Digital Transformation In Supply Chain Management

Digital transformation is a term that is thrown around a lot, and people have different ways to interpret what it means. Essentially, digital transformation is the integration of digital technology into all areas of a business. ARC Advisory Group, where I work, publishes an analysis of the 25 manufacturers with the most mature digital transformations. The report identifies the leaders and highlights best practices. The report takes a holistic approach to what a digital transformation means. “Leading companies take a strategic approach, integrating digital technology throughout their value chains. Design and engineering, production operations, maintenance, logistics, supply chain, business systems, customers, products, and organizational structure are subject to innovative change as companies examine and update processes and deploy new tools and technologies.”

The majority of time when people are discussing digital transformation, they are not directly referring to the digital transformation of supply chains. APQC conducts research on supply chain and logistics to help organizations assess the performance of their own processes and functions compared to their peers. Most recently, the APQC has conducted best practice and benchmarking research on digital transformation. This survey-based research gathers quantitative data as well as information on practices or performance drivers. The final report focuses on the current state of key practices in digital transformation in supply chain management, spread across multiple industries and over 1,100 respondents.

There are many areas of digital transformation within the supply chain. This report provides a cross-industry perspective on digital transformation in logistics including digital maturity in inventory management, transportation, fleet maintenance, safety and compliance, and more.

APQC Digital Transformation in Logistics Results

On average, respondents report allocating 14 percent of their logistics and warehousing annual budget to technology. Of this technology budget, an average of 30 percent is typically allocated to digital transformation. Let’s take a deeper look at a few technology areas.

Inventory Management

Respondents report a wide variety of maturity levels for digital transformation initiatives in inventory management. From a maturity standpoint, the majority of respondents are digitizing data and processes. But, it varies on how the data and processes are being used form a technology standpoint. Ninety-one percent of respondents are digitizing data and processes collectively; but, only 31 percent are using predictive analytics and 26 percent are using artificial intelligence. This means that 29 percent of respondents are digitizing data and processes but not using advanced technology to make the data more actionable. A mere 6 percent of respondents are digitizing data and processes, using predictive analytics or artificial intelligence, and using automation to act on recommendations.

Transportation

For transportation, the numbers are roughly the same as far as maturity within the digital transformation journey. Eighty-eight percent of respondents are digitizing data and processes. Predictive analytics is used significantly more than artificial intelligence to optimize, as 35 percent are using predictive analytics compared to 17 percent for artificial intelligence. For these respondents, only 3 percent are digitizing data and processes, using predictive analytics or artificial intelligence, and using automation to act on recommendations. These numbers show a significant gap around the use of advanced technologies for optimization and decision-making.

Warehouse Equipment / Facility Management

Ninety-two percent of respondents are digitizing data and processes in warehouse equipment / facility management. These numbers are line with the aforementioned technology areas for digital supply chain transformation. Thirty-one percent of respondents are using predictive analytics and 24 percent are using artificial intelligence to optimize. For these respondents, 6 percent are digitizing data and processes, using predictive analytics or artificial intelligence, and using automation to act on recommendations.

Two other issues that come in digital transformation are compliance and safety. Within these areas, respondents are at the low end of the maturity model. For both compliance and safety, nearly one-third of respondents do not have any digital transformation initiatives in place. Additionally, about one-third of respondents have only digitized data and processes in these areas. Very few respondents use artificial intelligence to optimize their data compared to those that use predictive analytics to optimize their digitized data and processes.

Final Thought

The APQC survey gives an interesting look at digital transformation in logistics. Currently, respondents are at the mid-point of the maturity model when it comes to digital transformation initiatives in inventory management, transportation, and warehouse equipment / facility management. However, these respondents are showing that their organizations are on the low end when it comes to compliance and safety. Moving forward, I expect to see more interest, more investments, and more movement in digital transformation in logistics.

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Article resource: https://www.forbes.com/sites/stevebanker/2023/04/09/digital-transformation-in-supply-chain-management/?sh=5772b2083311

Digital Transformation: The Ultimate Customer Experience Overhaul

Fifty-three percent of early digital adopters say customer experiences have become a priority for their organizations over the past 12 months. This figure rises to a whopping 93% for advanced digital adopters, according to a 2023 study conducted by Foundry, an IDG, Inc. Company. This survey highlights customer-centricity as a key approach to a successful digital revolution.

Bringing out the importance of putting the customer at the center of any digital transformation strategy, Jeff Bezos once said, “There are many ways to center a business. You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of day-one vitality.”

So how does digital transformation impact the customer experience?

By Offering A Hyperpersonalized Experience

Customers now expect brands to understand their needs and provide a more tailored experience. AI and machine learning can help make sense of the data shared by customers and allow for every customer interaction to be unique and personal.

Epsilon research (via TechCrunch) also indicated that 80% of consumers are more likely to make a purchase when brands offer personalized experiences.

Not just this, when brands get personalization right, marketing spending can deliver five to eight times the ROI and lift sales by 10% or more, according to McKinsey.

Three Ways To Implement Personalization Using AI

1. Analyze customer data: Since the standard CRM captures limited data and static buyer personas are too generic, AI uses the (vast) collected data and makes sense of it to get individual personalization right. Be it real-time location, context, behavior or values, AI works on critical customer variables and helps marketers understand what to offer, when and where.

2. Exclude data paralysis: Marketers have access to overwhelming sets of data. AI can help in this pursuit by collecting data that will continually evolve and adapt, helping marketers implement a more effective personalization strategy that delivers results.

3. Create evolving customer profiles: Using AI to create and target unique customer profiles can provide unique customer experiences. This way, each customer’s unique preferences will be catered to, leading to more successful campaigns.

By Establishing Omnichannel Functionalities

Businesses can tailor their messaging and promotions more effectively by offering a consistent customer experience across all channels.

The same is supported by a Forrester study that says that the companies with the strongest omnichannel customer engagement strategies enjoy a 10% Y-O-Y growth, a 10% increase in average order value and a 25% increase in close rates.

An omnichannel experience is made of three essential components: always available, consistent and personalized. So how can businesses provide an omnichannel experience to their customers?

Steps to Provide An Omnichannel Experience

• Define strategy and customer touchpoints: Have a clear plan in place with objectives, target audience, success metrics and adaptation. Identify customer touchpoints and pain points across all channels to upgrade the experience.

• Implement integrated technology: Invest in an integrated software architecture (omnichannel desktop, back-end interfaces and advanced analytics). This can include integrating your point-of-sale systems, customer relationship management tools and customer-facing platforms, among other tools.

• Get feedback and improvise: Employ platforms that use AI and advanced analytics to simplify heaps of customer feedback and customer-journey breakpoints. Monitor this and adjust your approach based on their needs and preferences.

By Providing Seamless Customer Support

Right from chatbots that lead to increased response time and reduced repetitive queries to decoding customer behavior and purchasing patterns, AI is the key to enabling real-time service for customer support platforms.

AI chatbots improve the customer experience by offering:

• Personalized conversation using stored data: AI chatbots can personalize their responses based on stored customer data, such as their purchase history and previous communication.

• Speedy and effective resolutions: AI chatbots can provide 24/7 support, personalized responses, faster response times, consistency, reduced errors, data collection and analysis, cost savings and increased customer satisfaction, loyalty and advocacy.

By Gathering Customer Feedback For Continuous Improvement

In today’s tech-driven world, traditional quantitative feedback-gathering methods are losing their importance. Reality demands companies leverage AI and machine learning to enhance their survey-forming and survey-analyzing efficiency.

While there are many ways to gather customer feedback via AI, the three most common approaches are as follows.

Three Ways To Gather Customer Feedback

1. Feedback tools: Use tools that empower AI to capture and analyze specialized vocabulary used by customers in answering feedback questions. The deep insights shared by AI can help marketers plan their customer satisfaction and retention strategies.

2. Automated forms: Automated forms are another effective way to gather customer feedback without disturbing the customer journey. The process can even be automated by telling the tools/AI processes when to shoot an email/SMS with the feedback form.

3. Customer surveys: While focus groups and personal interviews are still in vogue, customer surveys rolled out in bulk can work equally well. That is if AI is integrated to make sense of the answers, giving marketers insightful gleams into the customers’ needs and wants.

Conclusion

Digital transformation is key to maximizing customer experience in today's world. In the words of Mckinsey’s senior partner Tjark Freundt, “Effective customer-experience transformations require a clear vision and a customer-centric, ambitious articulation of goals.” This can be achieved with companies using AI-produced insights to see not just where customer problems are but what’s causing them and how they can be solved. And figures say that companies earning $1 billion a year earn an additional $700 million over three years by investing in customer experience.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/05/31/digital-transformation-the-ultimate-customer-experience-overhaul/?sh=2001056d4254

Low-Code/No-Code: Empowering Citizen Developers

 The demand for software has never been higher in today’s fast-changing environment. Business leaders began 2021 with concerns about talent shortages, and retaining top technology talent is likely the No. 1 priority for many global organizations. Because many technology organizations are struggling to find adequate developers with so many legacy subject matter experts retiring, the enterprise low-code application platform (LCAP) market is growing rapidly. Low code is about applying automation (visual full-stack development and deploy to any touch point) to software delivery and is a natural evolution of rising abstraction levels in application development. TechTarget defines it as "a visual software development environment that allows citizen developers to drag and drop application components, connect them together and create a mobile or web app."

6 Ways AI Transforms How We Develop Software

 AI is transforming all business functions, and software development is no exception. Not only can machine learning techniques be used to accelerate the traditional software development lifecycle (SDLC), they present a completely new paradigm for inventing technology.

Data Version Control: The Enabler Of Data Engineering Best Practices

 Data is the backbone of every business organization today, and its importance will only grow in 2023. There have been a lot of discussions lately about adopting version control practices for data. Many engineers believe that data version control is the obvious next step that would transform data pipelines from something that organizations maintain to something they engineer—just like code.

Setting KPIs For Software Development Teams As An Engineering Leader

 It's important to track, measure and assess the performance of your software development teams as an engineering leader. This way, you ensure that you'll come up with the highest quality product. This approach will help your team become more efficient and also help you generate substantial benefits in the long run.

To get the best-intended results from your engineering teams, leaders need to determine some essential KPIs to answer critical questions: How fast is your team moving? And what can you do to improve developer satisfaction and efficiency?

Key performance indicators (KPIs) are just like a map that helps you to determine how far you've come since you started. Having the right KPIs linked to your organizational goals lets you derive your progress within a specific time frame.

You might ask what key performance indicators are and how they can benefit engineering leaders. Please read on to learn every aspect of KPIs. This article will also cover setting KPIs for the software development team as an engineering leader.

What KPIs Are And How To Set Them

KPIs are a set of metrics that allow organizations and businesses to obtain qualifiable measurements over time to accomplish a specific business objective. The prime aim of key performance indicators is to drive engineering teams toward achieving goals and deliver valuable insights to make data-driven decisions for business processes.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/quickerbettertech/2022/11/10/on-crm-what-are-the-most-popular-add-ons-for-crm-applications/?sh=3487e26650f6

Setting KPIs can help engineering teams in the following ways.

• Understand what needs to be improved.

• Define a strict process to ensure consistent progress.

• Minimize the time required to complete a specific development project.

Setting KPIs For Software Development Teams

As a software engineering leader, you can set KPIs in the following ways.

1. Understanding How KPIs Will Be Used

The first step in setting KPIs is understanding how these indicators will be used to track and monitor your team's performance. Additionally, they should be clearly understood by all your team members.

Before setting key performance indicators, discuss the criteria with your teams and equip them with the right tools, knowledge and skills.

2. Linking KPIs To Your Business Goals

Software engineering leaders often adopt vague performance indicators that have no substantial impact. To get the best results, you must link KPIs to your objectives to ensure your team is on the right track.

Additionally, in order to give them the motivation to carry out their duties, your engineering team should also be aware of the organization's main objective.

3. Determining The Effectiveness Of Your Selected KPIs

The next step is identifying your KPIs as "smart" enough to deliver the best outcomes. You can use the SMART formula to check their effectiveness.

• Specific: Your selected KPIs should be focused on a specific objective to help teams develop the finest-quality product.

• Measurable: The performance indicator you select must be measurable and benchmarked against a determined standard.

• Achievable: Your selected KPIs should be well defined and achievable.

• Relevant: As mentioned earlier, your selected KPI should be relevant to your organizational goals.

• Time-Bound: Your key performance indicators should be deliverable and achievable in a set time frame.

4. Auditing KPIs

Make necessary changes depending on your customers' demands and market conditions.

KPI Vs. OKR

Both of these are agile goal-setting methodologies and are recognized for the profitability, productivity and visibility they offer to companies, but they differ from each other in certain aspects.

Objectives and key results (OKRs) help team leaders set, track and measure time-bound business goals. In contrast, KPIs are specific measures of success that allow engineering leaders to track team performance.

They differ in the following ways:

• KPIs evaluate your team's success, whereas the prime aim of OKRs is to facilitate ambitious goal setting and alignment for businesses.

• KPIs are the larger tracking areas with an extended period, while OKRs typically have quarterly cycles.

• KPIs are static performance indicators, but OKRs are meant to be malleable.

Benefits Of KPIs For Software Development Teams

Clear and well-defined key performance indicators can improve the performance of software development teams in the following ways.

Measuring Progress

Implementing the right KPIs can help engineering leaders track the performance and progress of their teams. Performance indicators also help you determine if the processes and policies are working together to improve operations.

Managing Performance

Setting key performance indicators helps teams and individuals maintain accountability and simplify communication, fostering positive performance. KPIs also encourage transparency by tracking and managing the performance of each team member.

Analyzing Trends

Implementing KPIs allows engineering leaders to identify positive and negative performance trends. It helps you spot the areas of work that require improvement rapidly.

Some KPI Examples For Engineering Leaders

As an engineering leader, you should be aware of the following examples of KPIs.

Cycle Time: Cycle time is a significant metric that indicates how quickly code goes from a developer's workstation to production. Identifying these valuable performance indicators helps engineering leaders accelerate time to market by identifying process bottlenecks.

Measuring cycle time in engineering departments can also help teams innovate faster and improve the sense of ownership.

Project Timeline: This is another valuable key performance indicator that helps you identify how work focuses and volume is modified over time. It also allows leaders to determine how their teams perform compared to market trends.

The Bottom Line

Key performance indicators are essential metrics to track, manage and analyze the performance of software development teams. As an engineering leader, you are responsible for ensuring that the KPI you select is relevant, achievable and measurable in the specific time frame.

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Article resource:https://www.forbes.com/sites/forbestechcouncil/2023/05/01/setting-kpis-for-software-development-teams-as-an-engineering-leader/?sh=111f1ac27cef

Five Common Mistakes To Avoid After CRM Go-Live

 I recently came back from an Alaskan cruise. This was a completely new experience for me since I have never been on a cruise before. With beautiful glaciers to climb, rivers to raft and wildlife to watch, Alaska impressed me with its raw, unspoiled beauty. Putting aside whether I am a cruise person or not, one thing I found interesting is that every evening when we walked back into our room, there was a brochure on the bed with everything we needed to know about the following day.

On CRM: What Are The Most Popular Add-Ons For CRM Applications?

 My company sells and implements five customer relationship management applications and we've been doing this for too long to mention here without depressing myself. We've implemented CRM systems at hundreds of businesses. Many of those also take advantage of the add-ons that their CRM vendor provides, usually through a marketplace or app store. Since most CRM systems moved to the cloud during the past ten years there's been a proliferation of add-on applications to fill the gaps in the features not provided.

So what are the most popular add-ons? Full disclosure: I didn't do this scientifically. But I can easily list out the add-ons that my clients use the most. Here are the top five in no particular order.

How To Set And Manage Key Performance Indicators For Software Engineering Teams

 Organizations use key performance indicators (KPIs) to measure their performance and progress toward specific goals. In software engineering, KPIs can measure the performance and productivity of software engineering teams. Setting and managing KPIs can be challenging for software engineering leaders, as they need to ensure that the metrics they choose are relevant, measurable and actionable.

The Role Of KPIs In Product Software Security

 Key performance indicators (KPIs) can be used in application security testing to measure the effectiveness of security testing and provide insight into the security posture of an application. Their purpose is to provide visibility into the effectiveness of an organization's application security testing program and to help identify areas for improvement. In a recent IDC survey (paywall) of mid-sized to large-sized software organizations, DevSecOps decision-makers identified the following as their top three KPIs for product security:

1. Vulnerability statistics

2. Compliance time and cost

3. Software build failures and delays

Let's consider each of these in more detail.

Transforming Cybersecurity Into A True Business Process

 Cybersecurity is an arms race of innovation. Cybercriminal gangs continually discover new and more inventive ways to breach their victims' defenses while the security industry toils to find ground-breaking ways to detect and block the attacks.

Yet despite investing in the most recent innovative technology and services, firms still fall victim to incoming threats.

In most cases, the issue is not about ideas or intentions but how security is executed and operationalized. Even when an enterprise has invested in all the right tech, it will not make much difference if the business has not invested in security effectiveness. This means ensuring the security stack is correctly integrated into the rest of the business and underpinned by the right processes and operating model.

Solving this issue requires a decentralized approach to security so that cyber risk is owned and understood by all stakeholders, executives and employees—not just the CISO and their security team.

The Critical Barriers To Cybersecurity Effectiveness

Many firms are still not measuring their security effectiveness, which means they cannot tell if their investments are having an impact. This stems from security's status as the "new kid on the block." While it has become an increasingly critical business priority and has earned a place at the boardroom table, security isn't always linked to broader business goals in the same way areas like finance and sales are.

This disconnect was less of an issue when cyber could more comfortably be considered a niche technical issue, a siloed department away from the rest of the enterprise. But today, security is a responsibility of the entire organization. The fact that the average cost of a breach now exceeds $4 million means few organizations can afford to ignore their cyber responsibilities.

Tackling this significant business risk demands a shift in mindset throughout the organization, particularly at the top. The highly complex nature of cybersecurity means non-technical executives and other stakeholders will be happy to assume that the CISO has things well in hand; however, this erroneous assumption can often lead to the rest of the organization avoiding accountability for security.

CISOs usually come from highly technical backgrounds and possess a breadth and depth of cyber knowledge—but they may not have the broader experience needed to relate this expertise to business operations. It's common to find highly knowledgeable CISOs who struggle to communicate cyber risk and put it into a business context.

Cybersecurity effectiveness hinges on understanding flowing both ways. Alongside non-technical stakeholders getting a clear picture of cyber risk, CISOs also need to recognize how security fits into the rest of the enterprise. They must be able to clearly communicate how security activity is enabling core business operations.

So how do they reach this point?

Developing Skills And Building Strategies

CISOs need to upskill and evolve. This means moving away from their traditional focus on technical enablement and toward a more simplified approach that non-technical stakeholders, company-wide, can better understand.

Making these changes requires self-reflection and honesty from CISOs about their skill sets and operating methods. They must recognize if their communication skills are bridging the gap between security and the wider business. Are they building a strategic plan that meshes with business priorities, or are they focusing on smaller, more easily solved tactical issues?

Developing a more strategic skill set will help CISOs operationalize security better. Cybersecurity is a journey that needs to map out outcomes, impact and the business' unique environment and operations.

Pursuing a tick-box approach is no longer enough. Simply putting measures in place to achieve regulatory compliance or cybersecurity certifications does not mean that these processes effectively keep the company secure.

Instead, cybersecurity effectiveness hinges on outcomes. Security must be a part of the business process, actively and measurably enabling business success. Once security is embedded in this way, all stakeholders will be able to understand security effectiveness and accountability just as quickly as they can for mainstays like sales and finance.

For example, have you considered what business functions will suffer from the biggest impact if they are affected by a breach? How does this view align with different stakeholders? If there are differing thoughts, how can they be unified and addressed?

What are your plans and preparations for an attack if you know your highest-risk assets? Do you have the processes, reporting, and communication to deal with a threat effectively and ensure long-term resilience?

A New Operating Model For Cybersecurity

Answering these questions requires an operating model that uses its technology platform to decentralize cybersecurity, turning complex data into something more digestible for stakeholders.

Because cyber risks threaten the entire business, improving security must be a company-wide responsibility. Everyone must be part of the cybersecurity process and aware of their role and responsibilities.

A top-down approach can help instill this sense of responsibility and bake security into the company culture without impacting the performance of core operations. Teams at the top, including the executive leadership team, can take accountability for each area, implementing and following the proper security measures.

Reinforcing this, the CISO must translate highly technical security issues into something the entire business can understand. This demands a robust set of KPIs for cybersecurity effectiveness, focusing on numbers that can be translated to the board and stakeholders to provide context. The right KPIs also make drawing a direct line between security targets and the wider organization’s business goals easier. This enables stakeholders to make more informed decisions about security investments.

Improving Cybersecurity Effectiveness Together

By fostering understanding and responsibility, security accountability becomes a shared concern across the company, enhancing cybersecurity effectiveness. With well-defined metrics, it's easier to evaluate processes and make necessary refinements. This enables businesses to fully utilize innovative technology, optimize their security stack, save costs and achieve optimal ROI, ultimately making cybersecurity a catalyst for success.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/04/11/transforming-cybersecurity-into-a-true-business-process/?sh=6af03b37454c

How To Make Sure That Your Product Is Ready For Software Development

Bringing any digital product into being is a complex process. Nevertheless, as software developers, we often meet clients who believe that properly written code can solve any problem and is the Holy Grail of building a product. The truth is; however, that code itself lies on the foundation of thorough preparations and includes a lot of stages and processes.

There are situations when a client approaches us after a failed attempt to build their application with other developers. Those other developers may convince customers that the proposal is perfectly complete, but in the end, they turn out to be unable to deliver the expected results exactly because they don't have a full statement of work.

The Importance Of User Experience Design

Based on hundreds of meetings, I find that one of the most overlooked parts of software development is user experience design. Sometimes, when we start talking about it with clients, they do not take it seriously since they believe that these conversations should be held with designers, not with software developers.

As a result of neglect of UX, I often meet clients who have only the idea of an application with a couple of vaguely described features. They see no more than 20% to 30% of their future product and believe it to be enough to start developing it.

UX is an important part of our process. Before writing code, developers need to know what exactly they are working on. I think it's particularly important to base solutions on specifications defined during user research and by the product team.

So, what needs to be done from the point of view of the design team prior to starting the code production per se? There are a few important prerequisites that can be combined into a sort of checklist.

Specify Target Audiences

It's next to impossible to develop a high-quality, sought-after product without knowing who is going to use it. Every function of your product should solve some problem a user has or help them with achieving their goals. It's necessary to define crucial features and extract non-functional requirements based on key user needs.

You can turn to the persona method (putting yourself in the shoes of your users) in order to systemize what you know about the target audience. It requires building personas for each user type of your product according to preliminary conducted user research. A persona usually consists of approximate age and income, location, lifestyle and goals that they want to achieve by using the product.

Defining your users helps with your empathy and understanding toward them. For instance, if we assume that your target audience lives in the U.S., they are very likely to use a two- to three-year-old iPhone. Does this impact what solutions you should employ while developing software? Yes, it does.

Define User Needs

When you know who you are developing your product for, you can better and more broadly understand what they may need from it. I like to use the user story method to describe functional requirements. User stories are a universal language that helps to efficiently convey to everyone on the team—from analysts to coders—what the product should do.

You can use the following format to craft a user story: "As a [role description], I want [capability], so that [received benefit]." In one sentence, it explains who your target audience is, what they should be able to do with your app and which benefits it should give them.

Describe MVP

Now that you understand what exact user needs your app is going to address, you can precisely describe the minimal viable product. The description should be short and clear, like an elevator pitch, and include a definition of the customers, the value of your solution and how it differs from competitors.

Make sure to also mention expected business outcomes and how you are going to measure them. In the end, define criteria for assessment of implementation correctness. Developers need to understand what results are expected from their work and be able to imagine the final product.

Make And Test Prototypes

When the key functions and target platform for your app are defined, it's time to convert them into simple black, gray and white wireframes linked together into a clickable prototype covering all user stories. The prototype is necessary to evaluate your ideas about the product and validate its functionality through usability testing.

It's much cheaper and faster to make sure that everything works properly during the prototype stage rather than when the app is released. During this stage, the team can identify all sorts of issues, from misleading button labels to holes in user flows.

As a result, you end up with the assurance that the product is going to work well and there will be no need to redevelop it again later. Besides, it's much easier to explain to software developers what you expect from them with a prototype. It saves a lot of time and money. When prototyping is completed, usually the stage of visual design starts, which defines how the interface of the product is going to look.

There's one more thing that you should know in order to successfully design your product. Before starting the UX process, it's crucial to determine prerequisites and your company's definition of complete for each step of this journey. To avoid any mess, you need to know what exactly is necessary to kickstart each phase and what outcomes you should get at the end of them.

It doesn't matter if you have your own developers team or you are outsourcing it; without this preliminary UX design work, I have found that no software development proposal can be as efficient as it needs to be and deliver optimal results.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource:https://www.forbes.com/sites/forbesbusinesscouncil/2023/04/11/how-to-make-sure-that-your-product-is-ready-for-software-development/?sh=232066b74dd3

The Role Of KPIs In Product Software Security

 Key performance indicators (KPIs) can be used in application security testing to measure the effectiveness of security testing and provide insight into the security posture of an application. Their purpose is to provide visibility into the effectiveness of an organization's application security testing program and to help identify areas for improvement. In a recent IDC survey (paywall) of mid-sized to large-sized software organizations, DevSecOps decision-makers identified the following as their top three KPIs for product security:

1. Vulnerability statistics

2. Compliance time and cost

3. Software build failures and delays

Let's consider each of these in more detail.

Rethinking Enterprise Software: Three Features You Should Look For In New Enterprise Applications

 Enterprise technology investments are among the most important infrastructure investments for startups, mid-size companies and large organizations alike. These tools may be used by every employee in the organization and, in many cases, will have a role in integrating and orchestrating myriad other task-specific tools. They're often the platforms upon which all other IT infrastructure is built.

Why Decision Intelligence Is The Next Digital Transformation

 Decision intelligence (DI) is how people make business decisions, regardless of their role or industry. It bridges the gap between analytics-focused data and AI platforms.

4 Steps To Simplify Your Technology Investments In 2023

 In a time when companies are trying to do more with less, there’s one thing that’s not lacking: technology. In fact, businesses have many choices when it comes to deciding what tools and solutions to invest in. It’s not likely to get any easier—there are more than 30,000 SaaS offerings available, and the global enterprise software market is expected to grow at an 11.1% CAGR to $404 billion by 2028.

On CRM: Do You Really Need A CRM Consultant? Really?

 Do you need an outside consultant to help you implement a CRM system for your business? The answer is: it depends.

How Corporate Leaders Can Help Software Developers Reach Their Full Potential

There’s an image many have of software developers—they sit in a dark room and code endlessly. But unfortunately, more often than not, developers don’t have the leeway to code in an efficient, uninterrupted manner.

How Machine Learning Will Transform Your Industry

 Machine learning is a rapidly growing field with endless potential applications. In the next few years, we will see machine learning transform many industries, including manufacturing, retail and healthcare.

In manufacturing, machine learning can be used for quality control, automation and customization. For example, machine learning can be used to detect defects in products before they reach consumers. It can also be used to automate repetitive tasks such as assembly line work. And finally, manufacturers will increasingly use machine learning to customize products for individual consumers.

In retail, machine learning can be used for data analysis to help businesses make better decisions about inventory and pricing. Personalization will become more common, with retailers using machine learning to recommend products to customers based on their past behavior. Robotics will also become more prevalent, with machine learning being used to automate tasks such as shelf stocking and order picking.

In healthcare, machine learning can be used for diagnostics, treatment and prevention. For example, machine learning can be used to diagnose diseases earlier and more accurately. It can also be used to develop personalized treatments based on a patient's characteristics. Machine learning can also be used for preventative care, such as identifying risk factors for disease and providing tailored recommendations for healthy living.

So far we have only scratched the surface of what is possible with machine learning. As technology continues to evolve, we will see even more amazing applications of this transformative technology.

Machine Learning In Manufacturing

In the past, quality control for manufactured goods was a time-consuming and expensive process that required human inspectors to examine each item for defects. However, machine learning can be used to automate this process by training algorithms to identify defects from images or other data sources. This can help reduce the cost of quality control while also increasing the accuracy of the inspection process.

Automation

Machine learning can also be used to automate manufacturing processes. For example, robots that are equipped with machine learning algorithms can be trained to perform tasks such as welding or fabricating parts. This can lead to a more efficient manufacturing process and can free up human workers for other tasks.

Customization

Another way that machine learning is transforming manufacturing is by enabling customization at scale. In the past, it was difficult and expensive to create customized products due to the need for manual labor and individualized production lines. However, machine learning algorithms can now be used to automatically generate custom designs based on customer specifications. This allows manufacturers to quickly and easily produce personalized products without incurring significant additional costs.

Machine Learning In Retail

In the past, retailers have relied on data from customer surveys and transactions to make decisions about their business. However, this data is often incomplete and doesn't provide a full picture of customer behavior. Machine learning can help solve this problem by analyzing large data sets to identify patterns and trends. This information can be used to improve customer service, optimize stock levels and make other strategic decisions.

Personalization

Machine learning can also be used to personalize the shopping experience for customers. For example, Amazon uses machine learning to recommend products that customers may be interested in based on their previous purchase history. This helps shoppers find what they're looking for more quickly and makes the overall shopping experience more enjoyable.

Robotics

Robots are increasingly used in retail settings to perform shelf stocking and order fulfillment tasks. While these machines cannot replace human workers completely, they can free up employees' time to focus on more critical tasks, such as helping customers. In the future, robots may become even more involved in the retail sector as machine learning technology develops.

Machine Learning In Healthcare

Machine learning is already being used in healthcare to diagnose diseases. For example, Google has developed an algorithm that can detect breast cancer based on images. In the future, machine learning will be used to diagnose more complex conditions such as Alzheimer's disease and cancer.

Treatment

Machine learning can also be used to develop new treatments for diseases. For example, a company called Insilico Medicine is using machine learning to develop new drugs for cancer and other diseases. In the future, machine learning will be used to develop more effective and personalized treatments for patients.

Prevention

In addition to diagnosing and treating diseases, machine learning can also be used to prevent them. For example, IBM's Watson system is being used to predict patients' risk of developing certain diseases. In the future, machine learning will be used to create more personalized and effective prevention plans for individual patients.

Conclusion

Machine learning is set to transform a wide range of industries in the coming years. In retail, machine learning will enable more accurate data analysis, personalization of products and services and even the use of robotics in stores. In healthcare, machine learning will revolutionize diagnostics, treatment and prevention. And in manufacturing, machine learning will improve quality control, automate processes and allow for greater customization. These are just a few examples of how machine learning will change the landscape of the industry as we know it. So whatever sector you're in, it's time to start preparing for the machine learning revolution.

While ML and associated technologies like natural language processing are gaining traction in current workflows, it's important to pay close attention to ethical standards that differentiate humans from machines. Today, ML has come to a point where it can replace humans in many intelligent tasks. The future is clearly AI/ML-driven, and it will eventually become part of our lives to the degree the mobile phone is. We will take it for granted. Given all of this, those using and developing AI must keep ethics in mind when dealing with it, whether that's focusing on consumer privacy rights or keeping up to date with laws and regulations surrounding the technology in this space.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/02/27/how-machine-learning-will-transform-your-industry/

ChatGPT, Machine Learning And Generative AI In Healthcare

Machine learning has finally captured the popular imagination in ChatGPT.

The free chatbot program, capable of generating a wide array of impressively human-like text from simple prompts, has chalked up copious headlines (and huge investment) since its public release late last year. In the space of eight weeks, Reuters reports, it attracted around 100 million active monthly users, making it “the fastest-growing consumer application in history.”

It is also already causing enormous debate about the future of journalism, academic testing, digital marketing and computer programming, among other specialties. It has even been dubbed the latest potential “Google killer.”

Without wading into any of those contentious issues, its potential for assisting in healthcare is incredibly exciting.

The Technology

First, let’s clarify the technology. The chatbot was developed by artificial general intelligence (AGI) research firm OpenAI on the company’s GPT-3 family of large language models (LLMs). It’s an example of conversational “generative AI”—basically machine-learning algorithms trained on massive troves of internet data to quickly generate new content (in this case, text) with minimal input. It can actually produce something from what it has “learned.”

While ChatGPT’s output is by no means perfect, the hype around it is warranted. The program and its underpinning models represent a big leap forward in sophistication and capability in natural language processing (NLP) technology—as well as an incredibly speedy evolution.

Consider that the final model of OpenAI’s GPT-2 deep learning neural network was released in November 2019 and was trained with 1.5 billion machine-learning parameters. The beta version of GPT-3 debuted in mid-2020 and was trained with more than 175 billion machine-learning parameters. By September 2020, Microsoft had licensed it for use in products. And by 2021, GPT-3 was fueling new Microsoft application features. This year, Microsoft has employed GPT-3 to add “intelligent recap” features to its premium Teams application, including “automatically generated meeting notes, recommended tasks, and personalized highlights,” and has just announced that the latest version of its Bing search engine will incorporate ChatGPT-like features.

OpenAI and Microsoft are not alone in advancing this type of technology. Alphabet (Google’s parent company) has its own experimental AI projects, such as Language Model for Dialogue Applications (LaMDA), and is currently releasing a ChatGPT-like conversational AI tool, dubbed Bard, to select testers. Meta (Facebook’s parent) and Quora have also joined the generative AI fray with their own chatbot examples. This technology seems to be everywhere all at once.

And that has a lot of people rethinking what generative AI makes possible, how quickly it can happen, and where it will make the biggest impact—healthcare is no exception.

Healthcare Potential

I have written before about the need for AI-powered decision intelligence and support systems in healthcare. Clinicians and healthcare workers could really use some relief from information and administrative overload. Generative AI may be able to help.

For example, last year Microsoft Research scientists published a paper on a project called BioGPT, “a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature.” They essentially took OpenAI’s GPT-2 and refined it with a large corpus of reputable biomedical literature, making a BioGPT that is better equipped to mine, analyze and “discuss” biomedical text—and that outperforms previous models on most tasks.

Now consider how such a domain-specific AI model might help with something like sepsis.

Mayo Clinic defines sepsis as “a potentially life-threatening condition that occurs when the body’s response to an infection damages its own tissues.” Sepsis can be caused by parasites, bacteria, fungal infections, and viruses, and physicians describe it as “hard to spot and easy to treat in its earliest stages, but harder to treat by the time it becomes evident.” Sepsis can present with a confusing “constellation of symptoms.” It is also the number one cause of death in hospitals.

Something like BioGPT could be used to:

• Analyze vast amounts of biomedical literature and extract relevant information related to sepsis to identify patterns and insights.

• Generate new biomedical literature related to sepsis, including combinations of hypotheses and theories that could guide future research.

• Aid in diagnostics and treatment for sepsis, and help in more quickly identifying targets for intervention.

In regard to a completely different but no less vexing area of healthcare, something like BioGPT could potentially disrupt the current healthcare coding and billing system. Costs associated with coding and billing in the U.S. are very high and “significantly exceed those in similar countries.” Generative AI could help by:

• Efficiently automating the process of coding medical procedures and services, freeing up time and resources for other critical tasks.

• Identifying and correcting coding errors, which are a common occurrence in the current system, reducing the risk of denied claims and other financial penalties.

• Identifying new and emerging trends in medical procedures and services to inform the development of new CPT codes, allowing healthcare providers to reflect the changing landscape of medical procedures and services more accurately.

The use of generative AI has the potential to greatly impact clinical understanding, diagnosis and treatment of complex medical conditions as well as improve the accuracy, efficiency and effectiveness of healthcare system function. This technology could quickly lead to improved patient outcomes, more streamlined processes, and reduced costs for both healthcare providers and patients.

We aren’t there yet, but it isn’t too early to start imagining ways for this technology to help.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/03/07/chatgpt-machine-learning-and-generative-ai-in-healthcare/?sh=49177ff1556f