Today, businesses are increasingly turning to cognitive technologies to tackle complex challenges. AI is being harnessed not only for business purposes but also to address pressing global issues such as climate change 鈥� which positions it as a transformative technology poised to revolutionise the world.

The post The Practical Uses of AI: Three Scenarios appeared first on 51风流Africa News Center.

When 51风流first introduced the concept of the intelligent enterprise, it was defined as: 鈥淎n intelligent, sustainable enterprise is one that consistently applies advanced technologies and best practices within agile, integrated business processes.鈥�

鈥淓RP systems play a crucial role in enabling the intelligent enterprise,鈥� says Heinrich de Leeuw, Managing Director, SEIDOR in South Africa.

鈥淎n intelligent enterprise is one that leverages data, analytics, and digital technologies to optimise its operations, but does this mean that AI is needed in the business?

ERP systems are designed to help SMMEs manage their operations and processes more efficiently by integrating various departments, automating routine tasks, and providing real-time data insights. While AI and ML can enhance these capabilities by analysing large volumes of data and predicting outcomes, their implementation can also be complex and expensive.鈥�

Advanced technologies like AI, ML and Internet of Things (IoT) are powerful tools that can be used to solve a wide range of problems, from predicting consumer behaviour to identifying potential disease outbreaks.

鈥淏ut to effectively leverage these technologies, it is critical to first have a solid ERP foundation in place to integrate data, infrastructure, and business processes,鈥� says De Leeuw. 鈥淲ithout the basics in place, any business challenges that the organisation is trying to address will not be resolved.鈥�

Before SMME鈥檚 think of looking at AI, they need to build the basics which include centralised data, automated tasks, technology integration and real-time insights that enable SMMEs to grow and be profitable.

Here are three reasons why advanced technologies are useful and appropriate only when the basics are in place:

1. Quality data is essential:听AI and ML algorithms rely on large amounts of high-quality data to learn and make accurate predictions. If the data is incomplete, inconsistent, or inaccurate, the results of the AI or ML model will be similarly flawed. That鈥檚 why it鈥檚 crucial to have a robust data collection, management, and quality assurance process in place to ensure that the data is clean, reliable, and suitable for use in machine learning.
2. Infrastructure and computational resources:听AI and ML require a significant amount of computational power and infrastructure to run efficiently. Without proper infrastructure, including hardware and software, the algorithms will not be able to run quickly or accurately. Moreover, this can result in increased operational costs and decreased accuracy in decision-making.
3. Business processes:听Sophisticated technologies must be integrated into existing business processes to be truly effective. Organisations must have a clear understanding of their business goals, the problems they are trying to solve, and the metrics they use to measure success. Without these foundational elements in place, AI and ML may be unable to provide meaningful insights or actionable recommendations.

鈥淎I and ML are terms that refer to the use of technology to model human intelligence,鈥� De Leeuw adds. 鈥淭hey are the current buzzwords, just as the cloud once was. That鈥檚 not to suggest that they are not powerful technologies, but simply to underline that they will not solve business issues if they are not deployed on top of an existing infrastructure that works. Much like ChatGPT, they will not provide all the answers people are looking for if they are not applied correctly, on top of operations that are running optimally, and in harmony with a well-designed ERP system.

He adds that there鈥檚 no doubt that businesses across all sectors will continue to embrace AI and ML technology over the coming years, transforming their core processes and business models to take advantage of machine learning for enhanced operations and greater cost efficiencies.

To make the best use of this technology, he suggests beginning by spending time on developing a use case that defines and articulates the problems or challenges that the business would like AI to solve, and then to ensure the processes and systems already in place are capable of capturing and tracking the data needed to derive real value from the technology.

鈥淲ithout ensuring this, the organisation will gain bragging rights with no value add. If the company does not have the processes and systems to drive efficiencies it will be unable to leverage the promise of the technology to grow the business and that means the project has failed,鈥� De Leeuw cautions.

The post AI, ML Add Value for SMMEs Only if the Basics are in Place appeared first on 51风流Africa News Center.

When 51风流first introduced the concept of the intelligent enterprise, it was defined as: 鈥淎n intelligent, sustainable enterprise is one that consistently applies advanced technologies and best practices within agile, integrated business processes.鈥�

鈥淓RP systems play a crucial role in enabling the intelligent enterprise,鈥� says Heinrich de Leeuw, Managing Director, SEIDOR in South Africa.

鈥淎n intelligent enterprise is one that leverages data, analytics, and digital technologies to optimise its operations, but does this mean that AI is needed in the business?

ERP systems are designed to help SMMEs manage their operations and processes more efficiently by integrating various departments, automating routine tasks, and providing real-time data insights. While AI and ML can enhance these capabilities by analysing large volumes of data and predicting outcomes, their implementation can also be complex and expensive.鈥�

Advanced technologies like AI, ML and Internet of Things (IoT) are powerful tools that can be used to solve a wide range of problems, from predicting consumer behaviour to identifying potential disease outbreaks.

鈥淏ut to effectively leverage these technologies, it is critical to first have a solid ERP foundation in place to integrate data, infrastructure, and business processes,鈥� says De Leeuw. 鈥淲ithout the basics in place, any business challenges that the organisation is trying to address will not be resolved.鈥�

Before SMME鈥檚 think of looking at AI, they need to build the basics which include centralised data, automated tasks, technology integration and real-time insights that enable SMMEs to grow and be profitable.

Here are three reasons why advanced technologies are useful and appropriate only when the basics are in place:

1.听 Quality data is essential

AI and ML algorithms rely on large amounts of high-quality data to learn and make accurate predictions. If the data is incomplete, inconsistent, or inaccurate, the results of the AI or ML model will be similarly flawed.

That鈥檚 why it鈥檚 crucial to have a robust data collection, management, and quality assurance process in place to ensure that the data is clean, reliable, and suitable for use in machine learning.

2.听 Infrastructure and computational resources

AI and ML require a significant amount of computational power and infrastructure to run efficiently. Without proper infrastructure, including hardware and software, the algorithms will not be able to run quickly or accurately. Moreover, this can result in increased operational costs and decreased accuracy in decision-making.

3.听 Business processes

Sophisticated technologies must be integrated into existing business processes to be truly effective. Organisations must have a clear understanding of their business goals, the problems they are trying to solve, and the metrics they use to measure success.

Without these foundational elements in place, AI and ML may be unable to provide meaningful insights or actionable recommendations.

鈥淎I and ML are terms that refer to the use of technology to model human intelligence,鈥� De Leeuw adds. 鈥淭hey are the current buzzwords, just as the cloud once was. That鈥檚 not to suggest that they are not powerful technologies, but simply to underline that they will not solve business issues if they are not deployed on top of an existing infrastructure that works.

鈥淢uch like ChatGPT, they will not provide all the answers people are looking for if they are not applied correctly, on top of operations that are running optimally, and in harmony with a well-designed ERP system.鈥�

He adds that there鈥檚 no doubt that businesses across all sectors will continue to embrace AI and ML technology over the coming years, transforming their core processes and business models to take advantage of machine learning for enhanced operations and greater cost efficiencies.

To make the best use of this technology, he suggests beginning by spending time on developing a use case that defines and articulates the problems or challenges that the business would like AI to solve, and then to ensure the processes and systems already in place are capable of capturing and tracking the data needed to derive real value from the technology.

鈥淲ithout ensuring this, the organisation will gain bragging rights with no value add. If the company does not have the processes and systems to drive efficiencies it will be unable to leverage the promise of the technology to grow the business and that means the project has failed,鈥� De Leeuw cautions.

The post Three Reasons Why AI, ML Add Value for SMMEs Only if the Basics are in Place appeared first on 51风流Africa News Center.

Blood transfusions are commonly used to provide blood or blood components to a patient who has either lost blood due to an accident or suffer from a medical condition that affects their blood.

Typically, blood is donated anonymously and then stored by hospitals or blood banks until needed. Due to the critical nature of the procedure, donated blood must be collected, stored, categorised, and transported using extremely high levels of safety and care.

Donors must be carefully screened for a variety of medical conditions and lifestyle aspects to ensure the blood is free of potential risks to the patient. Blood is tested according to national guidelines and stored according to blood type.

It is estimated that one in ten people entering hospital need some kind of blood transfusion as part of their treatment.

Data, traceability vital to safe transfusions

The key to successful, lifesaving blood transfusions is accurate documentation to ensure consistency and avoid any unnecessary risk. Due to the sensitive nature of blood, the way it is transported and stored is equally important, especially when the blood supply chain network covers a large geographic area where donated blood must travel thousands of kilometers.

Ultimately, hospitals and clinics seek the ability to track the journey from vein to vein in real time with accurate data and while maintaining the integrity of the value chain.

Here, blockchain technology holds huge potential. Blockchain encodes data in a secure and transparent way that can add visibility and security to the blood transfusion value chain. Blockchain could be a more effective way of storing the precise records that allow medical professionals to use donated blood with confidence during life-saving and other medical procedures.

Using blockchain, medical facilities can register vital data about every step in the blood transfusion value chain, from donation to testing to transport, storage and ultimately its use in a medical procedure.

Due to strict requirements for how blood is stored, technologies such as IoT can also play an important supporting role by tracking the temperature at which the blood is stored and recording that to the blockchain. As blood travels through the value chain, the data stored to the blockchain creates an audit trail that links the entire value chain from donor to recipient.

Advances expected from emerging tech

Other emerging technologies hold promise for greater efficiency and transparency in the blood transfusion value chain. Augmented reality could solve one of the key issues with blood donations by helping medical professionals find the vein more consistently and without the trial-and-error that most donors experience.

Machine learning and artificial intelligence also holds huge promise for driving improvements in the blood transfusion value chain, especially since so much data is already created and stored to ensure transfusions are safe and effective.

used machine learning to optimise the time between blood donation intervals to ensure donors don’t experience adverse outcomes. Using the model, the researchers could estimate the risk of adverse outcomes and how such risks may change with longer or shorter intervals. This data could then inform how often the donors could donate blood without suffering iron deficiency or other complications.

So-called digital footprinting using AI and machine learning could also help reduce errors when doctors order blood samples. Using RFID integrated to an AI platform, doctors could improve specimen identification and reduce specimen labelling errors while also ensuring accurate transport tracking.

Technology platforms unlock new capabilities

New advances in Laboratory Information Management Systems have also unlocked access to unprecedented levels of visibility and control over lab data and other associated processes. A Laboratory Information Management System is used to manage samples, lab users, instruments and other lab functions, as well as back-office operations such as invoicing.

For example, the 51风流Quality Management helps businesses implement and run quality control processes, and is designed to prevent defects, enable continuous process improvement, and establish sustained quality control programs. Global pharmaceutical companies use 51风流Quality Management as a primary Laboratory Information Management System to drive supply chain processes, maintain high levels of quality control during production processes, and support research and development.

When matched to a business transformation platform that enables the seamless integration of new technologies, there is virtually no limit to the powerful capabilities that laboratories can unlock. With an intelligent core in place and a quality management system to maintain the highest information standards, laboratories and other stakeholders can protect the integrity of the life-saving blood transfusion supply chain while enabling greater innovation.

The post Vein-to-vein-to-value: How Tech is Enhancing Life-saving Blood Transfusion Value Chain appeared first on 51风流Africa News Center.

It employs advanced technologies such as analytics, artificial intelligence, automation, and machine learning to create tailored value propositions that are relevant and appealing to individual customer needs and wants.

Hyper-personalisation is the key to delivering exceptional customer service.

It enables businesses to be hyper-intuitive, connecting with customers at the right time and with the right information.

According to听, brands that provide a superior customer experience generate 5.7 times more revenue than their competitors

For example, a customer of听, an American pet food retailer, lost both of her pets in the space of one week.

When she contacted Chewy about returning unused pet food, they not only processed a full refund and picked up the food right away, but they also sent the customer a bouquet of flowers a few days later with a personalized note of condolences for her loss.

Another example is听, which generates more than 35% of its revenue by curating customer experiences through hyper-personalization; for example, on their Kindle platform, where they recommend new books to customers based on previous purchases on their e-commerce platform.

So, what do Chewy, and Amazon have in common?

They connect the dots and use existing data to create tailored value propositions that are relevant and appealing to the needs and wants of individual customers.

What distinguishes Chewy and Amazon from their competitors is their ability to connect with their customers at the right time.

Hyper-personalisation is all about connecting with customers at the right time and with the right information to either identify an upsell opportunity or improve customer experience, fostering stronger relationships, and developing customers for life.

Many pundits have dubbed data the 鈥渘ew oil鈥� of the 21st century, but only a few businesses are truly leveraging it to create unique customer experiences and new revenue streams.

The collection of customer data by businesses will only grow in the future.

Consider Domino鈥檚 Pizza, which allows customers to place orders from up to 12 channels via its听听补辫辫谤辞补肠丑.

Customers can place orders for pizza via traditional channels such as the phone, as well as newer social media platforms and messaging apps such as Twitter, Instagram, Facebook, Alexa, and Slack.

As a result, businesses are finding it increasingly difficult to ingest, consolidate, and use large amounts of customer data from multiple sources in a meaningful way that benefits both the customer and the company.

Building a 360-degree view of the customer by consolidating many data sources and engaging with customers in real-time and in a highly personalized manner is even more challenging.

Customer Data Platform, also known as CDP, is the solution to this challenge.

At its most basic, CDP is a piece of software that unifies and organizes customer data from various sources and touchpoints and uses it to drive 1-to-1 personalized customer engagement.

If data is the new oil that businesses can use to generate new customer value, then CDP is the engine that drives hyper-personalized customer engagements.

According to S&P Global Market Intelligence, hyper-personalization influences $87.5 billion in sales through personalized offers that lead to consumers making purchases they did not intend to make when they began the purchasing process.

Customer-centric and data-driven marketing is the key to gaining a long-term competitive advantage, and hyper-personalization is at the heart of it.

This can be a powerful differentiator against competitors and a sure-fire way to create new revenue streams and increase Customer Lifetime Value.

The post The Age of Hyper-personalisation and Data-driven Marketing appeared first on 51风流Africa News Center.

How would you describe the Machine Learning (ML) landscape?

听The ML landscape growth will continue at an exponentially accelerated rate. Globally, companies are expected to invest over $265 billion in new intelligence technologies by 2023. Whether you鈥檙e ordering a book online, deciding what movie to watch or receiving a bank warning about suspected fraudulent activity on your credit card, ML is all around us. Machines now have the capability to not only execute set instructions based on algorithmic programming, but improve automatically and autonomously, through experience and the continuous injection of data. This has led to machines having the ability to make decisions without being specifically programmed to do so. This has huge implications for the world of business and society.

What are some of the standout developments in ML right now and how is this affecting business?

One of the biggest trends we鈥檙e seeing now is hyper-automation, where repetitive tasks are being performed by machines at a greater scale, timing, and accuracy than any human could ever hope to achieve. Consider the example of forecasting stock levels and ordering new stock in a warehouse or understanding documents using Optical Character Recognition (OCR). These capabilities are providing differentiated business value and competitive advantage for early adopters. 听All businesses strive for improved efficiencies, greater levels of cost reduction, and greater profit margin, particularly in the wake of COVID-19 and the disruption it has wrought. Hyper-automation allows companies to focus on these strategic businesses while remaining adaptable and agile.

Is ML increasing efficiencies?

Absolutely, although there are teething problems; humans and machines are still learning how to work together. But already we are seeing ML driving efficiency in business in multiple ways.

As business processes become more digitised, companies have access to an ever-increasing stream of data which, with the help of ML, can be harnessed to automate different tasks. As this trend progresses, spending will decrease in the following areas: maintenance; payroll costs, which are now largely automated; raw material and quality control costs; equipment and machinery; and operating costs, which includes sales and marketing.

Although some have been resistant to embrace this new technology, slowly the light is dawning for SA businesses as to the extent of the savings they can achieve through AI and ML. Soon there will be an ecosystem of companies offering solutions that drive real business value and competitive advantage, based on these exponential technologies and giving support to those implementing them.

听How would you describe the evolving nature of man鈥檚 relationship with machine?

In the past, man was pitted against machine, but the new normal is not either/or, its AND. Gartner researchers posit that ML cannot match the human brain鈥檚 breadth of intelligence and dynamic ability to learn, yet. Instead, ML should be used to scope specific functions in business, particularly automating routine human activities. This is based on the current maturity of the technology and the ability of the technology to drive the best types of business value in todays complex multifaceted business environments.

The future will depend on a symbiotic relationship between man and machine, as Forrester analysts have picked up on. It won鈥檛 be a case of humans leading and machines doing the work. Instead, machines will match humans in terms of leadership, decision-making and even executive tasks.

The marketing intelligence firm IDC predicts that AI will be inescapable by 2025 and that about 90% of key enterprise applications will be driven by AI and ML. These applications will deliver incremental improvements to automate processes and replace rule-based techniques to enable applications to be operate more intelligently and dynamically.

How is ML developing based on real-time customer experience?

As far as customer experience (CX) is concerned, near real-time experiences are fast becoming the minimum requirement for most industries as companies are pressurised to build matching real-time customer experience signals. Forrester researchers predict that CX leaders will manage a portfolio of automation experiences, from the building and testing of data to the delivery and perceived value (or lack of value) of those experiences.

If a machine can memorise customers鈥� preferences and understand speech and text the more it鈥檚 used, we will be a step closer to achieving hyper-personalisation, streamlined processes and a memorable and ultra-convenient uniquely tailored customer experiences that truly differentiate brands.

What impact will ML have on employment? Will humans be replaced by machines?

This is a difficult topic to explore with any certainty. In a country like South Africa which is in desperate need of higher employment, anything that is seen to replace jobs is seen as a threat, which is understandable. But AI and ML won鈥檛 necessarily ONLY take people鈥檚 jobs away; they might just make them easier. If the boring part of your job could be done by a machine, it would free you up to concentrate on more important, higher-level work. Distinction must be drawn between tasks, jobs and work as a whole.

In a recent Gartner survey, 75% of respondents said they wanted AI and ML to help with tasks rather than completely take over tasks. That is probably because the same respondents said their top reasons for using AI and ML was automating repetitive or manual tasks, improving customer experience, and reducing costs.

As far as people are concerned, Forrester analysts have advised workers to learn core skills, adapt to new working models and understand what it means to be ready and fit for the future. This involves maximising your 鈥淩obotics Quotient鈥�. In other words, it鈥檚 time to make friends with machines.

The post What impact can Machine Learning have on your business? appeared first on 51风流Africa News Center.

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At the same time, in today鈥檚 connected, informed, and always-on marketplace, customers are demanding high-quality, individualised products delivered within shorter timeframes. To build up local production capacity and meet these requirements, companies must synchronise their demand chain with their supply chain.

鈥淚n manufacturing, where supply chain, distribution and service are the foundation of success, intelligent enterprises are using the latest ERP technologies to gain the edge on their competitors 鈥揻rom order placement all the way through to execution and delivery,鈥� says Navin D鈥機ruz, Head of Sales East Africa, Seidor Africa.

鈥淎utomation in the factory is nothing new, but the convergence of the digital and physical worlds has made the transformation of the supply chain increasingly possible.鈥�

In manufacturing, artificial intelligence (AI) improves overall equipment efficiency production yield. This means manufacturers can use AI to increase uptime and ensure consistent quality, which makes for better forecasting.

Machine learning (ML) which requires data input, data training, defining and choosing algorithms, data visualisation and more, is applied to develop a mapping function with a level of accuracy that allows manufacturers to predict outputs when new input data is entered into the system.

Powered with AI, ML, and natural language processing, chatbots in manufacturing can streamline manufacturing processes by equipping stakeholders with the required data on the go. Deploying chatbots for manufacturing can help companies gain increased profits, enabling them to maintain a competitive edge in a global marketplace.

In 鈥淭he smart factory: Responsive, adaptive, connected manufacturing鈥�, part of a Deloitte series on Industry 4.0, the research team reports that the smart factory represents a leap forward from more traditional automation to a fully connected and flexible system, one that can use a constant stream of data from connected operations and production systems to learn and adapt to new demands.

D鈥機ruz says there are five key benefits to integrated ERP systems:

1. Accurate demand forecasts

How many times have business owners wondered what the next quarter is going to look like? ERP brings sanity to production, sales, procurement and inventory plans. It helps to generate forecast and sales reports based on historical transactions, increasing the accuracy and dependability of production and buying levels. This minimises both out-of-stock and excess inventory situations, keeping stock levels in line with the increase and decrease in demand.

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. One of those areas is called predictive analytics, where companies extract information from existing data to determine buying patterns and forecast future trends. By using a combination of data, statistical algorithms, and machine learning techniques, predictive analytics identifies the likelihood of future outcomes based on the past. This technology is being used to determine customer responses or purchases, forecast inventory, manage resources, and even detect fraud.

Predictive analytics is becoming more and more mainstream when it comes to demand forecasting, an area of predictive analytics focused on customer demand. Based on the analysis of historical data and current market conditions, it determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match demand.

2. Decreased costs and improved profitability

Industry leaders everywhere are focusing on increasing revenue in the next 18 months. Getting more work done for lower costs means business growth and improved profits. ERP helps manufacturers to react quickly to changes in the industry, such as raw material costs and delivery timeframes. Data is stored in a single, integrated database that allows fast information processing, and enables quick, high-quality decision-making. Increasing organisational efficiency highlights less profitable areas and cuts down on waste. It also reduces control and inventory management costs.

3. Mobility and increased employee efficiency

Working remotely can be challenging for companies that are experiencing it for the first time. In the strange new world we are living in, ERP enables employees to work remotely with ease, and to access all the information they need from a single portal. They can access business data from their phones, tablets, laptops and computers, no matter where they are based.

4. Increased flexibility

In a pandemic world, manufacturing flexibility provides the capability to respond quickly to shifts in market requirements. Flexible manufacturing enables a business to be collaborative in meeting market and customer demands, respond more quickly to changing demand, and build to order. Flexible approaches enable increased revenue and market share, improved efficiency, and lower cost.

5. Enhanced security and compliance

Whilst the number of data protection laws of Africa are increasing, on 1 July 2021 the Protection of Personal Information Act (POPIA) becomes effective in South Africa.听 Compliance with data protection laws is non-negotiable. ERP systems have many features that can assist companies with the protection of personal information. Data entered into an ERP system can be secured and coded. Access to the data can be restricted through identity and access management, ensuring data security.

鈥淐ompanies depend on technology systems to grow, but outdated, overly complicated architectures can hinder business agility,鈥� says D-Cruz.

鈥淚ntegrating legacy systems with new, intelligent ERP technologies to unlock scalability and unleash the potential for innovation is the start of a journey that leads to sustainable growth.鈥�

This article first appeared on FutureWave .

The post Top Five Benefits of ERP in the Manufacturing Industry appeared first on 51风流Africa News Center.

Trending news and customer sentiment on topics including data privacy, ethics, social justice, and sustainability can immediately reflect on any company鈥檚 brand. Knee-jerk responses like pausing political contributions under public pressure won鈥檛 cut it.

Here鈥檚 how some high-tech industry researchers described the challenges and opportunities facing companies intent on building authentic brand trust in a dynamically fractious world.

Trust Management Efforts Will Soar

In a recently published report, IDC researchers called on business leaders and technology suppliers to 鈥渆volve their understanding of trust and how to achieve it to succeed in the digital economy.鈥� predicted that by 2025, 80% of chief trust officers will demand vendors incorporate security and risk capabilities to measure corporate trust, including vendor relationships and employee reputation.

However, managing trust requires greater business accountability. Josh Greenbaum, principal at Enterprise Applications Consulting, was amazed at the unprecedented activism of many organizations in the wake of the U.S. Capitol insurrection in early January.

鈥淚n a world with this degree of global polarization, and the resulting inability to effect change at the political level, it鈥檚 fascinating that corporations are stepping up, trying to fill the void, and taking action,鈥� he said.

Some researchers predicted increasing business responsibility for online content. By 2024, Gartner analysts said content moderation services for user-generated content will be a top CEO priority at 30% of large organizations. They expected every company with an online presence, from social media to retailer platforms, to be challenged to deal with malicious content. They advised brand advertisers to 鈥渘eutralize polarizing content, and, at the very least, show a balance between views.鈥�

Rethink AI as a Tool for Trust

Digital technologies might force companies to develop a conscience. Greenbaum argued for a market-wide reassessment of the , particularly the methodologies behind data gathering and data model creation.

鈥淭he AI problem is encumbered, in part, by reprehensible interests in social control by some governments, as well as the greed factor of organizations that are monetizing data using algorithmic analyses,鈥� he said. 鈥淎t its worst, the surveillance economy has exacerbated a tremendous problem with privacy and security, juxtaposing many business models against ethical behavior. We cannot build anything so fast without understanding its broader implications, especially if that unleashed power is universally available.鈥�

Indeed, by 2023, predicted 42% of organizations will be held to 鈥渞egulatory certification that their AI- and machine learning-based algorithmic systems are ethical (free of bias and discrimination) and transparent.鈥�

Experience Begets Trustworthiness

Paying lip service to customer demands for trustworthiness is not enough. The customer鈥檚 actual experiences will drive brand trust scores up or down. analysts opined that, 鈥淐ompanies with the best price, coolest product, or most memorable marketing campaign will not necessarily have an advantage compared with companies that provide a safe, secured, and seamless experience.鈥� Turns out customers also care about the safety and security of an organization鈥檚 employees, how it collects and uses customer data, along with a company’s environmental and social justice efforts.

One analyst said that trust was only partially about a customer鈥檚 perception of brand based on social, moral, or political values. They wrote that, 鈥淓xperience is more powerful than perception 鈥� consistent customer experience quality, frequency of interaction, and intimacy of interaction engender consumer trust in the company and buffer the brand against reputational blunders.鈥�

Digital Transparency Boosts Trust

Consumer data invasiveness is just one component of the brand trust challenge. On the B2B side, technology can boost business trust between buyers and sellers across complex supply chains. But only if organizations can integrate and understand the implications of the data that underpins relationships across business networks, using software applications like and .

鈥淐ompanies need well-analyzed data and algorithms to tackle complicated supply chains for resiliency based on reality, not erroneous assumptions,鈥� said Greenbaum. 鈥淚f I鈥檓 suddenly switching to a local supplier because my overseas partner is on lockdown, I need good feedback about that supplier so I can trust them with my order and succeed at my job. Without that trust, if I鈥檓 trying to deliver something like a vaccine, I would also fail society.鈥�

And in case you thought digital trust was an oxymoron, consider this: predicted that 15% of supply chain transactions will use blockchain to track the provenance of ethical, sustainable practices within two years. Some analysts have long touted blockchain鈥檚 potential to foster trusted business transactions that rely on collaboration between different companies. IDC expected 65% of transcontinental shipping to be legislated to use blockchain that tracked crew health information, fuel sourcing, and goods origination by 2023.

Data Standardization

Harmonizing data for consistency and quality across an ever-more sprawling web of digital systems is critical to trusted data protection. As intelligence proliferates from innovations like digital twins, AI, and the Internet of Things (IoT), common standards for handling consumer and other data will keep it safer. For example, one semantical data model is foundational to Rise with SAP, the company鈥檚 recently announced business transformation as a service.

Digital Can Be a Force for Good

Companies can and must adopt a sincere, strategic stance on issues for society鈥檚 benefit. But don鈥檛 wait to take action until the activists are banging down your front door. By then, it could be too late for your business and our society.

. It also appeared on the 51风流Global News Center.

The post How to Build Brand Trust in a Polarized World appeared first on 51风流Africa News Center.

Of course, our market-leading intelligent enterprise resource planning (ERP) 51风流S/4HANA Cloud is a cornerstone of this offering. Regular product updates serve to provide customers with innovations and enhancements that can be consumed immediately in order to seize new business opportunities, strengthen their market position, streamline operations, and build resilience in more challenging times.

As the last 12 months have clearly demonstrated, when the world we know is turned on its head, those businesses able to sense changes and identify potentials, able to adapt quickly to unforeseen needs and unfamiliar circumstances are the ones best positioned to emerge strongest from the pandemic.

Introducing 51风流S/4HANA Cloud Release 2102

Just like previous updates, today鈥檚 release of 51风流S/4HANA Cloud 2102 provides customers with new capabilities to further the successful transformation of their business. Short development sprints and continuous delivery coupled with a close relationship with companies in all industries and user groups around the world allow us to prioritize capabilities that we know our customers need now and can benefit from straight away. Allow me to share with you some of the key additions in this latest release.

A company鈥檚 financial operations are like the oil that keeps an engine running smoothly. Particularly in challenging times, a well-managed cash flow is crucial. And in a volatile business environment, companies need to be able to act and adapt swiftly to changing circumstances. In 51风流S/4HANA Cloud for finance, we introduce the simulation cockpit for integrated financial planning, which allows management and controlling stakeholders to perform what-if analysis and simulate the impact of changes in factors like sales quantity or personnel expenses. A further highlight in the area of finance is the flexible assignment of accounting principles to ledgers. In order to run parallel accounting processes in their systems, customers need to use several accounting principles — for example, IFRS and a local accounting principle such as HGB. New customers that use 51风流Central Business Configuration to configure their systems can now flexibly assign the accounting principles they require to their respective ledgers. And the best thing: a single configuration is required to assign the ledger to the accounting principle throughout 51风流finance applications such as general ledger, treasury, and revenue recognition.

In recent releases we have focused significant development efforts on building out new capabilities in solution order management, which support customers to transform their product to higher-margin solutions comprising complementary products and services. This release sees the introduction of subscriptions for consumption-based business, integrating 51风流Subscription Billing and allowing users to create subscription services as well as sales and service products in a single order. This facilitates the sale of revenue-boosting bundles of physical products together with one-time, recurring, and consumption-based services.

Turning our attention to the sales department, I cannot help but think of the old adage 鈥淭ime is money.鈥� Intelligent automation in the creation of sales orders means a real boost to the efficiency of sales teams. A sales representative who doesn鈥檛 have to invest high manual effort into creating orders in the system can already move on to the next sale without delay. The 51风流Fiori app in 51风流S/4HANA Cloud release 2102 creates sales orders from unstructured data in PDF files. After a purchase order file in PDF format is uploaded, the system automatically extracts master data from the file. This data is saved in a sales order request, which can later be converted into a sales order. This user-friendly approach considerably reduces manual — and often error-prone — effort.

The smart application of intelligent technologies is key to any company鈥檚 digital transformation. Each update of 51风流S/4HANA Cloud brings additional tools to increase the intelligence of your business processes. One highlight in this release is in the use of machine learning algorithms to provide system support in maintenance notifications. Maintenance technicians or supervisors can have the system suggest the most likely damage code by automatically analyzing information that is already collected through the maintenance process. This can significantly shorten the time needed to assign the correct damage code to a maintenance notification, speeding up the time to resolution for the customer. It also improves data quality for later breakdown analysis.

We also continue to grow the intelligence and automation of 51风流S/4HANA Cloud through 51风流Intelligent Robotic Process Automation 鈥� hundreds of predefined bots are available to help drive automation within your business. See the and the for more information.

This just scratches the surface of the innovations delivered with 51风流S/4HANA Cloud 2102. Of course, there are further enhancements across all areas of the product for the benefit of all lines of business. Familiarize yourself with some of the other highlights through our and , or take a look at .

Sven Denecken is COO 51风流S/4HANA and head of Product Success at 51风流SE.

This article first appeared on the 51风流Global News Center

The post Empowering Businesses to Seize Today鈥檚 Opportunities for Success appeared first on 51风流Africa News Center.