What Data Science can bring to Logistics in 2019

Business process automation is usually associated with reassigning tasks to the robotic workforce. But actually, digital transformation starts with data science applications.

6 min read, Jul 25
data-science-logistics-2019

So, what is data science? It’s a field that focused on a thorough analysis of data combining statistical methods, machine learning algorithms, hardware powers and so on for that purpose. For business such deep analytics used for processing of all data concerning the workflow and its management to detect the weaknesses and strengths of the current operational models.

To perform comprehensive research and reach the appropriate accuracy level of results and corresponding with them adjustments to the business running it’s necessary to involve in the process professional data scientists and advanced digital solutions based on AI.

Upgrading this way the common business analytics allows improving not just everyday task performance. It also positively affects strategic planning thanks to novelty forecasting approaches that take into account more influencing factors including less predictable.

The progress in data science for logistics and supply chain became a game-changer that enabled further field development. Digitization of fields working with end-customers like retail stimulated raising of requirements for workflow speed, visibility, and agility within the whole goods path.

Data science applications in logistics

It’s all over the news how solutions based on artificial intelligence and its subsets like computer vision, image processing, NLP and, of course, machine learning for logistics, retail and eCommerce and the whole supply chain are taking business closer to the desired destination of cost-efficiency and ultimate stamp of reliability.

But as stated above the outcomes wouldn't be so impressive without techniques of data science applied to logistics planning, manufacturing workflow metrics, etc. So let’s explore more where should be established the foundation of digital transformation in logistics service providers to complete the ongoing process in the whole supply chain.

Strategy and goals

Data science methods allow the realization of predictive analytics for strategic planning making the business running more flexible in favor of the market needs. Demand forecasting, risks definition, required workflow optimization, etc. allow setting more realistic and yet ambitious goals for the determined period of time.

Routing and scheduling

Such data-driven processes that might require even real-time corrections must be formed with comprehensive considerations. Optimization of routing through factor and performance history analytics result in cutting of freight costs and operational expenses by making scheduling more advanced.

Management and workflow

Data science applications in logistics companies should, of course, cover control of internal and external business aspects by setting up big data processing. It concerns staffing and workforce governance, transportation, and warehousing management, marketing and performance tracking and evaluation

Collaboration and support

For the long-term partnerships and efficient cooperation, it’s important to find the best match as possible initially. Analysis of the available data concerning status, reliability, and correspondence of strategic goals makes it possible and demands transparency from service providers that also improve customer support.

Security and prevention

Data science in logistics industry takes the security and risk management to the next level. With a more accurate definition of hazards concerning health, weather, fraud, etc. it’s more feasible to take them into account fully in the strategy and come up with more efficient prevention methods like anti-theft solutions.

Data science trends in 2019

Being the engine of digital transformation tendencies in data processing approaches whether practical or theoretical shape up development directions of data science application fields. Therefore let’s explore what analytics methods, concepts, and technology will find the boost this year.

Analytical approach spread

As any new process integrated into the established workflow it requires notable financial, staffing and time contributions. The application of what can be described as modern data science to the fields that still entering the digital era can last a while due to the conservatism and rejection to take additional risks concerned with investments in novelties.

The rising number of data science use cases in logistics, supply chain, and other fields and their practical prove of cost-efficiency is a push towards technology for the SME sector that due to the smaller budget can always painlessly integrate new approaches into the workflow.

Privacy conflict resolution

Big data analytics allow raising the level of service personalization. But to the result accuracy, the type of data that should be included raises the question again where the border of the privacy zone of data whether it's personal or commercial is.

The biggest challenge remains on the theoretical side since considering that for data collection is used machine learning algorithms the practical implementation will mostly require just the list of adjustments for the existing models.

Ethical awareness

The previous point is one that the society the most familiar with thanks to all the drama around social networks. Nevertheless, data science isn’t just used in scopes based on physical or intellectual consuming of goods but also regulating fields like law, finances, etc.

Such application will reshape the professional interactions within and between domains. But the question is if society and business are ready for it. That’s why increasing awareness about AI innovations is the task even for the governmental sector since the IT domain is an inventor who propagating mostly for marketing reasons.

IoT and ML development

Wider application of data science induces the further development of IoT and ML to serve the field by automating the analytical and forecasting processes and integrating them whether in the company management systems or solutions used for improving customer experience.

The progress is possible since the spread of digital transformation trends around industries is resulting in also the appearance of the larger number of IT professionals in the scope of Internet of Things, Machine and Deep Learning, Big Data, NLP and Computer Vision.

DS solutions affordability

In scopes like supply chain and logistics automation trends are on the top of the charts for a while that’s why efficiency challenges concerning money and time expenses are starting to arise. It’s leading to a reconsideration of the data science algorithms and their technical implementation.

While it can be considered as a natural process of the field evolution the stimulus from the business sector is working as a catalizator redefining its directions and duration. The most powerful force on software provider and user sides are startups and small companies that require more efforts and innovative methods to survive on the market.

Data science and logistics

It’s not that evident but storing, transportation and delivery systems are data-driven and their optimality is dependent on how performance metric readings and facts about business and actual environmental conditions are processed, analyzed and forecasted.

Logistics trends and challenges are always shaping up each other. Data science allow taking the process of that mutual exchange under control by making the field more flexible and still more predictive. This tendency is opening the path to the status of the accessible and reliable service provider that are matching customer expectations.

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