Introduction to Supply Chain Analytics

Software to analyze supply chain logistics

Software for supply chain analytics typically comes in two different forms: integrated within supply chain software or as a separate specifically designed business intelligence and analysis application that is able to access the supply chain information. A majority ERP providers provide supply chain analytics and so do the suppliers of special SCM software. A few IT consulting firms create software models that can be custom-built and integrated into companies’ process of business.

Certain ERP and SCM vendors are introducing CEP on their platforms to provide continuous supply chain analysis in real time. A majority ERP and SCM companies have single-to-one integrations however, there isn’t a common. But, the Supply Chain Operations Reference (SCOR) model offers standard measures that allow you to compare the performance of your supply chain against industry benchmarks.

The ideal the software for supply chain analysis should be used across the complete chain, but in actual the focus is usually specifically on specific operational subcomponents for example, demand planning manufacturing production managing inventory and transportation management. As an example, supply chain finance analytics may help to determine the cost of capital that is rising or ways to increase working capital. procure-to pay analytics may help you find the most reliable suppliers as well as provide the early warning of budget excesses within certain cost categories as well as transportation analytics software that can forecast the effects of weather conditions on the shipments.

How supply chain analytics work

Supply chain analytics combines information from a variety of applications, infrastructures, and other sources and the latest technologies like IoT to enhance making decisions in the operational, strategic and tactical procedures that comprise the supply chain management. Supply chain analytics can help facilitate synchronization of the supply chain’s plans and execution, by enhancing transparency in real time about the process and its impact on both the customers and the overall bottom line. The increased visibility also increases flexibility within the supply chain by assisting decision-makers assess the tradeoffs that cost and service to customers.

The method of developing supply chain analytics generally begins with data analysts who are aware of certain aspects of company, like the elements that affect liquidity as well as inventory levels, waste, and levels of service. The experts search for relationships between the various elements of data to develop a model that maximizes the efficiency that the supply chain produces. They try out different versions until they are able to build a solid model.

Models for supply chain analytics which meet a predetermined level of performance are put in the production process by engineers working with data who have focus on scalability as well as efficiency. Data engineers, data scientists and business analysts work in tandem to enhance the way analytical data are presented and applied in the real world. Models of supply chain are refined in time, by linking the efficiency of the models used to analyze data on the production line and the value to business they bring to the business.

The characteristics of supply chain analytics

The software for Supply Chain Analytics typically contains the features listed below:

• The visualization of information. The ability to slice and dice data using various angles in order to increase understanding and insight.
• Processing of streams. Deriving insight from various streams of data generated through, for instance, the IoT applications, apps, weather reports, and other third-party information.
• Integrating social media. Using sentiment data obtained from social feeds to help improve demand management.
• Natural Language Processing. Extracting and organizing non-structured data that’s hidden within newspaper articles, newspapers as well as data feeds.
• Intelligence about location. Deriving insight from the location of data in order to better understand and maximize distribution.
• Digital counterpart of Supply chain. Organizing data into an overall model of supply chains that can be shared among different kinds of customers to increase the accuracy of predictive and predictive analytics..
• Graph database. Organizing information into linked elements makes it easier to identify patterns, connect them and enhance traceability of goods as well as facilities, suppliers and other sources.

The different types of Supply Chain Analytics

Commonly, the lens that is used to define the principal kinds of supply chain analytics is based on Gartner’s models of the four capacities of analytics: diagnostic, descriptive predictive, and prescriptive.

• Descriptive supply chain analysis employs reports and dashboards to determine what’s occurred. The process typically entails applying a number of statistical techniques to search through, organize and summarize details about activities in the chain of supply. It can help in answer to questions like “How have inventory levels changed over the last month?” as well as “What is the return on invested capital?”
• The diagnostic supply chain analytics can be employed to discover the reason things happen or aren’t performing as well as it could. As an example, “Why are shipments being delayed or lost?” for instance “Why is our company not achieving the same number of inventory turns as a competitor?”
• Predictive supply chain analytics help to predict what’s likely to occur from the perspective of the current information. As an example, “How will new trade regulations or a pandemic lockdown affect the availability and cost of raw materials or goods?”
• Supply chain analysis that is prescriptive can help to determine the optimal strategy for action by through optimization or embedding decision-making logic. This will help make better choices about the best time to introduce a new product, and whether or not to construct a factory, or determine the most efficient shipping strategy for every retail store.

Another method of breaking down different types of supply chain analytical is through their function and form. Consulting company Supply Chain Insights, for example, breaks down different types of supply chain analysis in the following ways:

• Workflow
• Support for decision making
• Collaboration
• Text mining using unstructured texts
• Management of structured data

In this way, various kinds of analytics are fed into the other in an ongoing, end-to-end process of better management of supply chains.

As an example, a business can make use of non-structured data mining to transform raw information from contracts as well as social media feeds, and news articles into structured information which is pertinent to suppliers. The improved, more organized data can then be used to streamline and enhance workflows for example, procure-to pay processes. Digital workflow data can be a lot easier to collect as opposed to data from manual workflows and thus making more information that can be used in decision support systems. Improved decision support can improve collaboration between diverse departments such as procurement, warehouse management as well as with suppliers.

The other emerging technologies offer options to boost the predictive models created through supply chain analytics. As an example, companies are now beginning to utilize the process mining technique to study the way they carry out the business process. Process analytics is a way to build an online digital representation of an enterprise that could aid in the identification of supply chain possibilities to automatize the logistics of production, procurement as well as finance. Augmented Analytics will allow business customers to answer questions regarding the company in plain English, and have short summaries of the answers. graph analytics could provide insight into the interactions among the different entities within the supply chain, for example the way that changes to a tier 3 provider could affect those in tier 1.

Uses of supply chain analytics

Planning for operations and sales makes use of supply chain analytics to align a producer’s supply with the demand. This is done by developing plans to align everyday operations to corporate strategies. Supply chain analytics can also be utilized to accomplish these things:

enhance the management of risk by identifying risks that are known and anticipating risks to come by analyzing trends and patterns across the supply chain

enhance planning efficiency by analysing customer data to determine factors that can increase or reduce demand

enhances order management by combining information sources that help assess levels of inventory, anticipate the demand, and pinpoint the issues with fulfillment

simplifies procurement through the organization and analysis of expenditure across departments in order to increase contract negotiation and find potential discounts or other sources.

boost working capital through improved methods for determining level of inventory required to meet satisfaction at a minimal cost of capital.

Supply chain analytics: A history

Supply chain analytics have their origins in Frederick Taylor, whose 1911 publication, The Principles of Scientific Management established the foundations for modern areas of industrial engineering as well as management of supply chains. Henry Ford adopted Taylor’s techniques to create the assembly line modern and supply chain, which was more efficient in production.

Mainframe computers helped to create the process of processing data by IBM researchers Hans Peter Luhn, who is credited with coining the concept of “business intelligence” within his 1958 article, “A Business Intelligence System.” Luhn’s work was instrumental in establishing the basis for various types of analytics using data for supply chain analytics.

in 1963 Bud Lalonde, a professor at Ohio State University, proposed that physical distribution management be integrated into materials management, manufacturing and procurement in what he referred to as business logistics. In the same year the management consulting firm Stafford Beer and others began investigating new ideas such as the feasible systems model that could be used for organizing information about business into a defined hierarchy in order to enhance the business’s plan and execute. The early 1980s were the time when this booming field became known by the name the management of supply chains.

When the internet was a major factor in the early 1990s, many people began to think about the possibilities of using it to the management of supply chains. One of the pioneers in this field was British technology expert Kevin Ashton. When he was a new product manager responsible for addressing the challenge of keeping popular lipsticks available in the stores, Ashton hit upon radio RFID sensors for frequency identification for a method to collect data regarding the movements of goods through in the supply chain. Ashton was the one who went to establish MAIT’s Auto-ID Center that perfected RFID sensors and technology, coined the term”internet of things” to define this groundbreaking new technology for managing supply chains.

In the 1990s, there was also advancement of CEP through research teams such as the the Stanford University’s David Luckham and others. CEP’s capability to collect information from live events enabled supply chain management to connect lower-level data pertaining factories’ operations, physical movement of goods, and even weather, into events that can later be studied through supply chain analytics software. As an example, the data on manufacturing processes can be extracted from factory data that could then be abstracted to business incidents that relate to stock levels.

A further turning point in the world of supply chain intelligence is the arrival of cloud computing. Cloud computing is an innovative method of delivering IT technology, infrastructure, and platforms as a service. Cloud computing provides a platform that can orchestrate data across many sources, the cloud has improved the performance of many kinds of analytics. This includes the supply chain analysis. Data lakes such as Hadoop let companies to store data from multiple sources and share them on a single platform. This further improved supply chain analytics through enabling businesses to connect more kinds of data. The data lakes have also helped in the implementation of advanced analytics that operate using a wide range of unstructured and structured data that came from various applications, events streams, as well as the IoT.

Recently, robot procedure automation -software to automate rote tasks that were previously done by human beings is now an extremely effective tool to improve productivity and efficiency of businesses. It also has the capability to connect data to analysis.

Additionally this, artificial intelligence called deep learning is being increasingly used for improving supply chain analysis. The use of deep learning is leading to advances in machine vision (used to enhance monitoring of inventory), natural language understanding (used to improve the management of contracts) and improvement in the routing model.

The future of supply chain analytics

Supply chain analytics are expected to continue to develop in conjunction with the advancement of models for analytics, data infrastructure and structures, as well as the capability to connect information across silos of applications. Over time, advanced analytics will result in increasingly autonomous supply chains that are able to handle and adapt to changing conditions similar to what self-driving vehicles are beginning to do. Additionally, advances on IoT, CEP and streaming technology will allow companies to gain insights faster through a wider range of information sources. AI methods will keep improving the ability of people to create precise and valuable predictions that can then be integrated into workflows.

Other emerging technologies that are expected to play an important role in management of the supply chain can be found in the following categories:

Blockchain. Blockchain infrastructure and technology promise to enhance traceability and visibility over a wider range within the supply chain. This same set of building blocks can be the catalyst for companies to adopt Smart Contracts for automation, control and manage transactions.

Gr APH analytics. Predicted to power over half of enterprise-level applications in the next 10 years graph analytics will assist suppliers better comprehend the relationships between different entities involved in the chain of supply.