The world has changed dramatically since March 2020. Supply chain models and concepts that we took for granted have been stretched, and in some cases, broken. New business models, new market opportunities, and new ways of working have emerged. As has been said by many, the world will never be quite the same. A recent report by McKinsey, “The future of work after COVID-19,” highlights that e-commerce has grown 2 to 5 times faster than before the pandemic, and that about 20% to 25% of the workforces in advanced economies could work from home between 3 and 5 days per week without a loss in productivity.

So, what is your company’s strategy for the future? Has your business model changed or evolved? Have your employee work patterns, procedures, and expectations changed? Have agility and innovation become more important? While many businesses are wrestling with these and other strategic questions, the important situation to avoid is the old, “Alice in Wonderland” scenario:

Stability is important. Hotels, casinos, and resorts have guests they need to serve right now, today. Hospitality software solutions in place help them do that by taking care of the basics: reservations, check-ins, folios and check-outs, along with a staggering variety of in-house services and their associated charges. Essential software like hotel PMS is pretty “sticky” that way; it’s easier to stick to what you’ve got than it is to change. Why fix what isn’t broken?

Here’s the thing. Five years from now – perhaps sooner – the way guests engage with hospitality providers will likely look very different. Innovative solutions and platforms will be necessary to scale to that new paradigm, whatever it may be. So how do hospitality organizations know when it’s time to start planning for that? How do decision-makers know when it’s time to get unstuck? Here are 3 considerations to help answer that question.

As new hospitality technology emerges and as organizations increasingly invest in it, the ways people do their jobs change in response. Most of the time, this means greater efficiency, and a way for teams to concentrate on key tasks while the technology takes care of the necessary minutiae. Some of the time though, the rise of certain technologies can cause some concerns that with certain functionality in place, the human factor will get lost.

Hospitality AI functionality in hotel revenue management and in hotel pricing tools is a case in point. The conversation around automation via algorithms has been too easily positioned with a “versus” between machine learning solutions and professional expertise. Yet, the features of the technology and years of knowledge and experience held by revenue and strategy teams isn’t adversarial. The relationship between them is more about achieving greater focus and clarity. How do science-based algorithms in revenue management and pricing solutions help your organization do that? Let’s look at some key points.

Traditionally, almost all business intelligence and analytics solutions were designed as general-purpose platforms or desktop tools suitable for any industry, department, or use case. That does mean, of course, that the burden of developing data models, data pipelines, dashboards, and reports falls to the customer. They effectively start with a 'blank sheet of paper,' which, in theory, can be developed to fit the organization's exact needs. However, the reality is that the organization may not have either the skills, resources, or time to develop an analytics solution that quickly meets business requirements.

The answer to avoiding this overhead of entirely homegrown analytics is to use a modern analytic application that can deliver ready-to-use analytics, right "out-of-the-box." Deployment and adoption of these analytic applications are accelerated because of built-in content developed by industry domain experts that understand the industries, roles, data, business applications, and specific analytics required by typical business user personas. A significant amount of development time can be saved because this domain-expertise-driven content can replace typical internal development processes.

Regardless of size or industry, asset-intensive organizations are always looking to do more with less. For a food and beverage manufacturer that means finding ways to extend asset life to minimize costs, improve food and worker safety and reduce waste while performing the right maintenance on the right equipment, at the right moment to avoid downtime.

Some businesses have been making do and getting by with asset management strategies that sell them short, driving up the cost of maintenance labor and materials and increasing the risk that critical assets will be down when they’re most urgently needed. The scene has been shifting over the past several years, with Enterprise Asset Management (EAM) offering greater visibility and sophistication for maintenance operations.

But business rarely sits still for very long. And with the rapid rise of Industry 4.0, it’s essential for asset management to keep up. Maintenance 4.0 is the set of tools and strategies that is helping food and beverage companies optimize operations by deploying the mountains of data now available to keep equipment and production lines in peak operating condition. Infor’s Best Practice Guide, “Why your EAM strategy must evolve to increase food safety,” explains how it works and lays out the five essential components of an Asset Performance Management (APM) platform.

Automotive manufacturers and their suppliers continue to increase their collaboration in various ways, whether this be through robotic process automation (RPA), data collection and exchange, or other methods that provide insights for scalability. With true, multi-enterprise collaboration, there are cross-training opportunities to convey customer needs and preferences for product innovation, giving manufacturers the ability to make rapid adjustments in real time. Collaborative behavior makes this happen, and it all starts with transparency.

However, building real trust, collaboration, and transparency goes beyond using a specific technology or workflow. At its core, this is about changing the culture of automotive OEMs and throughout their suppliers at every tier. Automotive companies who are true leaders in the industry consider the health of the entire supply network, not just their immediate interests. Making this change—as with almost all substantive transformations—requires change be from the top executive level down.

Disruptions to the supply chain

If any event reveals the need for resilient supply chains, it’s COVID-19. The disruptions shook every industry, including automobile. Early 2020 forecasts projected a significant drop in new vehicle sales, but ultimately turned out much better with only a 15% reduction from the total 2019 sales.

It’s no surprise the pandemic has prompted several automotive companies to embrace new supply chain strategies that allowed them to recover quickly while also setting them up for future growth. Many automotive companies stood up crisis teams and control towers to improve visibility and maintain profitability, which in turn developed into an advanced strategy around predictive risk management and multi tier supplier collaboration.

Even with the automotive industry embracing new supply chain practices, another disruption has risen in the semiconductor chip shortage. With modern vehicles often containing thousands of semiconductors, this crisis underscores another dimension of supply chain risk exposure and highlights the critical need to collaborate with multi tier partners for globally limited supplies.

With the rise of new digital marketing channels (such as search, display, mobile, and social media) as well as the continuing popularity of traditional marketing channels (including email marketing, direct mail, events, telemarketing, and more), today's marketer has plenty of ways to engage with customers. However, after purchasing a crazy amount of software and paying for expensive consultants to build multi-channel, multi-touch campaigns, one question remains: "where's the return on investment?"

Many marketers want to investigate their cross-channel efforts. However, they often use one tracking system for search, one for display, another for email marketing, yet another for social media, and the list goes on.

One of the common pushbacks with the use of analytics relates to the perceived ubiquity of imperfect data. This sentiment should be familiar to many: "We can't properly analyze and make decisions until we have corrected the quality issues in the data." Unfortunately, analytics must often be based on incomplete or "dirty" data. The first step in dealing with this reality is to understand and accept that information will likely never be perfect.

Sales professionals rely on an array of different criteria to be successful: strong personal skills, a good product, reliable technology, and efficient processes. To help sales reps be more effective, organizations have made huge investments in customer relationship management (CRM) systems and other technologies.

According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%? Despite the efforts of Cloud BI vendors making it easier for users to acquire, explore, and analyze data sources without IT dependency, lack of data literacy and analytic skills still hinder widespread adoption for data-driven decision making.  But the industry is undergoing a fundamental transformation. The mainstream arrival of Artificial Intelligence (AI) brings with it the potential to finally meet the demand for actionable, enterprise-wide, fact-based decision making.