Artificial intelligence in supply chain management: A systematic literature review

Less than one-third of companies perform an independent diagnostic at the outset, but this exercise can ensure companies have an accurate list of all the value-creation opportunities. Following the foundation of this study, where the emphasis is on how the different authors have applied different DL algorithms on the SCM and lack of consensus on SCM dimensions, different machine learning supply chain optimization aspects were recognized far from being utterly considered. Consequently, there is a marvelous opportunity to encourage researchers to advance the available knowledge in this area. To draw a future research agenda, we propose the research framework presented in Fig. This framework categorizes different areas that should be considered when using DL algorithms.

It’s also important to have multiple suppliers in different geographic locations so manufacturers can quickly switch from one supplier to another. Reliance on a single supplier for a critical component or raw material can cause production to grind to a halt. But even with these advances, global supply chains add several layers of complexity to inventory control. In light of the logistical and cost challenges faced in the past year, companies must do a better job stressing the importance of discipline.

Build a multichannel, responsive supply chain

The authors of Meisheri et al. (2021) addressed these challenges in a multi-period and multi-product system using DRL. Embracing change and fostering a culture of innovation will enable your organization to harness the full potential of AI-driven supply chain optimization and maintain a competitive edge in the future. Regularly assess the impact of AI solutions on supply chain performance and make adjustments as needed to maximize their effectiveness. Monitor key performance indicators and gather feedback from your team to identify areas for improvement and fine-tune your AI strategies.

Encourage a mindset of continuous improvement and responsiveness among your employees by promoting a data-driven culture. Empower your team to leverage insights from AI solutions and make data-driven decisions to improve performance across the supply chain. To successfully implement AI solutions, you need a team that combines supply chain management expertise with AI talent.

The Benefits of Supply Chain Network Optimization With AI

Journals of “SUSTAINABLE ENERGY GRIDS AND NETWORKS”, “REMOTE SENSING OF ENVIRONMENT”, and “INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH” have the highest citations that are 270, 138, and 64. “INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH” published three papers which is the highest number of papers with 38 total citations. “COMPUTERS IN INDUSTRY”, “INDUSTRIAL MANAGEMENT AND DATA SYSTEMS”, and “SENSORS”, each published two papers with 6, 14, and 23 total citations, respectively. Although we did not set any time limitation in our research query, as can be seen in Fig.

Finally, we’ll put our new skills to the test by optimizing a supply constraint problem using linear programming techniques. By checking the box below, you consent to GEP using your personal information to send you thought leadership content – such as white papers, research reports, case studies – and other communications. GEP representatives may contact you to provide additional information or answer questions. To ensure adoption of new solutions, companies must invest in change management and capability building. Employees will need to embrace new ways of working, and a coordinated effort is required to educate the workforce on why changes are necessary, as are incentives to reinforce the desired behaviors. This paper also proposes a conceptual framework that enables us to succinctly understand DL applications in SCM from a philosophy of knowledge perspective, and chart an agenda as a guideline for both practitioners and academic enthusiasts.

Machine learning and deep learning

During the training process, internal parameters of the unit including weight and biases are learned to produce outputs (Schmidhuber 2015). ANNs (also known as feed-forward NNs) are Multilayer Perceptrons (MLP) containing one or more hidden layers so that each layer has multiple hidden units (Alom et al. 2019). DNNs, by employing deep architectures in ANNs, are capable of representing learning functions with more complexity when the number of layers as well as units in a layer increases (Liu et al. 2017). In the retail industry, having multiple products with uncertain demands and different lead times makes determining the optimal inventory replenishment policy highly challenging (Meisheri et al. 2021).

Then, we’ll dive deeper into some of the specific techniques and use cases such as using neural networks to predict product demand and random forests to classify products. An important part to using these models is understanding their assumptions and required preprocessing steps. We’ll end with a project incorporating advanced techniques with an image classification problem to find faulty products coming out of a machine.

Systematic review results

The search was conducted on the most used academic search engine Scopus (Portugal et al. 2018) on August 14, 2021. Then we limited the selected material to articles published in peer-reviewed journals that have been 59 papers and excluded the other publications (8 reviews, 70 conference papers, 46 conference reviews, 3 books, 2 book chapters, and 1 Erratum). Finally, we studied the abstracts of 59 remained papers to make sure whether they are related to the scope of our review or not. Through this investigation, 16 publications that were not directly related to the supply chain or did not use DL algorithms have been excluded, and a final sample of 43 publications was selected for further review. The resulting benefits include enhanced forecasting accuracy, reduced lead times, improved customer satisfaction, cost reduction, and a more resilient and sustainable supply chain.

Tips for Overcoming Natural Language Processing Challenges

Tokenization is where all NLP work begins; before the machine can
process any of the text it sees, it must break the text into bite-sized
tokens. To perform the basic NLP tasks, we first will need to set up our programming
environment. You
will want to research these tasks further; there are ample resources
available online. We won’t focus much on rule-based NLP, but, since it has
been around for decades, you will not have difficulty finding other
resources on that topic. Rule-based NLP does have a room among the
other two approaches, but usually only to deal with edge cases. Heading into 2021 and beyond, NLP is now no longer an experimental
subfield of AI.

Natural language processing is a technical component or subset of artificial intelligence. This finance-specific language model
would have even better performance on finance-related NLP tasks versus
the generic pretrained language model. Dependency parsing involves labeling the relationships between individual tokens, assigning a syntactic structure to the sentence. Once the relationships are labeled, the entire sentence can be structured as a series of relationships among sets of tokens. It is easier for the machine to process text once it has identified the inherent structure among the text. Think how difficult it would be for you to understand a sentence if you had all the words in the sentence presented to you out of order and you had no prior knowledge of the rules of grammar.

Higher-level NLP applications

In law, NLP can help with case searches, judgment predictions, the automatic generation of legal documents, the translation of legal text, intelligent Q&A, and more. And in healthcare, NLP has a broad avenue of application, for example, assisting medical record entry, retrieving and analyzing medical materials, and assisting medical diagnoses. There are massive modern medical materials and new medical methods and approaches are developing rapidly. No single doctor or expert can be expert at all the latest medical developments.

Peter Wallqvist, CSO at RAVN Systems commented, “GDPR compliance is of universal paramountcy as it will be exploited by any organization that controls and processes data concerning EU citizens. For example, a knowledge graph provides the same level of language understanding from one project to the next without any additional training costs. Also, amid concerns of transparency and bias of AI models (not to mention impending regulation), the explainability of your NLP solution is an invaluable aspect of your investment. In fact, 74% of survey respondents said they consider how explainable, energy efficient and unbiased each AI approach is when selecting their solution.

What is Bag of Words?

By contrast, character tokenization breaks this down into 24 tokens, a 6X increase in tokens to work with. It takes natural breaks, like pauses in speech or spaces in text, and splits the data into its respective words using delimiters (characters like ‘,’ or ‘;’ or ‘“,”’). While this is the simplest way to separate speech or text into its parts, it does come with some drawbacks.

The data and modeling landscape in the humanitarian world is still, however, highly fragmented. Datasets on humanitarian crises are often hard to find, incomplete, and loosely standardized. Even when high-quality data are available, they cover relatively short time spans, which makes it extremely challenging to develop robust forecasting tools. We produce language for a significant portion of our daily lives, in written, spoken or signed form, in natively digital or digitizable formats, and for goals that range from persuading others, to communicating and coordinating our behavior. The field of NLP is concerned with developing techniques that make it possible for machines to represent, understand, process, and produce language using computers.

Without a strong foundation built through tokenization, the NLP process can quickly devolve into a messy telephone game. A large challenge is being able to segment words when spaces or punctuation marks don’t define the boundaries of the word. This is especially common for symbol-based languages like Chinese, Japanese, Korean, and Thai. NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence. An NLP-generated document accurately summarizes any original text that humans can’t automatically generate.

• A human inherently reads and understands text regardless of its structure and the way it is represented.
• This means that you can use the data you have available, avoiding costly training (and retraining) that is necessary with larger models.
• Name entity recognition is more commonly known as NER is the process of identifying specific entities in a text document that are more informative and have a unique context.
• And contact center leaders use CCAI for insights to coach their employees and improve their processes and call outcomes.

Text summarization is the process of shortening a long piece of text with its meaning and effect intact. Text summarization intends to create a summary of any given piece of text and outlines the main points of the document. This technique has improved in recent times and is capable of summarizing volumes of text successfully.

What is the starting level of planning graph?

In the legal domain, NLP models may need to be trained on legal documents and case law to extract information related to legal concepts, arguments, and decisions. Using a CI/CD pipeline helps address these challenges in each phase of the development and deployment processes to make your ML models faster, safer, and more reliable. Modern software development has embraced continuous integration and continuous deployment (CI/CD) to solve similar difficulties with traditional technology stacks.

Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. In the second half of the chapter, we will introduce a very performant
NLP library that is popular in the enterprise and use it to perform basic
NLP tasks. While these tasks are elementary, when combined together,
they allow computers to process and analyze natural language data in
complex ways that make amazing commercial applications such as chatbots
and voicebots possible. We’ve already started to apply Noah’s Ark’s NLP in a wide range of Huawei products and services. For example, Huawei’s mobile phone voice assistant integrates Noah’s Ark’s voice recognition and dialogue technology.

A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data. The main challenge of NLP is the understanding and modeling of elements within a variable context. In a natural language, words are unique but can have different meanings depending on the context resulting in ambiguity on the lexical, syntactic, and semantic levels. To solve this problem, NLP offers several methods, such as evaluating the context or introducing POS tagging, however, understanding the semantic meaning of the words in a phrase remains an open task. Information extraction is concerned with identifying phrases of interest of textual data. For many applications, extracting entities such as names, places, events, dates, times, and prices is a powerful way of summarizing the information relevant to a user’s needs.

As NLP technology continues to evolve, it is likely that more businesses will begin to leverage its potential. Many modern NLP applications are built on dialogue between a human and a machine. Accordingly, your NLP AI needs to be able to keep the conversation moving, providing additional questions to collect more information and always pointing toward a solution. In some cases, NLP tools can carry the biases of their programmers, as well as biases within the data sets used to train them. Depending on the application, an NLP could exploit and/or reinforce certain societal biases, or may provide a better experience to certain types of users over others.

Natural Language Processing (NLP) is the field of

The last two objectives may serve as a literature survey for the readers already working in the NLP and relevant fields, and further can provide motivation to explore the fields mentioned in this paper. Using these approaches is better as classifier is learned from training data rather than making by hand. The naïve bayes is preferred because of its performance despite its simplicity (Lewis, 1998) [67] In Text Categorization two types of models have been used (McCallum and Nigam, 1998) [77]. But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once irrespective of order. It takes the information of which words are used in a document irrespective of number of words and order.

Pretrained
models are models that have been trained on lots of data already and are
ready for us to perform inference with. Founded in 2016, Hugging Face is the newest
comer on the block but likely the best funded and the fastest-growing of
the three today; the company just raised a $40 million Series B in March
2021. Hugging Face focuses exclusively on NLP and is built to help
practitioners build NLP applications using state-of-the-art
transformers. Its library, called transformers, is built for PyTorch and
TensorFlow and supports over 100 languages. In fact, it is possible to move from PyTorch and TensorFlow for development and deployment pretty seamlessly. We are most excited for the future of Hugging Face among the three libraries and highly recommend you spend sufficient time familiarizing yourself with it.

Changing one word in a sentence in many cases would completely change the meaning. The object of NLP study is human language, including words, phrases, sentences, and chapters. By analyzing these language units, we hope to understand not just the literal meaning expressed by the language, but also the emotions expressed by the speaker and the intentions conveyed by the speaker through language. In the last two years, the use of deep learning has significantly improved speech and image recognition rates.

Read more about https://www.metadialog.com/ here.

IBM Watson Natural Language Understanding

Customers all around the world want to engage with brands in a bi-directional communication where they not only receive information but can also convey their wishes and requirements. Given its contextual reliance, an intelligent chatbot can imitate that level of understanding and analysis well. Within semi-restricted contexts, it can assess the user’s objective and accomplish the required tasks in the form of a self-service interaction.

Artificial intelligence is critical to a machine’s ability to learn and process natural language. So, when building any program that works on your language data, it’s important to choose the right AI approach. Instead, machines must know the definitions of words and sentence structure, along with syntax, sentiment and intent. Natural language understanding (NLU) is concerned with the meaning of words.

NLU vs NLP: What’s the Difference?

NLU relies on NLP’s syntactic analysis to detect and extract the structure and context of the language, which is then used to derive meaning and understand intent. Processing techniques serve as the groundwork upon which understanding techniques are developed and applied. Though looking very similar and seemingly performing the same function, NLP and NLU serve different purposes within the field of human language processing and understanding. The key distinctions are observed in four areas and revealed at a closer look. Natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related but different issues. Natural language processing works by taking unstructured text and converting it into a correct format or a structured text.

As a seasoned technologist, Adarsh brings over 14+ years of experience in software development, artificial intelligence, and machine learning to his role. His expertise in building scalable and robust tech solutions has been instrumental in the company’s growth and success. By way of contrast, NLU targets deep semantic understanding and multi-faceted analysis to comprehend the meaning, aim, and textual environment.

What is Natural Language Processing?

The future of NLP, NLU, and NLG is very promising, with many advancements in these technologies already being made and many more expected in the future. They say percentages don’t matter in life, but in marketing, they are everything. The customer journey, from acquisition to retention, is filled with potential incremental drop-offs at every touchpoint. A confusing experience here, an ill-timed communication there, and your conversion rate is suddenly plummeting. Given that the pros and cons of rule-based and AI-based approaches are largely complementary, CM.com’s unique method combines both approaches.

With the help of natural language understanding (NLU) and machine learning, computers can automatically analyze data in seconds, saving businesses countless hours and resources when analyzing troves of customer feedback. Sentiments must be extracted, identified, and resolved, and semantic meanings are to be derived within a context and are used for identifying intents. While NLU, NLP, and NLG are often used interchangeably, they are distinct technologies that serve different purposes in natural language communication. NLP focuses on processing and analyzing data to extract meaning and insights. NLU is concerned with understanding the meaning and intent behind data, while NLG is focused on generating natural-sounding responses.

Understanding Chatbot AI: NLP vs. NLU vs. NLG

He is the co-captain of the ship, steering product strategy, development, and management at Scalenut. His goal is to build a platform that can be used by organizations of all sizes and domains across borders. NLP stands for neuro-linguistic programming, and it is a type of training that helps people learn how to change the way they think and communicate in order to achieve their goals. So, NLU uses computational methods to understand the text and produce a result.

• Natural language processing works by taking unstructured data and converting it into a structured data format.
• To understand this, we first need to know what each term stands for and clarify any ambiguities.
• This book is for managers, programmers, directors – and anyone else who wants to learn machine learning.
• To simplify this, NLG is like a translator that converts data into a “natural language representation”, that a human can understand easily.
• The technology also utilizes semantic role labeling (SRL) to identify the roles and relationships of words or phrases in a sentence with respect to a specific predicate.

It’s a subset of NLP and It works within it to assign structure, rules and logic to language so machines can “understand” what is being conveyed in the words, phrases and sentences in text. In order for systems to transform data into knowledge and insight that businesses can use for decision-making, process efficiency and more, machines need a deep understanding of text, and therefore, of natural language. While both understand human language, NLU communicates with untrained individuals to learn and understand their intent. In addition to understanding words and interpreting meaning, NLU is programmed to understand meaning, despite common human errors, such as mispronunciations or transposed letters and words.

Read more about https://www.metadialog.com/ here.