloud Run or AWS Fargate offer a middle ground between opinionated serverless platforms and regular Kubernetes infrastructure. Depending on the use case, they can also contribute to infrastructure cost savings. As an added benefit, usage of cloud services reduces human costs associated with provisioning, configuration, and maintenance.

Reactive and proactive scalability

There are two types of scalability that companies can implement to improve the utilization of cloud resources and reduce cloud costs: reactive auto-scaling and predictive AI-based scaling. Reactive autoscaling is the easiest to implement, but only works for stateless applications that don’t require long start-up and warm-up times. Since it is based on run-time metrics, it doesn’t handle well sudden bursts of traffic. In this case, either too many instances can be provisioned when they are not needed, or new instances can be provisioned too late, and customers will experience degraded performance. Applications that are configured for auto-scaling should be designed and implemented to start and warm up quickly.

Predictive scaling works for all types of applications including databases, other stateful components, and applications that take a long time to boot and warm up. Predictive scaling relies on AI and machine learning that analyzes past traffic, performance, and utilization and provides predictions on the required infrastructure footprint to handle upcoming surges or slow downs in traffic.

In our past implementations, we found that most applications have well-defined daily, weekly, and annual usage patterns. It applies to both customer-facing and internal applications but works best for customer applications due to natural fluctuations in how customers engage with companies. In more advanced cases, internal promotions and sales data can be used to predict future demand and traffic patterns.

A word of caution should be added about scalability, regarding both auto-scaling and predictive scaling. Most cloud providers provide discounts for stable continuous usage of CPU capacity or other cloud resources. If scalability can’t provide better savings than cloud discounts, it doesn’t have to be implemented.

On-demand and low-priority workloads

To take advantage of both dynamic scalability and cloud discounts for continued usage of resources, a company can implement on-demand provisioning of low-priority workloads. Such workloads can include in-depth testing, batch analytics, reporting, etc. For example, even with lightweight CICD, a company would still need to perform service-level testing or integration testing, in test or production environments. The CICD process can be designed in such a way that heavy testing will be aligned with the low production traffic. For customer-facing applications, it would often correspond to the night time. Most cloud providers allow discounts for continued usage even when a VM is taken down and then reprovisioned with a different workload, so a company would not need to sacrifice flexibility in deployments and reusing existing provisioning and deployment automation.

The important aspect of on-demand provisioning of environments is to destroy them as soon as they are not needed. Our experience shows that engineers often forget to shut down environments when they don’t need them. To avoid reliance on people, we implement shutdown either as a part of a continuous delivery pipeline and implement an environment leasing system. In the latter case, each newly created on-demand environment will get a lease and if an owner doesn’t explicitly renew the lease it will get destroyed when the lease expires. Separate monitoring processes and garbage collection of cloud resources are also often needed to ensure that every unused resource will get destroyed.

An additional cost-saving measure that we effectively used in several client implementations is usage of deeply discounted cloud resources that are provided with limited SLA guarantees. Examples of such resources are spot (AWS) or preemptible (GCP) VM instances. They represent unused capacity that are a few times cheaper than regular VM instances. Such instances can be used for build-test automation and various batch jobs that are not sensitive to restarts.

Monitoring 360

The famous maxim that you can’t manage what you can’t measure applies to cloud costs as well. When it comes to monitoring of cloud infrastructure, an obvious choice is to use cloud tools. To make the most out of cost monitoring, cloud resources have to be organized in the right way to be able to measure costs by:

-Department

-Team

-Application or microservice

-Environment

-Change

While the first points might be obvious, the last one might require additional clarification. In modern continuous delivery implementations, nearly every commit to source code repository triggers continuous integration and continuous delivery pipeline, which in turn provisions cloud infrastructure for test environments. This means that every change has an associated infrastructure cost, which should be measured and optimized. We have written more extensively about measuring change-level metrics and KPIs in the Continuous Delivery Blueprint book.

Multiple techniques exist to properly measure cloud infrastructure costs:

-Organizing cloud projects by departments, teams, or applications, and associating the cost and billing of such projects with department or team budgets.

-Tagging cloud resources with department, team, application, environment, or change tags.

-Using tools, including cloud cost analysis and optimization tools, or tools such as Harness.io, which provides continuous efficiency features to measure, report, and optimize infrastructure costs.

With the proper cost monitoring and the right tooling, the company should be able to get a proper understanding of inefficiencies and apply one of the cost optimization techniques we have outlined above.

Conclusion

Cloud migration is a challenging endeavor for any organization. While it’s important to estimate cloud infrastructure costs in advance, the companies shouldn’t be discouraged when they start getting higher invoices than originally expected. The first priority should be to get the applications running and avoid disruption to the business. The company can then use the strategies outlined above to optimize the cloud infrastructure footprint and reduce cloud costs. Grid Dynamics has helped numerous Fortune-1000 companies optimize cloud costs during and after the initial phases of cloud migration. Feel free to reach out to us if you have any questions or if you need help optimizing your cloud infrastructure footprint.

manufacturing industry

AI is Transforming the Manufacturing Industry: Pros and Cons

The expansion of the global economy continuously triggers the use of new technologies across sectors. There’s no doubt that the manufacturing industry headlines the application of artificial intelligence technology. From product design, production, supply chain, and logistics, manufacturers are using AI software.

The use of these AI analytics and data has helped improve product quality and efficiency. It has also improved the safety of employees and delivery processes.

However, the AI-powered industrial revolution is not without criticisms. Thus in this post, we’ll consider the pros and cons of AI in transforming the manufacturing industry.

Pros of AI transformation of the Manufacturing Industry 

Generally, AI’s beneficial to the various aspects of manufacturing and product distribution. Here are the positives of artificial intelligence:

Predictive Analytics for Increased Production Output

AI manufacturing systems make use of predictive analytics and machine learning algorithms. Since the manufacturing sectors have a large volume of data, the AI predictive analytics is powered from this data. Data are kept in the cloud for analysis and monitoring of any process or equipment disruption.

With this predictive setup, companies can now easily apply a predict-and-fix maintenance model. The guesswork regarding what is wrong with the equipment or process is eliminated. Rather than stop the whole production to detect-and-fix the problem, AI predictions pinpoint anomalies more quickly. It likewise suggests tools and solutions to correct the problem.

Furthermore, manufacturers can also sync production schedules to enhance production output. A report from Mickensey says that an AI predictive maintenance model can increase productivity by 20%. And it can decrease maintenance costs by as much as 10%

Better Generative-Design Process

Another AI advantage is that manufacturers can create better ways of designing their products. With generative-designs, the designer can input product details. Such details include the type of material, appropriate production methods, budget, and time. The designer is also able to input all possible constraints. Using an AI algorithm, the details can be processed to meet a list of possible product options.

The appropriate solution is then tested to suit manufacturing conditions. What makes the generative-design stand out is it eliminating human bias design options. And then it proposes more suitable performance demands.

Improved Process Quality 

Artificial intelligence technology enables a more innovative production process and better product quality. It ensures that products meet the required quality standards and regulations. Manufacturers can achieve this by using equipment that operates with AI technologies like ML and big data.

For instance, tracking sensors could be used in logistics and haulage. It will help to monitor location, take stock, freight charges, and more. According to reports, automation of inventory improves process services by 16%. At the same time, inventory turnovers are likewise increased by 25%.

Such inventory data is used to check for any impending faults that may affect the product delivery service. Thus the company can attain a higher level of specialization. It also eliminates process downtime and increases productivity.

Ever-changing Market Adaptability 

Besides production, there are other significant aspects of manufacturing where AI is pivotal. These include distribution and supply chains, monitoring, customer behavior, and change patterns. Therefore, AI in manufacturing ensures that companies can predict possible market changes. With this, they can go ahead to strategize towards better production and other cost management processes.

Additionally, manufacturers can use AI algorithms to estimate market demands. Such estimates are possible because AI uses the information gathered from different sources. Such as consumer behavior, inventory of raw materials, and other manufacturing processes.

Optimizing Supply Change

When AI technology is adopted in the supply chain process, there’s transparency and increased data. It’s used to enhance manufacturing processes and customer service further. Data from multiple devices are collected and analyzed in real-time to get a more in-depth insight like a possible challenge. Manufacturers are then able to make informed industry-related decisions. AI helps minimize cost and time that may be incurred on warehousing and shipping in the event of any mishap.

AI tools and solutions also help schedule factory activities, demand and supply gaps, and avoid over or under production. Mckinsey estimates that AI technology-based supply chain management enables businesses to cut down forecasting errors by 20-50%.

 

 

 

Furthermore, AI chatbots enable taking care of client inquiries using human-type interactions. In turn, it helps to free up human resources. Such technology allows manufacturers to address clients’ requests and enquires quickly. For instance, a custom writing review service like Online Writers Rating may need to go through thousands of papers daily. And, at the same time, they’ll have to address customer inquiries. But with chatbots, AI provides the necessary customer support, while employees focus on the papers.

Cons of AI in the Manufacturing Industry

As earlier stated, AI in manufacturing is not without criticisms. These are contained in the following artificial intelligence cons:

It’s on the Pricey Side

Artificial intelligence implementation and maintenance costs are on the high side. The budget is one that is often too pricey for small companies and start-ups. Although AI cuts manufacturing labor costs, it still requires installation and maintenance fees. You also don’t want any cyberattacks on your systems, so you’ll also need to consider the cost of cyber threat protection.

Scarcity of Experts and Skills Persons 

Because AI tech is a continually evolving field, thus AI experts with the requisite skills are usually few. Since these tools need regular sophisticated programming, it’s essential to consider expert availability. And also, because they are in high demand, the cost of employing such hands will be on the high side.

Open to Vulnerabilities

Another artificial intelligence con is its vulnerability to cyber-attacks. A recent World Economic Forum report shows that cyber-attacks are among the top five global stability risks. Such information can be pretty scary for any manufacturer using AI software. As AI becomes powerful and wide-spread, cybercriminals are working hard to device new hacking methods. One minor breach can disrupt or fully shutdown a manufacturing business.

Conclusion

AI goes a long way in sustaining your manufacturing business, even amid constant change. It provides predictive analysis that can help manufacturers make more informed decisions. From the product design down to customer management, there’re several positives of artificial intelligence. These include an improvement in process quality, optimized supply chain, adaptability, etc.

However, AI technology isn’t without its cons. Such as expensive budgets and vulnerability to cyber-attacks. Yet the pros of AI outweigh these cons. Therefore, the manufacturing industry can only improve by leveraging AI applications.

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Frank Hamilton has been working as an editor at essay review service Best Writers Online. He is a professional writing expert in such topics as blogging, digital marketing and self-education. He also loves traveling and speaks Spanish, French, German and English.

data security

How Can Organizations Ensure Data Security

The cyber-security scene is advancing at a fast-paced rate and concurrently, advances in technologies are progressively becoming better at aiding cyber-criminals and hackers to take advantage of data security loopholes. The continuously growing scale of breaches and cyber-security attacks should be a major concern for all organizations. An example of such attacks is the WannaCry, a massive malware attack that affected over 150 countries, including the UK, Germany, India, and Japan. Considering all the sensitive data that organizations store online, including financial documents and customers’ private details, it’s evident that one breach could have a huge negative impact on their businesses. Here are a few measures organizations can take to ensure data security.

1. Protect the IT Infrastructure

Organizations need a secure and established IT framework to build a solid foundation for a healthy data security plan. As such, they should keep an eye on every component, including devices and systems. They should ensure all the computers and smart devices are adequately protected against advanced cyber-attacks and malicious hacks.

The IT team must ensure all systems are updated with the most recent operating systems and reliable anti-virus solutions. They must also put a configured firewall in place to ward off external attacks and unauthorized access on the network. NordVPN can be a great data protection tool, especially when browsing the Internet. By encrypting data, this VPN establishes an additional layer of security that keeps your browsing activity, financial information, and emails invisible to hackers.

2. Perform Comprehensive and Regular Audits

Data security measures can never be complete without thorough and regular audits. A regular audit is a practical approach that enables businesses to identify vulnerabilities in the existing security plan. Auditing data collected in the post-attack offers an organization a perfect understanding of the blunders that can result in similar breaches in the future.

This information can be instrumental in the creation of a more powerful data security strategy coupled with more reliable data security policies. So, businesses must perform comprehensive and regular audits to enhance compliance and get rid of potential risks.

3. Limit Data Access

Most companies give a few employees privileged access to their most valuable data. Consider who in the company has access to important customer information. Do you know everyone’s access rights? Knowing the details of every staff that has privilege access to data and reasons for accessing it can help you prevent data hacking, theft, and loss.

Organizations must limit data access. They should determine the kind of data that a staff member needs to access to carry out their work obligations effectively and make sure they have access to just what they require. In addition to safeguarding sensitive information from theft or loss, limiting access could ensure more efficient data management.

4. Remove Stale Information and Put Secure Backups in Place

Many companies in the healthcare, education, and finance sector handle sensitive data as an important part of their businesses. Having the right data disposal strategies in place can prevent redundant data from being stashed away and lifted at a later date.

Regular data backup is a fundamental part of a complete IT security strategy. Organizations should have robust backups in place to ensure they still have access to their sensitive information even after accidental file removal or a full ransomware lockdown. They should store their backup data in a safe, remote location far from their main places of business.

5. Change Your Mindset

Many organizations don’t give data security the seriousness it deserves. They have poor passwords, unencrypted sensitive files, and misconfigured AWS servers. Due to this sloppy attitude, it’s estimated that more than 4 billion data records with valuable information were breached within the first six months of last year.

Companies must change their attitude. They must view data security as their top priority. Everyone in the company must understand the value of data security, not just the top executives. They should embrace security best practices such as authenticating digital identities of all employees and customers as well as using up to date VPNs like the NordVPN.

The Parting Note

With cyber-security threats increasing rapidly in today’s world, it has become important to be armed with the right security tools and privacy improvements that are required to protect the organization’s most valuable asset, that is, the data. Data security should be given utmost priority and all staff members trained accordingly.

AI

AI and Cryptocurrency – How They Can Work Together Effectively

There will soon come a time when artificial intelligence will be running on top of cryptocurrency systems like Blockchains with its capability to increase machine learning capacity and create new financial products. It will take the technology leaps and bounds further in making it one of the mainstream emerging technologies.

According to the research being conducted about the future of AI, the market is estimated to grow to a whopping $190 billion worth of industry by 2025. Considering how much the market is expected to grow, Blockchain and AI convergence are inevitable.

Both the emerging technologies have been around for a decade now and deal with data and value. Where Blockchain enables a secure storage and sharing path of data, AI analyzes and generates significant insights from data to create value.

Having such similarities, there is no doubt that both the technological realms can be merged to create a more advanced and efficient machine learning blockchain system to benefit the masses. Let’s have a look at how Blockchain and AI are a perfect match.

How Blockchain and AI Is the Perfect Match

The following are some key pointers and examples that evidently showcase how combining Blockchain and AI is a consequent step forward in the right direction for increased efficiency and profitability.

Blockchain connecting with the AI basics

Firstly, it is essential to know that most of the hype surrounding startups integrating Blockchain with artificial intelligence is exactly just that, hype. Such companies are far too young and inexperienced in the industry to be talking about a big game. With few clients and less commercialization, it is understandably not possible to carry out such advance convergence.

The majority of such companies have raised money through the initial coin offering or the ICO. This means that the solutions they offer are as thoroughly evaluated as they would have been had the company raised a significant amount of venture capital money.

However, it is quite so possible that these companies may become successful in the future, but until then, they just create useless hype about the advancements in this technology.

Many people limit the usage of Blockchain technology and associate it with just cryptocurrency transactions. As a digital ledger that can record economic transactions, Blockchain can be expanded to virtually record almost anything of value.

There can be both public and private blockchains. Where the public ones are open to the public, the private or ‘Permissioned’ blockchains are restricted for usage by ‘invitation-only’ and mainly used in the corporate environment. This also makes them faster than public forums as the users are mainly trusted and verified personnel making the transactions verifiable faster.

One of Blockchain’s more important features is that it allows even the unrelated parties to carry out a transaction and share data through a mutual ledger. As cryptography validates the transactions, it makes it more efficient for participants not to rely on third-party evaluators to carry out a transaction. Deploying cryptography ensures that data transactions are secure, incorruptible, and irreversible once recorded.

Artificial Intelligence is not a term making rounds for a decade now. It very much comprises of every new technology that has near-human intelligence to carry out a task. AI models are used to assess, understand, classify, and predict using relevant data sets. Machine learning then cleanses the data as it gathers insights creating better useful data sets for use.

As it is evident, data is the central component to AI and Blockchain that allows a secure and collaborative effort towards data sharing. Both Blockchain and AI ensure the trustworthiness of data and extract valuable insights from it.

How Microsoft is Improving Machine Learning for Blockchain

According to the research conducted at Microsoft, the company is working on finding out ways to design efficient collaborative machine learning models hosted on public blockchains. The incentive behind this effort is to make AI decentralized and a more collaborative forum using Blockchain.

While there is no doubt that advances are being made in machine learning, the benefits that are being created as the results of these efforts are not as openly available. The masses have limited resources and cannot always access cutting-edge technology such as machine learning systems.

Such systems are highly centralized and used as the proprietary datasets. Not only are they costly to recreate, but even the best models can become outdated if not consistently refreshed with new data.

The idea is to allow advanced AI models and bigger datasets to be easily accessible, sharable, updated and retrained to increase the adoption, acceptance, and overall effectiveness of AI. People will soon be able to adopt this easy and cost-effective method to run and access advanced machine learning models through regular devices such as laptops and smartphone browsers and collectively participate in improving data sets and models.

Therefore, Microsoft is keen on developing what they call a Decentralized & Collaborative AI on the Blockchain framework. It will significantly increase AI community collaborations to retrain such models with valuable datasets on public blockchains. The machine learning models would be made free for public use as they would know the code they are interacting with.

Some applications that Microsoft is looking forward to integrating are virtual assistants and recommender systems like used by Netflix to recommend shows to its audience. Considering such models, Blockchain makes sense because of the increased security and how trustworthy it is for the participants.

The well-established nature of the blockchain system and the associate smart contracts ensure that the models will always perform up to the specific requirement. As the models are consistently updated on the Blockchain used unhinged by the user’s local device, every user gets to see the one genuine version of the model.

Hence, even though Microsoft’s framework isn’t favorable for operating at large scale for now, but sooner or later, it will be the norm. There is little to no doubt that organizations like Microsoft are doing advanced research and practical work to converge AI and cryptosystems like Blockchain. There is no doubt that cryptocurrency is the future of money. So it is in the best interests of the organization to start working on merging Blockchain and AI for improved benefits.

How can an organization merge Blockchain and AI?

Just as Microsoft, more advancement is made for combining Blockchain and AI for fulfilling specific usage requirements. Such cases will depend on the company’s specific needs, but the core preference would be related to data. This will allow companies to improve their digital and data capabilities by developing a combination of AI and Blockchain solution to fit their operations.

The very first step needs to be taken by the executives to identify the specific business needs and whether creating an AI and Blockchain system would address that need. This can become easier if the organization has already worked on AI and taken initiatives in other operations because now you can integrate Blockchain to improve them.

Similarly, if the company owns valuable data, they can monetize by converging a blockchain environment and sharing the data with AI model creators. For instance, a progressive car company like Tesla probably has a good collection of valuable data collected by its cars. They can put it on a blockchain system as their self-driving cars will continue to collect huge amounts of data that they can use to improve the neural networks powering self-driving operations and functions.

With a trusted name as Tesla, the public would not be too complacent about maintaining their privacy. Blockchain would allow the company to make the driver information anonymous to ensure privacy while collecting data to improve neural nets in use.

The company can even share anonymous data with car insurance companies. It would allow the insurers to price their insurance packages for self-driving cars more efficiently and with an educated mind, given how the risk profile of a self-driving car is different from that of a regular car.

The whole packaged win-win situation here is that where the company would improve its cars, the public would get advanced transportation, complete privacy, and the right insurance for the right price without getting exploited.

Using Digital Investment Assets for Trading through Blockchain

You must be already aware of how Blockchain is already a ready-made, and good-to-go digital ledger used to store and trade financial instruments such as cryptocurrencies and cryptographic tokens. However, Blockchain is still a nascent technology, been only around for a few years. Where cryptocurrency has definitely taken the world by storm, cryptographic tokens are comparatively more nascent.

Hence, it is evident that there is no probable activity and enough data yet to apply AI to financial products like a cryptocurrency that are traded through Blockchain. However, the upgrading technology and data sets show a promising future for AI taking insights from these data sets to create financial products and trade them autonomously.

How can an organization merge Blockchain and AI?

The convergence of artificial intelligence and Blockchain would be a huge step forward, and the process will cover four distinct yet inter-linked stages.

Stage I: Proof of concepts

Stage II: Asset tokenization

Stage III: Digital Investment Assets DIA

Stage IV: AI agents trading DIA

The four stages will represent how Blockchain is proof of concepts initially. On the second stage, assets are tokenized and traded. Tokens can represent underlying security methods, physical assets, cash flows, and utilities. This reduces the alleged transaction cost and decreases the time taken for settlement to improve audit accountability.

AI and Blockchain Applications

There is no denying that a decade back if someone would have presented us with an idea of magical internet money called crypto in the future, we would have laughed and made fun of the person for coming up with Superman and Kryptonite theories. Fast forward to ten years down the line, and cryptocurrency not only exists, but there are real-world integrations of its blockchain system with AI.

Smart computing power

Think of a machine learning code that would upgrade and retrain when given the right data. That is exactly what AI affords the users to tackle tasks more efficiently and intelligently.

Diverse data sets

The combination of Blockchain and AI can create smarter and decentralized networks to host various data sets. Creating a blockchain API would enable the intercommunication of AI agents resulting in diverse codes and algorithms to be built upon diver data sets, ensuring development.

Data protection

It doesn’t matter if data is medical or financial. Certain data types are too sensitive to be handled by a single company and their coding system. Storing such data on a blockchain and accessed through AI would give its users a huge advantage of personalized recommendations, suggestions, and notifications while securely storing data.

Data monetization

Data monetization would make both AI and advance Blockchain easily accessible to smaller companies. As of now, developing and growing AI is costly for organizations, especially those who do not own data sets. A decentralized market would create space such companies for which it is otherwise too expensive.

Trusting AI for decision making

AI is growing smarter with time. Through the use of blockchain systems like crypto, transactions will become smarter, making the process easier to audit.

Conclusion

All in all, the collaborative effort of blockchain technology and AI is still majorly an undiscovered territory. One of the main reasons why we still have yet to see a commercialized joint adoption of the Blockchain system and artificial intelligence is that the upscale implementation of their convergence is quite challenging.

Many businesses, although having ventured on with AI, are skeptical when it comes to conjoining Blockchain. They are in their early stages for testing the waters for AI and Blockchain coming together in isolation. As they continue to figure it out for appropriate public distribution, the convergence of the two technologies has had its fair share of scholarly attention as well. Yet still, projects solely developed to promote the groundbreaking match are still primarily not catered to.

There is no doubt that the potential of this combination is clearly there and developing, but how it will play out for future public use can be anybody’s call.

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Claudia Jeffrey is currently working as a Junior Finance advisor at Crowd Writer, an excellent platform to get assignment help UK. She is a self-proclaimed crypto-influencer. She has gained significant expertise and knowledge in this regard over the years and likes to share it with an interested audience.

risk

Why COVID-19 is a Galvanizing Moment for Eliminating Physical and Digital Supply Chain Risk

When the COVID-19 pandemic began, the resulting economic fallout was felt across borders and industries alike. From manufacturing to financial services, every industry has been scrambling to minimize the impact of the pandemic on the bottom line. For many businesses, this has helped serve as an urgent wake-up call to take proactive steps to identify and eliminate risk across their global supply chains, which typically span several tiers of suppliers dispersed across the world. Real-time supply chain risk visibility plays a critical role in avoiding business disruptions.

The Economic Risk

There is an immense economic risk that needs to be considered when a business operates a global supply chain. At the start of the pandemic, we witnessed the inevitable ripple effects across not just multiple industries but also across multiple different tiers of suppliers. For example, 3.74% of sub-tier suppliers in the Department of Defense’s ecosystem closed as a result of the pandemic. 75% of small businesses have reported that they have only enough cash in hand for 2 months or less. As suppliers struggle or go out of business, significant supply chain disruptions are common.

This instability coupled with the multitude of other economic crises facing the world, such as ongoing trade friction with China, could precipitate a fundamental collapse of global business as we know it. We must monitor our supply chains for more points of exposure to risks than ever before.

The Data Security Risk

With computer hacking having increased 330% since the start of the pandemic, global businesses also need to account for the cybersecurity risks involved with having a supply chain across multiple countries and potentially hundreds or thousands of suppliers. The data systems of global suppliers are a potential entry point to a brand’s or government agency’s data systems, presenting a major challenge across the global supply chain. Organizations must be able to assess and continuously monitor the strength of supplier data security measures and the changing cybersecurity-related risk associated with their suppliers.

Even after the pandemic subsides, the need for real-time risk monitoring in the extended digital supply chain will persist, especially as cybersecurity attacks grow in sophistication.

New Technology for Physical and Digital Supply Chain Risk Management

When it comes to monitoring risk associated with multiple tiers of suppliers, the majority of businesses are still way behind. According to Gartner, only 27% of companies perform ongoing third-party monitoring and only 2% directly monitor their 4th and 5th party suppliers. Although companies know they’re vulnerable to disruption by a sub-tier supplier, not enough are being directed or given the tools to actively monitor them effectively.

Historically, the majority of businesses attempt to identify, assess and manage supply chain risk manually and only periodically. This is because, previously, automation technology focused on making sense of large amounts of extended supply chain ecosystem data has not been up to the task. Much has changed. The global machine learning market was valued at just $1.58B in 2017 and is now expected to reach $20.83B in 2024, growing at a CAGR of 44.06%. New AI and machine learning-based technology is emerging rapidly and changing the game. This new technology can immediately illuminate risks across all tiers of a global supply chain because data on tens of millions of suppliers is continuously monitored from both a physical and digital supply chain perspective and across numerous risk factors.

Incorporating AI-powered solutions into your supply chain risk management strategy can automate the identification of risks that exist deep within a supply chain. In addition, adopting this technology ensures that an organization has continuous, real-time information to inform ongoing risk management efforts and identify problems before they threaten the business.

There is no way to know when the pandemic and its resulting implications will cease. Or when and where the next global event will happen. Looking ahead, successful businesses will be ready to continue functioning in a safe and secure way regardless of what issues they face. Supply chain-related blind spots and resulting disruptions can pose major complications for organizations that aren’t able to effectively identify and map risk. COVID-19 has driven a greater sense of urgency to shore up these problems. New technology for automated, continuous monitoring of supply chains end-to-end presents a new path toward operational resilience, business continuity, and overall health.

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Jennifer Bisceglie is the CEO of Interos, the first and only business relationship intelligence platform to protect enterprise ecosystems from financial, operations, governance, geographic, and cyber risk in every tier of enterprise supply chains, continuously.

vehicles

How Artificial Intelligence is Driving the Memory Market for Autonomous and Connected Vehicles

One of the important technologies that have emerged over the past few is that of artificial intelligence (AI). The technology is being utilized in various industries for making processes and operations simpler. Just like other industries, AI is also being widely utilized in the automotive industry for making vehicles safer and more secure. The technology is being utilized in infotainment systems that are now serving as personal assistants, aiding the driver by offering efficient navigational support, and responding to voice commands. This increasing utilization of AI is creating wide data storage capacity.

Autonomous and connected cars are generating large amounts of data, since they are extensively making use of electronic functions for providing greater efficiency, greater safety, driver assist capabilities, richer telemetric and entertainment functions, and communication between local networks and vehicles. Owing to these factors, the global memory market for autonomous and connected vehicles generated a revenue of $4,310.8 million in 2019 and is predicted to advance at a 23.9% CAGR during the forecast period (2020–2030), as per a report by P&S Intelligence. The major applications of the memory market in the automotive industry are telematics, navigation, and infotainment.

Out of these, the largest amount of data was generated by navigation features in the past, which can majorly be attributed to the surging adoption of these systems in vehicles. Navigation systems generate data related to alternative routes, shortest route, and traffic or checkpoints on the road, and need efficient storage mechanism. Apart from this, the telematics application is also predicted to make create demand for data storage capacity in the coming years, which is particularly because of the increasing preference for autonomous and connected vehicles. The system captures data via sensors, radars, and cameras.

Different types of memories in the automotive industry are NOT-AND (NAND) flash, dynamic random-access memory (DRAM), and static random-access memory (SRAM). Among all these, the demand for DRAM has been the highest up till now, owing to their effective storage of data and relatively low cost. Both commercial and passenger vehicles generate data, thereby creating a need for memory; however, the largest demand for memory was created by passenger cars in the past. This is because of the fact that passenger vehicles are produced more than commercial vehicles. Furthermore, new technologies are first implemented in passenger vehicles for testing purposes in the automotive industry.

In the past, North America emerged as the largest memory market for autonomous and connected vehicles, and the situation is predicted to be the same in the coming years as well. This can be ascribed to the presence of a large number of automotive technology companies and increasing sales of connected and autonomous vehicles in the region. Moreover, the disposable income in people in North America is high as well, owing to which, they are able to spend more on luxury vehicles that are equipped with advanced, connectivity, safety, and autonomous features.

Hence, the demand for memory in autonomous and connected vehicles is growing due to the increasing demand for safety features in vehicles.

Source: P&S Intelligence

innovation

Remote Innovation Is More Than Possible: Six Tips From a Tech and Digital Revolutionary

A few years ago, Centric Consulting team member Carmen Fontana launched her first Artificial Intelligence project. The goal? Craft machine learning to predict and manage human resources conundrums, such as project staffing. The initiative involved a new-to-Carmen technology, a dual-shore team and a healthy dose of ambiguity. We funded her anyway.

Carmen was participating in Centric’s newly minted innovation incubator which allows any employee to conceive and share product and process improvement ideas. Her idea was stellar, even if the roadmap was sketchy at best.

Carmen thought if companies like Netflix, Amazon and Spotify could observe, record and learn user behavior, allowing them to continually fine-tune their recommendation algorithms far beyond the scope of a traditional Boolean (and/or) statement, then HR could do the same with staffing.

Although much about this innovation journey may sound familiar — from the ambiguity of methods to the lofty (but vision-packed) goals — there’s one core element that most likely does not:

The entire project took place remotely. And we were even able to use it to guide our weekly staffing calls.

Since its inception 20 years ago, Centric has had a thriving “office-optional” workforce, which has grown from just a handful of people to more than 1,000 employees in 13 cities in the U.S. and India.

At a time when everyone is struggling to transition to remote work while innovating, we’ve won an award while doing just that. This year, we were included in Fast Company’s list of “100 Best Workplaces for Innovators.”

As we all hunker down in our separate home offices, physically apart, the stakes around innovation are only increasing. Innovation will remain a key differentiator in the market today and tomorrow. And there’s no turning back from the changes the pandemic has brought to the workplace.

Luckily, remote innovation is something that can be planned for, managed and grown, much like every other aspect of remote work. Below is our blueprint for keeping the creative wheels turning and amping up innovation when employees aren’t always working side-by-side:

Make Extemporaneous Encounters Intentional

The right collaboration tools can create the same sort of opportune encounters that Apple and Pixar champion while also facilitating remote collaboration. Microsoft Teams and Slack, for example, provide an online space for people to talk about new ideas and track progress on innovation projects.

While working on a recent Healthcare VR project, for instance we managed all of our interactions through a Microsoft Teams space — including meetings, brainstorming chats, project management and the collection of all of our teams’ output and materials.

Start a Problems-to-Be-Solved Repository

Nothing triggers innovation like having a problem you’re itching to solve. That’s where a remote repository of problems comes in handy. The more people contribute to the repository, the better: Innovation requires a lot of ideas coming in from a variety of people.

Although you do want to collect as many ideas as possible, you also want to provide some guidelines to make sure those ideas align in some way to larger company goals or to real client or industry challenges.  A repository can be a great tool for vetting which new ideas fit the bill.

A repository can also connect a firm’s natural innovators with employees who may not have an idea to offer but are strong problem-solvers and creative thinkers. Successful innovation efforts engage both types of people.

Hold Sessions Geared Towards Innovation-Generation

Whether in-person or remote, innovation-focused sessions for gathering and testing the latest thinking, ideas and problems are key. Employees usually leave these sessions energized and excited to be part of something new.

One recent example is Expedition: Data, an in-person event to encourage and develop machine learning and data science talent. Early this spring, Centric employees worked with Microsoft and RevLocal, a national digital marketing company, to come up with innovative ways to use Microsoft’s Azure Suite and other tools to improve RevLocal’s employee and customer retention. The winning team got bragging rights and $100 Amazon gift cards.

Institute A Virtual Innovation Lab

Too many organizations focus only on getting ideas, neglecting what comes next. If one of your employees has a concept they want to explore, do they know how to go about developing it?

Centric created its Virtual Innovation Lab to guide innovators as they explore their idea and see if it has legs. The lab acts as a collaboration portal and provides tools and resources for remote teams to work through the innovation lifecycle, helping them overcome major hurdles as they mature their concept and get it to the minimum viable product (MVP) stage.

Our virtual lab essentially provides a blueprint for rapid prototyping using agile development and human experience design principles, among other innovation frameworks. The goal is to help innovators quickly assess proof of concept and proof of value. This is important. If something works, that’s great, but is it feasible from an operational standpoint? Does it actually provide value to the end users or customers? Does it solve a real problem? If the answer is no to any of these questions, your innovator either needs to pivot or kill the project.

Be Deliberate About Forming Teams

Our virtual innovation process relies on agile development, which in its purest form requires teams to be together every day. So how do we get around that as a remote company? We’re very intentional about how we put teams together.

While self-forming teams can work and come together easily when you’re in an office setting, in a virtual environment, team formation needs to be more deliberate. To do this, get to know your internal network and who has what skills, capabilities and passions. Use that knowledge to build teams that will mesh well and play off one another’s strengths. The goal is to virtually replicate the relationships and collaborative spirit that happen effortlessly in an office.

Make Transparency Your Mission

As with any effort in your organization, communication plays a critical role. And in a virtual environment, it’s easy to forget to share information or see what your teammates are doing. That’s why we’ve made transparency a key focus for our virtual innovation lab.

Transparency is not only vital for networking and team building, but it’s also necessary for defining the success metrics that matter. Innovation isn’t easy — and intentionally prioritizing transparency forces learning and greater understanding. Perfection and polish are not required (at least not until the idea is commercialized). Drive the difficult conversations now, and always try to operate in the light.

Treat Failure As Additive, Not Subtractive

Many companies are failure-phobic, and in the interest of profits, many penalize employees and divisions for losing money. But innovation only succeeds through trial and error.

To innovate, you have to embrace failure and help your teams do the same. Give them the tools and the space to test new ideas or processes. Celebrate their efforts regardless of the outcome. Organize sessions – remote or in-person –  where they share stories about their failures. We have, and it has served us well.