Technology has been a passion of mine for a long time, so I have personally found it fascinating to watch the variety of opinions and perspectives unfold as technologies mature. It has challenged my thinking and preconceptions, and I recognize that as a leader and a human I need to address them.

I’ve covered everything from fundamental ethics and whether it is good or bad, through to more specific considerations like ‘what do I want from AI?’, and therefore ‘what might others want from it?’.

Naturally, it’s been hotly debated by my colleagues. The leadership team is considering how today’s and tomorrow’s versions of AI should shape our own role in AI creation and adoption.

We already know that artificial intelligence for IT operations (AIOps) is poised to take advantage of the benefits. At Avantra, we’ve long evangelized the value of automation to offset risk, better utilize skill and boost productivity and innovation. But the debate that surrounds AI has helped us understand that our next phase of technical development must be underpinned with even greater pragmatism and responsibility.

The latest numbers I saw suggest that every day, 100 million people are experimenting with the likes of ChatGPT and other Large Language Models (LLM), such as Bard. Numbers like this highlight the popularity and allure that machines still have. Just like the washing machine, if it makes life easier, why would you not use it?

Download: ChatGPT and Generative AI in the Workplace

Proceeding with Caution

However, even though world renowned university colleges are condoning the use of conversational AI tools, leading experts are urging caution — legalities, politics, economics and ethics are top of the list of concerns.

It was eloquently summed up by The Future Life Institute, which is made up of over 1,000 experts, in an open letter asking the industry to pause AI development, or risk humanity and society. LLMs are learning so much so fast, that we, as a species, haven’t had time to truly process the long term impact. Ethics are at stake.

Taking Responsibility is Urgent

I think it’s a responsible challenge. As I said before the headlines have provoked my own thinking to evolve and prompted me to consider whether such a warning could, should or even will stop our own industry from forging on.

This is where I think the application of AI must be balanced against the dilemma. Take the example of producing project documentation or new product technical summaries. Is using ChatGPT to create the first draft irresponsible or a boon for productivity, freeing up time for innovation in other areas? Similarly, with the introduction of ‘copilot’ tools, like Microsoft assistant, people can increase their productivity and have more time for other things, even just going to the gym. I can see how it could make a sustainable argument for a four day week and happier colleagues.

Can Conversational AI Help Our Industry?

Of course, in my world, the real advantage of introducing ML and AI is the ability to help customers find answers to the problems they face. Using conversational AI to mine a database of known and defined errors other businesses have encountered — be that on SAP or Google — would help practitioners arrive at answers far sooner and avoid a degradation in productivity.

The process would augment the value of the intelligence we aggregate and own and, as it’s a trusted source, accelerate decision making and the time to resolution (TTR). No human can realistically (nor would they want to) hold in their brain all the common problem scenarios and fixes.

I’ve tried to do this in my professional career, and though possible, it is exhausting. That’s why I believe, applying conversational AI to the common challenges our customers face would help highly qualified and skilled humans validate and implement the decisions they take.

I’d advocate that automating the interrogation of vast knowledge banks makes complete sense, especially when it helps skilled people get on with doing what they do best — running, managing, and developing world class ERP.

I should be clear that I am wedded to the notion that it’s important the wider industry runs the AI race in tandem with the ethics that protect humanity. We must thoroughly understand the implications at every point in development and put in place the checks, balances and regulation to ensure the values we hold dear are protected and enhanced, not obliterated.

In the world of AIOps there is real value to its adoption not least to ensure mission critical systems related to food supply or energy stay online. We must therefore consider the broad view of AI technology as well as our narrower domain. Only with a balanced view and appreciation of the accountability we assume as leaders, can we make the right choices.

John Appleby leads Avantra as the Chief Executive Officer. Before Avantra John served as the Global Head of DDM/HANA Center of Excellence at SAP and as the Global Head of SAP HANA solutions at Bluefin Solutions, subsequently acquired by Mindtree. John is a recognized thought leader in the SAP market and was part of SAP’s Mentors Group. John holds an MA in computer science from the University of Cambridge.

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To effectively navigate the digital transformation journey, there are several key topics that manufacturing companies should consider. Enterprise Resource Planning software and manufacturing solutions offer a broad spectrum of manufacturing planning, execution, and integration, making them great models to consider for digital transformation.

1. Change management enables teams to thrive in change

Efficient change management is necessary to ensure a smooth transition to new ways of working, manage supply chain disruptions and implement new business models, and support employees to thrive with change and navigate ongoing uncertainty.

Change management has become more critical since COVID-19 because the pandemic has significantly transformed how businesses operate, and companies have had to adapt quickly. All operations are in the hands of humans, and no matter how good technology is, execution will fail if individuals do not work as a team to support the company’s mission.

Manufacturing companies can best manage organizational change by setting up and managing a change management program and a change management organization. Specific methodologies to develop, deploy, and build a change management program are integral components in the digital transformation project for the respective project, and system integrators are helping with best practices and deploying them.

2. Business process management is key to digital transformation

Effective process management and a business process management organization as an endpoint for digital transformation are crucial because it enables organizations to optimize their business processes, improve efficiency, and achieve better outcomes.

Process management involves mapping and analyzing business processes, identifying areas of inefficiency or waste, and implementing process improvements. Organizations can reduce costs, improve quality, and increase productivity by optimizing business processes.

Related: Sustainability is the Key 2023 IT Trend

3. Sustainability – A new perspective for manufacturing

Sustainability is the third area of focus, driving digital transformation from both ends. It has emerged as a new initiative, pushing digital transformation forward. As a beneficiary of digital transformation, it gives manufacturing companies an entirely new world of data to measure sustainability goals such as carbon footprints or product lifecycle analysis to assess the impacts of products and what can be done.

Digital transformation to support sustainability for manufacturing is fundamental, considering the potential environmental effects of manufacturing processes, potential cost savings, reputation and branding, compliance, and innovation by adopting sustainable manufacturing practices.

Sustainability is still relatively new for most players in the general ERP space. However, the demand has never been higher than today. Managing sustainability goals can be challenging, requiring data availability, data quality, standards, frameworks, resources, investments to implement, and agreement on stakeholder expectations.

However, as with other areas in digital transformation, specific methods, and best practices are applicable.

4. The cloud is here to stay

The manufacturing industry may have been hesitant to adopt cloud computing due to concerns about data security, privacy and potential disruption to existing systems. However, as the benefits of cloud computing are more apparent, more and more manufacturers are beginning to adopt cloud-based solutions.

Security and privacy are a big concern for many; however, unless the manufacturing applications are entirely sealed off, the industry consensus is that security and privacy are a matter of managing policies rather than actual physical access issues. If done right, the cloud gives customers more control over privacy and security than traditional on-premises solutions.

Manufacturers today face a critical challenge – ensuring round-the-clock manufacturing operations with zero interruptions. Any potential disruption can have significant consequences with ramp-up and ramp-down processes requiring uninterrupted functions. However, manufacturers already offer cloud solutions with substantial development in this regard.

5. Cybersecurity challenges in manufacturing companies

In the age of digital transformation, cybersecurity is a critical concern for manufacturing companies. With increased reliance on interconnected systems, IoT devices, and cloud computing, the risk of cyber threats and data breaches is higher than ever.

Manufacturers must prioritize robust network security protocols, regular vulnerability assessment, and secure access controls to protect valuable assets. Implementing advanced intrusion detection and prevention systems, real-time monitoring, and incident response capabilities is essential for prompt threat identification and mitigation. Employee awareness and training programs also play a crucial role in preventing cyber-attacks.

By addressing these challenges, manufacturing companies can maintain operational integrity, protect sensitive information, and build customer trust. Cybersecurity must be fundamental to their digital transformation strategy for a secure future.

How to drive digital transformation

The points above highlighted the various aspects of digital transformation. However, the question that arises is how to tailor it to support the organization’s progress. The digital transformation process can be complex as it integrates new technologies, processes and data throughout the organization.

Adopting a strategic approach that aligns technology with business goals is necessary to prioritize digital transformation in the manufacturing industry. This can be achieved by following these key steps:

• Define business objectives: Identify the critical business objectives that digital transformation can help achieve.
• Evaluate current capabilities: Assess the current state of technology, processes, and data across the organization to identify areas of improvement and potential opportunities.
• Develop a roadmap: Chart a digital transformation roadmap, detailing the steps to achieve the identified business objectives
• Prioritize investments: Prioritize technology investments based on their potential impact on the business objectives identified in step one.
• Foster a culture of innovation: Encourage employees to experiment with new technologies and processes, thereby cultivating a culture of innovation.

Alexander Paulus is a Senior SAP Professional and SAP Project Manager with 15+ years of experience in various project roles, mainly in global ERP Implementation projects and Solution Architecture and Delivery & Operations Management. He is Managing Director at CNT Management Consulting Inc. and one of the key digitalization subject matter experts.

The post Five Key Topics for Digital Transformation in Manufacturing Companies appeared first on My TechDecisions.

Despite the importance and usefulness of ERP, the data from Panorama Consulting Group shows that only half (52%) of organizations are satisfied with the results of implementing these software solutions. However, the adoption of innovative technologies such as AI can increase the satisfaction of enterprises by enhancing ERP with additional capabilities.

AI Benefits for ERP

Today, AI-powered software systems have evolved enough to be able to read, recognize speech, execute transactions, place orders, make predictions and conduct other tasks related to enterprise resource management. Coupled with machine learning technology, AI-based ERPs can also continuously train to perform the aforementioned activities even better.

Today, ERP solutions empowered with AI are considered the best-of-breed enterprise technology in their niche. By purchasing or developing an ERP system integrated with artificial intelligence, an enterprise can achieve the following goals:

Performance and productivity improvement

Since AI allows for easy and quick automation of any work processes in ERP, organizations can continuously increase performance and, as a result, competitiveness.

Risk mitigation

By reducing the role of the human factor both in day-to-day ERP operations and decision-making, enterprises can eliminate a range of risks associated with manual work. For example, enterprises can utilize AI-based ERP to prevent mistakes during data entry.

Reduction of enterprise resource planning cost

The ability to cut and optimize costs is critical to the growth and development of any business, and this is where AI-powered ERP capabilities can come in handy. For example, by delegating tasks related to procurement to an AI tool, organizations can use fewer staff for resource management and thus save some money.

Related: Artificial Intelligence in Marketing

4 use cases of AI in ERP

Artificial intelligence can be implemented into enterprise resource planning in the following ways:

Autonomous Business Process Management

In recent years, organizations have been increasingly implementing ERPs with functionality for automating various tasks – creating invoices, processing orders, managing the supply chain, etc. However, by integrating AI and ERP, enterprises can go beyond that and choose a more revolutionary and advanced approach to workflow automation – autonomous business process (ABP) management.

Unlike conventional automation, whose primary goal is to replace manual labor with automated interactions, ABP focuses instead on continuous analysis and improvement of automated workflows. Now let us consider an example to see how it might work in practice.

For instance, an enterprise has an ERP solution that automates the procurement process. The software generates requests and sends them to suppliers when a particular item in the warehouse is running out. If this ERP is equipped with ABP capabilities, it can automatically detect bottlenecks in the procurement process, provide recommendations, and, if necessary, even make dynamic changes in the workflow.

Cross-Department Collaboration

The efficient work of a large enterprise requires all its departments to be synchronized with each other, and here ERP solutions prove indispensable. Working as a centralized data hub, an ERP system facilitates collaboration by providing employees with any data they need anytime via a single access point. Nevertheless, AI integrated into the corporate ERP can improve the collaboration process even more significantly.

Related: Future Technology & the Impact it Will Have on Collaboration Workflows

For instance, AI can help track the performance of enterprise employees, identifying those who reduced their activity or have issues with one or another aspect of ERP. The software can then automatically notify more experienced colleagues so they can help team members who are experiencing troubles. Also, if an ERP system is equipped with natural language processing, employees can use voice commands to manage their work calendars or quickly book meetings with colleagues.

Customer Service

What is more, the integration of AI and ERP allows organizations to improve the quality of customer service and support. For instance, an ERP system can quickly provide relevant data at the request of support agents, helping them with customer communication. Likewise, an AI-powered system can analyze a particular customer’s purchase history and transactions in just a couple of seconds, allowing agents to define a suitable solution to a customer’s issue.

Enterprises also can integrate ERPs with intelligent bots that can communicate with disgruntled customers or those who require help. Using machine learning models, these bots can continuously learn, which enables them to maintain a more human-like conversation and provide more accurate and relevant responses.

Advanced Analytics

Speaking of data analytics, it would not be an exaggeration to say that the capabilities of AI combined with ERP are almost limitless here. So, as we have already mentioned, AI can analyze workflows’ data to suggest and implement improvements, but these are not all the capacities of intelligent ERP analytics.

In particular, it is worth noting that the power of machine learning enables ERPs to identify hidden sales and marketing opportunities. For example, an ERP system can analyze people’s buying behavior and preferences to provide managers with insights into the most profitable market segments or marketing campaigns improvement.

Even more important, AI can provide forecasting capabilities, enabling making precise future predictions. This way, AI can help employees identify periods of high demand and recommend increasing or decreasing production volumes. In the same way, an ERP can help to identify changes in customer buying patterns, which can be critical in times when market conditions are constantly changing.

Final Thoughts

Enterprise resource planning systems play a vital role in how modern enterprises manage their corporate resources and ERP-related workflows. Since this software facilitates such different tasks as supply chain management, procurement, customer support, and many more, ERP is a must-have tool for any growing business.

However, the solution’s value can be significantly increased if an enterprise integrates it with artificial intelligence. This way, organizations can leverage autonomous business process management, foster collaboration and customer support, and improve analytics, thus enhancing their business efficiency and competitiveness.

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