Data Science, Big Data, Data Analytics

What Is Data Science?

Dealing with unstructured and structured data, data science is a field that comprises everything that is related to data cleansing, preparation, and analysis.

Data science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning data. This umbrella term includes various techniques that are used when extracting insights and information from data.

Some of the supply chain challenges that data science is helping to solve include:

Making the supply chain greener to minimize the environmental impact of global sourcing (e.g., shorter distances or consolidated shipments)

Increasing visibility into the supply chain and response time (e.g., through blockchain)

Adapting to demographic changes and customer expectations (e.g., free same day deliveries)

Allowing manufacturers to decrease their product life-cycle times (e.g., through better market insights and smart sourcing) to react to trends and demand more quickly

Increasing the product portfolio to serve not only the mass market but the entire demand curve (e.g., through mass-customization)

Some of application for Big data are as follows:

Demand Forecasting

With the ability to integrate more data with higher granularity, companies can utilize predictive and prescriptive analysis to improve the accuracy of demand forecasting. This includes driving improvements for finding optimal forecasting aggregation levels, optimizing for various forecasting horizons, or utilizing advanced forecasting algorithms.

Today’s companies need to be able to know how promotional activities by customers, competitors, and their own marketing department affect demand over various sales channels.

At the same time, the demand behavior is constantly being changed by external factors like product innovation, trends (e.g., demographic change), or governments (e.g., tariffs or new laws) to readjust their own product portfolio to the market demand.

The improvement in demand forecasting accuracy directly translates to improved performance for production scheduling and the correct inventory safety and cycle stock (e.g., more accurate data and automated parameter adjustments).

Distribution

Powered by real-time data, a more agile supply chain is able to optimize and reroute shipments that are affected by disruptions (like port congestions or weather) and automatically share these changes along the supply chain and inform direct customers. In distribution centers, more intelligent stocking in the network (e.g., bundling of shipments), improved sorting, and improved visual inspection all allow businesses to lower cost and increase on-time delivery.

Call Centers

Data science and AI enable maximization of customer support (to suppliers, consumers, wholesalers, and sales forces) through technologies like chatbots or voice-activated assistants to provide more accurate information more quickly (e.g., expected delivery times).

AI, blockchain, and advances in data integration will also support the detection of warranty and procurement fraud. “Smart contracts” will help to solve disputes quicker and increase compliance.

Sourcing/Procurement

Cognitive sourcing or procurement naturally is a data-rich function and will support improved supplier selection, including a fluid re-evaluation of opportunities. Augmentation and automation in this area will drive responsiveness and speed in product development cycles, exploit and increase production efficiencies to lower costs, increase product quality, and foster deeper relationships with suppliers.

What Is Big Data?

Big data refers to significant volumes of data that cannot be processed effectively with the traditional applications that are currently used. The processing of big data begins with raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, big data can inundate a business on a day-to-day basis. Big data is used to analyze insights, which can lead to better decisions and strategic business moves

Despite the largest growth of data analytics being experienced in downstream customer insights, analytics can have applications across the end-to-end supply chain. Supply chains that are embracing big data capability development, first need to become aware of the benefits that big data solutions can deliver to their operations. Decisions need to be made about the cost effectiveness of prioritizing certain parts of their operations. Holistic big data solutions applied to the whole supply chain can involve high costs, making supply chain decision makers more selective in customizing solutions to specific operations.

How is big data being applied in Supply Chain Operations?

Increased visibility of inventory levels, demand, and manufacturing capacity; hence more accurate production and distribution scheduling

Real-time conducting of big data analysis within the warehouse ERP system and identifying inventory levels, delivery miss-matches, and incoming deliveries.

More accurate estimation of demand by accessing data of sales, market trends, competitors’ data, and relevant local and global economic factors.

Monitoring of delivery routes, traffic data, weather in real-time and rerouting if necessary, for capacity and asset sharing.

Full transparency at the SKU level and fully automated replenishment systems combined with demand forecast data that eliminate under/overstocking and optimize inventory ranging.

Real-time optimization of complex webs of distribution hubs, plants, and warehouses based on the material flow data.

What is Data Analytics?

Data analytics is the science of examining raw data to reach certain conclusions.

Data analytics involves applying an algorithmic or mechanical process to derive insights and running through several data sets to look for meaningful correlations. It is used in several industries, which enables organizations and data analytics companies to make more informed decisions, as well as verify and disprove existing theories or models. The focus of data analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

5 Key Benefits to Harnessing the Power of Big Data

1. Enhance Customer Experience

Analysis of more varied data types, including social media data, can be used to improve the customer experience. For example, analysis of customer feedback regarding delivery and returns helps the company better understand the customer and offer an improved user experience, as well as introducing greater efficiencies and reducing waste.

2. Increase the Ability to Forecast Demand More Accurately

Instead of relying on the same rigid systems and inexact predictions from the sales team, companies now have access to huge and complex amounts of data from various sources, including suppliers and customers.

Advanced analytical tools can be employed to integrate data from a range of systems and, combined with external factors such as weather forecasting, competitors’ behaviour, and pricing, will significantly enhance the ability to forecast demand.

3. Solve More Complex Distribution Network Problems

Most complex distribution networks have developed organically over time into an almost impenetrable web of factories, warehouses, and distribution hubs which can struggle to adapt quickly to changing patterns of demand.

Companies can deal with this complexity more easily than in the past with the use of big data analysis. Big data provides the opportunity to solve much more complex distribution network problems by modelling outcomes in more detailed scenarios than ever before.

4. Improve the Efficiency of Delivery Route Planning

The huge amount of location data which is now available, combined with the latest advanced techniques in geo-analytical mapping, allows companies to model a greater number of potential routing scenarios as well as visualise those routes more dynamically at street level.

5. Develop Greater Collaboration in Your Supply Chain Network

The increased amount of data available to supply chain managers should be seen as an opportunity to improve the management of more complicated networks of suppliers and to develop greater collaboration.

Getting the most out of big data naturally requires an investment in technology, but also a culture change. Staff across different business functions need to be involved in the process of identifying which data is useful to them.

For example, it is very important to the supply chain function to have reliable manufacturing data so staff from manufacturing as well as other teams must be involved in the process of data collection.

Supply chain managers need to highlight the negative effect of poor data and place a high value on the collection of consistent, high-quality data at a strategic level. Valuing and rewarding staff who maintain a high standard of data collection is as important as investing in the latest analytical techniques.