Before we dive into details, let’s look at some definitions.

Data collection refers to the process of gathering and acquiring information, facts, or observations from various sources, in a systematic and organised manner. The collected data can be used for various purposes, such as research, analysis, decision-making, and problem-solving.

In today’s digital age, data collection has become increasingly prevalent and crucial, as it enables organisations and individuals to gain insights and make informed choices based on empirical evidence.

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The role of data collection in business decision making

Data collection plays a central role in business decision-making by providing the necessary information and insights for organisations to make informed choices and formulate strategies.

In the modern business landscape, where data is abundant, businesses that can effectively collect, analyse, and interpret data have a significant competitive advantage.

Some key ways data collection impacts business decision-making processes include:

Understanding customer behaviour

Data collection allows businesses to gather information about their customers’ preferences, purchasing behaviour, and demographics. By analysing this data, businesses can identify trends and patterns, enabling them to tailor their products, services, and marketing strategies to better meet customer needs.

Market analysis and competitor intelligence

Data collection helps businesses gain insights into market trends, industry performance, and competitor strategies. Analysing market data can help identify new opportunities, potential threats, and areas where a company can differentiate itself from competitors.

Product development and improvement

Through data collection, businesses can gather feedback from customers about their existing products and services. This feedback can be used to make improvements, address issues, and develop new offerings that align with customer preferences.

Optimising operations and processes

Data collection can be applied to internal operations, supply chain management, and production processes. Analysing operational data can lead to efficiency, cost reductions, and streamlined workflows, ultimately improving the overall performance of the business.

If you want to learn more about the advantages of using data in business, also read other texts written by our experts:

• Data transformation: the complete guide for effective data management
• Data modelling: a guide to techniques, models and best practices
• Data cleaning: benefits, process and best practices

Risk management

Data collection and analysis help businesses assess potential risks and vulnerabilities. By monitoring key performance indicators and relevant market data, companies can anticipate challenges and make proactive decisions to mitigate risks.

Financial decision-making

Financial data collection is crucial for budgeting, financial planning, and resource allocation. Accurate financial data enables organisations to make strategic decisions related to investments, pricing, and revenue management.

Employee performance and engagement

Data collection can extend to employee feedback, performance metrics, and engagement surveys. Understanding employee satisfaction and performance can lead to a more productive and motivated workforce.

Predictive analytics and forecasting

Data collection provides the foundation for predictive analytics, which involves using historical data to forecast future trends and outcomes. This capability helps businesses make proactive decisions rather than reacting to events after they occur.

Personalisation and customer experience

By collecting and analysing customer data, businesses can offer personalised experiences and targeted marketing campaigns, improving customer satisfaction and loyalty.

Compliance and regulation

In industries with strict regulatory requirements, data collection plays a vital role in ensuring compliance and meeting reporting obligations.

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The types of data collection: primary and secondary data gathering

Data collection can take many forms, including primary and secondary data gathering.

Here is an overview of how they differ:

• Primary data collection involves gathering original data directly from the source. Researchers or data collectors interact with individuals or entities to collect information through methods like surveys, interviews, questionnaires, observations, or experiments.
• Secondary data collection involves using data that has already been collected by others. This data can come from a wide range of sources, such as government agencies, research institutions, public databases, or other existing datasets. Analysing and utilising secondary data can save time and resources but might be less tailored to the specific needs of the current study.

Quantitative vs qualitative data gathering

Other types of data collection are called quantitative and qualitative data gathering methods. This is what they involve:

• Qualitative data collection focuses on obtaining non-numeric data, often used in social sciences, humanities, and other fields where understanding context, behaviours, and opinions is essential. Qualitative data can be collected through interviews, focus groups, content analysis, and more.
• Quantitative data collection focuses on gathering numeric data that can be analysed statistically. Examples are surveys, experiments, structured observations, and sensor data.

In-depth examination of various data collection methods

Data collection methods can vary based on the nature of the data being sought, the research objectives, available resources, and the target population.

Let’s look at some data collection methods in use:

Surveys and questionnaires

Surveys and questionnaires involve gathering information from a sample of individuals through a set of structured questions. They can be conducted on paper, via online questionnaires, telephone interviews, or face-to-face interviews.

They are efficient in collecting data from a large number of respondents and provide standardised responses for easy analysis. It’s worth remembering though that the wording and framing of survey questions can influence responses, and response rates may be affected by survey fatigue.

Interviews and focus groups

Interviews involve direct one-on-one or group interactions with participants to gather qualitative or quantitative data. Interviews can be structured, semi-structured, or unstructured, depending on the level of flexibility needed. They allow for in-depth exploration of topics and offer opportunities to clarify responses and probe deeper into participants’ perspectives.

On the other hand, they can be time-consuming, and the presence of the interviewer may introduce bias.

Observations and fieldwork

Observational data collection involves systematically watching and recording behaviours, events, or interactions in a natural setting. Observations provide firsthand, real-time data and are useful for studying behaviours or phenomena in their natural context.

They can however be influenced by the observer’s bias, and certain behaviours may be difficult to capture unobtrusively.

Experimental data collection

Experimental data collection involve manipulating one or more variables to observe their effect on the outcome of interest. They are often conducted in controlled settings.

Such experiments establish cause-and-effect relationships and allow researchers to control extraneous variables, but they may not fully capture real-world complexities, and ethical considerations must be taken into account when manipulating variables.

Document review

Document review involves the systematic examination and analysis of existing documents, records, or artifacts to extract relevant information. It is cost-effective, time-saving and non-invasive.

It’s important to remember though that the accuracy, reliability, and completeness of the data depend on the quality and credibility of the source documents.

Looking for information on how to automate certain business processes?

• Automated Data Processing (ADP): a tool for scalability and growth
• Data automation for business growth: everything you need to know

Probability sampling

Probability sampling is used to select a representative sample from a larger population and involves random selection, ensuring that every element in the population has an equal probability of being chosen. Its advantages include generalisability, statistical inference and reduced bias.

Yet it’s worth remembering that implementing probability sampling can be more challenging and time-consuming compared to non-probability sampling methods.

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Consequences of poor data collection: the hidden risks

Poor data collection can have far-reaching consequences such as flawed decision-making, compromised insights, and potentially damaging outcomes.

Let’s look at them in more detail:

1. Inaccurate analysis and decisions – If data collection is flawed or incomplete, the insights derived from the data will be inaccurate or misleading. Businesses may make ill-informed decisions that could lead to financial losses, missed opportunities, or ineffective strategies.
2. Biased results – Poor data collection can introduce bias into the data, either through the sampling process or the design of survey questions. Biased data can lead to unfair conclusions or discriminatory practices, affecting individuals or certain groups.
3. Missed opportunities and trends – Inadequate data collection may result in missing critical information and trends. Organisations might fail to identify emerging market opportunities, customer preferences, or potential threats, putting them at a competitive disadvantage.
4. Reputation damage – If data collected is mishandled, misused, or exposed due to inadequate security measures, it can lead to a breach of trust with customers, partners, or the public. This can damage an organisation’s reputation and result in a loss of customer loyalty.
5. Wasted resources – Poor data collection can lead to the collection of irrelevant or duplicate data. This wastes time, effort, and resources that could have been better allocated elsewhere.

To mitigate those consequences, it is essential to prioritise data quality, establish rigorous data collection procedures, and invest in data management systems and technologies.

Data collection best practices

As we discussed above, data collection is a critical process that lays the foundation for accurate analysis and informed decision-making.

To ensure success and maintain the integrity of data collection, several best practices should be followed:

Ensuring accuracy in data collection

Ensuring accuracy in data collection is crucial to obtaining reliable and trustworthy information for analysis and decision-making.

Key practices to achieve that include clearly defining research objectives, using valid and reliable data collection instruments, following standardised data collection methods, ensuring clarity and precision throughout the process and monitoring the system regularly to ensure errors are detected early on.

Maintaining ethical standards in data collection

Maintaining ethical standards in data collection is essential to protect the rights and well-being of individuals and to ensure the integrity and trustworthiness of research and business practices.

Key principles and practices to adhere include:

• obtaining informed consent from all participants before collecting their data,
• protecting participants by ensuring their personal information is kept confidential and secure,
• collecting only the data necessary to address the research objectives,
• having respect for vulnerable populations and respecting culture and social norms.

For more information on business ethics:

• Code of ethics in business: benefits, importance and its impact
• How can a strong set of work ethics ensure solid software delivery?

Data privacy and security: a non-negotiable aspect

Data privacy and security are non-negotiable aspects in all data collection methods. To achieve them, remember to protect individual rights, build trust, mitigate data breach risks, comply with regulations and safeguard sensitive information.

Implementing strong security measures, obtaining informed consent, conducting data protection impact assessments. and staying up-to-date with data protection regulations. These efforts not only protect individuals’ rights but also contribute to a more trustworthy and responsible data-driven society.

Read more about this topic:

• GDPR compliance: essential principles and tips for your business
• Data security and privacy: the 50+ generation’s biggest concern

Overcoming challenges in data collection: create an effective strategy

Creating an effective data collection strategy involves careful planning, consideration of potential challenges, and the implementation of solutions to overcome them.

Here’s our step-by-step guide to developing one:

1. Define clear objectives: Start by clearly defining your research or business objectives. Understand what data you need to collect, why you need it, and how it will be used to achieve your goals.
2. Choose appropriate data collection methods: Select data collection methods that align with your objectives and the nature of the data you need. Ensure that the chosen methods are suitable for the target population and are likely to give reliable results.
3. Design data collection instruments: If applicable, design data collection instruments such as surveys, questionnaires, or interview guides. Ensure that they are clear, unbiased, and relevant to your research objectives.
4. Pilot test the instruments: Before full deployment, pilot test your data collection instruments with a small group of participants to identify and address any issues, ambiguities, or errors.
5. Address sampling challenges: If your data collection involves sampling, carefully address potential sampling challenges. Use probability sampling when possible to ensure representativeness, and pay attention to issues like non-response bias or sample size.
6. Train data collectors: If data collection involves human interaction, provide comprehensive training to data collectors to ensure consistency and standardisation of the data.
7. Establish data privacy and security protocols: Implement robust data classification measures to protect participant information and ensure compliance with relevant data protection laws. Establish secure data storage and access controls.
8. Minimise non-sampling errors: Identify and minimise non-sampling errors, which can occur during data entry, data recording, or data processing. Conduct regular data quality checks to ensure accuracy.
9. Anticipate and address data collection challenges: Identify potential challenges that could arise during data collection, such as low response rates, uncooperative participants, or incomplete data. Develop strategies to address these challenges proactively.
10. Monitor data collection progress: Regularly monitor the progress of data collection to ensure it is on track and meeting the objectives. Be prepared to make adjustments if needed.
11. Maintain clear communication: Communicate with stakeholders and participants clearly and transparently about the process, its purpose, and the importance of their participation.
12. Record detailed documentation: Keep detailed documentation of the data collection process, including any modifications, issues encountered, and how they were resolved.
13. Plan for data analysis and utilisation: Consider how the collected data will be analysed and utilised to achieve the research or business objectives. Ensure that the data is relevant and sufficient for your analytical needs.
14. Evaluate and improve: After data collection is complete, evaluate the effectiveness of your data collection strategy and identify areas for improvement in future projects.

A good data strategy is always key. But if you are keen to do it right, you may need to work with specialists, experienced in this kind of projects.

At Future Processing we offer several data solutions that may have huge impact on your business. Get in touch with our team to see how you can make the most of your information assets and take your organisation to the next level.

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As a result, the new knowledge and observations based on machine learning will revolutionise supply chain management in organisations.

Key challenges in the logistics industry

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Companies nowadays are faced with a series of new challenges, apart from the growing customer expectations: transport complications, remote work, shortages resulting from unexpected growth in demand, and so on. The pandemic has forced businesses to revise their global supply chain strategies and adapt them to the new reality.

1. Resource planning

Resource planning is a crucial element of supply chain management as it allows companies to cope with and adjust to unforeseen shortages. No company that deals with supply chains would like to stop the production to look for a substitute supplier. Likewise, no company would like to be stuck with excess supplies which block their capital and increase storage costs.

Supply management is largely based on keeping balance in purchase order synchronisation to maintain the operation flow and avoid stocking goods that are no longer necessary or usable.

2. Quality and safety

Due to the growing demand for on-time delivery and the need to keep assembly lines running, quality and safety assurance has become a huge challenge for supply chain companies. Resigning from some quality and safety standards may cause real hazard. Moreover, environmental changes, trade conflicts, and economic pressure connected with supply chains may also bring about problems and risks.

3. Problems resulting from supply shortages

One of the most common problems in logistics is related to supply shortages. Luckily, by implementing ML in the supply chain, you will be able to get a deeper understanding of this problem and its various facets. Demand and supply forecasting algorithms analyse numerous factors to enable early planning and storage. By providing new insights into the various aspects of supply chains, ML solutions make supply management much easier.

4. Inefficient supplier relationship management

Another challenge that logistics companies face is a sudden shortage of supply chain specialists. This can actually make supplier relationship management problematic and ineffective. Thanks to ML, you can get a useful insight into supplier data and make real-time decisions.

Benefits of using machine learning in the supply chain

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There are many benefits of machine learning for supply chain management, including:

• effectiveness – ML systematically accelerates cost reduction and improves quality,
  
• product flow optimisation in the supply chain – companies don’t need to store large amounts of supplies,
  
• smooth supplier relationship management – thanks to simpler, quicker, and tested administrative procedures,
  
• practical conclusions – enabling quick problem solving and constant improvement.

The most important examples of using machine learning in the supply chain

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1. Supply chain planning

Supply chain planning is one of the most important elements of supply chain management. Machine learning is a very effective technology when applied to the constant search for the key factors impacting the efficiency of a supply chain. Apart from making better decisions concerning the supply chain, ML technologies minimise human interference.

2. Warehouse management

Proper warehouse and supply management is necessary for effective supply chain planning. Storing supplies and maintaining them in good condition is a costly process. Both excess and insufficient supplies may pose a real challenge to a company.

Thanks to access to the latest data about the market, ML tools can predict growth in demand and enable restocking in advance or prevent overstock of goods or important manufacturing components.

3. Stock management in a warehouse

Machine learning can be used in warehouses to automate manual work, predict possible problems, and limit paperwork for warehouse employees. Thanks to NLP and OCR technologies, warehouse specialists are able to automatically register parcel delivery and status changes. Machine vision can automate barcode and label reading, which accelerates and facilitates the whole process.

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Smart warehouses are fully automated buildings, where most of work is done with the help of autonomous robots or software. Autonomous mobile robots use computer vision to identify routes and move to selected areas of a warehouse, helping to receive, pack, unpack, transport, load, and unload goods.

These robots use object classification, detection, and segmentation to:

• navigate around the warehouse;
  
• find the right object attributes and sizes;
  
• avoid obstructions;
  
• detect visual damage on a parcel;
  
• find free space for a box;
  
• monitor the right positioning of a package;
  
• avoid collisions with vehicles;
  
• check the warehouse inventory and create automatic real-time images.

AMRs reduce the number of warehouse management errors and limit human involvement in the warehouse, which consequently lowers the risk of accidents. This way complex tasks are made simple and all operations become more profitable. In fact, Alibaba and Amazon have transformed their warehouses into productivity utopias thanks to the use of automation.

4. AI in logistics for demand forecasting

Demand forecasting is a branch of predictive analytics used to predict the demand for products and deliveries in the whole supply chain, also in uncontrolled conditions. There are several crucial benefits of accurate demand forecasting in supply chain management, including maintenance cost reduction and inventory optimisation.

By using ML models, companies can benefit from the power of predictive analytics for demand forecasting. ML algorithms are able to quickly analyse large and diverse sets of data, thus improving the accuracy of demand forecasting.

What’s more, ML models are capable of identifying hidden patterns in the historical data concerning demand. ML in the supply chain can also be used to detect particular problems before they even manage to disturb the business in any way. When a company has access to a solid supply chain forecasting system, it can react to new problems and risks even before they occur.

5. Logistics route optimisation

ML offers numerous benefits for supply chain networks: reduction of transport costs, improvement of supply efficiency, and risk minimisation for suppliers are the three crucial advantages. To reduce the cost of shipping and accelerate the process, you can use AI to decide which routes are the best. This is particularly important for big e-commerce companies with plenty of customers. ML can help you optimise routes in real-time.

The technology can be used to track the weather and road conditions and to give recommendations regarding route optimisation and shortening the time of driving. Thanks to this, lorries can be redirected at any moment if a more profitable route is available.

Moreover, ML can help in learning where a given parcel is located in the logistics cycle. It allows tracking product location during transport and provides insight into the conditions of transporting. Special sensors allow monitoring various parameters, including humidity, vibrations, and temperature.

6. Selection of suppliers and supplier relationship management

The choice of reliable suppliers and maintaining good relationships with them may be a challenging task. If you make a wrong choice or a mistake in managing your relations, your company may suffer. In the worst-case scenario, it can even go bankrupt.

However, if you take advantage of ML technologies for supplier relationship management activities (e.g. audits and solvency rating), you will receive reliable forecasts for every interaction with potential or current suppliers. This will help you avoid errors and build mutually beneficial cooperation.

7. Workforce management

Machine learning can play a crucial role in production planning optimisation. Workforce management is a must in every modern organisation. It includes various processes, such as recruitment; employee retention, development, and transfer; performance management; and others.

ML and AI solutions can facilitate production planning and streamline your workforce management strategy. What you get by this is a satisfied team. This is important, as employees who like their organisation and work environment are more productive. With a happy team, your company will be definitely more successful.

8. Autonomous vehicles

Logistics and shipping are the key areas of the delivery process. Goods must be delivered to a customer or contractor. Numerous restrictions are related to this field. For example, drivers have to stop driving after a certain amount of time. They need to have a break or be replaced by another driver.

However, having at least two drivers in every delivery vehicle may be expensive; what’s more, the need to wait for a driver to rest may extend the shipping time considerably.

Autonomous vehicles can become a solution to these problems as they reduce the costs and the time necessary for shipment. Such vehicles are not advanced enough for the time being, but when they get fully launched on the market, they will markedly improve logistics.

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9. Drones used in delivery

Delivery drones are the latest solution to aid companies in delivering products to the least accessible sites. Companies often have difficulty in delivering parcels to places where land transport is either dangerous or fallible, or sometimes even impossible.

Drones have revolutionised the logistics sector, especially for pharmaceutical companies, which deliver products with short expiry dates. Problems with transport often result in product wasting or the need to invest in specialised warehouses, which is rather costly.

10. Marketing and sales departments

AI solutions play an important role in facilitating marketing processes in logistics companies. Email marketing is a great example of that. This time-consuming task can be automated thanks to AI. Marketing specialists can now concentrate on more creative tasks, while AI-based software takes care of all the repeatable tasks.

11. Chatbots – AI automating customer service

Every organisation that deals with logistics knows how much work it takes to offer high-quality customer service. Customers normally expect a company to answer their questions quickly and solve their problems as soon as possible. Delivery processes are complex and rather unpredictable, which is why problems occur from time to time. ML-based chatbots are trained to understand specific keywords and phrases. They are widely used in supply relationship management as well as sales and purchase management, which is why employees are able to focus on value-adding tasks rather than become frustrated with responding to basic questions. Moreover, chatbots are able to analyse customer experience and make conclusions regarding possible improvements. What follows is that nowadays, company can better understand their customers’ needs and react to them promptly.

12. Customer satisfaction improvement

For a company to succeed, its customers must be satisfied. One of the ways to make them feel satisfied is to recommend suitable products at the right time. Recommendation systems based on customers’ preferences are integrated with mobile or web apps so to personalise their experience. By means of sentiment analysis, companies can divide products into successful and unsuccessful ones, based on the reviews and ratings given by the customer. This also helps in improving user experience.

What’s more, customers expect to receive up-to-date information about the delivery status. Thanks to ML, it is possible to predict the delivery time, accounting for all the changing conditions. When the delivery time is predicted more accurately, user experience is enhanced.

13. Real-time product pricing

Dynamic pricing is advanced time-based pricing, reacting to changes in demand and supply as well as to the changing prices of competitive or dependent products. Thanks to this approach, companies can offer optimum prices for their goods to attract more buyers. Dynamic pricing software makes use of ML algorithms to analyse customer historical data in real time. This way, organisations can react to changes quickly and adjust pricing to the new circumstances.

14. Product damage detection

Nothing can disappoint your customers more than opening a package with what they’ve just purchased only to find out that the product inside is damaged. This usually ends up in negative reviews and customers leaving your business. Logistics centre normally run manual quality inspections to check containers and packaging for any kind of damage in transit. The development of ML technologies allowed increasing the scope of quality assurance automation in the supply chain life cycle. ML solutions are perfect for visual pattern recognition and there are also numerous potential applications in physical inspection in the whole supply chain network. ML algorithms which quickly detect comparable patterns in many sets of data turn out to be very efficient in quality control automation in logistics centres by isolating parcels with damaged goods.

15. Back-office task automation

Contemporary companies can automate many back-office tasks. ML in logistics helps create transport schedules, assign tasks to particular employees, and implement parcel tracking in a warehouse. Robotic process automation (RPA) helps in analysing and generating reports or sending automatic emails to stakeholder.

16. Fraud prevention

ML algorithms are able to both improve the quality of the product and lower the risk of fraud by automating control and audit processes and performing real-time results analytics to detect anomalies or deviations from standard patterns. What’s more, ML algorithms can analyse huge amounts of data to protect your business from fraud. For instance, when it comes to the supply chain, ML helps identify fraudulent transactions, prevent authorisation abuse, accelerate fraud investigations, and automate the processes of counteracting fraud.

Examples of companies using machine learning to improve supply chain management

Amazon

Amazon is one of the most renowned leaders of the e-commerce supply chain, using technologically advanced and innovative ML-based systems, such as automated warehousing and drone deliveries.

Thanks to the reliable supply chain, Amazon has direct control over its main areas, including packing, order processing, delivery, customer service, and reverse logistics, based on ample investments in smart software, transport systems, and warehousing.

Microsoft Corporation

Microsoft’s supply chain is chiefly built on predictive analytics, machine learning, and business analytics.

The tech giant has a huge portfolio of products which generate vast amounts of data that must be centrally integrated for the purposes of predictive analytics and improvement of operational performance.

ML technologies allowed the company to build a smooth and integrated supply chain that enables real-time data capture and analytics. Moreover, Microsoft’s dynamic supply chain uses early warning systems to reduce the risk and respond to queries quickly.

Alphabet Inc.

Alphabet is a famous and innovative tech tycoon, whose operations are based on a flexible supply chain that facilitates cooperation between regions.

Alphabet’s supply chain uses ML, AI, and robotics to become fully automated.

Procter & Gamble

P&G is a leading consumer goods corporation with one of the most complex supply chains and a vast product range. The company skilfully uses ML technologies and advanced analytics for comprehensive product flow management.

Rolls Royce

Rolls Royce, the legendary British car manufacturer, produces autonomous ships, where ML and AI substitute for human crew. In the ships, special algorithms are used to examine their surroundings on the water and properly classify the objects based on the level of hazard they pose to the vessel. In the new system of autonomous ships, such technologies will be used for autonomous navigation, obstacle detection, and communication management. ML algorithms can also be used for the monitoring of engine efficiency, safety, and cargo.

UPS

UPS is a multinational shipping & receiving company, which claims to deliver thousands of parcels daily, with about 100 deliveries done by each UPS driver per working day.

To ensure smooth and timely delivery, UPS makes use of a perfectly optimised navigation system called On-Road Integrated Optimization and Navigation (ORIAN). The system makes sure that UPS drivers use the optimum delivery routes in terms of distance, fuel, and time.

According to UPS, ORION uses highly advanced algorithms to collect and process large amounts of data to optimise routes for the drivers. This way, they can ship and receive parcels in a much more efficient way. The system uses online map data to calculate the shipping distance and time and to find the most cost-effective routes.

How to make ML help in supply chain management?

1. Understanding the supply chain structure

Before implementing ML in the supply chain, you need evaluate its whole structure:

• Define the critical elements of your business
  
• Run a detailed analysis of your supplier network
  
• Identify hidden relationships as well as mutual relationship nodes
  
• Give a quantitative diagnosis of the relative fragility of the supply chain
  
• Find the bottlenecks and risk factors in the supply chain
  
• Make tangible comparisons with other companies and industry benchmarks
  
• Evaluate the supply chain safety
  
• Assess your functional maturity in the context of the process, people, and technologies

2. Planning your objectives and the steps necessary to achieve them

To understand in what way the use of machine learning in the supply chain might be beneficial to your business, you need to go through the discovery stage and calculate the short-term and long-term ROI. It is also important to draw up a detailed plan to define your goals and the requirements that must be met to achieve them. In other words, the business problem at hand must be defined in the categories of machine learning.

3. Ensuring an effective ML engineering process

Successful use of machine learning in the supply chain largely depends on:

• Creating a multi-functional team of professionals experienced in data science, DevOps, Python, Java, QA, Business Analytics, etc.
  
• Starting by describing the business problem
  
• Deciding on the adequate success factors
  
• Selecting the right technology stack
  
• Taking data readiness into account: focusing on data quality and quantity
  
• Creating, training, testing, and optimising the model
  
• Implementing the model
  
• Monitoring the model efficiency

Wrap-up

Improving the supply chain efficiency is a key task for every company. Innovative technologies such as machine learning makes it easier to deal with challenges related to changeability and accurate demand forecasting in global supply chains.

Machine Learning makes it possible to detect patterns in the supply chain data, based on algorithms that quickly identify the factors with the hugest success impact as far as supply networks are concerned, and learning in the process at the same time.

Advanced technologies can also help logistics companies in providing the possibility of product, logistics, warehousing, and final delivery at various levels.

AI in logistics improves the quality of customer service. Thanks to the use of ML-based solutions, your products will be delivered in perfect condition and on time. Additionally, your team will have more time and business information to help the managers make effective decisions and ensure the development of your company.

AI is quickly developing in all sectors and it has already proven to be a useful tool of supply chain management. Without ML, companies like Amazon, Nike, UPS, or Walmart wouldn’t be able to work as quickly as they do. AI guarantees not only efficiency and customer satisfaction but also safety in warehouses thanks to the qualities of autonomous vehicles, which make the workflow exceptionally smooth and error-free.

However, to be able to make full use of AI, companies need to plan their future and begin investing in ML and related technologies right away to be able to enjoy increased profitability, productivity, and availability in the supply chain industry.

Data Science & Engineering 

Process data, base business decisions on knowledge and improve your day-to-day operations.

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What are recommendation systems?

The key to successful sales of products and services lies in close scrutiny of customers, especially in relation to their reactions to particular trade offers. Being able to get insight into customers’ tastes enables businesses to adjust their offer to satisfy their audience. Recommendation systems respond to this need, as their goal is to predict how a given person might rate a specific product. By analysing the visits to your online shop using a recommendation engine, you can adapt the way its content is viewed to users so that it draws their attention and, as a consequence, increases the value of the shopping cart.

What are recommendations good for?

Recommender systems have a number of applications, but their primary purpose is to address every user as a unique individual. A person is the focus of recommendations and the task of the system is to get to know this person, especially when it comes to the individual characteristics that differentiate them from other people. Based on the acquired information, the system browses the shop offer to find the products which are best suited to the particular customer’s needs. As a result, the offer presented to the customer is carefully adjusted to their individual preferences. This mechanism can be used in various industries including healthcare.

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These are the main goals of recommendation systems:

• Increasing sales volumes – one of the principal methods of increasing the sales of regular offers is marketing. Marketing activities involve addressing the whole population or a selected target group and directing special offers and promotional material to them. As far as recommendation systems are concerned, sales offers and promotional activities are directed to specific users. This approach increases the likelihood of selling the promoted products.
  

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• Customer retention – one of the greatest problems of modern companies, in particular those based on the subscription model, is the churn rate. Retaining customers for a long time is necessary to get a return on the investment made to acquire them. Popular methods of customer retention, such as discounts and coupons, are often rather costly and with short-term effects. This problem can be overcome by adopting an individual approach to customers. Recommendation systems help in understanding the needs and objectives of customers and, eventually, in selecting the correct activities to maintain the relationship with them.

• Increasing customer satisfaction – the use of recommendation systems helps increase customer satisfaction by enhancing the functionality of the sales service. Spotify, the popular music streaming service, offers 70 million tracks – trying to find the music that you like in such a crowd may be a cumbersome task. Here’s where recommendation systems come in handy: they shorten the distance between the customer’s need and satisfaction. Not only do they help find the search product; they also discover the needs that customers are not even aware they have.

How do recommendation systems work?

Recommendation engines are based on state-of-the-art functions of machine learning.

ML algorithms build mathematical models to make predictions or decisions without being explicitly programmed to do so by humans. They are used in a variety of applications where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. The training data for algorithms includes, for example, user behaviour (ratings, clicks, purchase history), user demographics (age, education, income, location), or product attributes (book genre, film cast, food origin).

How to present the recommendations

Preparing the presentation

Since lists are the most popular method of presenting the recommended items, a proper ranking model is necessary to assign rating to every item and rank them from the most to the least attractive ones.

The basic ranking function which increases consumption is the popularity of the item. However, popularity is the opposite of personalisation. If a list is based on popularity, every user gets the same order. This is why it is essential to personalise the experience.

Personalisation can be achieved by completing the system with ratings given by the user to other items. And yet, this is not enough either, because in this approach, the prioritised items might be too niche and the most interesting suggestions might get omitted. It’s easy to picture a situation where a user gives a low rating to a film with a terrible plot even though they love the particular film genre. To make up for this, instead of focusing on popularity or predicted rating only, the ranking should balance these factors.

Learning to rank

There are numerous ways of constructing the ranking function, from simple rating methods to pair preferences and ranking optimisation. One of the available options is combining the popularity factor with the user rating predictions.

You must bear in mind, though, that in the case of ranking recommendations, personalisation is essential: the goal is not to create a universal concept of accuracy but to find ways of optimising customised models.

With large sets of data available, in terms of both the amount and the type, it is vital to adopt an in-depth approach to model selection, training, and testing. This is why all-encompassing approaches to ML algorithms are usually adopted: from unsupervised learning methods, such as cluster analysis, to a series of supervised classification methods, which have given optimum results in various contexts.

Supervised learning happens when the data set supplied for training includes the expected output: for instance, a collection of emails including the information which of them is spam and which isn’t. In this case, a new email should end up in the inbox or in the spam, in accordance with its content. Unsupervised learning, on the other hand, makes it possible to process untagged data to identify previously undetected patterns, e.g. collecting press articles on similar subjects.

These are some of the most important algorithms:

• Linear regression
  
• Logistic regression
  
• Elastic nets
  
• Singular value decomposition
  
• Restricted Boltzmann machine
  
• Markov chains
  
• Latent Dirichlet allocation
  
• Association rules
  
• Gradient-boosted decision trees
  
• Random forests
  
• K-means
  
• Affinity propagation
  
• Matrix factorization
  
• Support vector machines
  
• Neural networks

In the last couple of years, there have been many new algorithms specifically designed to learn to rank, such as RankSVM and RankBoost.

There is no universal method of choosing the best model for a given ranking task. The simpler the feature set is, the simpler the model. It is easy to fall into a trap where a new attribute shows no value because the model is unable to learn it. Or another trap: deciding that a more powerful model is not useful only because there are no attributes that use its advantages.

A/B testing process

Although ratings prove very useful in deciding whether a given model deals well with training data, you cannot be sure that the results will translate into actual improvement of user experience. This is why it is necessary to implement A/B testing to test new algorithms. Tests are normally made on thousands of users and 2 to 20 elements which are variants of the basic idea. With A/B tests, you can check out brave ideas or test multiple projects at once. All in all, their key benefit is that they help you make data-based decisions.

To measure the model performance, a number of factors can be taken into account: from ranking measures, such as NDCG (Normalised Discounted Cumulative Gain), mean mutual rank, and fraction of concordant pairs, to classification indexes, such as accuracy, precision, recall, and F-score.

The analysis shows to what extent these factors correlate with tangible results in A/B tests. However, since mapping is not perfect, performance is used only as a guideline to making informed decisions concerning further tests. If model tests confirm the hypothesis, A/B tests are designed and run to prove that the new functionality is relevant from the user’s point of view.

Application

One of the most popular recommender systems has been created by Netflix. Netflix is based on the subscription business model, which means they need to not only acquire new users but also to retain the current ones. For this reason, it is very important for them to retain the users’ attention and to encourage them to extend their subscription.

The current video base of Netflix includes over 5500 TV shows and films and it is regularly updated. The wide range of content allows Netflix to satisfy their customers’ needs but there is also a risk involved. If users need to take a lot of time looking for a film they want to watch, they might become uninterested and cancel the subscription as a result.

To prevent this, Netflix uses a recommendation engine that helps users find new films. The effectiveness of this approach is confirmed by the fact that most shows watched by Netflix users get through to them by means of the recommendation system. The system enables Netflix to retain their regular customers, which means they also retain profit and, additionally, acquire new users. If recommendations are effective, they attract new users, who want to watch shows they find interesting without spending a lot of time looking for them.

Other online services also use effective AI-based recommendation engines. One of them is Spotify. Spotify puts together a unique list called “Discover Weekly” for every user. The list contains 30 songs selected based on the user’s personal preferences.

As far as machine learning is concerned, Spotify uses a model based on the multi-armed bandit problem that balances between exploitation and exploration. In this case, exploitation refers to providing recommendations in the app, based on the previous music and podcast choices.

Exploration is the opposite of exploitation. It is based on users’ uncertain engagement and it serves as a research tool that provides information on how people interact with the suggested content. Exploration makes it possible to discover new interesting items for users, something they haven’t heard before. Thanks to this balanced approach, shelves and cards are personalised for new and current users.

Unlike traditional department stores, online sellers such as Zalando need to use digital representations of their offer to attract clients. This normally means that there is a database gathering information about the properties, prices, and images of particular items. And yet, the relationship between these attributes and the features that customers actually look for (e.g. how much a given object fits their style) is much more complex.

The recommendation engine is able to provide accurate suggestions concerning a given product or recommend an accessory to match the outfit. One of such networks is Style2Vec, which allows saving an image of a piece of clothing so that its qualities can be easily modified: for instance, you can change the colour, the length, or the cut. Browsing the base with Style2Vec consists in adding and subtracting traits of the clothes.

Close picture

The results can be used to generate entire outfits and collections based on users’ preferences. This allows:

• recommending similar or matching products,
  
• following the trends and, consequently, launching new products that meet the customers’ needs,
  
• shortening the shopping time.

Another popular platform using an impressive recommendation engine is YouTube. The system consists of two neural networks responsible for different sorts of data filtering.

The first one filters user data to find correlations between users’ preferences, whereas the other sorts videos based on their characteristics and other data, including their ratings, number of views, comments, and descriptions. YouTube recommendations are displayed both on home screen and in the “Skip to next” section, where suggestions show up as you watch a video.

Summary

Comprehensive use of data and practical application of artificial intelligence determine the success of the entertainment and e-commerce tycoons. Thanks to access to unlimited sets of data, they are able to offer extremely accurate recommendations.

The benefits of ML-based solutions outweigh the effort that must be put in their implementation. The fact that this area is still developing is also beneficial. The future may bring brand-new and incredibly effective AI-based methods of interacting with users.

Are you wondering how recommendation systems can help your company? Contact us so that we can help you find the best solution for your needs. You can also learn more about data science.

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