The growing industrial competition requires effective control over the company’s time and resources as this plays a significant role in creating or breaking the company. One such prominent resource segment is Logistics and Supply Chain. Gartner’s 2017 CSCO survey indicates that over 3/4th chief supply officers admit the need for digital transformation in the related process. And shipping chain management gets its boost from data analytics. The modern logistics market is witnessing huge demand owing to its advancement in shipping activities. As a result, the business entities can adjust shipment patterns, predict customer’s buying behavior, ensure on-time deliveries, reduce cargo risk and identify miscalculations. The dynamic and demanding market scenarios where both organizations and consumers expect cheaper and faster shipping operations data analytics does wonder.
What is Supply Chain Data Analytics?
According to IDC, in the year 2017, supply chain data increased by 50 times in half a decade making it difficult for humans to process. Thus, supply chain management is considered a suitable place to implement AI-driven cognitive technologies. As a result, supply chain data analytics facilitates easy understanding, reasoning, interacting, and learning of enormous data size with great speed. It leads to a significant shift of unstructured data into forecasting and inefficiency identification. Thus, the customer-oriented supply chain process can produce breakthrough ideas.
A wide variety of benefits data analytics provide logistics and supply chain ecosystems are worth the cost of technology. Moreover, the responsive features of supply chain data analytics are dominating the business sphere.
- Connected and collaborative approach
- Existence of cyberware
- Cognitively enabled technology
Unlike other departmental applications of data analytics, supply chain makes use of and benefits from all the popular types of data analytics applications available, say predictive analytics, descriptive analytics, prescriptive analytics, and cognitive analytics.
Types of Supply Chain Data Analytics
The supply chain analytics is bifurcated into five broad categories to provide multi-dimensional and deeper insight into the supply chain data. These supply chain analytics types form the core of predicting healthy prescriptions and details.
1. Descriptive Analytics
Descriptive analytics is the main stuff of the supply chain dashboards which defines what is happening now and how would it impact future actions. It focuses on building summary numbers, a healthy inventory system, customer service level, empowering internal and external lead time for the suppliers. As a result, it helps to provide strong visibility into the future using prominent tools and techniques which further creates powerful supply chain management in the organization.
2. Prescriptive Analytics
Prescriptive analytics is meant to determine the future results of today’s decisions. Thus, it helps to improve the decision-making process in the organization despite the bulkiness of the data. According to the study of Gartner, 85% of organizations use prescriptive supply chain data analytics for strengthening their future explanatory conditions. Thus, it helps to manage and understand the future scenarios of business implications. Moreover, prescriptive analytics plays a prominent role in mitigating potential risks and disruptions.
3. Business Analytics
Using business analytics for smoothening supply chain analytics has brought significant change in business operations. The profound deep learning and machine learning algorithm help to solve business problems and leverage maximum business value. As a result, focuses on collaborating businesses with logistic partners to facilitate to combat disruptions and time-consuming processes. Moreover, it leads to lesser storage costs and availability of raw materials according to the demand. Thus, business analytics provides better efficiency and effectiveness for the supply chain management.
4. Big Data Analytics
It is the combination of various tools, processes, algorithms which helps to interpret useful insights for information interpretation. The supply chain management relies on ERP and storage systems which are driven by data analytics. As a result, it led to the great shift from automation process to future-oriented data integration processes which helps to establish better decision-making. Big data analytics empowers the organization with 3Vs – volume, variety, and velocity to enable a collaborative supplier network.
- Forecasting demand of the product accurately and enhance product output.
- Improves performance which leads to better efficiency and speed.
- Reduces return costs and improves visibility.
5. Advanced Analytics
It is a combination of mathematics, data science, and artificial intelligence which makes it the prominent tool for supply chain analytics. These tools help to process operations more accurately by forecasting demand and recommend safety stock levels for the effective inventory management system. Furthermore, it is a proven technology to identify the root cause of the delivery failures by paying attention to the data-driven early warning trigger tools. It leads to locate geographical supply chain growth analytics.
Use Cases of Supply Chain Data Analytics
The supply chain data analytics is on its way to gear up the logistics management and movement. It certainly begins by drawing suitable predictions by filtering and studying the huge data sets.
|Business Forecasting||As the name indicates data analytics is the full-fledged solution to grab command over the supply chain by providing accurate predictions using the data generating at various stages in the supply chain process. The prediction for tomorrow serves as the basis of planning today. Majorly, the focus lies in demand forecasting which is never linear due to the existence of numerous variables. Thus, the consideration of past events and current trends through data analytics helps to determine future market scenarios.|
|Transport Management Systems (TMS)||The data analytics allow the company to predict the transportation requirement and meet the demand at a minimal cost. The process includes identifying the fastest routes based on traffic, distance, delivery point, time, the number of logistics, and weather. Thus, the AI-driven tool provides an immense boost to the transport management system which is a crucial part of every organization.|
|Last-Mile Delivery||Accurate the prediction better is the plan, especially while talking about shipping. Inarguably, shipping is not a job but a responsibility to deliver and share customers’ belongingness. Thus, an organization has to be prepared with last-minute delivery options which only comes with the timely prediction of the situation, and supply chain data analytics helps the company to do that.|
|Inventory Management||The supply chain data analytics help to determine the optimal level of inventory and meet shipping demands at a reduced cost. Additionally, the level of safety stock reduces with the multiple distribution centers making it extremely useful for supply chain managers.|
|Establishing Pricing Strategies||Price forecasting helps to adjust to the dynamic market nature by framing suitable mitigating strategies. Anything coming as a shock leads to significant losses and a panic state of mind for the business entities. Therefore, it is safer to take the remarks of data analytics into consideration while planning operations and fixing prices for products & services.|
|Healthcare Supply Chain||The healthcare supply chain data analytics use the predictive format of analytics for creating automated processes and enhancing the supply chain management. As a result, it helps to classify spending data and illustrate the comparative system for finding the prevailing gaps, if any. Moreover, the data-driven approach enables healthcare organizations to manage their operations with immense transparency in supply chain management. Thus, it leads to significant growth in operational revenue backed by the identification of better opportunities. Ideally, with the traditional approach of supply chain management, the organization faced poor patient outcomes and high-cost involvement which is not the case with the contemporary techniques of supply chain analytics.|
Metrics used for Supply Chain Data Analytics
The concept of supply chain focuses on two major areas – increasing productivity and customer satisfaction. As a result, each supply chain analytics metric revolves on measuring the performance and productivity at large. Though the choice of metrics varies for each organization, their outcomes are always result-oriented.
|Perfect Order||The order metric helps to track storage, manage costs, identify delivery operations and fetch customer satisfaction. Thus, paves for establishing effectiveness and efficiency in the supply chain mechanism. It is calculated as (Total Number of Orders – Number of Error Orders) / Total Number of Orders) * 100. Some of the major elements of the perfect order metrics which define delivery quality are full, damage-free, and on-time delivery along with proper documentation.|
|Customer Order Cycle Time||The metric provides in-depth insight into the matters relating to the product and service. Thus, it is calculated as the difference between the purchase order creation date and the actual (requested) delivery date. The invoicing times, account payables, and account receivables are considered to draw the right conclusion.|
|Fill Rate||The crucial metric relating to the supply chain helps to understand the complete performance of the domain. Major types of fill rates are order fill, line fill, and unit fill. The overall fill rate (percentage) is calculated as the difference of total items and the number of shipped items divided by a total number of items.|
|Cash to Cash Time||It is a financial ratio revealing the essential insights regarding supply chain operations. Here, the fair analysis is drawn by measuring the time duration (in days) between the payment for raw material and receiving payment for final products. Lower the cash to cash KPI is., better the profitability rate of the company is.|
|Inventory Days of Supply||It indicates the number of days inventory can sustain without restocking. Thus, it helps to understand after how long inventories in the warehouse require restocking so that they do not replenish and lead to stock-related catastrophe. It is calculated by dividing inventory in hand with the average daily inventory use.|
|Accuracy of Freight Bill||The smooth movement of inventory items from factory to warehouse requires extra cautions to avoid any damage and impact on cash flow. Moreover, it can impact the overall reputation of the company if items are defective or not delivered on time. It is calculated by dividing by a number of correct freight bills with total freight bills to attain the figure in percentage. Thus, it helps to meet customer’s expectations and identify the negative trend.|
Approaches of Supply Chain data Analytics
The modern approach of data analytics has brought brighter days for supply chain management. Each approach is a detailed description of what customers expect and how organizational resources respond to them.
|Clustering Analysis||It works by parting the group of data objects into sub-parts on the basis of their similarities. The key target here is to analyze the customer behavior and logistics availability which are the core of supply chain management. Time-series Forecasting: The most common algorithm of time-series helps to mine sequential and complex data at equal time intervals. Being centered on all customer-related data, it works effectively for supply chain data analytics scenarios too.|
|Artificial Neural Networks||Being a set of input and output units, the algorithm allows the connection of data on various layers to derive accurate correlations. As a result, predictions are made in due consideration of each data segment.|
|Regression Analysis||To deliver accurate prediction here continuous-valued functions are utilized. The method uses two distinct values for concluding results. The first one is the value of the response (dependent) variable and another one is the predictor (independent) variable.|
|Support Vector Machine||The algorithm uses non-linear mapping to transform training data into data classes of higher dimensions to forecast the demand. The usage extends to personal care, household, and certainly the business sector.|
Techniques Used for Supply Chain Data Analytics
Over the year, the growing complexities in the supply chain ecosystem have led to the development of various technologies and softwares for addressing the gaps. Thus, the Implication of supply chain analytics in the business sphere owes a lot to its powerful techniques. The powerful softwares and interfaces are the USP of the supply chain analytics and help to create an ecosystem full of progressive possibilities in almost all industry types.
1. Supply Chain Analytics using R
The open-source programming language is based on statistical graphics and computing. Generally, it facilitates publication-ready platforms whereas graphic features help in creating storage of reusable analytics for the future. The application of thousands of SKUs and multi-dimensional distribution networks leads to better supply chain forecasting. As a result, the entire supply chain operational speeds up with healthy planning, strong variability, and the creation of design-effective process building.
2. Supply Chain Data Analysis Excel
MS Excel is the powerhouse of statistics and analysis which sheds its beneficial features to the supply chain data analytics environment too. The pivot tables, graphs, and other analysis tools make it convenient for supply chain analysts to draw suitable conclusions. Moreover, with the use of advanced formula the inventory, cost, and supply chain management receives immense smoothness. The effective use of MS Excel considers providing a precise presentation for the predicted information because without proper delivery of every data remains undervalued.
3. Supply Chain Dashboard Power BI
With the large data size, it becomes difficult to pool relevant information for organizational supply chain use. Thus, the business intelligence tool acts as the best solution to streamline supply chain operations and deliver the best outputs. Using the supply chain power BI dashboard organizations are able for enhancing on-time shipping rate, inventory turnover, velocity, days of supply, establishing accuracy for freight bills, and identifying reasons for return. As a result, it helps to get actionable information for real-time implementation.
Supply Chain Data Analytics Tools and Solutions
In recent times, data analytics is playing a crucial role in managing supply chain operations and management. As a result, it creates room for improved forecasting and planning for supply chain managers. Most of the tools and solutions are driven by data analytics act as the comprehensive supply chain management solution.
|KPMG Spectrum Third Party Intelligence||It is a business intelligence tool that addresses the complex challenges of supply chain management and makes it possible to identify vulnerabilities to avoid future disruptions. Its intuitive interface is designed to collect supplier’s financial data and identify threats in the operational process. The software acts as the security shield for preventing mishappening in the supply chain ecosystem by creating vulnerability visualization.|
|PeopleSoft Supply Chain Analytics||The software provides real-time information to manage operation performance activity relating to supply chain management. Thus, it helps to track profitable products, identify quality issues, capacity, customer demand, etc. to create a healthy balance in the supply chain atmosphere. Also, provides clarification about the availability and accessibility of warehouse & freight carriers. As a result, strengthens the organizational operations.|
|Intellestra by Voxware||The software empowers the distribution and logistics managers by anticipating future supply chain requirements. The in-built sophisticated algorithms of Intellestra help to analyze present and past activities and lead to informed prescriptive analysis. Moreover, it is specifically built for the supply chain and is supported by a strong graphical presentation. The application has gained wide expectance for its potential to collect data from various sources.|
|Halo Supply Chain Analytics & Business Intelligence Software||The supply chain analytics software from Halo BI is known for data discovery and establishing a secure environment for conducting operations. It is designed specifically for customer-driven supply chains and business users and helps to integrate data with strong visualization through a single platform. Largely Halo supply chain analytics focus on S & OP, supplier management, and demand planning. Additionally, it also brings an essential regulatory framework into the limelight.|
Supply Chain Data Analytics Companies
The supply chain analytics companies act as the major key players for developing a healthy ecosystem for the same. There are few t major industrial players who have paved the way for the long-term sustainability of supply chain data analytics companies.
|Senseye||Senseye was founded in the year 2015 as a private entity in the United Kingdom. It provides easy accessible and affordable online services for fetching data from electronic equipment. As a result, it helps to forecast and predict business strategies by using these valuable and realistic data. Hence, Senseye provides immense support for enhancing supply chain operations.|
|Jupiter Intelligence||The US-based company is emerging as the global leader as the supply chain analytics company in less than half a day. It provides proficient services for managing risks that are expected to arise from natural changes or calamities. Thus, it includes through study and accurate prediction building for extreme weather conditions, sea-level rise, rising temperature, earthquakes, and other such phenomena which impact the supply chain operations on the greatest scale.|
|Citrine Informatics||Being founded in the year 2013, Citrine Informatics uses machine learning and data aggregation methods to develop advanced materials. These materials support the supply chain mechanism by focusing on each aspect from catalysis to energy. As a result, with accurate forecasting of material requirements, the cost involvement reduces radically. Furthermore, the applications provided by the company are best-suited to leverage healthy benefits to the supply chain industries.|
|Descartes Labs||The company has created its huge by using deep learning algorithms in the supply chain ecosystem by making the use of remote sensing algorithms. These algorithms command softwares to visualize the changing scenarios over time. Such applications from Descartes tracks use massive data from satellite imagery and help to draw suitable prediction which helps to connect all the operational dots. The online data analytics services provided by Descartes labs have benefitted several companies across the world.|
|Atos||The company is known as Atos SE, where SE stands for Societas Europaea. It is a digital service provider serving global clients with its consulting and system integration service for supply chain data analytics. Today, it is the company with annual revenue of EUR 12 billion and 1,00,000 subject-matter experts across 72 countries making the reliable choice for industries across the world.|
|CB Insights||It is a US-based company with a forward-looking intelligence approach to serving health companies in streamlining their supply and logistics management. The company mosaic makes the use of a quantitative framework along with public data which leads to strong insight formation. Also, facilitates suitable strategy formation with the help of accurate predictions.|
Examples of Supply Chain Data Analytics
Supply chain analytics has attracted major industrial giants for contributing towards its growing glory. Some big moves by logistics leaders have reshaped the supply chain ecosystem with the use of advanced AI-driven technology.
- DHL Supply Chain had plans to invest $350 million towards the deployment of the predictive analytics concept in its various locations. It is planned with the intention to reduce cost and upscale process efficiency on a mass scale.
- Maersk Line is a Danish shipping company that is presently operating in 130 countries and owns 600 container vessels and beyond. The company brought predictive technology to use for gaining better visibility to identify waste and avoid it at the earliest stage. As a result, it helped in saving millions of dollars.
- UPS, a renowned company saves around $50 million by handling 19 million packages with 96,000 vehicles each day. It is a result of data analytics efficiency where the company invests around $1 billion annually. Besides improving operational efficiency, the company uses profound technology for network planning at large.
- Amazon, a brand that does need an introduction is known for anticipatory shipping where data analytics play a crucial role. The technology helps the company to accurately predict the availability of delivery drivers, the number of shipments, and storage requirements.
- DB Schenker, a global logistics company is a success story of data analytics. The AI-driven decision support tool is used at the company’s warehouse location. As a result, it helps to stimulate scheduling and supply chain operations. The company has also created “Industrial Data Space” to facilitate exchanges of secure data between companies using predictive maintenance and supply chain analytics.
Using Big Data in Supply Chain Data Analytics
Big data plays an instrumental role in enhancing the overall supply chain data analytics ecosystem by addressing all the pinpoints strategically. The application of big data for improving supply chain performance extends from identifying alternatives to the delivery time and bridging the gap between the two. As a result, acts intelligently for improving supply chain and logistics productivity. It also plays a crucial role in augmenting data-driven decisions and direct towards cost efficiency. Some of the growing importance of big data in supply chain analytics is as follows:
- It helps to identify customer behavior and usage patterns that directly impact the revenue segment of supply chain management.
- Enhance the concept of inventory management and makes it completely automated.
- Streamline the E-commerce operations where inventory to logistics everything holds great importance.
The data-driven predictive and prescriptive solutions are opening doors for better expansion of logistics and supply chain management. According to the study of the Council of Supply Chain Management Professionals, 93% of shippers and 98% of third-party logistics consider data-driven technology a crucial part of supply chain management. Moreover, 71% of these professionals believe that it improves overall work quality and performance which was earlier considered to be a barrier in the Deloitte study of 2017. Furthermore, the MHI annual industry survey suggests that 57% of the companies that do not use data analytics presently plan to incorporate them by 2025. Thus, the application of data analytics in supply chain management is no longer a choice, but a necessity to maintain strong competition amongst market players.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics