How can managed services address skill shortages?

Organizations of all sizes have begun to realize that they must digitalize their operations rapidly to maintain competitiveness. Whether they are upgrading from legacy technologies or venturing into the digital arena for the first time, their survival depends on successful digital transformation.

The biggest contributor to digital success is no longer access to capital. A recent poll of C-suite executives revealed that businesses are more concerned about technical resource shortages. Skill shortages are a clear and present threat to your organization’s digital future, but managed service providers (MSPs) provide a viable solution.

Here are six ways that MSPs address shortages of skilled resources:

• By proactively aligning your technology and business objectives

Continuing to rely on aging technologies and legacy platforms can generate unnecessary skill shortages. The older a technology is, the smaller and more expensive its relevant talent pool will be. If you find yourself in desperate need of a COBOL or FORTRAN developer, there’s a strong possibility that your IT budget would be better spent on platform modernization.

Instead of blindly providing support for your existing IT infrastructure, MSPs continually evaluate and suggest improvements to your digital systems. If there are obvious ways that newer technology can reduce operating costs or generate more revenue, your MSP will let you know. You might not need to hire those expensive legacy platform consultants after all.

• By continually upgrading resources’ skills

As an important corollary to section 1, managed services also helps you avoid skill stagnation.

Suppose you rely solely on your in-house IT department. In this scenario, you need to keep an eye on technology trends to determine when to upgrade your staff’s technical skills. If you fail to do so, then your support staff will remain stuck in the past. This will either hold back progress because you’re reluctant to modernize your digital infrastructure, or it will create new skill gaps if you decide to go ahead with system upgrades. You’ll also be responsible for all training and certification costs.

Your MSP, on the other hand, is responsible for ensuring that their teams are sufficiently trained and experienced, no matter which technology platform you currently use. If your business applications are upgraded or replaced, then it’s your MSP’s responsibility to ensure that the team assigned to you is fully capable of supporting them.

• By sharply reducing resource onboarding time

Sometimes you need to move quickly to capitalize on a lucrative opportunity, but you don’t have the right personnel to get the job done. You could try to find experts with the right skill set and experience in your specific industry, but that might take weeks or months. By the time new resources sign on the dotted line, your window of opportunity would be long gone. You can’t retrain your existing team overnight, either.

Having access to an MSP opens up new avenues for increasing operational agility. Instead of waiting to find and onboard a new skilled resource, your MSP can quickly assign the perfect expert from their large, elastic resource pool. By engaging your MSP’s specialists, your new project can get started in a matter of days. Your MSP will scale down your assigned team once the project is complete, providing higher ROI and lower TCO.

• By providing affordable access to large teams of skilled resources

Highly skilled resources don’t come cheap, and so many companies only have one or two experts on their payroll for specific mission-critical technologies. This is a fairly high-risk strategy. If your resident expert becomes unavailable for an extended period, your organization becomes exposed to business risk. In the event of a service disruption, you would be forced to seek expensive, per-incident technical support by an external organization with a limited understanding of your IT infrastructure.

Reliable MSPs have large teams of experts on their roster who can be assigned to support your applications, hardware, and other digital resources. That way, your business continuity doesn’t rely on the availability of a single expert and you don’t need a large team of highly paid experts on your payroll to manage each major component of your technology footprint.

• By supporting 24x7x365 global operations

If your organization maintains operations in multiple time zones, then having a large support team is especially important. Most MSPs offer remote support options with resources stationed in different international regions. The size of their team and their geographical coverage allow them to provide high-quality, around-the-clock support at a lower price point than you can offer in-house.

• By providing fully managed, flexible team composition

As your organization’s mix of digital tools evolves, so do your support requirements. For example, if you decide to start an enterprise-wide project to optimize your existing digital operations, your existing support crew might not be the best fit for the job. So do you hire new resources on short-term contracts for the duration of the optimization project?

MSPs provide a better solution. Since managed services are proactive in nature, your MSP will seamlessly manage the transition from a support-focused to an optimization-focused team, and then transition back to a support focus when the project is complete. Any adjustment in their team makeup is just another aspect of their full-service approach to addressing your digital needs.

Conclusion

MSPs provide large, agile, and flexible teams of highly trained experts to address your organization’s digital needs. They offer great value by taking on the risks and responsibilities associated with skill gaps and evolving business needs. Partnering with an MSP frees you from many of the complexities associated with recruiting and maintaining your own team of highly compensated technology experts.

To learn more about how managed services can help you optimize your workforce, please contact Visionet.

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.