ML use cases in Logistics, Tourism, Food Industry & Accommodation

Bhanu Shahi
Jun 13 · 15 min read

Contributors: Bhanu Shahi, Vaideswar Reddy & Prashant Jha

ML use cases in Logistics

Logistics refers to the overall process of managing how resources are acquired, stored, and transported to their final destination.

Photo by TimeLab Pro on Unsplash

The term logistics is now used widely in the business sector to refer to how resources are handled and moved along the supply chain. Companies today need smart, flexible, and proactive supply chain decision-making solutions to cater to the dynamic demands of the market.

How to ensure efficient logistics management?

This is an open question for many suppliers, distributors, manufacturers, and retailers.

Machine learning holds the answer to many well-known as well as emerging logistics challenges.

Smart Logistics

There are numerous use cases of machine learning in logistics. ML in logistics allows us to save time and money because it helps in automating various time-consuming processes.

Let's see some use cases of ML in logistics.

Demand forecasting

Demand forecasting is believed to be one of the major implications of Machine Learning in logistics. It plays a crucial role in maintaining a balance between demand and supply. Using past data and existing models, shippers can effectively generate reports on the consumption and predict what would be the demand. This in a way accelerates delivery and reduces wastage.

With improved accuracy in demand prediction:

  • Customers are less likely to experience stockouts that lead to more customer satisfaction.
  • Local warehouses/ retailers can reduce the holding costs.

Route optimization

Finding the best possible path from source to destination is what we call route optimization. This reduces the time taken to deliver a package and also, improves the efficiency of the system.

Route Optimization

ML models help businesses to analyze existing routing, track route optimization. It uses shortest path algorithms in the graph analytics discipline to identify the most efficient route for logistics vehicles. Therefore, the business will be able to reduce shipping costs and speed up the shipping process.

Lesser Distance Travelled — Lesser Emissions

Optimized delivery routes move through far shorter distances, avoiding traffic and unnecessary detention. This means that they use lesser fuel leading to far lesser emissions. In this way, ML is adding to the environment.

Supply Chain Visibility

Machine learning is one such technology that helps track products and machinery in real-time. Right from the production phase to the last mile distribution of goods, one can manage and monitor vehicles and keep track of the shipment. Continuous monitoring of devices leads to better delivery and improved status of shipment.

Image Source: Unsplash

Damage detection

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages. Companies can determine the damage depth, the type of damage, and take action to reduce further damage.

Dynamic Pricing

Dynamic pricing is real-time pricing where the price of a product responds to changes in demand, supply, competition price, subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-time so that it can respond to demand fluctuations faster with adjusting prices.

Let’s see a case study of logistic industry giant Loginext.

Case Study: Customer-Centric Live Logistics Optimization in LogiNext[1]

LogiNext provides delivery route planning software that helps in effective carrier management and last-mile delivery optimization. Its industry-leading machine learning-enabled algorithm makes it the most advanced delivery route planning software in its space. LogiNext is boosting the logistics efficiency for their clients.

Image Source: Loginext Website

Let’s understand how LogiNext boosted logistics efficiency for one of their clients. The client was one of the largest online home goods retailers in North America with more than 60 million active online users, 10 million products, and 20,000 suppliers.

LogiNext helped that client as follows:

  • Right from the point, a customer ordered furniture to the point they received it in perfect condition right in their rooms, LogiNext’s logistics optimization software worked to make the entire process smooth and error-free.
  • The shipment was analyzed and automatically allocated to the best-suited driver or truck. Each driver/truck had its own skill-sets associated with them.

Let’s consider a scenario where the shipment is a king-size bed with a mattress. The driver/handler must be an expert in loading and moving such items. They should be well-versed with delivering it in the house of the customer in the preferred room. If assembly is required, the handler or helper should be trained in such work.

Such auto-allocation based on the shipment requirements is essential to create a great delivery experience for the customer.

Advanced Route Planning and Quick Dispatch

LogiNext’s logistics optimization software supports Advanced Route Planning and Quick Dispatch. Moreover, they are also supporting live tracking of all trucks with instant notifications of all on-ground events (ranging from unplanned delays to successful deliveries) in a single dashboard and giving the client the means to totally control their distribution in one go.

End-to-End Visibility

Such end-to-end visibility cut down the reaction time to any and all instances on the ground giving the client the agility and responsiveness to give the best delivery experience to all their customers.

End to end visibility in LogiNext

LogiNext’s logistics optimization software took on all the ‘big challenges’ of clients and comes through successfully at the other end for the complete business landscape.

Great Delivery Experience

One of the major boosts that came through LogiNext’s optimization was the creation of a great delivery experience for each customer. With on-time deliveries and live tracking of shipments coming in, the customer’s expectations were not just properly managed but also exceeded.

And also Proper driver-shipment mapping ensured high-quality interactions with the customer, boosting their satisfaction.

So for that particular company optimized logistics movement with LogiNext saved up to 18% of their total logistics costs which were then turned back into benefits for the customers creating a more efficient and transparent eco-system.

Let’s see some ML use cases in Tourism and Food Industry.

ML Use Cases in Tourism and Food

The restaurant business is one of the trickiest businesses in India and many people just follow their gut feeling to open a restaurant and most of them fail. I don’t have any stats to present here but only a few of the new restaurants are able to survive in India and most of them just shut after going cashless.

This is what I’m talking about.

But!

This can be turned around.

How?

With the data.

Yess! That’s true. But who has the data for it? Think about it. I think you know the answer, give it a thought and scroll down then.

.

.

.

I’m talking about them. These are the food delivery companies in India that have the data.

Zomato and Swiggy are the food delivery giants in India and they are operating in most of the cities. They have an enormous amount of data about what people like to eat, what price they are happy to pay for it, they can answer your query about the restaurant business in India better than anyone else. There are chances that they know the food trends in your area better than you where you are brought up. They should start providing services with the data to the people who want to make it into the food industry.

So now we’d come to the questions that you should ask before opening a restaurant:

  • What type of food is popular in your locality?
  • What type of food is not available in your locality that you can try?
  • What is the average number of orders a restaurant gets? Here you should look for only those kinds of restaurants that you want to open. You shouldn’t consider large restaurants if you’re going to open a mid-size outlet.
  • What should be your target customer category, it should be middle-class people, rich people, or maybe students and millennials. You have to be sure about it since it’s hard to have something for everyone when you’re just starting.

These are examples of some crucial information that you should be aware of before opening a restaurant. The reason behind the failure of restaurants in India is running out of cash after a certain period and not being able to generate profits. Before opening a restaurant, you should try to find a balance between providing quality and making profits. With the help of Data, you can take these kinds of crucial decisions like what price range you should keep, how many people you’d need to manage the restaurant, etc.

Ok! So now you know what to do when you want to open a restaurant. Thank me later. You can offer me a treat as well. You know! I’m a foodie 😉.

Ok! Enough talk about the food, I know most of you have started feeling hungry. But bear with a little more, Let me make you a little nostalgic too about your last vacation. Yup! Let’s talk about how AI and Data Science can impact the tourism industry.

AI can help to provide a Personalized Experience to tourists. You might not have enough information about a new tourist, but I think there is a way. We can make clusters of tourists from the existing data of tourists, who visited in past. Whenever a new tourist arrives, we can check which cluster he belongs to and we can get some idea about what this person likes.

Organizations can use the trends and seasonality in the data to predict the footfall of tourists every month in advance. This prediction can help businesses in many ways. They can start preparing in advance about how they’d like to make profits from the tourists.

There is so much scope of advertising on social media based on traveling history or even for first-time travelers. I’ll take the example of Instagram. Most people search for places and hashtags (like #Goa etc.) when they’re planning a vacation. Instagram can easily conclude that this person is gonna visit this place and show him ads from the local businesses from that place. This can also help the person to figure out what places he should visit when he gets there. It’s a win-win for both tourists and local businesses.

ML use cases in Hospitality Industry

Why is Machine Learning Becoming Important in the Hospitality Industry?

Machine Learning is playing an increasingly important role in hospitality management, primarily because of its ability to carry out traditionally human functions at any time of the day. This potentially means that hotel owners can save significant money, eliminate human error and deliver superior service.

In particular, customer service is a vital part of the travel industry, with hotels often living and dying based on the way they treat their customers. With artificial intelligence, the possibilities for improving this aspect are almost endless, ranging from increased personalization to tailored recommendations.

One of the key customer service challenges for hotels is responding to customer questions quickly and artificial intelligence now provides an additional option for tackling this problem. Moreover, it has the capacity to assist with tasks like data analysis and, through data collection, can effectively “learn” and adapt to customer interactions.

Energy management

Hilton’s Energy Program

The LightStay program at Hilton predicts energy, water, and waste usage and costs. The company can track actual consumption against predictive models, which allows them to manage year-over-year performance as well as performance against competitors. Further, some hotel brands can link in-room energy to the PMS so that when a room is empty, the air conditioner automatically turns off. The future of sustainability in the hospitality industry relies on ML to shave every bit off of energy usage and budget. For brands with hundreds and thousands of properties, every dollar saved on energy can affect the bottom line in a big way

Human Resourceshiring

IHG employs 400,000 people across 5,723 hotels. Holding fast to the idea that the ideal guest experience begins with staff, IHG implemented AI strategies to“find the right team member who would best align and fit with each of the distinct brand personalities,” notes Hazel Hogben, Head of HR, Hotel Operations, IHG Europe. To create brand personas and algorithms, IHG assessed its top customer-facing senior managers across brands using cognitive, emotional, and personality assessments. They then correlated this with KPI and customer data. Finally, this was cross-referenced with values at the different brands. The algorithms are used to create assessments to test candidates for hire against the personas using gamification-based tools, according to The People Space. Hogben notes that in addition to improving the candidate experience (they like the gamification of the experience), it has also helped in “eliminating personal or preconceived bias” among recruiters. Regarding ML uses for hiring, Harvard Business Review says in addition to combatting “human bias by automatically flagging biased language in job descriptions,” ML also identifies “highly qualified candidates who might have been overlooked because they didn’t fit traditional expectations.

Hotel Upgrades

A 2018 study showed that 70% of hotels say they “never or only sometimes” promote upgrades or upsells at check-in (PhocusWire). In an effort to maximize the value of premium inventory and increase guest satisfaction, Accor Hotels partnered with Nor1 to implement standby Upgrade®. With the ML-powered technology, Accor Hotels offers guests personalized upgrades based on previous guest behavior at a price that the guest has shown a demonstrated “willingness to pay” at booking and during the pre-arrival period, up to 24 hours before check-in. This allows the brand to monetize and leverage room features that can’t otherwise be captured by standard room category definitions and to optimize the allocation of inventory available on the day of arrival. ML technology can create offers at any point during the guest pathway, including the front desk. Rather than replacing agents as some hotels fear, it helps them make better, quicker decisions about what to offer guests.

Reviews

The luxury Dorchester Collection wanted to understand what makes their high-end guests tick. Instead of using the traditional secret shopper methods, which don’t tell hotels everything they need to know about their experience, Dorchester Collection opted to analyze traveler feedback from across major review sites using ML. Much to their surprise, they discovered Dorchester’s guests care a great deal more about breakfast than they thought. They also learned that guests want to customize breakfast, so they removed the breakfast menu and allowed guests to order whatever they like. As it turns out, guests love this.

Data Analysis

Another way in which AI is being utilized within the hotel industry away from pure customer service is in data analysis. In this capacity, the technology can be used to quickly sort through large amounts of data and draw important conclusions about customers, or potential customers.

An example of this has been seen with the Dorchester Collection hotel chain, which has made use of the Metis AI platform. By using this technology, the company has been able to sort through data collected via surveys, online reviews, etc. and the AI has been able to then analyze this to draw conclusions about overall performance

Chatbots in the travel industry

Chatbots are like speaking to your imaginary friend, but the beauty of it is that Chatbots do respond and they are often very insightful. This Artificial Intelligence development has also shown major success in the hospitality industry as it has taken flight in various sectors in this industry.

Some call chatbots that are used in hospitality, Intelligent Travel Assistants. These Intelligent Travel Assistants work 24 hours a day, 7 days a week, and have no holidays. This is the reason why they have been adopted in the hospitality industry that requires customer support around the clock. The services offered by chatbots vary and are mostly task-specific.

For example, some chatbots might be programmed with AI that resolved hotel customer complaints. Other chatbots answer questions related to having a great time at a certain destination. While on the other hand, other chatbots answer basic questions like where you can get pizza or other services.

Other chatbots help travelers plan their trip in an effortless and streamlined manner. You get two types of chatbots, the first type uses predetermined answers that are manually programmed. The second type is a bit complex and answers some complicated questions that need a bit of thinking. According to your needs, you can choose which one you need.

You can embed your chatbots on the most popular social media channels or on your website. Some hotels and hospitality service providers have opted to have their own app, in which they imbed their chatbot.

You can get yourself a good chatbot and start customizing it to your own needs and then implement it on your website, app, or social media pages. The instant responses to questions you customers may have will help you retain those customers and even grab the attention of new customers,

Sarah Perry who works as the head of the customer service for a grademiners review online service, MyAssignment says that both pre-sale and after-sale support is paramount, not just for the hotel industry but every business. Before joining the online writing service, she spent almost two decades in the hospitality industry.

Using chatbots, they have considerably reduced their response time, and the waiting time is zero. Even a bigger benefit is that customer issues are resolved at a high rate and the need to email or talk to an executive is also reduced.

Forecasting customer trends

Another way to find out when is the best time to run promotions and special offers is consumer trend forecasting software. These Artificial Intelligence and Machine Learning run systems will collect historical data and let you know the trends that will happen in the hospitality industry.

For example, these predictive systems will let you know what will happen during the upcoming Christmas in your area. Using these systems, you will know which season of the year will have many travelers in your city.

Consumer trend forecasting systems are based on the truth of history repeating itself as the historical data provided yields accurate predictions concerning this industry.

Deep learning, technology closely related to machine learning can also be used in applications that will show you the best pricing structures of your business.

As a result, without risking your profits, you can gain a competitive advantage over your competition. The data that needs to be processed to get the perfect forecast for the best price of your product or service include:

  • The season and time of year
  • Competition pricing structures
  • Events that will be taking place
  • Third-party promotions

With this data, you can get the correct price to set on your products and services to attract most of the clients with only a small price to pay. To implement this system in your business, you can search for price optimization software that runs on machines or deep learning.

Dynamic pricing automation

Data science allows hotels to predict demand and patterns of customer behavior more accurately. That’s why global chains such as Marriott International and AccorHotels have data scientists and analysts on board. These specialists develop and deploy pricing models using data about hotels and their competitors.

Some hotels rely on RM solutions to be in control of their revenue. Such software defines the optimal room rate in real-time using machine learning. These RM systems automatically consolidate and analyze large amounts of internal and external data from multiple sources to detect patterns and anomalies.

One such solution, the OTA Insight platform, for example, is used by Carlson Rezidor, AccorHotels, Fusion Hotels and Resorts, Sydell Group, Hilton, Crowne Plaza, and other global and regional hotel brands.

Conclusion

Efficient logistics and transportation are the need of the hour for supply chain enterprises. Logistics technology is capable of improving routing, scheduling, and end-to-end supply chain optimization, with the added value of speed, cost-effectiveness, and overall supply chain efficiency. For India, NITI Aayog’s vision of a digitized and modern logistics system across varied territories is an achievable dream with the help of Data Science, Machine Learning, and Artificial Intelligence. Researches are going on in this field and many more advancements are yet to come.

The logistics discipline in tourism aims to support the main activities of this sector. In the case of tourism companies, it is about providing services that complement the main activities such as a hotel, whose essential function is to host tourists; a restaurant, whose basic function is to produce food and serve them; a travel agency, provide tour packages to attractive places and transport, among other types of companies. AI has automated most of these things but still, a lot of advancement scope is there in this field.

The hospitality industry depends on knowing the trends of the industry and when to run exposure marketing campaigns. Machine learning and Artificial Intelligence have developed technologies to tackle those problems and satisfy customers. Chatbots also have a great impact in retaining business from returning customers by the quick response system chatbots use that is available around the clock every day.

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Happy Reading..!!

REFERENCES:

  1. Case Study on LogiNext: https://loginextsolutions.com/blog/case-study-customer-centric-live-logistics-optimization-for-large-online-retailer/
  2. https://www.thepeoplespace.com/practice/articles/how-international-hotel-giant-ihg-using-artificial-intelligence-bring-hr-and
  3. https://hbr.org/2017/05/8-ways-machine-learning-is-improving-companies-work-processes
  4. https://www.phocuswire.com/Guest-Experience-report
  5. https://www.otainsight.com/
  6. https://www.analyzedu.com/writing-services-reviews/grademiners-com-review.html

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Bhanu Shahi

Written by

Aspiring Data Scientist | Machine Learning | NLP | Time Series | Python, Tableau & SQLExpert | Storyteller | Blogger | Data Science Trainee at AlmaBetter

AlmaBetter

Your home for data science. Concepts, ideas, codes and blogs from students of AlmaBetter. We are building the next-gen AI ecosystem https://www.almabetter.com