Supply chain analytics (SCA) leverages statistical modeling, predictive algorithms, and prescriptive recommendations to help businesses optimize complex global supply chain operations. This 3200-word guide will explain what SCA is, types of techniques used, real-world benefits, common challenges faced, and helpful learning resources to start driving value.
What is Supply Chain Analytics and Why Does it Matter?
Supply chain analytics refers to the process of aggregating, cleansing, and analyzing supply and operations data to generate actionable insights that drive smarter decision-making. It moves beyond traditional rearview descriptive analytics to predict likely futures and prescribe optimal actions using quantitative methods.
As supply chains grow more global and complex, the need for end-to-end visibility and fact-based decisions increases. Mature analytics capabilities help businesses respond quicker to market changes, mitigate disruptions, reduce costs, improve customer service levels, and boost financial performance.
According to Gartner, the SCA software market surpassed $1.5 billion in 2021, growing at a CAGR of 12% annually. Adoption continues rising as leaders recognize analytics’ ability to deliver material value.
For example, Intel achieved $3 billion in cost savings within 5 years of investing in supply chain analytics, data science, and process digitization. Product availability increased from 75% to over 95% as well.
Types of Analytics Powering Supply Chain Insights
There are four main types of analytics leveraged across supply chain management functions:
Descriptive Analytics for Rearview Visibility
Descriptive techniques visualize historical data to gain visibility into past supply chain performance using KPI dashboards, trends, aggregated reports, and more. While purely retrospective, this insight uncovers improvement opportunities.
UPS relies heavily on descriptive analytics to monitor service levels, optimize routes globally, and manage one of the largest commercial airlines fleets exceeding 240 planes.
Diagnostic Analytics to Understand Root Causes
While descriptive analytics shows what happened, diagnostic techniques analyze why it happened through correlation analysis, drill-downs, and statistical modeling. Diagnosing the root cause of issues allows corrective actions.
For example, labeling anomalies as special cause vs. common cause variation helps separate one-off events from systemic defects needing process redesign.
Predictive Analytics to Plan for the Future
Predictive analytics leverages time series forecasting, multivariate regression, simulation, machine learning, and other statistical techniques to predict demand, delays, and disruptions before they occur. This powers supply chain planning and continuity.
Manufacturers rely extensively on predictive analytics for demand sensing and positioning inventory. Logisticians use data science to optimize routes accounting for traffic and weather risks proactively.
Prescriptive Analytics to Recommend Optimal Actions
Finally, prescriptive analytics combines optimization algorithms, business rules, and AI to recommend the best decision options for given conditions. Inventory reorder points, dynamic pricing, and sourcing policies represent common applications.
For instance, IBM’s omni-channel order management solution prescribes customized fulfillment plans for each order based on customer preferences, item availability, and cost factors dynamically.
While descriptive insight remains foundational, incorporating predictive and prescriptive analytics drives large performance gains and cost savings.
For example, leveraging algorithms for demand forecasting and inventory optimization delivered 25% less stockouts and 20% inventory cost reductions for Procter & Gamble.
How are Companies Utilizing Supply Chain Analytics?
Industry leaders apply SCA across sourcing, manufacturing, inventory, transportation, service, and more to unlock value:
1. Strategic Network Optimization
Descriptive analytics uncovers network inefficiencies while predictive models and optimization prescribe the best supply chain footprint considering costs, risks, and service.
2. Supply Forecasting
By applying machine learning to point-of-sale, firm orders, and macroeconomic datasets, businesses predict market dynamics 6-24 months out, enabling smarter supplier contracts.
3. Dynamic Inventory Optimization
SCA informs ideal stocking levels, placement, and policies minimizing total landed cost using advanced algorithms to incorporate risks.
4. Intelligent Order Promising
Analytics assigns customized cycle times for high-value orders dynamically factoring in priority, likelihood of delays, and customer history to set accurate delivery expectations.
5. Proactive Risk Management
Businesses track leading indicators, model disruption likelihoods in real-time, and prescribe mitigations leveraging weather data, transport visibility, and news analytics.
The Department of Defense cut $3 billion in logistics costs implementing predictive threat analytics and AI across fuel, munitions, medical, and maintenance supply chains.
Overcoming Key Challenges with Supply Chain Analytics
While impactful, scaling analytics faces common barriers like:
Data Quality Issues – With supply chain data dispersed across disconnected systems, ensuring integrity and governance remains challenging. MDM and Golden Records help.
Legacy Technology Constraints – On-premise solutions often lack the advanced computational capabilities required for optimization algorithms and machine learning relative to cloud-based apps.
Lack of Skills – From data engineers supporting data flows to data scientists building predictive models, talent shortages hinder progress. Partnering with external experts can help fill gaps.
Culture Resistance – Instilling an analytics culture requires executive leadership, training, and new incentives rewarding evidence-based decisions over intuition.
Evolving Analytics – With new data sources like IoT and satellite imagery emerging, supply chain analytics demands flexibility to incorporate new signals and ask new questions.
While adopters must overcome these barriers, the long-term benefits for those harnessing analytics continue growing as algorithms and data get smarter over time.
Helpful Resources to Boost Supply Chain Analytics Expertise
Here are great educational sources on supply chain analytics and data science for those looking to expand skills:
Blogs & Websites
- Logistics Viewpoints by Oracle
- Supplychain247 by SCDigest
- Lora Cecere (Supply Chain Insights)
- Mike Gilliland (Practical Analytics)
Online Learning Platforms
- Coursera SCM Modeling courses
- edX MicroMasters Programs
- LinkedIn Learning video tutorials
Associations & Communities
- Institute for Operations Research and Management Science
- Council of Supply Chain Management Professionals
- INFORMS Analytics Society
Podcasts
- Supply Chain Now on Risk & Resilience
- Supply Chain Leaders on Technology & Innovation
Books
- “Business Forecasting” by Michael Gilliland et al
- “Supply Chain Analytics” by Steven Melnyk
- “Logistics Clusters” by Yossi Sheffi
For supply chain teams seeking to optimize complex global operations, boost resilience, and improve the customer experience, advanced analytics continues proving essential. This guide summarized key types of techniques, real-world benefits, implementation challenges, and educational resources to help begin or accelerate your analytics journey.
