The impacts of climate change and geopolitical conflicts coupled with volatile commodity prices and inflation are only exacerbating food supply chain challenges. Food prices are expected to rise 80% by 2050, and crop yields are forecast to decrease up to 30% by 2080. During a recent event at 麻豆原创鈥檚 Hudson Yards office in New York, agriculture experts and policymakers discussed how organizations in the private and public sector can work together to build a more sustainable food supply. 

鈥溌槎乖� continues to co-innovate with agribusiness leaders to digitalize the farm-to-consume value chain,鈥� said Anja Strothkamper, global vice president of Agribusiness and Commodity Management at 麻豆原创. 鈥淓very organization in the ecosystem has the opportunity to come together and make a significant contribution towards affordable, efficient, and resilient farming, food production, and distribution. We collaborate across the food chain including agricultural production and farming, origination and trading, commodity processing, food manufacturing and packaging, and retail. These digital ecosystems help companies manage risk, improve decision-making, and maximize crop yields.鈥� 

Data Insights Reduce Complexity for Cost-Efficient Sustainable Farming

Case in point is the collaboration between 麻豆原创 and VISTA that uses satellite images and AI-fueled algorithms to create digital twins that can simulate predictive forecasts for optimized decision-making in farming. VISTA is a subsidiary of BayWa, Germany鈥檚 largest digital agricultural company. I sat down with Strothkamper and Tobias Fausch, Baywa鈥檚 CIO, who shared how working together is cultivating the future of agriculture.   

鈥淪atellite imagery plus AI models capture and analyze a wide range of data in digital twins, including soil quality, crop variety and health, water availability, weather conditions, and other farming activities,鈥� said Fausch. 鈥淭he technology simulates yield predictions, calculating various scenarios based on precise risks by field location and weather patterns. Farmers can automate optimal soil preparation, irrigation and fertilization, crop rotation, and harvest times for sustainable efficiency and high yields. Even with the effects of climate change, these models reveal that we have sufficient resources to better manage global, end-to-end farming for adequate food production.鈥�

In one example, a soybean farmer in India used VISTA to harvest the highest yield ever, despite a drought in the country. Fausch said that optimizing crop yields sustainably bolsters food security and also makes economic sense.

鈥淯sing the technology, we can automatically calculate the availability of water for not just individual farms, but also a region or country,鈥� said Fausch. 鈥淭his data can help governments better manage water resources for maximum crop yields in the face of events that make crop yields unpredictable. For example, if there鈥檚 a drought in one part of the country, water could be transported into dryer regions for storage and irrigation where it鈥檚 needed most.鈥� 

Digital Platform Scales Up Secure Food Supply Chains

Accessibility to advanced technology is crucial to organizations in the food value chain, especially smallholder farms. The collaboration between 麻豆原创 and VISTA offers growers of all sizes a simple yet powerful tool to help capture intelligent data in the field using their mobile device. Available on , VISTA is integrated with .

鈥淭he digital twin is the smart superpower, and 麻豆原创 makes the information available across the digitalized value chain, triggering business processes like automated irrigation and fertilizer services, increased storage and transportation capacities for higher yields, or accurate government subsidy payments for fertilizer,鈥� said Strothkamper. 鈥淧ublic and private sectors can partner to extract value from the predictive modeling data. Farmers can increase yields, banks would have the traceability for investment decisions in startups to grow the economy, insurers can track claims, and governments can better plan and respond to dynamically changing community needs.鈥�

Generative AI for Intelligent Agriculture

麻豆原创鈥檚 agriculture solutions are evolving to incorporate the latest technologies like AI for valuable business results. An uses 麻豆原创 Intelligent Agriculture to help smallholder farmers digitalize operations, providing advice based on captured data in the field and beyond. Farmers have already improved crop yields based on harvest time guidance.

鈥淲e built this solution through our ongoing collaboration with organizations that are part of the 麻豆原创 Advisory Council for Agribusiness,鈥� said Strothkamper. 鈥淐limate change has rendered time-honored farming practices irrelevant. Generative AI can democratize intelligent agriculture with data-driven insights. Tools based on ChatGPT and other large language models that provide advice based on tremendous amounts of data throughout the ecosystem are the vision for the sustainable food value chain of the future.鈥� 

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Contemporary businesses often grapple with the dual challenge of managing cost pressures while simultaneously driving innovation to maintain competitiveness. On one hand, business models must be further optimized to unlock efficiencies and margin potential through standardization and automation. On the other hand, the exploration of novel business models is critical, despite associated uncertainties and the potential need for extensive transformation efforts.

To manage and further optimize ongoing business operations, outsourcing entire business processes to external service providers rather than operating them in-house can prove beneficial. Similar to the software as a service (SaaS) business, there is an emerging market of service providers that use cloud-based technologies to assume responsibility for the execution of business processes.

BPaaS in Practice

BPaaS means entrusting the service provider to manage and maintain the defined business process. In contrast to business process outsourcing, BPaaS is an evolution in which the provider has automated the delivery of business process outcomes to a high degree using modern technologies such as machine learning and robotic process automation (RPA). Obvious areas of application include business processes with a high number of transactions that are highly repetitive and homogeneous, which includes invoice verification in procurement, receivables management in sales, and auditing of travel expense claims.

For instance, 麻豆原创 offers as part of the 麻豆原创 Concur portfolio. Intelligent Audit is a customized service for travel expense claim auditing, enabling customers to ensure compliance with their company travel policy. This helps ensure that the amounts entered match the receipt data, that the correct tax rates are applied, and that the costs being charged are within company-specific limits. Moreover, employees are given direct and specific instructions about what to do to correct any incomplete or incorrect claims for travel expenses.

麻豆原创 already provides this service to more than 4,000 customers and has continuously increased process automation over more than 10 years. At the same time, 麻豆原创 cooperates with experienced third-party service providers that perform some of the audits that still need to be carried out manually. Last but not least, it is necessary to fulfill country-specific tax laws as well as other compliance requirements.

Advantages for Customers

The BPaaS model improves customers鈥� ability to accurately predict costs because the services are usually billed based on usage. In the example of travel expense auditing, a fixed amount is paid for each audited claim for travel expenses, regardless of how complex each individual audit actually is. Additionally, performance-based remuneration can be agreed upon with the respective provider. One study commissioned by 麻豆原创 revealed that 94% of all executives already believe that modern technologies can improve the quality of audits.

BPaaS generally results in the advantages for customers that include:

• Focusing on core competencies
• Outsourcing of repetitive operational activities
• Achieving process results that are only possible through cross-company best practices
• Predictable usage-based costs

Advantages for Providers

Providers use modern technologies to significantly reduce the volume of manual activities and, in turn, aim for a higher level of automation. Due to the increasing number of customers, the sheer amount of business process data being accumulated is giving rise to another growing asset: the ability to dynamically determine best practices and thus continuously improve and optimize the service. This increases the value of the service for each individual customer.

The emergence of federated learning has become its own field of research within machine learning because it enables providers to address the requirements relating to data security and data protection more effectively. In addition, the higher level of automation results in greater profitability for the provider.

BPaaS thus generally results in the following benefits for providers:

• A significant reduction of manual activities through modern technology utilization
• Dynamic determination and continuous further development of best practices based on business process data from a wide customer base
• Improved profitability 鈥� and competitiveness 鈥� due to the increasing level of process automation

Innovation at All Levels

Table 1: Process automation in cloud computing

The obvious areas of application for BPaaS include business processes with a high number of transactions and highly repetitive and homogeneous work聽steps that do not have many industry-specific or regional differences.

BPaaS can be viewed as the logical progression of the cloud service model because many BPaaS services will be implemented on the basis of familiar SaaS solutions. At the same time, today鈥檚 SaaS solutions can already facilitate integrated automation of certain process steps.

Finally, customer-specific requirements and enhancements can be implemented using tools for process automation from known platform-as-a-service (PaaS) environments. This means that the provider can also implement the necessary 鈥渓ast mile鈥� of process responsibility.

麻豆原创 itself will only offer a few selected business processes as BPaaS. In keeping with our corporate strategy as a global provider of cloud services, such an offering is only pursued if there is significant market demand and a high degree of automation can be achieved. Therefore, the large majority of BPaaS solutions are and will be offered via our partner ecosystem. In particular, our advisory partners have the in-depth expertise and extensive capacities required to offer BPaaS across larger business domains.

It is crucial for companies to remain in contact with their service providers to pursue the right strategy for their business regarding where they retain business process responsibility and where they hand over responsibility to service providers. When opting to use BPaaS, companies can redirect the saved time from operational process optimization toward innovation, ultimately achieving the entrepreneurial ambidexterity described above.

Claus von Riegen is head of Innovation Strategy and Services at 麻豆原创.

麻豆原创 doesn鈥檛 just encourage its customers to embrace this concept: its IT team applies the same technology-driven business transformation processes internally to continually improve business functions throughout the company. The intelligent enterprise solutions created and rolled out by the 麻豆原创 IT team significantly enable 麻豆原创鈥檚 day-to-day business success. These solutions are developed in close consultation with business stakeholders to help ensure they effectively support the real-world needs of the end user.

鈥淭he chief information officer is the guide to a successful digital transformation journey. To ensure superior business outcomes, IT leaders need to work closely with the business to support their transformation journey with game-changing technology 鈥� no matter if it鈥檚 about rapidly adopting new business models or optimizing business processes end-to-end,鈥� said Sebastian Steinhaeuser, chief strategy officer, 麻豆原创 SE.

Partnering for Success

It began as a project to upgrade 麻豆原创鈥檚 sales technology infrastructure at the end of 2021. 麻豆原创 IT collaborated with colleagues from sales teams to visualize and successfully deliver a solution that met core business requirements and empowered end users to thrive. This partner approach led to a new 麻豆原创 sales engagement platform that has streamlined sales processes using advanced automation, cloud-based tools, and data analytics.

To be successful, sales executives must clearly understand their customers鈥� needs. Especially in the world of cloud-based technology, appropriate recommendations and effective price negotiations require transparency and quick access to reliable data and advanced insights. Sales teams need the latest technological developments to make informed decisions and maintain their competitive edge.

麻豆原创 sales representatives now have a single-solution experience that offers improved access to competitive intelligence and compelling content to drive better deal win rates. Through process automation, an intuitive user interface, and reduced complexity, it has become the go-to hub for employees in sales and marketing to manage engagements with prospects and customers. And optimized digital demand generation is driving continuous pipeline growth, while workflow automation has improved sales efficiency.

Machine Learning Refines Opportunity Forecasting

Machine learning is one of many exciting technological advances in the headlines. Its wide application potential is helping companies speed up processes and automate tasks. It can simplify sales operations by automating tasks such as lead scoring, customer segmentation, and sales forecasting. Using machine learning algorithms, sales teams can identify the best prospects, segment customers quickly and accurately into meaningful groups, and predict future sales trends.

One area where machine learning is instrumental at 麻豆原创 is predictive opportunity scoring, which helps make revenue forecasting more accurate and results in considerably lower error rates for 麻豆原创 sales teams. And, by continuously aggregating and maintaining revenue forecasts predicted through machine learning with verbal forecast data from account executives, the sales organization has instant access to live revenue forecasts in the sales platform鈥檚 鈥渄igital boardroom.鈥� These types of enhanced automated business processes can reduce manual efforts but also allow for retaining expert knowledge at the same time.

Increased Margins Through Optimized Sales Support

Transparency is an important aspect in process optimization. It is also a critical component of business accountability and an important factor for employees, customers, and investors. Especially in reference to sales and deal support, it helps reduce the risk of errors and allows for more accurate and reliable contract processing. Here, 麻豆原创 IT moved sales processes to tool-based processing of digital contracts, which provided increased transparency for account executives during the contract renewal phase and further reduced the margin for errors.

鈥溌槎乖粹�檚 IT organization invested time and worked with us to fully understand the fundamental requirements of our sales and post-sales operations. Now with the new sales engagement platform providing improved revenue forecasts and critical transparency for better price negotiations, we freed up time for our sales teams to focus on what they do best: selling. It鈥檚 a win-win,鈥� said Murat Moustafa, global account director 鈥� PricewaterhouseCoopers, 麻豆原创 Deutschland SE & Co. KG.

In the era of digital transformation, transparency, cross-organizational collaboration, and leveraging innovation leads to new technology solutions that are truly based on the requirements of the user.

Florian Roth is chief digital and information officer at 麻豆原创 SE.

• New Offering Will Unite 麻豆原创 with Google Cloud鈥檚 Data and Analytics Technology, Making Enterprise Data More Open and Valuable and Advancing Enterprise AI Development

The offering enables customers to build an end-to-end data cloud that brings data from across the enterprise landscape using the solution together with Google鈥檚 data cloud, so businesses can view their entire data estates in real time and maximize value from their Google Cloud and 麻豆原创 software investments.

Data is the cornerstone of digital transformation and AI development. Organizations spend significant resources building complex data integrations, custom analytics engines, and and natural language processing (NLP) models before they start to realize value from their data investments. Data originating from 麻豆原创 systems, in particular, is among organizations鈥� most valuable assets and can contain critical information on supply chains, financial forecasting, human resources records, omnichannel retail and more. 麻豆原创 Datasphere combines this mission-critical data with data from across the enterprise landscape, regardless of its origin. Being able to combine 麻豆原创 software data and non-麻豆原创 data on Google Cloud from virtually any other data source, means organizations can dramatically accelerate their digital transformation with a fully defined data foundation that retains complete business context.

鈥淏ringing together 麻豆原创 systems and data with Google鈥檚 data cloud introduces entirely new opportunities for enterprises to derive more value from their full data footprints,鈥� said Christian Klein, CEO and member of the Executive Board of 麻豆原创 SE. 鈥溌槎乖� and Google Cloud share a commitment to open data and our extended partnership will help break down barriers between data stored in disparate systems, databases and environments. Our customers not only benefit from the business AI already built into our systems, but also from a unified data foundation.鈥�

鈥溌槎乖� and Google Cloud now offer an incredibly comprehensive and open data cloud, providing a foundation for the future of enterprise AI,鈥� said Thomas Kurian, CEO, Google Cloud. 鈥淔ew resources are as important to digital transformation as data. By deeply integrating 麻豆原创 software data and systems with our data cloud, customers will be able to utilize our analytics capabilities as well as advanced AI tools and large language models to find new insights from their data.鈥�

麻豆原创 and Google Cloud鈥檚 new open data offering complements the RISE with 麻豆原创 solution and will enable customers to:

• Access business-critical data in real time: The integration between 麻豆原创 Datasphere and Google Cloud BigQuery allows customers to access their most critical data in real time without data duplication. This joint offering can unify data from 麻豆原创 software systems, such as 麻豆原创 S/4HANA and 麻豆原创 HANA Cloud, providing organizations with a comprehensive view of their most important data on Google data cloud.
• Simplify data landscapes: 麻豆原创 and Google Cloud co-engineered powerful data replication and federation technologies, which allow businesses to integrate 麻豆原创 software data with BigQuery environments and leverage 麻豆原创 and Google Cloud鈥檚 leading data analytics capabilities. Now, customers can federate queries across 麻豆原创 Datasphere and BigQuery to blend data from 麻豆原创 and non-麻豆原创 software. This eliminates common data silos from sources that span marketing, sales, finance, supply chain and more. For example, customers with wholesale business distribution models can now have full visibility into their products as they go through the sales pipeline and reach customers.
• Create trusted insights with Google Cloud鈥檚 advanced AI and machine learning (ML) models: Businesses will be able to use Google鈥檚 AI and ML services to train models on data from 麻豆原创 and non-麻豆原创 systems.
• Perform advanced analysis: Organizations can utilize the analytics capabilities of the 麻豆原创 Analytics Cloud solution in Google Cloud to analyze financial and business outcomes while improving the accuracy of models. With a simple integration to data in BigQuery with 麻豆原创 Datasphere, customers can plan with a single, comprehensive view of their businesses.
• Utilize joint solutions for sustainability: 麻豆原创 and Google Cloud are exploring ways to combine 麻豆原创 Datasphere with broader ESG data sets and insights powered by Google Cloud to accelerate sustainability journeys with actionable insights.
• Use (麻豆原创 BTP) on Google Cloud globally: 麻豆原创 will advance its multi-cloud offerings by expanding regional support of 麻豆原创 BTP and on Google Cloud, which includes support for 麻豆原创 Analytics Cloud and 麻豆原创 Datasphere. 麻豆原创 and Google Cloud intend to launch 麻豆原创 BTP in five new regions this year, building to a total of eight regions supported by 2025.

The companies also plan to partner on joint go-to-market initiatives for enterprises鈥� largest data projects, enabling customers to adopt data products from both 麻豆原创 and Google Cloud. Visitors to the 麻豆原创 Sapphire conference can see demos of joint AI and data solutions at Google Cloud鈥檚 booth. These include how enterprises can apply generative AI to common workflows and applications, such as using a chatbot to search, create and edit purchase requests. The 麻豆原创 Sapphire conference takes place May 16鈥�17 in Orlando, Florida.

Visit the 麻豆原创 News Center. Follow 麻豆原创 on Twitter at .

About Google Cloud

Google Cloud accelerates every organization鈥檚 ability to digitally transform its business. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology 鈥� all on the cleanest cloud in the industry. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

About 麻豆原创

麻豆原创鈥檚 strategy is to help every business run as an intelligent, sustainable enterprise. As a market leader in enterprise application software, we help companies of all sizes and in all industries run at their best: 麻豆原创 customers generate 87% of total global commerce. Our machine learning, Internet of Things (IoT), and advanced analytics technologies help turn customers鈥� businesses into intelligent enterprises. 麻豆原创 helps give people and organizations deep business insight and fosters collaboration that helps them stay ahead of their competition. We simplify technology for companies so they can consume our software the way they want 鈥� without disruption. Our end-to-end suite of applications and services enables business and public customers across 25 industries globally to operate profitably, adapt continuously, and make a difference. With a global network of customers, partners, employees, and thought leaders, 麻豆原创 helps the world run better and improve people鈥檚 lives. For more information, visit .

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Even as far back as the 1950s, computer scientists were already experimenting with this concept of data modeling. In the 1980s, knowledge graphs were a much-discussed topic in the context of expert systems, and in the 2000s they once again came to the attention of the scientific community, as they took on a fundamental role in Semantic Web. Finally, in 2012, it was a search engine that gave knowledge graphs their big appearance among a wider audience: Google announced that its users would be able to search for 鈥渢hings not strings.鈥� Rather than just trying to match keywords from a query, the search engine would also place them in the right context 鈥� all with the aim of delivering better, more intelligent results.

鈥淧ut very simply, knowledge graphs are a technology that seeks to turn data into machine-interpretable knowledge,鈥� says Michael Burwig, innovation engineer at Potsdam. Their key focus is to model the relationships between objects, which are shown as a network of interconnected points.

鈥淜nowledge graphs allow us to map how we humans understand the world 鈥� how we go through life, accumulate knowledge, and how we contextualize that information in our minds,鈥� Burwig explains. This ability allows humans to draw conclusions that produce fascinating 鈥渁ha鈥� moments. Ultimately, this is also the goal of business software: to connect knowledge and find solutions, ideally in an automated way. There are many potential use cases of knowledge graphs, such as knowledge and data management and chatbots. Detecting insurance fraud is one example, but, in short, they can be used in any scenario in which patterns are examined to identify exceptions and anomalies.

The Potential of Hybrid AI

Burwig believes, however, that automated insights like these have not yet become part of everyday practice. 鈥淗ybrid AI is a major step in this direction. It combines semantic technology, in other words knowledge graphs, and static machine learning, which is now being used for a host of scenarios of this kind,鈥� he says.

Dr. Jan Portisch, lead architect at Value Accelerator Delivery, describes the difference between classical machine learning concepts and hybrid AI machine learning as follows: 鈥淐lassical machine learning methods use extracts of existing databases. These methods draw on such data sets, which are effectively snapshots without any context, to create machine learning content. With knowledge graphs, on the other hand, new data can be added at any time so that they keep on 鈥榣earning鈥� and, unlike static methods, stay current.鈥�

Even though knowledge graphs are not new, it took the massive increase in computing power that we have seen since the 2000s to unleash their full potential, Portisch explains. And since this technology is not yet widely taught at universities, few developers know much about it. 鈥淎nother difficulty is that designing graphs is highly complex, with developers having to think beyond their own application. The modeling behind the graphs has to be complete and semantically accurate. Nonetheless, their potential is huge,鈥� he says.

As an architect, Portisch is involved in creating a central graph for process knowledge at 麻豆原创. The 麻豆原创 Signavio Value Accelerator Delivery team collates and models the process knowledge 麻豆原创 has built up over the past 50 years to give customers an integrated view. In the future, the accelerator will offer a semantic search where users will be able to formulate a business problem they鈥檇 like to solve and the system will display a list of processes and data that are impacted by this problem, Portisch explains. Further, this capability could be a helpful transition tool in migration projects or could provide presales teams with the visibility they need when tailoring solutions to customers鈥� specific situations.

Felix Sasaki, expert in Knowledge Graphs & Semantic Technologies in the 麻豆原创 AI unit, explains additional benefits: 鈥淪tandards-based knowledge graph technologies facilitate the modeling of business scenarios. So-called constraints complement the existing logic-based modeling. Since constraints can easily capture the knowledge of business experts, modeling becomes easier. In addition, knowledge graph-based vocabularies like schema.org have found widespread adoption and thus help to find a more ubiquitous language.鈥�

Decoupling Business Expertise and Application

For 麻豆原创, the power of this technology lies in how graphs can be combined in different ways and, therefore, in how data models 鈥� and ultimately the applications themselves 鈥� can be integrated and composed.

Portisch explains how it鈥檚 important to recognize that RDF 鈥� Resource Description Framework, a syntax used to model metadata for Internet resources 鈥� is a publicly acknowledged standard and that knowledge graphs are already widely used outside the corporate world. One of the best-known knowledge graphs is ; another example of a large-scale graph is . 鈥淭hese graphs are public resources that can also be used commercially,鈥� Portisch says.

That creates interesting use cases. For example, private corporate data could be combined with public data. A supplier system running on 麻豆原创 software could automatically import metadata from a graph about companies it interacts with, such as the type of business, company logo, and who their managing directors are.

鈥淪emantic Glue鈥� 鈥� Integration by Design

Burwig sees another advantage of these graphs in their 鈥渟emantic glue.鈥� Because of their flexible structure that can be enhanced in real time, individual graphs can be 鈥済lued鈥� together to link up data silos. Unlike graphs, the table structure of a relational database has to be defined at the beginning 鈥� and changing that structure later requires a lot of work. But graphs can store and link metadata in a semantic data layer that is separate from the data stored in an application鈥檚 tables. This makes it possible to consolidate data across different products and data silos.

Graphs offer huge advantages over relational databases in certain scenarios, such as calculating the shortest route or associating a specific material with a customer material. 鈥淚f such a scenario is based on a relational database, the query would have to touch applications from across the entire 麻豆原创 world. But with a graph, it would be a simple problem to solve,鈥� Portisch says. While relational databases usually offer greater performance within their applications, graphs always have an advantage when it comes to creating bridges, known as joins, between data structures and recognizing the correct context.

Because it is possible to extend knowledge graphs, they represent a perfect data model for businesses to start small with and then, as an iterative process, to roll out to more locations and build on. For example, a company could begin with just one use case or department and then gradually roll the model out to the rest of the organization. Any additional data models are 鈥済lued鈥� to the graph as new nodes and edges.

The Situation Knowledge Graph: One of the First Knowledge Graphs at 麻豆原创

One of the most advanced knowledge graphs to date at 麻豆原创 serves as the basis for the Explore Related Situations app, which is part of the Intelligent Situation Automation service on for situation handling in 麻豆原创 S/4HANA.

The business background: around 4% of the automated business processes within a business require manual intervention because of an unforeseen event, such as a late payment, a delayed delivery, or a transport issue.

The situation knowledge graph links these exceptional events to their business entities, enabling the user to better understand the situation in a business context and therefore helping solve the problem and optimize business processes. Further analyses of these situations often reveal relations of issues to certain materials or partners. 鈥淭oday, this kind of knowledge is often hard-coded,鈥� says Dr. Torsten Leidig, an architect in 麻豆原创鈥檚 Situation Handling team. The knowledge graph that underpins the Intelligent Situation Automation service enables a business expert or a key user to model processes and understand them within a comprehensive business context. A problem that has been detected can be resolved automatically based on simple rules and without additional programming.

Dr. Knut Manske, engineering lead for Situation Handling and Responsibility Management at 麻豆原创, describes the graph as a layer that stretches across various 麻豆原创 applications. To summarize the capabilities of situation handling, he explains: 鈥淭he knowledge that is embedded in 麻豆原创 applications is extracted and made usable for algorithms. Situation handling runs alongside the application, analyzing data and reacting to information about data changes or events. The aim is to show solutions that span various applications or business areas.鈥� Customers and partners can define situations themselves without ever touching the application. A selected group of customers is currently validating the solution.

Looking Ahead

Currently just a prototype, the 麻豆原创 鈥淏usiness Decision Simulator鈥� innovation project simulates the effects of internal and external factors on a company and presents the user with potential future scenarios and recommended responses. 鈥淥ne possible question could be: what does a bushfire in Australia mean for my global pharmaceutical company鈥檚 value chain and thus the achievement of our targets in Europe?鈥� Burwig explains. 鈥淥n the one hand, knowledge graph technology can help express the complex relationships between real-world events and business-related processes. Additionally, an extensive knowledge base makes it easier for the user to choose the best possible response to opportunities and risks.鈥�

When asked about the possibilities of graph technology, Burwig says: 鈥淭he potential of knowledge graphs 鈥� at least that鈥檚 the dream 鈥� is that someday there will be extensive graphs modeled on deep knowledge and experience that can be accessed by programs. This means that with every change in technology, we鈥檇 only have to recode the application and not the knowledge stored inside it.鈥�

While the decoupling of data from applications and the accompanying scalability of solutions and programs are important aspects for an IT company, Burwig also sees a vision that goes beyond the 麻豆原创 context: symbolic AI, artificial intelligence that is linked to a large number of interdisciplinary knowledge networks, could have the potential to generate new knowledge that has not been explicitly modeled. Linking scientific content could have the power to accelerate the discovery of new drugs or forge innovations between different science disciplines that don鈥檛 interact on a large scale today.

In the history of humanity, things have often been 鈥� and still are today 鈥� 鈥渋nvented鈥� multiple times, at least in part from lack of knowledge about other relevant findings. Connecting global research projects within one given discipline has increased sharply within the past years, thanks to the availability of extensive digital content. Uniting scientific knowledge across the individual disciplines 鈥� for example, mathematics, biology, medicine, and chemistry 鈥� could uncover new insights and lead to exciting interdisciplinary inventions, Burwig concludes.

The Virtuous Circle Between Knowledge Graphs, Machine Learning, and Natural Language Processing

Academic communities of symbolic AI and machine learning in the past had few touchpoints in terms of research methods and researchers. This is changing 鈥� among others leading to so-called hybrid AI. This results in various virtuous cycles in which knowledge graphs, machine learning, and natural language processing (NLP) cross-pollinate each other. Johannes Hoffart, head of the Chief Technology Office in the AI Unit at 麻豆原创, explains the relation between machine learning and knowledge graphs: 鈥淜nowledge graphs enable data scientists to work with complex and heterogeneous data sources. Their flexible schema can be easily extended and contains powerful data validation capabilities. At the same time, knowledge graphs facilitate access to and exploration of data, as they represent data and its schema such that it鈥檚 easier for humans, and also large language models, to understand.鈥�

Christian Lieske from 麻豆原创鈥檚 Language Experience Lab adds about the relation to natural language processing: 鈥淜nowledge graphs can feed NLP and can be fed by NLP. Take the detection of new business entities as an example: a knowledge graph can inform NLP about known entities and NLP can add additional entities to a knowledge graph.鈥�

To get further insight around knowledge graph technology, read this study co-authored by Portisch: .

麻豆原创 is moving into the second year of its transformation of support. Andreas Heckmann, executive vice president and head of Customer Solution Support and Innovation at 麻豆原创, is excited about all that鈥檚 planned. 鈥淲e鈥檙e making great progress on our comprehensive vision to serve customers better along the entire lifecycle. We look forward to providing an even better experience around customer self-services,鈥� says Heckmann. 鈥淭his year is going to be a year of getting even more prescriptive and offering even more guidance to our customers. Functionality will be substantially expanded with a lot more content added for new products.鈥�

In this conversation, Heckmann gave more details on the goals and progress of this vision.

Q: 麻豆原创 Business Transformation Center will be introduced at this year鈥檚 麻豆原创 Sapphire, held in Orlando, Florida. Can you give details on what鈥檚 planned?

A: Customers will see a first version of what we call 麻豆原创 Business Transformation Center. This is part of the business transformation suite. The focus is to support customers in the planning stage of their 麻豆原创 S/4HANA software transformation. With this, customers can simulate their transformation and plan and anticipate what the transformation could look like when complete. Let me give an example. If a customer says, 鈥淲hat will it look like if we exclude certain company codes or business data?鈥� they will see in real time what this means to the amount of data and information needing to be transformed. The ultimate result is up-front insights that help customers shape and drive their transformation planning.

Can you share what鈥檚 planned around 麻豆原创 Cloud ALM?

We鈥檒l deeply embed business process modeling capabilities of the 麻豆原创 Signavio portfolio in 鈥� our strategic cloud-based platform for customers to manage their entire application lifecycle. Customers can get access to a world-leading business process management solution to discover and adapt 麻豆原创 best practices for accelerated solution delivery. Our focus is to give customers the best possible experience for their business users.

We鈥檙e also extending access to the to our 麻豆原创 Enterprise Support on-premise and cloud edition customers at no extra cost. Our customers can use the solution to help automate testing during their 麻豆原创 projects and validate their business processes when new 麻豆原创 releases are applied to the 麻豆原创 solution landscape. We expect to seamlessly integrate the Tricentis testing capabilities with a fully automated setup within 麻豆原创 Cloud ALM. These cloud-based test automation capabilities will help ease the testing of the 麻豆原创 solutions and help facilitate the adoption of 麻豆原创 cloud product releases.

In a world where we want to innovate and deliver value to end users and customers at an always higher speed, 麻豆原创 and Tricentis are significantly lowering the barrier to test automation. I鈥檓 very satisfied with the great partnership with Tricentis. This unique solution now brings test automation capabilities fully integrated in our business transformation suite for 麻豆原创 Enterprise Support customers.

You mentioned plans for new support functionality. Can you give some examples?

We are reshaping our content and knowledge management strategy to give customers a more robust search experience that leverages artificial intelligence (AI), machine learning optimizations, and analytics functionalities. This search index scans knowledge from many different data sources during a single search. “” will be the main entry point to 麻豆原创 support and for searches. Additional features help us understand and optimize the search and knowledge relevancy at every step of the support journey.

Within , we expect to see progress on the bidirectional dialogue in and 麻豆原创 for Me. This proactive, two-way communication will help keep customers informed about news related to the solutions they are using, alert them to potential issues to watch out for, and deliver support-critical information they need to know.

Furthermore, our focus for Built-In Support is to provide predictive and preventative support. Our biggest wins are when we resolve potential problems without customers even knowing a problem might exist or could have occurred. Our AI services help us continuously improve the quality of real-time recommendations for when customers search for a solution to an issue or create a case for support.

This year, Built-In Support will expand into more 麻豆原创 solutions and will grow the functionality and adoption across our cloud solutions.

There鈥檚 also a lot going on in 麻豆原创 internally as part of the transformation of support that indirectly benefits customers. Can you describe a few of these indirect benefits?

An important internal change is that we鈥檙e increasing our capability of cross-expert interaction. More and more cases require close collaboration of various experts with different knowledge, experience, perspectives, and so on. We鈥檝e introduced an internal framework and technology that lets experts dynamically assemble and quickly collaborate on the same problem. This reduces redundancy, eliminates handover delays, and collapses the time it takes to get experts aligned. That obviously accelerates the resolution substantially for our customers.

We鈥檝e greatly improved the predictive capabilities of our cloud solutions. These predictive capabilities will give us early indications when something isn鈥檛 right. This, in turn, prompts us to start analyzing the situation and take steps to mitigate and resolve issues before the customer even recognizes or experiences problems or service degradation on their end.

These capabilities have been successfully tested and applied to a set of our solutions. This year we鈥檒l extend the reach of these technologies and onboard increasingly more solutions. Plus, we鈥檒l also integrate this more closely into our processes so our experts can respond swiftly to evolving situations.

What suggestions do you have for customers in 2023?

While our focus is to be more prescriptive and provide more guidance to our customers, it鈥檚 important that they also make an effort to stay up to speed on the things we鈥檙e doing. Follow closely on what we鈥檙e doing, embrace the new ideas and improvements, and start consuming them. It鈥檚 my motto to 鈥渄elight customers always,鈥� and we鈥檝e had the customer in mind with all the improvements that we provide and whatever we do.

Customers should talk within their organizations about how they can use all of these improvements. If you have questions, reach out to us. Let鈥檚 explore together how you can make best use of them. At the end of the day, we can invent the best features and options, but if customers stick to their old ways and don鈥檛 embrace new approaches, they鈥檙e missing out on the many benefits that can make their work lives easier. That would be a shame.

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Regina Postman is part of Customer Solution Support and Innovation Communications at 麻豆原创.

When we started to talk, Andreas Heckmann, executive vice president and head of Customer Solution Support & Innovation at 麻豆原创, stated, 鈥淎lthough it was one of the most challenging years in my professional history, it was still a year filled with opportunities. We are seeing traction in the transformation of customer support I described 11 months ago, and we delivered a great amount of product innovations to our customers.鈥�

In this interview, Heckmann gives insight into how the transformation is continuing.

Q: How has 2022 been going for you in the transformation?

A: I鈥檓 proud of the notable, award-winning product releases that are directly relevant to supporting economic needs and helping with sustainability. Take, for example, the solution. We鈥檙e enabling agribusinesses to tap into their vast source of farming data by leveraging data science and machine learning. This farming intelligence helps them leverage processes and services to better forecast and increase farming yields and quality. It will also help them use the right balance of water resources and help keep fertilizer and crop protection to a minimum. This will help agribusinesses play their part in becoming more sustainable and efficient.

Let me also point out the Hasso Plattner Founders鈥� Award-winning . It鈥檚 helping prevent pharmaceutical drug counterfeiting by enabling traceability and verification of products. With an estimated 50% or more of drugs in Africa being counterfeit, this product is definitely helping businesses to protect their intellectual property and, more importantly, people鈥檚 health and lives.

Another exciting sustainability-focused app release this year is the . In this project, we developed a solution extension to help neutralize carbon emissions. Our customers can access data collected from business travel and consider ways to watch their carbon footprint.

All of these solutions make me proud of our Customer Solution Support & Innovation organization and underscores the many ways we have our customers鈥� goals and priorities front of mind.

With 麻豆原创 Cloud ALM being available for a while now, how do you see it being taken up?

is the strategic platform to help customers manage their entire application lifecycle, both on premise and in the cloud. We will close out this year seeing quite an amazing uptake and increasingly more customers using the solution to help implement and operate their cloud and hybrid environments. There鈥檚 a big uptake on 麻豆原创 Cloud ALM, with functions being used more intensely for managing project teams and business processes. And it鈥檚 also getting third-party products integrated to give customers even more access to content such as guidance, tools, and checklists for their implementation projects and operations.

What progress are you making around Real-Time Support during this transformation?

We鈥檙e seeing momentum in continuously transforming the support experience for customers towards a more personalized and preventative experience. This could be when they search for a support answer in or , or when they report an issue. At the beginning of this year, we received a for the innovative way we鈥檙e capturing our customer problems in a structured and guided fashion. It鈥檚 what we call support assistant. This assistant guides customers through the process of logging an incident. Machine learning functionality suggests answers from our knowledge repositories along the way. Often the answer is found in the first bunch of suggestions and no incident needs to be created.

If no answer can be provided instantaneously, the incident information collected is packaged for a support engineer to process further during a or a scheduled appointment. We found that real-time interactions get completed on average within 30 minutes and two of every three issues are solved in the first chat. And these interactions support nine different languages, allowing for multi-language dialogues in real time. For either way the customer decides to go, the support assistant and help customers get their issues resolved much more quickly and efficiently.

Now imagine, we鈥檙e already working on the next generation of the entire support experience for customers to look forward to. With Built-In Support, we鈥檙e making significant progress as the tool for continuous two-way dialogue with customers. It鈥檚 our goal to proactively notify customers about critical issues or relevant new features and to take care of any potential problem before a customer becomes aware of it. We鈥檙e also eagerly awaiting the go-live of our new customer facing portal, 麻豆原创 for Me, with its completely redesigned user experience.

麻豆原创 is getting into customer-centric observability. What does this mean?

Customers trust us with their critical business processes. Service availability is the foundation of that trust and naturally a prime topic for us. We see it as a prerequisite for customer success. A focus of our transformation is to become more predictive and proactive. It鈥檚 what we鈥檙e calling customer-centric observability. This is a methodology that focuses on observing real user experience and outcomes to get advanced warnings ahead of customer-reported incidents and to quickly react upon them. By monitoring 1.5 billion system actions daily, we鈥檙e proactively watching for potential issues and preventing them from happening.

What can we expect for 2023?

Personally, I’m excited to see the launch of the bi-directional dialogue in Built-In Support and 麻豆原创 for Me. This means customers can look forward to getting an even better support experience in 2023. In addition, they should expect to get significantly improved results and optimized recommendations when searching for solutions or creating incident tickets for support. Plus, there will be more times when potential problems are resolved without customers even knowing any problems existed. While we will certainly not be done next year, given all we鈥檝e planned for our customers, I鈥檓 confident that we鈥檒l already begin reaping the harvest.

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Regina Postman is part of Customer Solution Support & Innovation Communications at 麻豆原创.

Let me describe three big examples of innovation developed to make your lives easier: continuous updates that are no longer disruptive to the business; artificial intelligence (AI) and machine learning that produce more precise search results; and real-time support at your fingertips that is built into your application.

Continuous Updates That Are No Longer Disruptive

System updates are important as they provide new or improved functionality, enhance stability and security, and enrich the user experience. However, what鈥檚 been a challenge in the past for on-premise software is the downtime, test efforts, and spike in system issues that often followed a bigger release cycle. For cloud solutions, you should 鈥� and already can 鈥� expect ready-to-use, manageable updates with minimum disruption.

With the , our application lifecycle management platform developed on 麻豆原创 Business Technology Platform (麻豆原创 BTP), we solved that issue by introducing 鈥渄eploy with confidence鈥� as a new way of how we develop this solution. Deploy with confidence works to increase developer productivity and helps ensure high quality while delivering daily system updates. From a technical side, it incorporates many development tools to help automate the daily deliveries of multi-microservice software-as-a-service applications like 麻豆原创 Cloud ALM.

All this allows us to provide regular feature updates to 麻豆原创 Cloud ALM with daily deployment and enables a robust lifecycle without disruption or downtime. Additionally, as our teams are in continuous exchange with customers using 麻豆原创 Cloud ALM, we can quickly bring their feedback into the solution to further improve our software quality. Introducing deploy with confidence as the development method for 麻豆原创 Cloud ALM is a great example where we as a team leveraged innovation to increase productivity and quality while at the same time lifting customer experience to a whole new level.

AI and Machine Learning: More Precise Search Results

Continuous updates are one thing. What about using technology to get answers faster? With companies working in a faster-paced digital world, making decisions and finding answers also need to move faster. This is also the case when business processes aren鈥檛 running as expected.

Think about yourself and if you are someone who prefers to find answers on your own and not wait on support to respond. You鈥檒l be happy to know we have various ways to help you find the right answer. When you search in or 鈥�,鈥� you can get an increasingly personalized search experience that considers the topics most relevant for you. As this applies machine learning to improve search relevance and we are continually optimizing our knowledge base for better discoverability, you鈥檙e more likely to get the needed answer instantaneously 鈥� listed as a proposed solution within the first bunch of search results.

When you actually contact 麻豆原创 support directly, your request and all of its context, such as system and product information, text input, and soon even attachment data, are evaluated in real time by a highly optimized, AI-powered algorithm to provide you with precise solution recommendations. This Incident Solution Matching algorithm helps to make sure you are shown the best available solutions even before you send a ticket to 麻豆原创 or start an Expert Chat session. This helps reduce your effort to solve the problem.

Real-Time Support and Answers at Your Fingertips

It鈥檚 definitely an advantage to tap into modern technology for finding the answers you need. How about the convenience behind it and having answers at your fingertips? One of the things we鈥檝e learned from research and talking with customers is how annoying it can be when you must drop everything you were doing and leave your IT workspace to raise a ticket in a central support portal. This meant you would need to rethink what you were doing when the issue came up and where it happened. Plus, you needed to enter all the contextual information the portal doesn鈥檛 have, such as the system where it happened or the application you were using when running into problems.

We鈥檝e made this obsolete and convenient: is available in many of our cloud solutions and can be used right away, if needed. It鈥檚 a click of a button to ask a question, search for solutions, get tailored recommendations for your problem, or to just report an issue. Built-In Support knows where you were working when the issue came up and what you were doing when you got the error message. Through the usage of AI and machine learning technologies, the tool either already finds possible answers while you are typing in your issue, or it pulls together the necessary information for the support engineer. This is a more personalized support experience tailored to your specific situation. How convenient to not need to post a ticket elsewhere or explain your situation.

And even better, this click can get you connected in a or a scheduled appointment with a support expert. You can more quickly describe and resolve your question or problem 鈥� avoiding the ping-pong back-and-forth happening through a traditional ticket resolution. Talk about a way to free up time and resources to focus on the business.

On top of this, Built-In Support will evolve towards bi-directional communication: if there鈥檚 a brand-new solution to a critical issue in the application you are currently working with, why not get it shared with you right away? And if there is supporting information around a new or changed feature in the app you might have questions about, why not see it published proactively before you run into problems? This is exactly the future-looking direction of Built-In Support. Tailored to every screen of your application, it can proactively provide you answers and solutions and not wait until you run in to a problem and need to report it. Built-In Support is ending the one-way communication. If we know a customer has a problem but isn鈥檛 yet aware of it, we鈥檒l proactively take care of it.

In Summary

All this sounds smart, right? Let me summarize my key messages to you. You鈥檝e learned that 麻豆原创 system updates are important as they bring you new functionality, but they don鈥檛 need to be disruptive. Finding answers to your support problems is just as quick as what you know from a regular Web search. And finally, you鈥檒l experience the convenience of having support at your fingertips and getting your support questions and issues quickly worked out without needing to leave the application you are working in.

I hope I got you excited about some of the existing support innovations that will further improve your support experience. And we鈥檙e not stopping there. We鈥檙e already working on more great things.

Andreas Heckmann is executive vice president of Product Engineering and head of Customer Solution Support and Innovation at 麻豆原创. Follow him on and .

鈥淢anufacturers can no longer create one product for one set of customers,鈥� said Mike Lackey, global vice president of Solution Management for Digital Manufacturing at 麻豆原创. 鈥淭hey have to be agile enough to deliver mass-produced and individualized products at scale. The intelligent factory delivers on highly changeable customer expectations by connecting business data directly with the shop floor for agility based on unexpected events as they arise. Leading manufacturers are digitally transforming to infuse intelligence into every aspect of the business and the factory while controlling costs and quality for a stronger future.鈥�

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Connected Data across Ecosystems Boosts Business Agility

Automating processes on the factory floor was just the first step in modern manufacturing. The next step is gaining intelligence by connecting data from business systems like finance and planning and demand to factories, warehouse distribution, and logistics providers. Information needs to flow securely between customers, suppliers, and other partners. analysts said that due to increased competition for wallet share, 65% of G2000 original equipment manufacturers (OEMs) will integrate customer insights with service work orders to better personalize engagements, increasing satisfaction by 15% by 2025. By next year, analysts expected 30% of manufacturers will share applications with industry ecosystem partners to improve visibility and operational efficiency and ensure safety, security, and quality.

鈥淚ntelligence means that you can鈥檛 look at a single factor or a single factory,鈥� said Lackey. 鈥淭he intelligent factory links together your entire global operation as one entity to become the Intelligent Enterprise. As a manufacturer, your intelligent factory drives your intelligent supply chain that鈥檚 able to adapt quickly moving production lines between facilities when disruptions happen, sourcing alternate materials if supply chains are disrupted, or even switching out production for completely new products should consumer demand suddenly spike.鈥�

Bringing Intelligence Supply Chain-Wide

Technology advances are making yesterday鈥檚 impossibilities everyday realities. By next year, analysts expected 50% of all supply chain forecasts will be automated using artificial intelligence (AI), improving accuracy by five percentage points. By 2025, researchers predicted 30% of G2000 manufacturers will embed connected technologies to increase product reliability using operational insights that ensure uptime and support an optimized maintenance supply chain.

鈥淭he intelligent factory is self-correcting and self-learning,鈥� said Lackey. 鈥淔or example, machine learning builds intelligence into your processes for more robust and continuous improvements in quality control. When you combine business information like market demand drivers, cost structures, inventory, suppliers, and delivery dates with shop floor data, you have the intelligence to reduce inefficiencies and focus on benefits for the greatest competitive advantage. This is how our customers are digitally transforming using .鈥�

Technology Advances Energize Business Transformation

With greater intelligence from digital innovations like AI, machine learning, and the Internet of Things (IoT), manufacturers are using previously unused data to adapt operations and cross industry boundaries for expanded business opportunities. analysts predicted that by 2025, 65% of global manufacturers will invest in edge AI as a part of their IoT-enabled hyper-automation strategy, up from less than 10% today. In just a few years, analysts expected 40% of G2000 organizations will use AI, data governance, and a transformed organization to develop a resilient and distributed operational decision-making framework that drives 25% faster change execution. Products-as-a-service is among the biggest market growth areas. analysts predicted that by 2026, 30% of software development teams will focus on turning traditional products into outcomes-as-a-service.

鈥淭he intelligent factory supports new cloud-based services models as they emerge from board-level strategies,鈥� said Lackey. 鈥淚ntelligence from connected data across factory operations and the business is crucial to deliver on the contract for usage-based services where the manufacturer owns the product throughout its entire life cycle.鈥�

Delivering products against the plan is all well and good, but we live in a world of disruptions. It could be floods and wildfire shutdowns impacting the flow of materials, spiking consumer demands after an influencer鈥檚 social media post goes viral, or ongoing pandemic lockdowns anywhere in the world. In this kind of market, manufacturers have discovered that factory automation alone won鈥檛 build business resilience. As lot sizes decrease and customer expectations rise, Industry 4.0 initiatives are making factories more intelligent for agile business that builds customer loyalty.

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But what happens when the answer is 鈥淣o, actually, I didn鈥檛. You were all out.鈥�? You cannot teleport out-of-stock item onto the shelf for the customer鈥檚 experience to be made whole immediately.

Create one too many of these unfortunate experiences, and the customer is apt to move their shopping elsewhere, where the experience is smoother and more rewarding.

Future of Retail Technology at Brick-and-Mortar Stores

The customer friction can be costly. But problems like stockouts and long lines 鈥� and the customer friction they beget 鈥� are promising to become a thing of the past for retailers that adopt the most recent innovations in machine learning, artificial intelligence (AI), and the Internet of Things (IoT).

With smart technologies like touchless shopping and automated on-shelf forecasting, customers are delighted with a frictionless in-store experience that resembles the ease of online shopping. Innovative intelligent stores, or 鈥渟mart stores,鈥� are popping up everywhere.

S.MART Pulls Back the Curtain with an Immersive Retail Journey

Enter the future of retail: At the , curious minds can step into the intelligent store 鈥淪.MART鈥� for a journey into an innovative, sustainable retail enterprise in action.

With centers across the globe, 麻豆原创 Experience Center locations are state-of-the-art innovation spaces for reimagining business using 麻豆原创 solutions.

In October 2022, the 麻豆原创 Experience Center in Walldorf will officially open its doors, which will be the first of many milestones to come on an ongoing transformation journey toward an even more captivating exploration into 麻豆原创 innovation worldwide.

The S.MART component provides a taste of all the available uses cases of 麻豆原创鈥檚 retail technology ecosystem in one immersive grocery store experience. Peruse the aisles as a customer while simultaneously seeing how various types of data are captured in the back office, in order to gain insights into the shopping behaviors and interests of customers, more sustainable business practices, improved product performance, and more efficient staffing.

To kick-start their own transformations, retailers can request a customized visit, where the 麻豆原创 Experience Center is set up as a secure testing environment for use cases specific to their business needs.

How Digital Transformation Solves 10 Traditional Retail Challenges

The value of building a store like S.MART goes beyond keeping shelves nicely stocked. Here are 10 common challenges retailers experience and how digital transformation through an intelligent store can solve them.

1. Respond to customer expectations of personalized and immersive experiences

Personalized loyalty programs in-store and online offer customers the discounts and rewards they appreciate and are likely to use. 麻豆原创 solutions allow for businesses in the tech ecosystem to effectively create products like augmented reality (AR) on mobile shopping. AR technology enables personalized experiences by turning a two-dimensional search into a full-fledged 3D immersion. The camera on the consumer鈥檚 device scans their real-world surroundings and mirrors the product in that environment. That way, consumers can virtually try before they buy. Learn more about how 麻豆原创 supports AR technology like Adloid here. These types of customer-centric tools can help ensure that you are responding to customer expectations of streamlined, digital experiences and keep them coming back for more.

2. Optimize product placement 颅颅

Heatmapping allows for visualizing visitor traffic, while camera streams and 3D vision sensors give demographic and soft biometric details like time of day, age, gender, and time spent at locations. Armed with this data, you can identify heavily trafficked areas and patterns in customer behavior, so you can better strategize product placement and limited time offers.

3. Prevent stockouts

Demand forecasting helps prevent shelves from going bare. Out-of-stock items, or stockouts, are caused by one or a cluster of causes: unusually higher demand, supply chain disruptions, inventory miscounts, and improper planning.

Forecasting technology helps to deter those challenges. Additionally, with technology apps that maintain one consistent source of stock information across the supply chain, you can maintain a more accurate record of goods and further boost forecasting efforts. With the latest AI, you can more effectively keep your shelves stocked, and avoid lost sales.

4. Keep shelves tidy

Today鈥檚 customers expect stores to look tidy and organized. AI connected to video cameras can interpret if a product is misplaced, while sensor-based technology identifies if a shelf is empty. Store personnel are alerted so they can act immediately and keep the shelves pristine.

5. Make the most out of limited storage space

Deep learning and machine learning applied by AI surfaces actionable insights related to customer behavior and supply chain fluctuations, enabling for improved forecasting and more accurate inventory decisions.

With automated sub-daily demand forecasting, you order what your customers are likely to buy at a particular time of day 鈥� no more, no less, making the most out of your shelf and storage space and delivering what customers want when they want it.

6. Reduce food spoilage

According to a , 鈥淎long the food chain, approximately 15% of food losses by weight and value derive from transport, warehouse, and retailing activities. However, for a variety of reasons, temperature abuse and improper handling in transport seems to dominate.鈥�

An effective way to make a dent in that problem is by using smart technology to improve cold storage issues throughout the supply chain, from packing to marketing. At the retail location, as soon as freezers are open past a set period or there is a deviation in the temperature of the products, an alarm is triggered to alert store personnel to address the problem.

7. Become a more sustainable enterprise

Historically, retail companies are a major contributor of carbon emissions and waste, putting them under the scrutiny of consumers and regulators. With demonstrable sustainability practices, retailers can successfully differentiate their聽brands, attract new customers, and drive life-long loyalty. By harnessing the power of AI, you can perform accurate and responsive planning, thereby optimizing the flow of goods to reduce emissions, overstocks, and food waste.

8. Minimize annoying checkout lines

We have come far from long checkout lines, noisy cash registers, and paper receipts. Customers can simply add products to their virtual baskets and leave the store without any wait time at the point of sale. As noted, today鈥檚 consumers are digitally savvy shoppers who expect retailers to provide a streamlined experience in-store and online. 鈥淛ust walk out鈥� experiences help recreate the simplicity and ease of shopping online.

9. Comply with maximum occupancy limits

When it is important to know that maximum capacities are not exceeded, and costly fines are avoided, you don鈥檛 need a person standing at the door with a tally counter in hand. Automated analysis of all persons entering and exiting the store are kept in real time, and store personnel is notified when maximum capacity is reached. Plus, with historical data, you can plan staffing needs accordingly.

10. Motivate employees

Machine learning and AI technologies from 麻豆原创 can serve to reduce friction points in the workforce. For example, insights into the busiest times and store sections can inform optimized staff schedules, reducing stress burnout. Automated alerts regarding empty shelves and improper product placement can help simplify an employee鈥檚 workday, thereby increasing engagement. And streamlined forecasting and replenishment processes replace cumbersome and inaccurate manual efforts, enabling store associates to be more efficient and successful in their inventory keeping responsibilities.

With intelligent features that foster a future-forward work environment, employees are more empowered, productive, and happier. And at the end of the day, a happy workforce thrives on serving happy customers.

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Markus Marsch, head of Value Experience Strategy and Operations, explains how retail experiences like S.MART are the future of retail.

More Inspiration on the Way

麻豆原创 Experience Center locations around the world showcase innovative solutions and use cases for all industries and lines of business. We are creating more of these experiences 鈥� like an 麻豆原创-driven retail fashion studio in New York and a smart cities experience in Dubai 鈥� to showcase how 麻豆原创 builds the innovative solutions that run the world鈥檚 most intelligent and sustainable enterprises.

connect businesses to 麻豆原创 solution experts, inspire transformation through innovative demos and immersive stories, and engage enterprise transformation through hands-on workshops.