How is machine learning making vending machines smarter?

Machine learning is all about recognizing subtle patterns in data and then extracting important insights to solve complex problems. Machine learning helps organizations change generic, one-size-fits-all processes into efficient, contextually sensitive processes that save time, effort, and money.

Vending machine management is an example of a set of processes that machine learning can analyze and enhance. The vending machine industry represents a huge opportunity, with as many as 31.6 million machines in operation and a market size of over $30 billion by 2025. Real-time vending machine data collection and analysis is becoming easier with the introduction of connected vending machines, which are expected to exceed 3.6 million by next year.

In this blog post, we’ll discuss how machine learning can give forward-thinking vending machine businesses a massive competitive advantage over their competitors.

Machine-specific resupply

People in different countries and cities tend to favor different snacks and beverages. Even within the same city, SKUs that sell well in a vending machine in a movie theater might not perform as well in a vending machine in a gym. If vending machine user preferences vary from location to location, why should vending machine businesses stock each machine identically?

You can apply machine learning techniques to your company-wide vending machine sales data to determine the best product assortment for each machine. With more accurate demand forecasting for each product and location, your machines won’t run out of popular items between deliveries or carry as many products that aren’t selling.

Addressing seasonal demand

Your customers’ preferences change depending on the time of day, day of the week, and the weather. Even if each of your machines is stocked with the right product assortment for its customers, that assortment should be adjusted from resupply to resupply to match these gradual shifts in demand.

Machine learning techniques like time series analysis can identify annual, monthly, weekly, or other cycles of rising and falling demand for each product. You might discover that people switch to cheaper snacks towards the end of the month, or they might prefer chocolate over potato chips in the winter. The great thing about machine learning is that you don’t need to come up with an explanation for why people’s preferences change; you just need to follow the data.

Optimizing resupplies

Taken to the next level, machine learning can help you find an optimal replenishment schedule for each of your vending machines. Some might need to be restocked once a week, while others might need a refill every other day. The goal is to optimize sales against resupply costs.

Even though your stock will vary from shipment to shipment, sophisticated planning software powered by machine learning can tell you how much of each product to load onto each truck. Planning software can also find the best route for each of your delivery trucks as they wind through your network of vending machines. Your drivers will receive delivery schedules that help them restock more machines using less time and fuel.

Machine learning can even help identify the best time to visit each machine; a time of low demand for that machine (to minimize lost sales during downtime) and low traffic on the truck’s delivery route (to minimize time and fuel costs).

Perfecting your plan-o-grams

You can even apply machine learning on a more fine-grained scale. Analysis of sales across your vending machine network can help you improve revenue by adjusting each machine’s plan-o-gram. Some products might require more than one facing. You might learn that people unconsciously prefer items from a specific row or column. Positioning competing or complementary items next to each other might positively or negatively influence buying decisions. Machine learning can suggest changes that influence people to spend more, but are too subtle for human decision-makers to identify unaided.

Better long-term product planning

Machine learning can also guide your company’s overall product offering. Non-seasonal drops in demand might suggest that a specific product has begun to fall out of favor. This information can help you phase these products out of your catalog before they become dead stock.

You can also use non-seasonal increases in demand to identify the types of product that are gaining popularity. Techniques like collaborative filtering can provide recommendations for similar products that your customers might also like.

Smart vending machines and personalization

Newer “smart” vending machines allow users to pay for products using their smartphone. In addition to providing customers more convenient payment methods, this also enables vending machine businesses to send users personalized product recommendations and special offers. Market basket analysis is a machine learning technique that uses a customer’s purchase history to determine their unique preferences and generate relevant suggestions for their next purchase.

The ability to send targeted messaging to an existing customer is a very powerful advertising channel that turns a “passive” vending machine that waits for customers into an “active” virtual salesperson that lets your customers know what’s new and what they might enjoy.

Conclusion

Machine learning has become an affordable tool that businesses can use to improve efficiency and align their products and services more closely with real-world demand. Vending machine operators can use machine learning to refresh their overall product line and tailor individual machines’ stock according to hyperlocal and seasonal differences in customer preferences.

Machine learning can optimize vending machine replenishment schedules and delivery routes, and can also adjust machines’ plan-o-grams to gently convince customers to spend more. A global beverage brand recently increased revenue by 6% and reduced restocking trips by 15% by adopting machine learning. Smartphone-enabled vending machines can even generate demand by pushing highly personalized messaging to their customers.

Vending machines are just one example of how machine learning is revolutionizing the retail industry. For more information on how you can apply these techniques to your own business, please contact Visionet.

Why is eCommerce integration important?

A decade or two ago, eCommerce might have seemed to be reserved for large corporations with annual revenues in the hundred millions. For the past several years, however, the barriers to entry for online commerce are so low and the potential returns so high that establishing an online store should be an integral part of virtually every business plan. Opting for pure conventional commerce is no longer viable.

So you’ve either already set up an online storefront or are actively pursuing a digital commerce solutions. That’s great, but is it enough?

The woes of digital solitude

You might discover that their eCommerce platform is a rather solitary creature that doesn’t play well with the rest of your organization’s digital ecosystem. It might have its own set of reports and dashboards, separate from reports on your financials and internal operations… or it might not have these reports at all. The former scenario is far from ideal, while the latter is simply unacceptable.

In addition to reconciling reports from multiple sources, your staff will probably spend several hours each week manually rekeying customer and product information between your eCommerce platform and other information systems like your ERP platform, warehouse management software, or shipping solution. Those lost hours would be better spent closing sales or serving customers.

Disconnected eCommerce and warehouse management systems slow down your pay-to-order cycle. The longer it takes to process an order and ship the right product, the higher your cost will be per order. Factor in the errors resulting from manual entry and the potential costs are even higher. If your order processing workflow involves email or spreadsheets, then there is ample room for improvement.

Using a standalone eCommerce system also makes it trickier to inform online shoppers about product availability. If your inventory management solution isn’t integrated with your online store, how will shoppers know whether the items they want are in stock?

The cross-channel conundrum

For most retailers, however, eCommerce is just one slice of their sales pie. Your business probably has old-fashioned brick-and-mortar stores, a wholesale business, or even telephone shopping where customers choose products out of a printed catalog.

Bringing together current and accurate information from each of these channels is extremely useful. You can enable cross-channel fulfillment, allowing customers to pay using one channel, receive items using a second, and return items using a third. You can maintain a shared inventory so that your customers have access to your complete product catalog no matter how they choose to shop. You can also provide consistently excellent customer service by sharing customers’ complete support and order history across call centers, retail locations, and fulfillment centers.

Without digital integration across eCommerce, supply chain, and finance, efficient cross-channel fulfillment, inventory, and support is virtually impossible. Many businesses maintain separate product catalogs, customer records, and delivery information for their various retail channels. They spend a lot of time asking their customers to repeat their personal details and reason for calling. They miss sale opportunities because some products aren’t available via a particular channel. They require customers to return to their point of purchase for exchanges and refunds. In short, they fail to offer their customers excellent customer service.

Integration using a pre-built integration solution

Integrating eCommerce with the rest of your business systems is a great investment, but there’s more than one way to achieve unified commerce. Some retailers embark on custom enterprise integration projects, which tend to be expensive and time-consuming. However, it’s possible to achieve the same results more affordably and in less time using ready-made integration solutions designed specifically for eCommerce platforms.

While pre-built integration solutions usually offer better ROI than custom integration, there are a few important factors to keep in mind.

Get an integration solution that supports bidirectional integration. If you want to synchronize business information between Magento and Microsoft Dynamics 365, for example, the integration solution needs to send order information from Magento to Dynamics 365 and inventory information from Dynamics 365 to Magento to be truly effective.

Make sure that the integration solution deploys quickly. It should be compatible with your eCommerce platform and ERP system to enable rapid integration out of the box. Since more and more digital business solutions like eCommerce and ERP platforms are moving to the cloud, the solution should support cloud-based solutions.

You should choose an integration solution by a provider that offers complete implementation and support services. Ideally, their operational model should include both onshore and offshore components to give you an optimal mix of responsiveness and cost-effectiveness.

Conclusion

eCommerce has become an essential part of every successful business, but setting up an online store is only half the battle. It is just as important to establish automatic communication between your eCommerce system and other digital business solutions like your ERP system and warehouse management solution. For more information on how a pre-built eCommerce integration solution can improve operational efficiency with optimal time to value, please join us for our webinar on February 27, “Making Omni-Channel Real in B2B and B2C Commerce, Unifying Magento with Dynamics 365”.

Swimming with the Digital Commerce Sharks

Amazon is a leader in eCommerce and offers unmatched value to its customers with its 2-day and next-day delivery promise. Amazon even extends its 2-day shipping service to other sellers using “Amazon Shipping”. Amazon can provide the 2-day delivery promise using its extended network of distribution and fulfillment centers across the country. For any other B2C or B2B company to match this level of guarantee would mean a huge investment for which they can’t leverage the economies of scale that Amazon can.

While there is no guessing how far Amazon’s monopoly in eCommerce and fulfillment will grow, there is something other businesses can do. But in this scenario of “everyone else versus Amazon”, sellers, distributors, and shipping carriers will have to join forces, so to speak, to thwart or at least match Amazon on its promise.

The first step in this process is for organizations to understand how they can expand their existing network, either utilizing their own facilities or through partnerships.

1. Brick and mortar retailers who have extended their retail store networks have to look inwards, leverage their real estate holdings, and adopt a model where the retail stores are used as distribution centers to fulfill online orders.


2. Businesses can utilize 3PL/4PL services to extend their network to areas with no current physical presence. There are many options available in this space and more startups are coming up with tailored value-add for 2-day shipping.


3. Big-box stores have an existing network of distribution centers and stores that can be of huge value to many smaller eCommerce businesses. Big-box stores can become part of the solution by opening their distribution networks and letting sellers leverage these extensive networks as well to provide faster deliveries, returns, and customer support.

For this model to succeed, adjustments have to be made to supply chain operations, merchandizing, forecasting, and replenishment processes. These changes have to be driven by technology in a bid to flexibly automate as much of their operations as possible.

• Internal stores should be empowered to run pick, pack, and ship operations. A decent WMS solution capable of automatically manifesting to common shipping carriers and rate shopping for specific delivery is a must-have.
• Robust integration is required to succeed in partnerships with 3PL/4PL and 2-day fulfillment startups.
• Establish a process to automatically route online orders to the nearest fulfillment center (own or partner). Modern distributed order management (DOM) features handle order routing with decent accuracy. Establishing and maintaining zones and defining distribution priorities is the first step towards putting together a DOM system.
• An agile demand and inventory planning, allocation, and replenishment process is fundamental for such an operational shift. Machine learning and artificial intelligence needs to lead the way in approximating true weekly demand and feeding to weekly distribution planning and replenishment from main distribution centers down to fulfillment centers. This process is key to the success of the operation, and it has to be accurate and automatic.
• One big internal challenge in true omni-channel adoption is achieving consensus on how revenue centers are defined and how profitability is measured for online versus retail. If retail stores are acting as fulfillment centers, they are essentially consuming their sellable stock and there is no easy way to trace sales lost due to stockouts or unavailability of specific SKUs. One way to make this kind of model successful is to look at cross-channel revenue and profits separately for each store. Stores can also see their individual numbers go up with the success of the operation and maintain higher levels of safety stock at store locations instead of stocking only at the main distribution centers.

Amazon is big, it’s growing, and it’s here to stay. So is 2-day shipping. Sellers, distributors, and retailers must think creatively to counter the looming threat to their business. In order to survive and thrive, competitors must beat Amazon at its own game.

How is machine learning used in finance?

From screening and approving loans to managing assets and preventing fraud, machine learning plays a crucial role on many levels in financial institutions. In this blog post, we’ll explore some ways that machine learning improves business processes in the financial sector.

Read full blog post here: https://www.visionetsystems.com/blog/how-machine-learning-used-finance

Customer segmentation

Machine learning algorithms are far more effective for personalizing your customer experience than entire teams of employees. Simple demographics can’t fully explain actual consumer behavior, so financial organizations should use machine learning to segment consumers by their level of sophistication and financial acumen, and then customize products and services accordingly. All relevant customer interaction data is used to train these algorithms, which then automatically builds statistical models that help correlate customers’ preferences with their demographic, behavioral, and other characteristics.

“Next Best Offer” recommendation

The “Next Best Offer” strategy can provide personalized financial product and service recommendations for each customer by analyzing past behavior. This technique uses collaborative filtering (CF), a specialized component of machine learning. User-based CF uses the opinions and behavior of similar customers to predict a specific customer’s inclination towards purchasing a specific product, while product-based CF identifies products that customers have exhibited a similar preference for. If executed properly, this is a win-win approach: customers get their desired products and services, and financial institutions develop valuable relationships with their customers.

Customer churn prediction

To reduce customer attrition, financial organizations need a way to find patterns in customer activity that predict dissatisfaction and churn. Once you have identified the factors that might convince a specific customer to close their account or decide against renewing their service, you can take appropriate action to prevent lost business. Machine learning techniques can be used to analyze many more relevant dimensions and data points than a team of humans can, and they often reveal patterns that are invisible to unaided analysis by trained data scientists.

ATM cash optimization

To accurately predict cash demand for each ATM in a network, it is crucial to develop an intelligent cash management system based on machine learning that can help financial institutions lower operational costs and improve the return on their cash assets. ATM cash management has usually been performed manually, but financial institutions have begun to apply machine learning to historical cash withdrawal data to forecast cash demand on a per-ATM basis with unprecedented accuracy. Used in machine learning applications as approximators, artificial neural networks (ANNs) help avoid instances of ATMs running out of cash without allocating large amounts of excess cash to sit unused in ATMs. Machine learning also helps improve operations by determining optimal replenishment schedules and could even recommend the most profitable locations for future machines.

Underwriting

Underwriting is a core process for insurance companies, and finding and training competent underwriters are effortful and time-consuming tasks. Furthermore, even expert underwriters are ultimately human beings, so unconscious bias and costly mistakes are an inevitability. Underwriters have begun using machine learning techniques to better gauge risk and provide more accurate premium pricing.

A machine learning agent can be trained to use historical data for underwriting evaluations and statistics to evaluate an underwriting decision and provide a certainty rating (from 0 to 100 percent certain) for its evaluation. This helps human underwriters decide whether to accept or adjust the machine learning agent’s evaluation. Using the agent’s evaluation reduces underwriters’ workload, enabling them to focus on more complex evaluations that were assigned low certainty ratings by the agent.

Customer service

Customers nowadays expect prompt access to accurate information and rapid issue resolution. To cater to this, banks are rolling out chatbots and investment service applications for smartphones that provide customers 24/7 access to financial services and support. Modern chatbots use sentiment analysis to adjust their response depending on a customer’s apparent goal, emotional tone, and other relevant factors. Machine learning is used to detect common conversational themes and improve customer service over the long run.

Financial institutions also use machine learning to anticipate customer behavior and generate predictive insights that improve customer service and reduce attrition. By mining patterns in transaction data, companies can accurately forecast demand and personalize products and services. Millennials, the demographic cohort that will soon be the primary target of financial institutions, find these capabilities especially appealing. Leveraging them gives banks and financial institutions the opportunity to secure more clients than competitors who continue to rely on traditional online banking portals.

Fraud detection

Detecting fraud involved having a sizeable team of skilled professionals dedicated to the task. This was costly and time-consuming. Automation efforts over the last century focused primarily on using computers to create and run through lists of hundreds of compliance rules. If a particular document or account failed to follow any of these, the fraud detection software raised a red flag.

While this checklist-style solution works well for known forms of fraud that are easy to define as a set of rules, it doesn’t help financial institutions find instances of fraud that lack a formal definition. Machine learning significantly reduces security susceptibilities in finance by leveraging copious volumes of data and utilizing supervised and unsupervised learning to detect fraudulent transactions and discover new patterns in transaction history that might suggest fraud.

Process automation

Traditional automation techniques have been improving operational efficiency in financial organizations for decades. However, these older methods lack the agility required to adapt to change. As customer preferences, regulations, and other market forces continue evolve, business processes and workflows need to be adjusted, too.

Using machine learning in process automation allows an organization’s information systems to detect changes in the types of requests they process and make changes to the way they handle those tasks. For example, if a traditional automated system receives an invoice that doesn’t conform to a particular format, it forwards this exception to a human worker for further processing. After that human worker performs the correct series of steps, the invoice re-enters the automated workflow. No matter how many times similar exceptions occur, a traditional system will continue to forward them to a human, which essentially turns that automatic process into a manual process.
However, if you infuse this automated system with machine learning, then the system observes the steps that the human worker performs on the exceptional invoice and uses that information to adjust its behavior. It is then able to detect future instances of this new type of invoice and process them correctly. Machine learning finds patterns among the exceptions and forms new rules for the system to follow.

Content creation

Machine learning has begun to assist humans in creating financial summaries, company profiles, stock reports, and other business documents. Instead of spending hours or days producing these documents, machine learning algorithms can draft them in minutes. They use hundreds of samples of similar content (written by humans) to compose text that sounds natural, uses the right tone, and follows other context-appropriate writing conventions.

By using content creation solutions powered by machine learning, business executives can reduce the hours they spend each day preparing business letters and emails to the mere minutes it takes to review computer-generated text. Any corrections that they make will also feed back into the machine learning algorithm to continually improve its performance.

Conclusion

The use cases for machine learning in finance are both numerous and highly valuable. Not only does it help businesses to customize their customer experience but enables them to provide personalized products and services based on consumer behavior. Machine learning techniques predict and help reduce customer dissatisfaction and churn, while accurate prediction of cash demand in ATMs helps control costs and improves the return on cash assets. Chatbots provide round-the-clock customer service with superhuman speed and consistency. Machine learning underwriting agents help human underwriters make the best use of their time and effort. Fraud detection algorithms help find suspicious activity that even the best human experts might miss. Machine learning process automation keeps improving as new requirements emerge. Even business correspondence isn’t an exclusively human activity any more. Machine learning ensures that these digital financial solutions continue to perform correctly, even as the needs of financial institutions evolve substantially over time.

Why is EDI integration important to your business?

Electronic data interchange (EDI) enables the smooth, rapid, and structured exchange of important data between businesses. However, businesses have come to realize that manually entering data to be sent via a third-party EDI solution is still fairly time and labor-intensive. To further enhance business growth, companies that use EDI solutions can automatically synchronize all inbound and outbound partner data with their ERP system.

This blog post will shed light on some of the core advantages that EDI integration brings to your business.
Accuracy

Inaccurate information and failure to comply with EDI standards or individual trading partners’ internal policies can result in chargebacks or expensive errors. You might accidentally instruct a supplier to ship products to the wrong warehouse or even lose business by incorrectly processing a major customer’s order.

Sending business information directly from your ERP via integrated EDI technology greatly reduces the chance of making these mistakes. ERP platforms use various methods to validate information against a set of business rules to ensure compliance. Minimizing manual actions during inter-organization data exchange also prevents most errors, which helps your business avoid unexpected costs, reduce customer attrition, and preserve healthy margins.

Increased Efficiency

Compared to stand-alone EDI systems, ERP-integrated EDI solutions don’t require you to dedicate hours or days to manual input. Inbound and outbound partner communication can also be automated by initiating an EDI transaction on a fixed schedule (like sending an end-of-week report) or when a specific set of conditions arises in your ERP system (like requesting replenishment when you’re out of stock). This results in faster order processing and delivery, increased operational agility in response to changes in your supply chain, and improved relationships with suppliers, vendors, distributors, and other trading partners.
Reduced Costs

Automatic, paperless partner communication drastically saves costs by at least 35%. Savings can be as high as 90% with the use of electronic invoices. Rapid EDI communication also helps you control costs by allowing you to reduce inventory levels and shorten order processing and delivery times. However, manually keying business information into a separate EDI system introduces errors, delays communication, and increases labor costs.

Integrating your EDI and ERP systems allows you to send verified, standards-compliant information to suppliers and trading partners without needing to pay a worker to manually key in that information. You can avoid costs associated with SLA violations, performance gaps, and delays by using ERP integration to virtually eliminate manual error and ensure that your documentation process conforms to EDI standards.

Data Security

If an unauthorized person gains access to your business secrets, that’s bad enough, but someone manages to transmit that data, that’s far, far worse. EDI system integration helps keep your valuable business information safe by only granting EDI access to authorized ERP users. Each ERP user can be granted or denied access to EDI capabilities based on their specific role. Since most ERP platforms offer sophisticated auditing capabilities, you can also keep track of who sent or requested a specific document, and when.

Conclusion

Seamless EDI-to-ERP integration makes partner communication paperless, which results in reduced operational latency and errors, reduced costs, and better relationships with business partners. It also enables authorized users to exchange business information safely and securely. To learn more about how EDI integration maximizes the effectiveness of B2B communication, please contact PartnerLink.

Reinsurance risk mitigation using digital risk management solutions

Reinsurers face a high level of risk from multiple market forces, including claims leakage, the arrival of new players, poor management of aging recoverables, and inadequate business intelligence capabilities. Implementing the right digital solution is absolutely crucial for minimizing these risks.

What good risk management solutions offer

The “right” solution is one that addresses all of your organization’s risk management needs without requiring extensive customization. The customization process delays time to value, increases implementation costs, reduces adoption rates, and increases the chance of unexpected results. Your digital risk management solution should include out-of-the-box capabilities like claims and event management, policy management, technical accounting, statistical and regulatory reports, and advanced business intelligence.

Ensuring successful solution implementation

Successfully implementing a risk management system isn’t easy. In addition to ensuring that the solution possesses all the functionality your organization needs, you must also integrate it with your other existing information systems and configure it to align properly with company-specific internal policies and workflows.

The implementation project’s success hinges on open and efficient communication between your solution implementation partner and internal project team. Delays in the decision making and approval process on either end can cause the project to go over budget or create gaps in your expectations and actual solution functionality.

Avoiding claims leakage

Digital risk management systems can help reduce claims leakage using self-auditing capabilities, improved monitoring claims personnel, robust claim data modelling capabilities, and integrated, automated claims workflows that minimize manual intervention.

Enhancing operational processes

Automating internal business processes and integration with external systems are important ways to improve efficiency and minimize errors. Automated processes are also easier to audit and analyze, providing better visibility and accountability.

It is critical to adopt modern technology to achieve operational efficiency and mitigate risk. Digital technology also helps reinsurers control costs across the whole reinsurance value chain while also improving business relationships. Choose a dependable technology vendor who has experience in handling reinsurance projects and a solid implementation track record. Please contact Visionet Systems today for a complimentary solution demo.

5 ways of achieving flawless EDI integration with Microsoft Dynamics 365

EDI is the most widely used structured electronic data exchange between organizations. However, not all EDI solutions are created equal. Instead of operating as stand-alone applications that require manual entry and their own maintenance regime, leading EDI platforms integrate seamlessly with ERP software and other business applications to eliminate manual rekeying and duplication of business information.

Microsoft Dynamics 365 is a powerful cloud-based ERP solution. A fully integrated EDI solution can extend this power by directly connecting your implementation of Dynamics 365 to your trading partners’ ERP systems. Decision makers need to choose an EDI solution that integrates rapidly with Dynamics 365 and takes full advantage of Dynamics 365’s analytics, workflows, and other productivity-enhancing capabilities.

In this blog post, we’ll consider several factors that are important for effectively integrating your EDI solution with Dynamics 365:

The advantage of native integration

Some EDI solutions are designed to natively integrate with Dynamics 365. If you choose the correct one of these solutions, you don’t have to worry about compatibility or security issues – everything just works. This is the best way to avoid compromises or complications during or after solution implementation.

Choose a reliable integration partner and platform

If you decide to implement an EDI solution that isn’t specifically designed to integrate with Dynamics 365, choose an integration partner that possesses in-depth experience with integrating EDI solutions with Microsoft platforms. Since Dynamics 365 runs on the Microsoft Azure cloud platform, your partner of choice should be familiar with Azure-compatible enterprise application integration (EAI) tools and methodologies. To minimize business risk and avoid future upgrade costs, the integration platform should be highly secure and scalable.

Onboarding new EDI trading partners

In addition to the many security and regulatory concerns associated with transmitting sensitive data between organizations, each business that you partner with usually has its own set of information policies and standards. While integrating your EDI solution with Dynamics 365, make sure that the integration provides enough flexibility to accommodate these partner requirements.

Eliminate manual processes

The ROI of automating EDI processes varies depending on the frequency and importance of your data exchanges with other organizations. If you send or receive just a few documents each month, a fully automatic solution might not deliver enough value to justify the cost of implementation.

While integrating your EDI platform with Dynamics 365 will automate many manual processes, some ancillary processes might continue to be performed manually. Before you go the extra mile and attempt to eliminate these additional steps, define your specific EDI integration goals and determine the value you expect from automating each manual process. This will give you a clear picture of what you stand to gain from end-to-end automation of supply chain communication.

Data accessibility and privacy

If there are regulations or internal policies that prevent you from storing some types of business information in the public cloud, you will have to take this into consideration while planning to integrate your EDI solution with Dynamics 365. Instead of simply using Dynamics 365 or Azure cloud storage, you might have to implement a hybrid solution. These requirements add cost and complexity, so you should be aware of them before you begin integration.

Conclusion

Organizations that prepare a complete roadmap of the EDI integration process are rewarded with faster time to value, lower implementation costs, fewer delays, and higher ROI. For more information on best practices for integrating EDI with Dynamics 365, contact PartnerLink for a complimentary consultation.