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Manufacturers have started implementing features of the Fourth Industrial Revolution (4IR) to be more flexible and responsive and make more intelligent, data-driven decisions. One of the technologies of the 4IR is the Internet of Things (IoT). Manufacturing can not only use IoT for manufacturing, but also to give inventory management greater flexibility and improve decision-making.

IoT works by harnessing a connected mesh of devices with computers that enable the devices to communicate. Using this to track goods in real-time in warehouses and throughout the supply chain can save time, improve warehouse efficiency and prevent stock-outs.

How IoT works with inventory

With inventory management being a key factor in profitability and cost containment, active inventory management is essential. How much inventory a company holds depends on demand factors, supplier lead times, manufacturing capacity, and stock-holding policies. In many cases, these are slow manual processes. However, if IoT is used for inventory management, much of the information could be accessed in real-time for better and faster decisions.

As part of an ERP system, this data can be centralized, allowing for a single source of truth that the whole enterprise can use to improve efficiency, reduce waste and cost, and provide better customer service.

Six benefits of IoT for inventory management

1. Determine inventory availabilityBusinesses can get detailed, real-time insights into inventory in various locations. This includes visibility into stock levels and status, which improves decision-making, prevents shortages and overstocking. In a crisis, information at this detail could be crucial.
2. Automated replenishmentIoT sensors can trigger replenishment orders when inventory levels drop below a predefined threshold, automating replenishment in the ERP to prevent stock-outs as well as optimizing inventory levels.
3. Supply chain optimizationIoT data can be used to optimise supply chain operations, including transportation, warehousing, and distribution. By monitoring inventory levels and locations throughout the supply chain, businesses can make more informed decisions about logistics.Real-time inventory data can improve collaboration across the supply chain. The participants in the supply chain — manufacturers, suppliers, distributors — can use the information to coordinate delivery with production schedules, and enhance logistics to boost overall supply chain efficiency.
4. Enhanced warehouse managementUsing IoT sensors to track the location of items within a warehouse can optimize storage space, identify stock that will expire, and improve picking efficiency. Inventory counts are updated automatically, reducing the need for manual stocktaking.
5. Improved Decision MakingStoring real-time data from IoT devices in an ERP system can help to identify trends, predict demand, and make informed decisions about inventory management.
6. Better efficiency and productivityUsing IoT for manufacturing in inventory management reduces manual stock reviews and speeds up searching for inventory items. Allowing staff to concentrate on higher-value tasks improves performance and productivity, leading to cost savings.

Pre-requisites for using IoT with inventory

Before a business can adopt an IoT-based inventory management system, it must have the necessary infrastructure.

Manufacturing ERP

IoT for manufacturing is creating data at an exponential rate. The ability to manage this flood of data is becoming critical. The answer to overcoming the data deluge can be found in the adoption of a manufacturing ERP system. An ERP enables the automation of processes and provides the tools for inventory management. It can be used to deliver up-to-date information on critical business processes and allows streamlining of the inventory management process.

As well as benefiting the organization internally, the ERP system can share data across the entire business ecosystem. By doing this, manufacturers can unlock additional value and accelerate innovation.

Cloud infrastructure

Most manufacturers will not have the on-premise systems or infrastructure to handle IoT data. A cloud service like the Microsoft Azure IoT Hub can be used as a management service to handle IoT devices. This service connects to all IoT devices wherever they are and provides a unified, central location where devices can be configured, turned on or off, settings changed, and firmware updated.

A manufacturing ERP that supports IoT for manufacturing will have a message queue service and a queue data structure to manage IoT messages from an IoT Hub. In addition, the ERP should have a service to aggregate and analyze the IoT data received, and structure the data in a meaningful format that can be used to provide analysis and empower accurate and real-time decision-making.

Using IoT to manage inventory in an ERP

The digitization of the manufacturing process using IoT is a long-term project. To benefit from IoT for manufacturing, an ERP system can prepare a manufacturer for the changes. Adding inventory to the IoT project will further improve the benefits of the project.

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In an era where the threat of supply chain disruptions is constant, reshoring manufacturing has become a strategic imperative for manufacturers worldwide. Driven by a growing need for resilience, flexibility and proximity to core markets, it’s more than a temporary trend; it’s a strategic reorientation of global manufacturing practices.

The primary objective of onshoring and/or nearshoring is often to regain control over production, improve product quality, reduce lead times, and foster innovation. Additionally, the desire to shorten supply chains and improve responsiveness to customer demands is encouraging more manufacturers to explore reshoring.

A compelling argument for reshoring

Onshoring and/or nearshoring manufacturing operations offers several compelling advantages. Here are the key benefits:

• Improved quality control: Onshoring allows for closer oversight of manufacturing processes, enhancing the ability to maintain high quality standards.
• Shorter supply chains: By reducing the geographical distance between production and consumption points, onshoring and nearshoring can lead to shorter supply chains, faster delivery times, and lower transportation costs.
• Better communication: Reshoring can eliminate language barriers and time zone differences that may affect communication with foreign manufacturers.
• Positive public perception: Consumers often view onshoring positively, associating it with job creation and domestic economic growth.
• Greater intellectual property protection: Onshoring can provide better protection for a company’s intellectual property rights, a significant concern when manufacturing overseas.
• Government incentives: Many countries offer incentives to support onshoring and nearshoring efforts.

Strategies for effective onshoring and nearshoring

Geopolitical upheavals, tariff hikes, regulatory changes, climate-related weather shocks and post-pandemic supply chain crises are ongoing challenges for manufacturers around the globe. In defining their strategies for onshoring, manufacturers need to consider all these factors as well as the challenges and benefits inherent in making the transition.

It is also critical to select locations that fit the manufacturer’s medium- to long-term vision for the future of its operations. Scenario planning is essential for anticipating the impact of policy changes to aspects such as tariffs, immigration and regulatory incentives on operations and supply chains. Some may find strategic advantage in reshoring certain operations, while others might benefit from establishing manufacturing presences in multiple regions to provide options as trade policies evolve.

Successfully navigating the shift to onshoring or nearshoring requires transparent communication throughout supply networks as well as data-driven decision-making processes and an organizational culture that embraces change rather than resists it.

While challenges like labor costs and infrastructure upgrades exist, careful consideration of workforce, infrastructure, policies and the transition process can lead to successful reshoring initiatives which in turn lead to more competitive and sustainable operations.

Capitalizing on technology to adapt at speed

Technology provides manufacturers with the visibility and flexibility they need to adapt quickly. Strategic investments in digital capabilities will help weather market uncertainties – regardless of where they are – and position manufacturers for greater competitiveness well into the future.

A digital manufacturing solution will form the foundation of any onshoring or nearshoring strategy. It’s an ideal opportunity to accelerate advanced manufacturing practices that support a number of key business imperatives while at the same time increasing overall competitiveness and resilience.

There’s no doubt that automated technologies like robotics and Artificial Intelligence are making onshoring and nearshoring more cost-effective and efficient. AI and machine learning can deliver transformative value for supply chain management. By automating the multi-sourcing process, manufacturers can rapidly identify those suppliers which provide the optimal combination of price, quality, and rapid delivery. Predictive analytics can be used to process vast amounts of internal and external data to detect potential inventory shortfalls before they manifest as crises. Similarly, rapid scenario planning allows manufacturers to test various responses to potential disruptions before they occur.

Improved visibility into real-time costs enables manufacturers to make data-driven decisions that protect margins while staying competitive. Automation features further help enhance productivity by streamlining operations, reducing waste, and driving overall efficiency.

Furthermore, digital manufacturing software helps prevent excessive inventory buildup caused by overcautious ordering. Advanced AI models can analyze complex market signals to generate demand forecasts with unprecedented accuracy, providing essential guidance for production scheduling. By balancing supply against accurately predicted demand, manufacturers can optimize inventory levels even in the face of market volatility.

Transforming operational efficiency – and the bottom line

There’s no silver bullet – manufacturers must evaluate multiple elements as well as future scenarios to determine a comprehensive footprint strategy that captures long-term strategic advantage. However, it is clear that reshoring manufacturing can have a transformative impact on a company’s operational efficiency and bottom line.

By asking the right questions, developing robust scenario planning, and pairing it all with a data-driven approach, manufacturers can ensure that their onshoring and nearshoring strategies deliver both immediate benefits and long-term strategic value. As the shift accelerates, onshoring and nearshoring will become central pillars for the global manufacturing landscape, positioning high-performing manufacturers to thrive in a dynamic, high-stakes global economy.

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In today’s unpredictable economic landscape, resilience is everything. From fluctuating inflation to fragile supply chains, businesses — especially manufacturers and distributors — are feeling the pressure. While market giants like Howard Lutnick point to long-term optimism in trade and growth, most CFOs and finance leaders are asking a more immediate question:

“How can I protect my cash flow now?”

The answer lies in one of the most overlooked but powerful levers: accounts receivable. And that’s where Credit Hound steps in.

The Cost of Chaos: What Economic Uncertainty Means for AR

When the economy wobbles, late payments spike. Customers delay invoices, teams scramble to follow up, and revenue forecasts become unreliable. Manual processes — like chasing emails, spreadsheets, and phone calls — can’t keep up.

Every day spent waiting on overdue payments is a day your working capital shrinks.

Credit Hound: Turning Chaos into Control

Credit Hound, a cloud-based credit control solution integrated with SYSPRO, helps you weather volatility with three essential capabilities:

Automation:

Cut 95% of manual collection tasks with smart reminders, chase letters, and to-do lists. Finance teams stay lean, focused, and proactive—even in times of uncertainty.

Visibility:

Interactive dashboards give real-time insights into debtor status, aged receivables, and dispute patterns—so you know who owes what and can act fast.

Control:

With features like PayThem integration for one-click payments and aged debt reduction by up to 50%, Credit Hound turns your AR from a stressor into a strategic asset.

In a Tight Economy, Time Is Money

Credit Hound delivers:

• • Faster payments and reduced debtor days
  • Improved cash flow and working capital
  • Better customer communication and fewer disputes

• Quick setup and fast ROI with a 30-day free trial

Don’t Just Survive. Lead.

In uncertain times, the businesses that lead are the ones that automate smartly, see clearly, and control confidently. Credit Hound helps you do all three—so your finance team stops chasing and starts collecting.

Ready to see Credit Hound in action? Register for our upcoming webinar: Stop Chasing, Start Collecting: Strengthen Cash Flow in Uncertain Times

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In the fast-paced world of smart manufacturing, making quick, accurate and informed decisions is essential. Real-time decision-making, powered by artificial intelligence (AI), is revolutionizing smart manufacturing processes. It enables manufacturers to make decisions about production schedules, inventory management, quality control and more, all based on live data.

AI facilitates real-time decision making by processing and analyzing data at a speed that far surpasses human capability. AI algorithms can review data from various sources such as IoT devices, production lines, and supply chain management systems, and provide valuable insights instantly. For instance, AI can predict machinery failures, allowing for timely maintenance and preventing costly downtime.

Optimizing the impact of AI on smart manufacturing

AI has impacted several smart manufacturing technologies and processes, and this transformation is reshaping industries and driving them toward efficiency and innovation. That said, manufacturers need to take several steps to successfully enable these technologies.

The best approach to smart manufacturing is to … well, be smart. Figure out exactly what you want to achieve – what is the specific business objective? Define that clearly and get business buy-in. That’s the most effective way to reap the rewards of your investment in AI, real-time decision-making and, ultimately, smart manufacturing.

You will also need to address any challenges that exist in the traditional decision-making processes to avoid replicating these as you transition towards smarter decision-making. Next, you will need to evaluate the plethora of reporting, Business Intelligence and AI tools that will create more value than their out-of-the box counterparts.

Once you’ve evaluated the tools available, make sure to engage the services of an experienced consultant to assist in implementing the appropriate solutions for smarter manufacturing. Without this expertise, achieving a satisfactory AI implementation is extremely challenging.

Combining AI with digital manufacturing solutions for maximum benefit

When integrated with a digital manufacturing solution, AI further streamlines processes, enhances data accuracy and strengthens decision-making. Together, these technologies can identify inefficient processes and propose solutions to cut costs and enhance efficiency. AI also enables predictive diagnostics, thereby minimizing resource waste. It can empower digital manufacturing solutions to think, learn and make decisions, a role that was once exclusive to humans.

In conjunction with predictive AI, Generative AI-powered digital manufacturing solutions can analyze vast data sets to offer actionable insights. They reveal patterns and trends, allowing informed decisions on production schedules and resource allocation. They can also identify market opportunities by analyzing market data, customer feedback, and competitor products and accelerating research and development.

To summarize the benefits of a combined AI-digital manufacturing solution:

• Empower leaders with quick access to actionable insights, enabling smarter, faster decision-making.
• Real-time insights: Data on production, inventory, and other critical metrics, allowing for informed decisions.
• Predictive analytics: Forecast potential issues, such as equipment failures or supply chain disruptions, enabling proactive measures.
• Helping to build smarter, more agile businesses.
• Fueling the shift to smart manufacturing and more connected operations.
• Powering growth through smart, data-driven manufacturing.

Practical examples of AI-driven decision-making in smart manufacturing

AI’s impact on real-time decision making is visible across various aspects of manufacturing. Here are a few examples:

• Quality control: AI can analyze product images in real time to identify defects, enabling quick corrective actions and reducing waste.
• Inventory management: AI can predict demand patterns, allowing manufacturers to optimize inventory levels and avoid overstocking or stockouts.
• Energy efficiency: AI can monitor energy usage in real time and suggest ways to optimize energy consumption, leading to cost savings and sustainable practices.
• Process optimization: AI can analyze production data to identify inefficiencies and offer recommendations for process improvements.

Driving smart manufacturing into the future

Ultimately, the power of real-time decision-making lies in its ability to provide manufacturers with the insights they need, exactly when they need them. By integrating AI into their processes, manufacturers can make more informed, data-driven decisions, leading to improved efficiency, reduced costs, and enhanced competitiveness.

It’s clear that AI plays a pivotal role in enabling real-time decision-making in smart manufacturing. By integrating AI into their operations, manufacturers can gain a competitive edge and drive their industry into the future.

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Traditional ERP systems were deployed on-premise, where the company took responsibility for everything, including installing the hardware, running the system, and most IT issues. Manufacturers are now able to choose a different deployment method – cloud ERP – as the backbone of their digital transformation strategy. A cloud-based ERP solution is located off-premises, and is accessed using the Internet. The software vendor is responsible for maintaining the hardware and dealing with IT issues. There are many other benefits of cloud ERP for mid-size manufacturers.

Considerations and Benefits of Cloud ERP

1. Financial considerationsConsiderations for deploying a cloud-based ERP include cost and time-to-value. Various reports mention significant savings on IT infrastructure, and a shorter time for Return on Investment. A cloud-ERP results in lower capital expenditures compared to on-premise implementations. For manufacturers, cloud ERP can reduce the total cost of ownership and decrease maintenance costs.Manufacturers considering a cloud ERP should ensure that it complies with data sovereignty and data residency legislation in their locations. Cloud ERP providers should offer data residency options and ensure data is stored within the specified jurisdictions to comply with regulations.
2. Business benefitsCloud ERP solutions keep up-to-date with technology innovations, allowing manufacturers to make use of the latest developments, such as Industry 4.0 and IoT.The scalability of a cloud ERP system allows manufacturers to scale operations with lower marginal cost. If a new site is created, it is easy to connect it with the rest of the organization via the Internet. If production operations are subject to seasonal demands – expanding or reducing production – the company can quickly scale up or down its use of cloud ERP resources.For back-office staff and other stakeholders, a cloud ERP system provides location independence, allowing them to access the system remotely from anywhere and at any time.A cloud ERP system can improve and expand the business ecosystem by giving suppliers, customers and other partners an easier way to integrate their systems with the manufacturer.
3. Technical benefits of cloud ERPA cloud ERP removes the stress and effort associated with maintaining applications on-premise. An ERP in the cloud is looked after by a skilled team of technical staff who are certified in all the knowledge needed to run an ERP system.Maintaining an ERP system usually involves upgrading it periodically as new features and functions are introduced. For an on-premise system, this usually entails a lot of work before upgrades are implemented, and the live system has to be taken offline to install the upgrades. The benefit of a cloud ERP is that downtime is significantly reduced. For certain industries which need their systems to be compliant, a cloud ERP allows companies to test changes first on a separate cloud instance to ensure the compliance of the new system can be signed off before it is made live.Cloud ERP systems usually provide much better disaster recovery services than most manufacturers can afford. This ensures that data and operations can be restored quickly in case of a disaster.
4. Data management benefitsIt is easier and more cost-effective to manage data in the cloud than in many on-premise systems.With almost unlimited data storage in the cloud, it is easier for manufacturers to leverage the deluge of data that’s coming in from connected sensors in their factories, inventories, and raw materials, and from signals along the supply chain.Greater data security and privacy are other benefits that a cloud ERP offers. For example, cloud ERP providers will be ISO27001 compliant (a standard for information security management). Another standard that a cloud ERP will have is SOC compliance, which certifies that customer data and other sensitive information are protected.
5. Extensibility and integration benefitsCloud ERP systems have greater options for extensibility, allowing them to connect to many other services in the cloud. These services can be on other cloud platforms.A cloud ERP will also have the integration capability to connect not only to cloud services but also to on-premise services. This allows it to integrate with production systems like shopfloor data collection which can be implemented across all production locations to improve job scheduling.

Cloud ERP – the key to digital manufacturing success

Manufacturers in the SMB sector need to decide how to allocate scarce funds to ensure the business can grow. The key to success in the digital era is selecting a cloud ERP platform, designed for manufacturers, that is cost-effective, scalable, secure, and allows adoption of new technological capabilities. A cloud ERP is the best strategy to enable business growth as it is scalable and adaptable to accommodate future growth and evolving business needs.

The post The Smarter Manufacturing Blog appeared first on Syspro.

The importance of supply chains

The supply chain is the journey that raw materials, components, and other goods take before they go into production or assembly, and then sold to customers. Many supply chains span across continents and are becoming increasingly complex. A resilient and trusted supply chain is now seen as critical and manufacturers need visibility over it to address any supply chain disruptions.

There have been some disruptions to supply chains that many businesses never even envisaged, for example, the Ever Given blocking the Suez Canal in March 2021. However, manufacturers should plan for certain eventualities, such as:

• congestion and bottlenecks disrupting the flow of goods;
• material shortage or scarcity;
• supplier problems and delays;

Supply chains and ERP

Companies need to take proactive steps to mitigate the impacts of supply chain disruptions. Trying to achieve this with legacy systems and manual processes is impossible. For a functioning supply chain, an ERP system is needed to consolidate and integrate processes across the business and provide the tools to achieve an optimized supply chain. A modern ERP system can help cut costs, improve operational efficiency, and enable real-time visibility enabling better informed decision-making.

Real-time visibility and data-driven decision-making

Supply chain management involves the continuous planning and orchestration of goods to meet customer demands. An ERP is a highly effective tool for consolidating and analyzing data pertaining to the movement of goods, from their origin to the point of demand.

One of the key benefits of an ERP in counteracting supply chain disruptions is the real-time visibility it can offer across the supply chain. It allows manufacturers to understand how goods are flowing to the shop floor and identify bottlenecks quickly. This information can help plan the arrival of goods so that production is scheduled. In turn, this can be used to provide customers with a more accurate timeline for delivery. When manufacturers use business intelligence capabilities in an ERP with real-time data and reporting capabilities, they can track and improve supply chain performance.

Greater supply chain visibility

An ERP enables manufacturers to see the location of inventory throughout the supply chain so they can respond quickly to supply chain disruptions. It allows them to make more informed business decisions, and have insight into every part of the supply chain to identify problems and address them speedily. An ERP system can assist in:

• managing the complex operations of a supply chain;
• notifying the manufacturer when problems occur;
• streamlining processes and eliminating inefficiencies.

Within supply chain management, the management of dispersed warehouses, especially across multiple locations, presents substantial challenges. These challenges can quickly escalate without proper tools and strategies. Warehouse management is key in supply chain operations, and an ERP system allows it to be optimized. Through ERP systems, organizations can achieve heightened inventory visibility of stock in every location, effectively mitigating the risks associated with stock-outs and overstocking.

Using inventory management to minimize disruptions

Deploying an ERP system as part of a supply chain management strategy also enhances information about stock data to improve stock levels and turnover. Manufacturers can accurately track shipments, inventory levels and demand to assist decision-making about inventory. This is particularly valuable where inventory surplus carries a high cost or stock-outs want to be avoided.

Organizations often struggle to access real-time updates on logistics and inventory, leading to decision delays. ERP integration alleviates this challenge, empowering businesses to connect with local and external activities, and gain timely cross-channel inventory visibility. This transformative step enhances overall supply chain management and operational efficiency.

How collaboration can mitigate supply chain disruptions

A well-functioning supply chain relies on the collaboration between internal departments and external partners to minimize supply chain disruptions. Trying to manage the information using emails or spreadsheets is error-prone and cannot scale as supply chains grow and get more complex. An ERP helps by:

• unifying information in a shared data model so that all stakeholders have access to the same real-time data, creating transparency and eliminating information silos;
• enabling communication with suppliers which creates better coordination and ensures disruptions are identified quickly and addressed accordingly;
• speeding up access to information means faster decision-making and quicker response to unforeseen circumstances.

With effective communication across supply chain partners, manufacturers can also identify if alternative sourcing options are available.

Better forecasting of supply needs

Effective demand forecasting is an essential aspect of supply chain planning. However, it can be a daunting task, given the numerous variables that are subject to constant change.

Manual processes are not conducive to forecasting supplies, but a manufacturing ERP can use sophisticated algorithms to analyze sales data, customer behaviour and market trends to forecast demand. It allows manufacturers to anticipate changes in demand and ensures they have the right resources at the right time. This leads to improved inventory planning and optimized stock levels, helping manufacturers to tackle supply chain disruptions.

An ERP can improve supply chain management

Using an ERP system to support a company’s supply chain can streamline and automate business processes and reduce manual errors and costs. It increases supply chain transparency and helps manufacturers tackle delivery challenges. By providing a unified data model, both internal staff and external partners can be kept up-to-date on activities throughout the supply chain. The real-time data that the ERP system can present creates better visibility and allows quicker decision-making when supply chain disruptions occur, so appropriate actions can be taken before the situation becomes too serious.

The post How ERP Can Help Mitigate the Impact of Supply Chain Disruptions appeared first on Syspro.

Businesses are realising the importance of implementing cybersecurity to protect their IT systems. For manufacturers, this includes data in their ERP system and external systems like IoT (Internet of Things). A manufacturing ERP system contains sensitive information ranging from supplier and customer information to critical intellectual property information in BOMs (Bill of Materials). Because of this, it can be a prime target for cybercriminals due to the sensitive data stored. Cybersecurity in ERP has therefore become a focus for manufacturers.

How cyber-attacks can affect business

Cybercrime taps into the vulnerability of business systems and can pose multiple threats in the supply chain, billing, inventory and production.

Various sources have put the average cost of recovering from a ransomware attack as US$1million – US$4million. Attacks can affect the prices that customers pay as well as their outstanding orders, and so threaten the manufacturer’s reputation.

Components of an ERP to protect

Protecting the data in an ERP system involves shielding the various components of the system. The components are:

• core ERP system: the component that provides the capability for managing core business processes, such as financial management, inventory management, supply chain management, and customer management;
• database: the data of an ERP is stored in a centralised database. Everything from customers, suppliers, inventories, transactions, product information and data obtained from external systems is stored there;
• reporting and analytics: an ERP contains data used for reporting and analytics that enables users to analyze and get insights into business operations;
• integration systems: every manufacturer uses some data from other systems with its ERP, so integration protection is important.

Six ways to protect data in an ERP

Implementing cybersecurity in ERP means looking at various strategies. ERP vendors provide the tools and recommend best practices to protect their ERP solutions.

1. Software updates: security technologies keep evolving to address new threats, therefore, businesses need to protect their ERP system by installing the latest version of the ERP software. Besides enabling the latest features, newer software versions remove vulnerabilities that may put a business at risk.
2. Access rights: ERP software includes a hierarchy of access rights which can be applied across an organization to restrict users’ access to only those areas that they use. However, many companies avoid the effort to apply this hierarchy and give employees full access rights. This opens up opportunities for cybercriminals to access sensitive information. Manufacturers should instead ensure that employees are provided with role-based access and form part of groups with associated security and clearance authorizations.
3. Multi-factor authentication: one-factor authentication, or single sign-on, of a userID and password is now outdated and opens another opportunity for malicious access. Instead, businesses need to have an extra layer of security with multi-factor authentication involving a security method where users must provide multiple forms of verification (like a password plus a code from a phone) to access a system, adding an extra layer of protection.
4. Network security: the ERP system should be protected from network-based attacks and denial-of-service attacks by using network security measures. Firewalls, intrusion detection and prevention systems, and routine network scans are some examples of the security measures that fall under this category.
5. Protecting external data: sensitive data coming from external sources should be protected by encryption while it is in transit.
6. User education: It is vital for employees to understand potential dangers and learn best practices for using the ERP system. They need to be aware of maintaining good password hygiene and recognizing social engineering attempts. In a recent survey it was revealed that 49% of respondents use the same login credentials for multiple work applications, and 36% use the same credentials for personal and professional accounts.

Cybersecurity in ERP

It is essential for businesses to implement a comprehensive security strategy that includes regular security assessments, vulnerability testing, employee training, and stringent access controls to reduce the impact of cybersecurity threats and protect their ERP solution from security breaches.

ERP security is achieved by getting the security of the composite elements right. The biggest challenge is likely to be identifying these components. Companies must also recognize that their employees are their most significant vulnerabilities.

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In a previous blog AI and Machine Learning in Manufacturing ERP: Key Benefits, we discussed the benefits of using AI in manufacturing and how it could be enhanced with an ERP system. While manufacturers are keenly interested in using AI, the main question they have is what are the best use cases for AI in ERP?

There are so many options for using AI in ERP that it can be tempting to rush into a project without thinking it through properly. Despite the hype around AI, many manufacturers have still to implement it in their manufacturing systems. According to SYSPRO research with Frost and Sullivan Complex Manufacturing Outlook: Emerging Trends and Opportunities, only 19 percent of companies in the complex manufacturing sector have adopted AI. In the mid-marketing manufacturing sector, the adoption is lower.

Where AI can add value to ERP

As was pointed out in the previous blog, there are many areas where AI can benefit a manufacturing ERP.

• Using AI to predict when maintenance will be required to prevent unexpected downtime and to extend equipment lifespan.
• AI can be used to improve demand forecasting, enabling better inventory management.
• Vision systems incorporating AI can inspect products for defects in real time, ensuring consistent quality and reducing human error.
• AI algorithms can analyze production data to optimize schedules and allocation of resources, increasing throughput and reducing production costs.
• AI can provide real-time insights and analytics, enabling manufacturers to make informed decisions based on accurate data.
• For product design, generative AI can analyze large datasets and existing designs to come up with new design concepts that would be unlikely with traditional methods.

A new use case of AI in ERP is Agentic AI, which refers to AI designed to operate autonomously and make decisions on behalf of humans or other systems. Agentic AI can adapt and learn from past data, allowing it to perform complex tasks without constant human supervision. It provides the potential for virtual assistants which can understand and process natural language more effectively.

Challenges of using AI in ERP

One reason why AI initiatives have not been adopted more quickly is the experience manufacturers had with the high costs associated with Industry 4.0 deployments, which did not often provide short-term benefits.

Manufacturers who implemented Industry 4.0 or Smart Manufacturing in the past can see many of the same problems with AI:

• fragmented or poor-quality data,
• integration with existing systems,
• workforce readiness.

AI is only as good as its data

The right data in the right format is the foundation of AI success. Companies that fail to check and provide the right business data for AI will find that AI produces unreliable results. The adage of ‘garbage in, garbage out’ still applies with AI and manufacturers that want to start an AI project must invest in cleaning their data first.

AI requires a unified, consistent dataset, but many companies operate with multiple, disconnected systems that contain overlapping but inconsistent data. These need to be consolidated before implementing AI to avoid misleading predictions.

Since AI became so prominent in 2023, there has been a perception that AI requires large language models (LLMs) and that the bigger a model, the better it is. Only a few companies can afford the infrastructure and other costs of this type of AI. However, small language models (SLMs) are now appearing that offer significantly improved costs, and more importantly, the fine-tuned and specialized knowledge that manufacturers will require.

Integrating AI

For any AI system to be useful, it must be able to integrate with data in other systems. The advantage of AI as part of an ERP is that the integration is built in, therefore the data is already available for the AI system to use.

Workforce not ready for AI

Many manufacturers are struggling to recruit and maintain sufficient staff just to implement ordinary operational improvements. Very few companies have the level of maturity – skill, knowledge, understand – to make AI initiatives succeed. Since many staff are apprehensive and need support, the transformation needed will take time.

How to start an AI project

Several experts in the ERP arena have the following advice.

1. Identify a key area, for example, demand forecasting in the supply chain process, which can benefit from AI. This will help to create momentum and build trust across the organization.
2. Focus on integration with other systems: this ensures that new technology complements existing systems rather than replacing them outright.
3. Ensure data collection and management is prioritized. It is critical to gather accurate and relevant data from various sources within the manufacturing process.
4. Select the right AI solution that aligns with business needs.
5. Start with a pilot project to test the effectiveness of the focused AI project. This makes it easier to evaluate the benefits and make adjustments before full-scale implementation.
6. The human factor in complex projects is crucial. Therefore, get buy-in from staff on the AI project, involve them in the implementation, and train them on how to use the AI tools.

The potential of AI in ERP

The integration of AI in ERP can help to revolutionize how manufacturers operate. However, despite its promise, the adoption of AI remains low. To harness AI effectively, manufacturers must approach implementation with due care and without rushing into it. By following a structured roadmap, manufacturers can unlock the potential of AI in ERP, driving efficiency, innovation, and competitiveness in an increasingly complex market.

The post How Manufacturers Can Start Using AI in ERP appeared first on Syspro.

In manufacturing, performance improvement, cost reduction and process optimization are crucial. Manufacturers have adopted innovative solutions and technologies to deal with these issues. Artificial intelligence (AI) and machine learning (ML) in manufacturing ERP have recently added a new realm of technology that can address the complex operations found in manufacturing.

There is no question that AI and ML will have important roles in shaping the future of manufacturing ERP. Given the recent developments in computing and the ability of AI models to learn and adapt, AI and ML will increasingly be used to improve efficiency, productivity, and creativity across manufacturing.

What is AI and ML?

AI is the term for computer software that gets information about the environment and takes action in response to that information, and objectives that are set. AI systems get better and more accurate as they collect and analyze more data. AI can perform tasks often performed by humans with a higher degree of accuracy and intelligence. While it does not replace human intelligence, the insights that come from using AI can significantly augment human capabilities.

ML is a form of AI that enables a system to learn from data rather than through explicit programming. As the ML process trains on data, it is then possible to produce more precise models based on that data. An ML model is the output generated when you train your ML algorithm with data

Benefits of AI and ML in manufacturing ERP

Maintenance, process control, productivity, and supply chain optimization are some of the common areas where AI and ML can make an impact on manufacturing ERP.

Industrial engineers have used heuristics-based rules, what-if analysis, and physics-based simulation to get predictive and prescriptive insights for several years. AI and ML can now augment and enhance those heuristics to get results quicker and provide more in-depth analysis to help manufacturers improve how they monitor and control processes and operations.

AI and ML in production control

• ML models trained on operational data from an ERP can help achieve significant benefits in key operational metrics like throughput, and OEE.
• Combining real-time data from connected machines with AI and ML can provide insights into machine loads, streamline the production process and improve production scheduling.
• AI and ML can analyze machine data from a manufacturing ERP to predict when equipment might fail, allowing for timely maintenance and reducing downtime.
• AI can help manufacturing ERP systems in optimizing the use of materials and resources, reducing waste and lowering costs.

AI and ML in inventory and supply chain management

• Applying AI to demand forecasting can reduce forecasting errors and costs by optimizing inventory levels to match demand through analyzing data from an ERP on sales history, customer behaviour and industry patterns.
• By analyzing external data, AI can identify potential supply chain disruptions, and suggest alternative suppliers.
• AI can help to solve complex delivery constraints and improve logistics costs by finding patterns in data from intelligent sensors on trucks and packages.
• By applying AI to shipping and supplier data, manufacturers can evaluate supplier performance, and uncover which shipment centres are most accurate in catching errors

AI and ML in quality management

Using AI to evaluate component images in the production line can identify in real-time deviations from quality standards, improving ERP quality management and testing.

Other benefits of AI and ML for manufacturers

• Using the large amounts of data in the manufacturing ERP, AI-powered decision support tools can analyze data to find patterns and trends in seconds, helping managers make informed decisions quickly.
• AI algorithms can simulate a variety of scenarios, evaluate potential outcomes and identify the most effective strategies.
• AI integrated with tools like ERP can provide more personalized experiences by analyzing user roles, past data interactions and common workflows.
• AI models can also improve customer service by personalizing interactions through analyzing customer data.

Manufacturing benefits of Generative AI

Generative AI, a subset of AI, can generate new content or designs without needing training data. Unlike traditional AI which primarily analyzes existing data, generative AI learns the underlying patterns and structures of input data to produce similar but new outputs. In manufacturing, this can be the creation of optimized design alternatives for parts or products.

Manufacturers are beginning to employ generative AI in their product design and development. By inputting parameters and requirements into generative design software, companies can obtain optimized design solutions that not only meet their criteria but also present options they might not have considered.

Generative AI can also be used for information summarization. In this way, it could help with the skilled labor shortage in manufacturing by assisting production workers in learning how to use their ERP to improve understanding and solve problems.

Where to Start with AI and ML

There has been so much hype around AI since the launch of ChatGPT at the end of 2022 that many manufacturers may consider they are being left out of the AI revolution. That is not the case. An article in the Havard Business Review noted that as much as 80 percent of AI projects fail. In order to ensure a greater chance of success, companies need to do three things.

• Choose a business case that has a narrow scope, for example, demand forecasting.
• Select the right tool. An ML solution is more appropriate for demand forecasting than generative AI.
• An AI model is only as good as its data. Manufacturers must make sure they have high-quality data from their ERP as well as external data, and have it in the right format to get the results they need from their AI project.

Key benefits of AI and ML in manufacturing ERP

Three key benefits of using AI and ML in manufacturing ERP are that it can trigger significant improvements in productivity, efficiency, and operational excellence. However, while the benefits of AI and ML in manufacturing are clear, when it comes to the adoption of AI, there is still much for companies to learn and understand first.

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