The Power of Supply Chain Analytics

Brief History

Contrary to popular belief, the concept of analyzing data to manage the supply chain has been around for quite a while. At the beginning of the 20th century, Henry Ford adapted ideas from Frederick Taylor’s “The Principles of Scientific Management” to create and improve his assembly line. Numerous companies over the decades followed suit.

The introduction of the internet and electronic data interchange in the early 1990s enhanced the exchange of information between companies working together. Cloud computing, Enterprise Resource Planning (ERP) systems, and Artificial Intelligence (AI) took flight soon after, enabling companies to gather data, make accurate predictions, and decide based on well-founded insights in real time.

Importance

Analytics is invaluable in optimizing the modern supply chain. Companies across sectors, from manufacturing and retail to construction and healthcare, will benefit from leveraging advanced supply chain data analytics.

An effective supply chain analytics system provides the following benefits:

• Provides end-to-end visibility – Overstocking, stock-outs, bottlenecks, cost overruns, and longer lead times are some of the most common issues plaguing companies in the network. These are all resolved with the help of tracking, tracing, and monitoring tools offered in conjunction with analytics systems.
• Enables real-time data gathering and evaluation – In the past, team managers had to be on-site to gather information on spreadsheets and make complex calculations that took weeks or months to finish. Now, with data analytics in the supply chain, businesses can automate these processes and ensures accuracy, minimizing expensive adjustments and do-overs.
• Mitigates risks and manages uncertainties – Maintaining supply chain sustainability and continuity is only possible if managers proactively manage risks and effectively forecast fluctuations. Companies that leverage advanced analytics gain more visibility into the current operations and future trends, enabling them to handle any situation or challenge.

Types

There are five main categories of supply chain analytics that are sometimes combined for maximum efficiency. These types are: .

• Descriptive – Answering the question “What happened?”, this type involves reviewing past operational performance. The results can help companies make data-driven decisions for adjustments, improvements, and changes.
• Diagnostic – This zooms in on historical data to determine why something happened. By utilizing this, organizations can identify the root cause of issues and develop targeted solutions.
• Predictive – This answers the question: “What might happen in the future?”. It applies complex mathematical techniques such as statistical modeling to forecast future trends, helpingbusinesses improve supply chain resilience.
• Prescriptive – This utilizes data from internal and external sources, such as the experience of other companies, to determine possible outcomes. By answering the question “What should be done next?”, relevant personnel can recommend the best course of action.
• Cognitive – One of the newest and most advanced supply chain analytics examples, this leverages AI and machine learning to evaluate large amounts of data. It is valuable today because it makes nuanced conclusions, incorporating human intuition and experience.

Implementation

Analytics in the supply chain is a continuous process and should be integrated into various stages, including procurement, production, and distribution. It can also be performed on an ad-hoc basis, particularly when significant changes in the organization, regulations, or market conditions occur. Here is a step-by-step guide:

Step 1: Collect Data

The first step involves gathering raw data from sources within the supply chain (e.g., ERP systems, Internet of Things (IoT) devices, supplier information, and market research). This is crucial as it is the foundation of any analytics process and the basis of future evaluations.

Here, companies do the following:

• Utilize digital checklists and templates to automate data collection, improving efficiency and reducing errors on the job.
• Validate the accuracy and extensiveness of information gathered.
• Standardize formats to simplify integration and analysis.

Step 2: Integrate Data

Data collected from different sources should be combined into a unified repository. By centralizing relevant information, managers across the network can quickly access the information hub, make a critical analysis, and then collaborate for subsequent steps. This step involves the following:

• investing in data integration tools and platforms to streamline this process, particularly in acquiring real-time insights; and
• establishing data governance policies to ensure quality and integrity.

Step 3: Clean Data

Despite meticulous data gathering, miscalculations, inconsistencies, and inaccuracies may still exist. By going over documentation processes, managers can do the following:

• improve data quality and make reliable evaluations;
• develop data cleaning procedures to maintain consistency; and
• know the right time to perform regular audits to identify and address quality issues.

Step 4: Choose the Right Tool for Analytics Modeling

Analytics modeling is the heart of the entire process. Here, managers extract actionable insights from the data collected, integrated, and cleaned to improve decision-making. There are various tools for every type of analytics. Choosing the right one is essential in acquiring the most accurate forecast and settling on the best way forward.

Step 5: Monitor Continuously

Successfully evolving with emerging challenges and new data requires agility and adaptability. Businesses can achieve this by maintaining oversight of the supply chain operations with monitoring sensors, regular audits, and inspections, constantly analyzing the data collected, and sustaining any improvement made over time.