Alexis Kestler
Supply chain analytics has become an invaluable practice for boosting business performance. This definitive guide will explore how advanced data analysis can transform global supply chain planning, execution, and collaboration.
I have over eight years of experience as an analytics consultant helping Fortune 500 manufacturers, retailers, and logistics firms leverage supply chain data. By integrating analytics across upstream and downstream processes, I have delivered millions of dollars in cost savings and profit growth for clients.
Now let me break down exactly how you can replicate similar success through supply chain analytics mastery.
Supply Chain Analytics 101
You may be wondering—what exactly constitutes supply chain analytics? Supply chain analytics refers to the strategic practice of collecting and analyzing data from across your operational flows to drive better planning and execution. This could range from supplier performance metrics to logistics costs, demand forecasts to inventory optimization techniques.
As per industry body IDC, the global supply chain analytics market will expand at a CAGR of 10.3% from 2022-2027, reaching a valuation of $9.88 billion. This exponential growth underscores the competitive necessity of analytics adoption. Top players like Apple, Amazon, and Walmart are leading this charge through investments in data science capabilities.
In fact, Walmart leverages an AI-powered supply chain analytics platform spanning over 3500 suppliers globally. By continually sensing temperature deviations and shipment status during transport, they have managed to reduce annual food wastages by $2 million while improving compliance. Their analytics models can now predict supply shortages weeks in advance to take corrective actions.
So clearly, there is ample precedent of market leaders using supply chain analytics for enriching visibility, coordination, and control across end-to-end operations. Now let us explore the various facets of this fascinating discipline.
Types of Supply Chain Analytics
Modern supply chain analytics comprises four key branches catering to diverse decision needs:
1. Descriptive Analytics
Descriptive techniques revolve around understanding past supply chain performances through data aggregation, monitoring, and visualization. It creates a “single source of truth” by converging information from multiple touchpoints across your workflow like:
- Orders, forecasts, and planning from sales channels
- Procurement systems tracking purchase costs and supplier ratings
- Production data covering machine performance, quality, output
- Inventory management systems overseeing stock levels across facilities/in-transit
- Transport management systems with carrier performance stats
- Logistics costs and customer delivery timelines
- After-sales cycle times and satisfaction metrics
By gathering such data onto a common platform, you gain comprehensive visibility into operational health. Now you can slice and dice information in different ways to reveal key insights like:
- Which product configurations have the maximum quality defects or returns?
- Which regions have the highest logistics spend and longest delivery times?
- How severe was the impact on inventory and costs from recent supply shortages?
- Which departments have excess workforce allocation affecting cost efficiency?
Such descriptive analytics fuel data-backed assessments instead of subjective opinions to identify improvement areas.
2. Predictive Analytics
While descriptive analytics provides rear-view mirror visibility into supply chain outcomes, predictive techniques forecast future performance. Predictive models ingest historical data as well as leading indicators to make educated guesses.
For example, time series-based forecasting algorithms can process past sales, pricing, marketing spend, and macroeconomic datasets to predict next monthly or quarterly demand. Manufacturers dimension production cycles based on such demand sensing to optimize utilization and margins.
Retailers combine public data on weather, events, demographics with their own sales data to forecast store-level footfall and inventory needs. Predictive analytics minimizes stock-outs during peak seasons and overstocks during lean periods.
Transportation firms rely on geospatial analytics correlating shipment locations, weather, traffic patterns to predict ETAs and reroute goods dynamically in the event of delays. Maintaining such ETAs ensures high contract renewal rates.
So whether it is smoothening production planning, aligning inventory to demand, or improving last-mile efficiency, predictive insights enable smarter supply chain planning.
3. Prescriptive Analytics
The next evolutionary wave in supply chain analytics is prescriptive analytics. While predictive models foresee what might happen, prescriptive techniques recommend the best course of action in response. It brings together advanced data analysis with business rules and constraints to share data-backed decisions.
For instance, your freight analytics dashboard may detect through track and trace data that your critical machinery component shipment will reach four days late from South Korea due to typhoon disruptions. Dynamically knowing this, what should your mitigation response be?
Your prescriptive engine would then share options like:
- Pay 30% premium for 2-day air freight to bring half the shipment faster
- Outsource 20% order quantity to an alternate supplier in Japan for just-in-time delivery
- Leverage safety stock buffers to manage till full delivery arrives
It would also provide the cost/benefit trade-offs for each choice considering margins, customer sensitivities, and relations with existing suppliers. This enables determining the optimal decision aligning with business priorities beyond just reacting.
4. Cognitive Analytics
Cognitive analytics in supply chain aims to emulate human thinking through AI/ML capabilities. The system continuously learns from new data to drive automation and enhancement of analytical models.
For example, IBM helps leading apparel firms anticipate shopper preferences and trends using cognitive analytics. Algorithms ingest customer feedback and public social data to develop heatmaps of trending fashion styles, colors and fits. This allows proactively designing collections aligning with such sensing months before traditional design cycles. Hit ratios and full-price sell-through then improves.
I have personally helped industrial manufacturers in spotting early warning signals of machine failures through vibration sensors and IoT data. By using self-learning algorithms instead of static thresholds, the predictive maintenance models detect fault patterns more accurately. Scraps and downtime reduce by over 20% as per client reporting.
So in essence, cognitive analytics takes data-driven decision support to the next level through contextual recommendations and continuous model improvement.
Now that you understand the analytics types let‘s explore how supply chain data actually gets leveraged.
How Does Supply Chain Analytics Work?
There are five steps involved in turning raw data into actionable business insights:
Step 1: Data Ingestion
The first supply chain analytics step entails aggregating relevant operational data through appropriate connectors. This could include:
ERP Data: Sourcing performance stats from procurement modules and production schedules from MRP systems
Logistics Data: Inbound/outbound tracking data from 3PL/4PL partners spanning freight, fleet, warehouse, and yard management
Channel Data: Point-of-sale data from dealers, wholesalers and ecommerce portals providing regional demand patterns
External Data: Macro-economic, weather, traffic, geopolitical data from web APIs to enable contextual modeling
IoT Data: Machine sensors, fleet GPS pings, asset trackers capturing equipment efficiency and ambiance condition metrics for precision tracking
With the rise of IoT, cloud supply chain management systems, and analytics-ready transportation/warehouse modules, collecting such high velocity and variety of data has become easier.
Step 2: Data Processing
Post data gathering, the associated metadata like units of measurement, timestamp, data source, sequence etc. needs harmonizing for standardized modeling and analysis. Master data management ensures metrics across various systems have consistent identifiers and attributes through:
- Mapping part numbers, UPCs, SKUs to items
- Geocoding facility addresses, shipment routes
- Categorizing carriers, channels, suppliers appropriately
- Structuring unstructured text/notes
- Managing data lineage and processing pipelines
Such data stewardship is foundational to quality analytics.
Step 3: Exploratory Analysis
Before correlating data and calculating key performance indicators (KPIs), exploratory analysis enables auditing information quality through:
- Statistical analysis to identify missing values, outlier readings
- Data visualization for gaps in reporting periods
- SQL querying to test for duplicates or obsolete datasets
- Spotting invalid correlations, say sudden demand drop without sales promotions ending
Such profiling ensures models provide reliable outputs based on factoring correct inputs.
Step 4: Model Development
Now advanced analytics capabilities can be leveraged through quantitative modeling and business rules automation including:
Predictive Modeling: Forecast expected demand, pricing, margins based on historical patterns
Prescriptive Modeling: Define optimal inventory norms, safety stock levels, order quantities after statistical significance testing
Machine Learning: Continuously improve forecast accuracy based on new data by auto variable selection, dimensionality reduction
Optimization: Simulation of production scheduling, shipment routing to enhance process efficiency
The specific models and methodologies leverage depend on the context and analytical maturity.
Step 5: Visualization and Consumption
Finally, the strategic insights, recommendations, algorithms generated are packaged into intuitive dashboards, reports and alerts customized for business teams through:
- Interactive data visualization enabling self-service analysis by managers
- Scheduling automated emails with key metric updates and notifications for timely tracking
- Publishing APIs for easy plug and play integration with planning tools
This facilitates easier data exploration, drill downs and simulation by users to drive adoption.
Now that the supply chain analytics mechanism is clearer, let us examine why it matters.
Benefits of Embracing Supply Chain Analytics
Here are five compelling business benefits of embracing analytics across operational flows:
1. Boost Margins
Matching demand more tightly through cognitive planning improves capacity utilization by 19% and reduces product obsolescence by 23%, directly enhancing profitability as per McKinsey. For slow moving items, markdown optimization algorithms determine optimum discounting.
2. Forge Risk Resilience
Analysis of supply chain risk scenarios through simulations allows creating mitigation strategies. The ripple effect of single point failures like sole source factory shutdown reduces through multi-vendor data models. Inventory gets redirected optimally when patterns sensing fleet diversion probability spikes due to weather disruptions.
3. Enable Innovation
Analytics uncovers room for improving legacy processes through benchmarking and diagnostics. Case in point—leading cement firm Halcon leveraged prescriptive analytics to reduce truck empty running by 11% through route optimization and predictive maintenance. New digital technologies get prioritized based on projected operational impact.
4. Boost Productivity
Analytics platforms continuously mentor staff to resolve process issues through training modules revealing performance gaps. Intel enhanced semiconductor equipment productivity 4% by using predictive models for earlier maintenance scheduling. Automated analytics workflows also optimize repetitive planning tasks.
5. Attain Sustainability
Data intelligence helps taking greener actions whether by optimizing transportation loads for lower emissions or analyzing customer sentiment for reusable packaging. Microsoft boosted server workload energy efficiency around 30% using ML optimization. Clorox increased share of eco-friendly shipments 14% through routing enhancements.
So in summary, analytics creates a virtuous cycle of better visibility leading to sharper planning and decision automation for boosting efficiency, resilience and innovation edge.
Overcoming Barriers in Supply Chain Analytics Adoption
Introducing analytics into complex global supply chain environments has its fair share of challenges which could delay value realization:
Legacy Technology Constraints – Most supply chain processes leverage outdated tools lacking analytics readiness and data integration capabilities. For example, longtime warehouse management solutions have rudimentary reporting and third party analytics dependencies. Evaluating modern cloud-based SCM modules with embedded analytics is advised rather than just retrofitting old systems.
Spotty Data Health – 30-40% of average supply chain data tends to have quality issues like incompleteness, inaccuracy, inconsistency, and duplication. Such problems get amplified in outsourced operations where information captured externally may lack reliability due to process gaps. Investing in master data management and strong data governance through centralized architecture, common standards, access controls and periodic auditing becomes vital.
Analytics Talent Crunch – Translating supply chain data Context into tangible business recommendations requires both statistical as well as operations knowledge which can lengthen competency development. Being clear on required skill sets during analytics team structuring and providing adequate learning exposure helps get the roles impactful over time. Augmenting through external specialists is an option.
Lack of Operational Sponsorship – Analytics projects often have technical team execution without ongoing executive oversight. But influencing enterprise processes requires getting key business stakeholders like warehouse heads, procurement VPs bought in for unified vision and change endorsement. Ensuring their teams participate in analytics design through iterative feedback and tying usage to individual KPIs improves adoption.
Spotty Governance – Supply chain analytics maturity relies on the virtue of governance standardizing platforms, models, metrics and user access protocols. For example, creating centralized supply chain data lakes removes fragmented analytics. Meta-data management ensures naming conventions enable analysis. Loosely governed initiatives struggle gaining footing at scale.
While the barriers seem formidable initially, a phased roadmap addressing the key pain points through structural enhancements ultimately eases things out.
Now let us explore a few real-world examples.
Supply Chain Analytics in Action
Here are few instances of analytics modernization delivering tangible results:
Automotive – Margin Uplift
Business Issue: Leading automotive OEM faced severe margin erosion given rising steel prices and currency fluctuation impacting imported components purchase costs. But requesting repeated price hikes from dealers was unviable.
Analytics Solution: Built an ML-based shoulder price optimization tool factoring in vehicle demand elasticity, regional price sensitivities, competitor actions to prescribe optimized markups for maximizing dealer margins at minimum required hikes
Business Impact: Increased vehicle margins by 2% through calibrated price increases while limiting customer attrition
Retail Grocery – Inventory Reduction
Business Issue: Major grocery chain lacked visibility into intra-day store wise spot demand due to constantly changing footfalls and quick moving merchandise having a short shelf life. High stock-outs at some outlets and write-offs at others.
Analytics Solution: Track item-level consumption through IoT sensors on shelves and AI vision analyzing camera feeds to sense real-time store traffic changes. Then dynamically route inventory across outlets through prescriptive analytics minimizing aged stock.
Business Impact: 28% improvement in fresh product availability, 23% lower multi-site inventory, 15% stock rebalancing through just-in-time transfers
Industrial Manufacturing – Downtime Minimization
Business Issue: Discrete manufacturer suffered over 220 hours of downtime annually costing $15 million productivity loss given outdated preventive maintenance norms and lag in technicians’ scheduling delays finding right spare parts catalogs.
Analytics Solution: Digitize maintenance history data from repair logs to determine ideal preventive schedules through predictive modeling, smartly allocate spares inventory and technician work orders using multi-constraint scheduling algorithms.
Business Impact: 28% downtime reduction through demand-based maintenance saving $4 million annually by optimizing planning
So in summary, real gains rely on melding analytics into specific operational contexts through purposeful data modeling, pragmatic analysis and sharp visualization targeted towards business issues.
Getting Started – A Practical Roadmap
Launching a successful supply chain analytics program warrants a thoughtful rollout plan:
Start Small, Scale Fast: I would advise piloting the analytics initiative for a specific product line or region first to build credibility before expanding across larger footprint. Especially, when the legacy ecosystem lacks maturity. Pursue quick wins to showcase benefits over 6 to 9 months at a contained level.
Take Outside Help: Developing advanced analytics capabilities requires a good understanding of mathematical concepts and solution know-how, hence supplementing through external specialists helps accelerate capability building. Evaluate different solution providers for advisories during design phase before eventually getting technical development support.
Prioritize Master Data Foundation: Often analytics aspirations remain checked by spotty data quality which distorts models. Ensure foundational data standards for supply chain entities get defined upfront spanning products, suppliers, logistics lanes, facilities etc. While perfect data is unviable, drive consistency.
Leverage Hybrid Tech Options: Getting held up by current technology constraints will hamper progress. Hence explore newer SaaS analytics options with prebuilt models versus undertaking multi-year modernization. Stitching niche modules creates unified visibility at much lower TCO and faster speed.
Align with Specific Pain Areas: Maintain a business outcome aligned roadmap prioritizing specific initiatives that target defined operational issues quarter to quarter. Continually enrich models, widen deployment scope based on relevance versus getting sidetracked. Celebrate micro-wins through the transformation journey.
Stay tuned for part two of this series where we will do a deeper dive into supply chain analytics education. For any queries on putting analytics to work for your operations or exploring technology solution options, feel free to drop me a note!
