Twenty-five analytical cases. One page. No content maze.

What Is It?

Supplier Performance Metrics are a set of quantifiable measures used to gauge and monitor the efficiency, effectiveness, and reliability of suppliers within a supply chain.

• On-time delivery rates - Measuring supplier reliability
• Quality of goods - Defect rates and conformance
• Cost-effectiveness - Value for money assessment
• Responsiveness - Reaction time to changes
• Compliance - Adherence to contracts and standards

Business Objectives

Optimized Performance

Using metrics to drive improvements in supplier performance, leading to better efficiency and quality across the supply chain.

Cost Reduction

Identifying areas for cost savings and reducing risks associated with supplier inefficiencies and poor quality.

Strategic Relationships

Developing strategic relationships with key suppliers based on performance data and mutual improvement goals.

Key Challenges

• Data Accuracy and Collection Gathering accurate and timely data from multiple suppliers can be challenging, especially across different systems and geographies.
• Setting Relevant Metrics Identifying the most relevant and impactful metrics that align with specific business goals requires careful analysis.
• Supplier Collaboration and Compliance Ensuring suppliers understand, agree with, and adhere to the set performance metrics requires ongoing communication.

Data Requirements

Internal Data

• Delivery Records - Historical data on delivery times, quantities, conditions
• Quality Inspection Reports - Defect rates, non-conformances
• Procurement Data - Purchase orders, prices, volume discounts

External Data

• Market Intelligence - Dun & Bradstreet, ThomasNet insights
• Trade Associations - Industry benchmarks and standards
• Credit Ratings - Moody's, S&P financial stability ratings

Analytics Methods

Statistical Analysis

Benchmarking supplier performance and identifying outliers

Predictive Analytics

Forecasting future supplier performance based on historical trends

Multivariate Analysis

Understanding impact of various factors on supplier performance

Data Envelopment Analysis (DEA)

Measuring efficiency of suppliers relative to each other

Key Performance Indicators

Quality Rating

Conformity of goods to quality standards

Cost Reduction

Achieved through effective supplier negotiation

Compliance Rate

Adherence to agreed terms and regulations

What Is It?

Supplier Risk Analytics refers to the use of data analysis techniques and tools to assess and manage risks associated with suppliers in the supply chain.

• Supplier performance - Historical reliability and consistency
• Financial stability - Credit scores, liquidity, market position
• Compliance - Regulatory adherence and certifications
• Operational risks - Geographic, political, environmental factors

Utilizing predictive modeling, risk scoring, and scenario analysis to anticipate potential issues and mitigate risks.

Business Objectives

Risk Identification & Mitigation

Proactively identifying risks and implementing strategies to mitigate them before they impact operations.

Supply Chain Resilience

Enhancing the resilience and reliability of the supply chain through informed supplier management.

Optimized Supplier Relations

Building and maintaining effective relationships with suppliers based on risk assessment and performance.

Key Challenges

• Data Availability and Quality Ensuring access to accurate and comprehensive data on suppliers for meaningful analysis.
• Complexity of Risk Factors Identifying and assessing a wide range of risk factors that vary across different suppliers and regions.
• Dynamic Market Conditions Adapting to rapidly changing market and geopolitical conditions that can influence supplier risk.

Data Requirements

Internal Data

• Performance Data - Delivery, quality, responsiveness history
• Financial Data - Credit scores, financial statements
• Compliance Records - Regulatory and contractual adherence

External Data

• Market Data - Economic indicators, geopolitical events
• Data Providers - Dun & Bradstreet enriched profiles
• Industry Trends - Sector-specific risk factors

Key Insight: Enriching internal data with external data can significantly enhance supplier risk analytics. Only both perspectives provide the full picture!

Analytics Methods

Risk Scoring Algorithms

Developing a scoring system to quantify risk levels for each supplier

Scenario Analysis

Monte Carlo simulations and what-if analysis to evaluate impact of potential risk events

Network Analysis

Evaluating interdependencies within the supply chain to understand ripple effects

Predictive Modeling

Anticipating future risks based on patterns and early warning signals

Key Performance Indicators

Supply Chain Resilience

Ability to respond to and recover from risks

Cost of Risk Management

Expenses related to managing and mitigating supplier risks

What Is It?

Supplier Capacity Planning involves predicting and managing the capacity of suppliers to meet current and future demands of the business.

• Production capabilities - Understanding supplier output limits
• Lead times - Planning for order-to-delivery windows
• Potential bottlenecks - Identifying constraints early
• Demand alignment - Matching supply with production schedules

The goal is to ensure a smooth and uninterrupted supply chain flow.

Business Objectives

Supply Chain Efficiency

Optimizing the flow of goods from suppliers to ensure timely production and delivery.

Cost Reduction

Minimizing costs related to overproduction, storage, and expedited shipping by aligning supply with demand.

Risk Mitigation

Reducing the risk of stockouts, production delays, and excess inventory.

Digital Supplier Connectivity

Streamlining collaboration between companies and trading partners is a business imperative.

For Buyers

• Share demand and forecast plans with suppliers
• Communicate stock on hand and min/max inventory levels
• Integrate supplier replenishment plans into ERP
• Run SMI programs with standard or consigned stock

For Suppliers

• View stock on hand and demand plans
• Receive projected stock alerts for compliance
• Gain early visibility into customer demand
• Plan and execute replenishments against targets

Data Requirements

Analytics Methods

Linear Programming

Optimizing supplier selection and capacity allocation while minimizing costs

Monte Carlo Simulations

Assessing impact of uncertainty in demand and supply lead times

Time-Series Analysis

Forecasting demand trends and planning for seasonal fluctuations

Constraint-Based Optimization

Accommodating multiple variables and restrictions in supplier capabilities

Key Performance Indicators

Supplier Lead Time

Time from order placement to receipt

Fill Rate

% of orders fulfilled on first shipment

Flexibility

Ability to handle varying order sizes

What Is It?

Compliance Tracking involves monitoring and ensuring that suppliers adhere to specific industry regulations, legal requirements, and organizational standards throughout the supply chain.

• Establishing compliance criteria - Defining standards and requirements
• Assessing supplier activities - Monitoring adherence
• Using data analytics - Evaluating compliance levels automatically
• Managing documentation - Tracking certificates and records

Business Objectives

Risk Mitigation

Reducing the risk of legal penalties, reputational damage, and supply chain disruptions.

Standardization

Standardizing processes and policies to ensure consistent compliance across all suppliers.

Ethical Supply Chain

Upholding ethical practices, sustainability, and corporate responsibility.

Common Document Types

Certificate of Analysis (COA)

Formal laboratory document detailing results of analyses

Technical Data Sheet (TDS)

Comprehensive product specifications and composition

Safety Data Sheet (SDS)

Occupational safety and health information

Automated Compliance Check Process

Document Upload

OCR Extraction

NLP + ML Processing

Compliance Check

Accept/Review

Key Processing Steps:

• OCR - Extract text, handwriting, and data from scanned documents
• NLP - Map terminology, verify mandatory information, confirm signatures
• Validation - Check parameters against specifications

Analytics Methods

Data Mining

Uncovering patterns indicating non-compliance

ML Classification

Categorizing activities as compliant or non-compliant

Natural Language Processing

Analyzing contracts, audit reports, regulatory documents

OCR Services Available

Cloud

Google Cloud Vision, Amazon Textract, Azure Form Recognizer

Specialized

ABBYY, Kofax

Open Source

Tesseract OCR

Key Performance Indicators

Compliance Rate

% of suppliers meeting standards

Cost of Compliance

Expenses for managing compliance

Supplier Risk Profile

Accuracy of risk profiles

What Is It?

Raw Material Quality Analysis involves assessing and ensuring the quality of raw materials received from suppliers through testing and verification processes.

• Quality testing - Laboratory and inspection procedures
• Traceability - Tracking materials through the supply chain
• Complaint handling - Addressing and resolving quality issues
• Root cause analysis - Identifying sources of defects

Business Objectives

Quality Assurance

Ensuring consistent quality in raw materials to maintain high standards in the final product.

Cost Reduction

Reducing costs associated with poor quality materials - waste, rework, and product recalls.

Supplier Relationships

Strengthening supplier relationships by collaboratively improving quality standards.

Analytics Challenges

Consistency

Ensuring consistent methods and standards across all quality assessments.

Traceability

Accurately tracking materials back through the supply chain.

Data Integration

Combining supplier, testing, and customer feedback data.

Predictive Analysis

Identifying quality issues before they impact production.

Data Requirements

Analytics Methods

Statistical Quality Control

Monitor and control quality

Root Cause Analysis

Identify underlying causes

Machine Learning

Predict quality outcomes

Connection to Manufacturing

Early detection of material quality issues prevents downstream production problems.

Key Performance Indicators

Supplier Quality Rating

Performance and defect rates

Time to Resolve

Speed of fixing issues

Cost of Quality

Rework, returns, scrap costs

What Is It?

Manufacturing Cost Analysis examines and optimizes expenses associated with production, focusing on the Bill of Materials (BOM) .

• Raw materials costs - Direct material expenses
• Component costs - Purchased parts and assemblies
• Labor costs - Direct and indirect labor
• Overhead allocation - Factory and operational expenses

The aim is to reduce costs while maintaining or enhancing product quality.

Business Objectives

Cost Reduction

Identifying opportunities to reduce direct and indirect costs associated with the BOM.

Resource Utilization

Optimizing use of materials and resources to minimize waste.

Process Improvement

Streamlining manufacturing processes for greater efficiency and cost savings.

The Bill of Materials (BOM)

"BOM Explosion refers to the extensive list of raw materials required to manufacture, design and fix a product or service."

Engineering BOM (EBOM)

Design-focused bill of materials from engineering perspective

Manufacturing BOM (MBOM)

Production-focused bill of materials for manufacturing

Using graphs for BOM querying can transform EBOM into MBOM efficiently.

Data Requirements

Analytics Methods

Activity-Based Costing (ABC)

Allocate overhead to products based on actual resource consumption

Linear Regression

Understand how volume/material costs affect total cost

Pareto Analysis

Identify the "vital few" cost drivers (80/20 rule)

Multivariate Analysis

Understand complex relationships between cost factors

Key Performance Indicators

Cost of Goods Sold

Direct costs attributable to production

Gross Margin

Revenue minus COGS as % of revenue

ROI Raw Materials

Returns on materials used in production

What Is It?

Production scheduling organizes and allocates resources, tasks, and timelines to manufacture goods efficiently.

• Sequence planning - Ordering operations optimally
• Resource assignment - Allocating machines and production lines
• Timeline management - Start and finish times for production runs
• Constraint handling - Working within capacity limitations

Business Objectives

Maximize Output

Increase production volume within the same time frame through streamlined operations.

Reduce Turnaround

Accelerate production cycles to deliver products to market faster.

Resource Utilization

Allocate resources effectively to minimize waste and reduce costs.

Meet Customer Demands

Ensure timely order fulfillment to enhance customer satisfaction.

Production Schedule Constraints

Optimization Approaches

Changeover Minimized

Focuses on reducing time and cost of switching between products

Makespan Minimized

Focuses on completing all jobs in the shortest possible time

Analytics Methods

Linear Programming (LP)

Optimize schedules while considering constraints

Mixed Integer LP (MILP)

Handle discrete variables in scheduling problems

Heuristic Algorithms

Genetic algorithms, simulated annealing for complex problems

Google OR-Tools

Open-source optimization suite for scheduling

Key Performance Indicators

On-time Delivery

% of orders shipped on or before promised date

Capacity Utilization

Extent to which manufacturing capacity is used

What Is It?

Batch Size Optimization finds the most cost-effective quantity of units to produce in a single production run.

• Setup costs - Fixed costs for each production run
• Holding costs - Inventory storage expenses
• Production costs - Variable manufacturing costs

The goal is to balance economies of scale with responsiveness to market demand.

Business Objectives

Cost Efficiency

Reduce changeover costs, inventory holding, and capital tied up in unsold goods.

Production Flexibility

Enhance ability to respond to demand changes without excessive costs.

Process Improvement

Faster turnaround times and increased throughput.

Economic Batch Quantity (EBQ)

The standard formula for optimal batch size:

Q = Batch Size

D = Annual Demand

C O = Setup Cost per batch

C C = Holding Cost per unit/year

d = Daily demand rate

p = Production rate per annum

Cost Components

Costs that INCREASE with batch size

• Materials and labor costs
• Cost of handling materials
• Storage and warehousing
• Capital tied up in inventory

Costs that DECREASE with batch size

• Setup cost per unit
• Paperwork and order costs
• Machine changeover per unit
• Administrative overhead per unit

Analytics Methods

EOQ Model

Economic Order Quantity for minimizing inventory costs

EPQ Model

Economic Production Quantity for manufacturing

Queueing Theory

Analyzing waiting line scenarios for batch processing

Simulation Modeling

Assess different batch sizes under varying conditions

Key Performance Indicators

Production Efficiency

Productive vs total available time

Capacity Utilization

Use of available production capacity

Cost per Unit

Total cost associated with each unit

What Is It?

Predictive Maintenance (PdM) uses data analytics and machine learning to predict equipment failures before they occur.

• Real-time monitoring - Continuous equipment health tracking
• Historical analysis - Learning from past failure patterns
• Pattern recognition - Identifying early warning signals
• Proactive scheduling - Maintenance based on actual condition

Business Objectives

Reduce Downtime

Minimize unplanned equipment outages that cause costly production delays.

Extend Equipment Life

Optimize maintenance to extend operational lifespan of assets.

Cost Efficiency

Lower maintenance costs by performing maintenance only when needed.

Maintenance Maturity Levels

1️⃣

Reactive

Fix when broken

2️⃣

Preventive

Scheduled maintenance

3️⃣

Condition-based

Monitor and respond

4️⃣

Predictive

Predict and prevent

The P-F Curve

The relationship between Potential Failure and Functional Failure:

Potential Failure (P)

Equipment begins showing signs of degradation - detectable through sensors and monitoring.

Functional Failure (F)

Equipment can no longer perform its intended function - production stops.

P-F Interval: The window for predictive maintenance to prevent failure

Analytics Methods

Anomaly Detection

Z-score or Grubbs' test to identify outliers in performance

Machine Learning

Random Forests, SVMs for pattern recognition and failure prediction

LSTM Networks

Time-series forecasting for equipment health trends

Survival Analysis

Estimating time until equipment failure

Key Performance Indicators

MTBF

Mean Time Between Failures

Maintenance Cost

Reduction in maintenance expenses

Equipment Uptime

% of operational time

What Is It?

Machine Learning in Quality Control applies AI algorithms to monitor, analyze, and improve product quality in manufacturing.

• Pattern identification - Learning from historical quality data
• Anomaly detection - Spotting defects automatically
• Predictive quality - Anticipating issues before they occur
• Reduced manual inspection - Automation of quality checks

Business Objectives

Enhanced Quality

Higher standard of product quality through precise AI-driven inspections.

Cost Reduction

Reduce costs from defects, recalls, and manual inspection labor.

Efficiency

Streamline quality control for faster production cycles.

Computer Vision Applications

Vision-Guided Robots

Precise positioning for pick & place

Anomaly Detection

Identify irregularities in products

Defect Detection

Automated inspection on production lines

Packaging Inspection

Check color, size, integrity

Barcode Scanning

Large-scale label verification

Safety Monitoring

PPE compliance & hazard detection

Case Example: Microchip Inspection

CNN for Microchip Quality

Convolutional Neural Networks designed to quickly and accurately find problems in microchips using sound wave images.

This ML application is more efficient and accurate than previous methods, representing a significant step in using advanced technology for quality assurance.

Analytics Methods

Image Classification (CNNs)

Automatically identify defects from product images

Image Segmentation

Localize defects by distinguishing parts within images

Autoencoders

Anomaly detection via reconstruction errors

Transfer Learning

Leverage pre-trained models with limited data

Key Performance Indicators

Defect Rate

% failing quality standards

Detection Accuracy

ML model precision in identifying defects

Quality Yield

Products meeting criteria without rework

What Is It?

A digital twin is a virtual model that accurately reflects a physical manufacturing process using real-world data.

• Predict performance - Simulate outcomes before implementation
• Optimize processes - Test improvements virtually
• Identify issues - Detect problems before they occur
• Support innovation - Experiment with new techniques

Business Objectives

Process Optimization

Identify and implement manufacturing improvements using the digital twin.

Cost Reduction

Reduce costs from downtime, defects, and inefficient processes.

Innovation Acceleration

Experiment with new manufacturing techniques rapidly.

Digital Twin Classes

Component Twins

Basic unit - smallest functioning component

Asset Twins

Two+ components working together

System Twins

Assets forming entire functioning system

Process Twins

Complete production facility

Digital Twin in Supply Chain

Shop Floor Level

Real-time monitoring of production equipment

Supply Chain Level

End-to-end visibility across suppliers & distribution

Key Capability: Linking shop floor digital twin to supply chain digital twin

Analytics Methods

CAD Modeling

Creating geometric and material properties of assets

3D Simulation

Simulating behavior under different conditions

Agent-Based Modeling

Agents interacting to mimic complex system behavior

System Dynamics

Understanding process behavior over time

Key Performance Indicators

Time to Market

Concept to market duration

OEE

Equipment efficiency & productivity

Production Yield

Good quality vs total output

What Is It?

OEE is a standard metric for measuring manufacturing equipment effectiveness, combining three key factors:

• Availability - Equipment uptime vs planned production time
• Performance - Operating speed vs designed speed
• Quality - Good products vs total production

The aim is to identify improvement areas and enhance equipment productivity.

Business Objectives

Optimize Equipment

Maximize utilization and efficiency of manufacturing equipment.

Reduce Downtime

Minimize equipment downtime and production defects.

Cost Savings

Lower operational costs through improved efficiency and quality.

OEE Calculation

OEE = Availability × Performance × Quality

Availability

(Run Time / Planned Production Time) × 100%

Performance

(Ideal Cycle Time × Total Count / Run Time) × 100%

Quality

(Good Count / Total Count) × 100%

ML Root Cause Analytics

Using machine learning feature importance to identify OEE improvement factors:

1

Data Prep Collect & train ML model

2

Feature Analysis Identify top-ranked features

3

Root Cause Investigate key factors

4

Validation Test in real-world settings

5

Iterate Implement & monitor

Follows CRISP-DM methodology - highly iterative, aligns with agile principles

Analytics Methods

Statistical Process Control

Monitor & control using statistical methods

Time Series / ML

Forecast patterns in equipment performance

Regression Analysis

Identify factors affecting efficiency

Key Performance Indicators

OEE Score

Combined metric (world-class: 85%+)

Equipment Downtime

Non-operational time reduction

Production Yield

Products meeting quality standards

What Is It?

Demand Forecasting uses historical data, statistical algorithms, and market analysis to predict future customer demand.

• Historical analysis - Past sales data, trends, and patterns
• External factors - Market conditions, seasonality, promotions
• Quantity estimation - Products/services consumers will purchase
• Decision support - Inventory, production, and sales strategies

Business Objectives

Inventory Optimization

Balance stock without overstocking or stockouts

Resource Allocation

Efficient workforce and capacity planning

Cost Reduction

Minimize excess inventory and lost sales

Customer Satisfaction

Ensure product availability

Competitive Advantage

Anticipate customer needs better

Forecasting Layers in Planning

Strategic (Long-term)

Network planning, capacity investments

Tactical (Mid-term)

Master planning, procurement

Operational (Short-term)

Production and distribution scheduling

Real-time

Consolidated orders, immediate adjustments

Forecasting Approaches

Univariate

Single variable over time as input

• Sales data of one product
• Classical time series methods
• ARIMA, Exponential Smoothing

Multivariate

Multiple input variables considered

• Sales + promotions + price + weather
• Machine learning approaches
• Random Forests, Neural Networks

Analytics Methods

ARIMA

Capture trends and seasonal effects

Machine Learning

Random Forests, Gradient Boosting

Deep Learning

RNN, LSTM for sequential patterns

Facebook Prophet

Open-source tool for seasonality

Key Performance Indicators

Forecast Accuracy

MAPE, RMSE metrics

Inventory Levels

Optimal stock management

Stockout Rate

Reduce out-of-stock events

What Is It?

Inventory Level Optimization strategically manages stock to meet demand while minimizing costs.

• Demand forecasting - Predict future requirements
• Lead time assessment - Factor in supplier delivery times
• Optimal quantities - Right amount at the right time
• Cost balancing - Avoid overstocking and stockouts

Business Objectives

Cost Efficiency

Reduce storage, insurance, spoilage

Working Capital

Improve cash flow

Responsive SC

Adapt to demand changes

Customer Satisfaction

Product availability

Economic Order Quantity (EOQ)

The optimal order quantity that minimizes total costs:

Q* = √(2DK / h)

D = Annual demand

K = Fixed order cost

h = Holding cost/unit/year

Analytics Methods

EOQ Model

Optimal purchase quantity

Monte Carlo Simulation

Account for uncertainty

Safety Stock Calculations

Buffer against variability

ABC Analysis

Prioritize by value

Key Performance Indicators

Inventory Turnover

Frequency of stock replacement

Service Level

Meet demand without stockouts

Order Cycle Time

Order to dispatch time

What Is It?

Multi-Echelon Inventory Optimization synchronizes inventory decisions across all supply chain stages.

• Raw materials - Supplier inventory levels
• Work-in-progress - Manufacturing buffers
• Finished goods - Distribution centers & warehouses
• Network optimization - Right inventory at right place & time

Supply Chain Echelons

Suppliers

Manufacturing

Distribution

Retail

Customer

Each echelon requires coordinated inventory policies

The Bullwhip Effect

Small demand fluctuations at retail cause progressively larger fluctuations upstream.

Multi-echelon optimization mitigates this by:

• Coordinating inventory policies across levels
• Sharing demand information throughout the chain
• Optimizing network parameters jointly

Analytics Methods

Multi-Echelon Algorithms

Upstream & downstream policies

Stochastic Optimization

Handle demand/supply uncertainty

Simulation Optimization

Evaluate different scenarios

Deep Reinforcement Learning

Dynamic reorder policies

Key Performance Indicators

Total Inventory Cost

Combined cost across all echelons

Service Level

% demand fulfilled on time

What Is It?

VMI is a supply chain initiative where the supplier manages inventory levels at the customer's premises.

• Demand forecasting - Supplier predicts customer needs
• Stock optimization - Automated optimal levels
• Auto-replenishment - Supplier initiates orders
• Data sharing - Real-time inventory visibility

CMI vs VMI

Customer Managed (CMI)

Customer monitors inventory and places orders when needed

Vendor Managed (VMI)

Vendor monitors inventory and delivers products proactively

Analytics Methods

Time-Series (ARIMA)

Predict B2B demand

Machine Learning

Complex B2C patterns

EOQ & ROP

Inventory optimization

Dynamic Programming

Sequential replenishment

Key Performance Indicators

Inventory Turns

Turnover frequency

Service Level

Demand met from stock

Fill Rate

First shipment fulfillment

What Is It?

Supply Chain Segmentation divides the supply chain into distinct groups based on specific criteria.

• Customer needs - Service level requirements
• Product type - Demand patterns & value
• Market demands - Volatility & seasonality
• Logistics requirements - Lead times & handling

Segmentation Types

Supplier

Strategic importance, performance, risk (Kraljic Matrix)

Product

Life cycle, value, demand volatility

Geographic

Regional characteristics & requirements

Customer

Demand patterns, profitability, service needs

ABC-XYZ Matrix

Analytics Methods

K-Means Clustering

Group by similarity

Predictive Analytics

Segment performance

Decision Trees

Auto-assignment rules

Key Performance Indicators

Customer Satisfaction

Tailored service

Cost-to-Serve

Aligned strategies

Profit Margins

Segment optimization

What Is It?

S&OP is a collaborative process aligning sales forecasting with operational planning.

• Strategic alignment - Long-term goals and capabilities
• Tactical planning - Medium-term resource allocation
• Operational execution - Short-term activity coordination
• Cross-functional - Sales, operations, finance integration

S&OP Process

1

Data Gathering

2

Demand Planning

3

Supply Planning

4

Pre-S&OP

5

Executive S&OP

6

Implementation

Control Tower Data

PLAN

• Demand forecasts
• Sales plans
• Capacity plans

SOURCE

• Supplier performance
• Purchase orders
• Inventory levels

MAKE

• Production schedules
• Manufacturing KPIs
• Quality metrics

DELIVER

• Logistics data
• Order fulfillment
• Customer service

Key Performance Indicators

Forecast Accuracy

Plan vs actual

Demand/Supply Balance

Alignment metric

Plan Adherence

Execution accuracy

What Is It?

Supply Chain Risk Analysis identifies, assesses, and manages risks that could disrupt operations.

• Risk identification - Across all echelons
• Impact assessment - Probability and severity
• Mitigation planning - Strategy development
• Continuous monitoring - Real-time visibility

Key Insight

"Improve your end-to-end transparency by connecting the entire value chain with a seamless flow of data"

— McKinsey

Risk Categories

Supply

Supplier bankruptcy, quality issues, capacity

Demand

Volatility, forecast errors, market shifts

Operational

Equipment failure, labor, process issues

Environmental

Natural disasters, pandemics, climate

Geopolitical

Trade disputes, regulations, instability

Financial

Currency, credit, commodity prices

Analytics Approaches

Risk Scoring

Quantify risk across network

Scenario Analysis

Evaluate risk event impacts

Network Analysis

Dependencies & cascading effects

Machine Learning

Predict risk events

Key Performance Indicators

Risk Exposure Score

Aggregate risk level

Time to Recovery

Disruption recovery speed

What Is It?

Customer Satisfaction Analysis evaluates customer preferences, experiences, and satisfaction levels.

• Customer surveys - Direct feedback collection
• Purchase history - Behavioral patterns
• Service interactions - Support experience
• Social media - Public sentiment and reviews

Key Performance Indicators

Net Promoter Score

Likelihood to recommend

CSAT Score

Satisfaction rating

Churn Rate

Customer attrition

On-Time In-Full (OTIF)

Customer satisfaction in supply chain starts with a customer-centric definition:

OTIF = % of orders delivered on/before requested date with complete quantity

Analytics Methods

Sentiment Analysis

NLP for feedback

Decision Trees

Churn predictors

Survival Analysis

Time to churn

What Is It?

Order Fulfillment Analytics identifies root causes behind On-Time In-Full (OTIF) delivery failures.

• Process analysis - Map actual fulfillment flows
• Bottleneck detection - Find delays and inefficiencies
• Root cause analysis - Identify underlying issues
• Continuous improvement - Monitor and optimize

Process Mining Failures

Bottlenecks

Flow slowdowns causing delays

Deviations

Actual vs intended process

Re-Work Loops

Repeated tasks

Invisible Tasks

Unrecorded activities

Handovers

Excessive transfers

Violations

Compliance issues

Key Performance Indicators

OTIF Rate

On-time & complete

Cycle Time

Order to delivery

Perfect Order

Zero issues

Fill Rate

First attempt

What Is It?

Network Design strategically shapes the supply chain structure for optimal efficiency.

• Location decisions - Where to place facilities
• Capacity planning - How much capacity at each site
• Flow optimization - How goods move through network
• Cost/service balance - Trade-off optimization

Business Objectives

Cost Efficiency

Reduce transportation, inventory, operations

Service Level

Improve delivery times

Flexibility

Adaptable to change

Resilience

Continuity against disruptions

Analytics Methods

Network Analysis

Visualize flow of goods

Simulation

Test under stochastic conditions

Scenario Analysis

What-if evaluation

Mixed Integer Programming

Optimize locations

Key Performance Indicators

Total SC Cost

Production + storage + transport

Carbon Footprint

Environmental impact

Flexibility

Adaptability score

What Is It?

Transportation Cost Analysis optimizes costs of moving goods through the supply chain.

• Mode selection - Choose optimal transport type
• Route optimization - Find best paths
• Consolidation - Combine shipments
• Cost trade-offs - Balance cost vs speed

Transportation Modes

Air

Fastest, highest cost

Road

Flexible, door-to-door

Rail

Bulk, cost-effective

Sea

Lowest cost, slowest

Intermodal

Combined modes

Analytics Methods

Linear Programming

Optimize mode/route selection

Network Flow

Analyze transport networks

Simulation

Evaluate scenarios

Cost-Benefit Analysis

Compare options

Key Performance Indicators

Cost/Unit

Per unit shipped

Cost/Mile

Per distance

Freight % Rev

vs Sales

On-Time %

Service level

What Is It?

Delivery Time Prediction uses data analytics and ML to forecast when orders will arrive.

• Customer expectations - Provide accurate estimates
• Operations planning - Better resource allocation
• Service improvement - Meet promised times
• Competitive advantage - Reliable predictions

Prediction Horizons

E-Commerce

Days

eBay, Amazon style

Food Delivery

Minutes

Uber Eats style

Last Mile

Variable

DHL, FedEx style

Analytics Methods

ML Regression

Continuous time values

Time Series

Temporal patterns

Gradient Boosting

Feature interactions

Neural Networks

Complex scenarios

Key Performance Indicators

Accuracy

Prediction precision

On-Time %

Met predictions

CSAT Impact

Satisfaction effect

MAE

Mean absolute error

What Is It?

Route Optimization determines the most efficient sequence of stops for delivering goods.

• Minimize distance - Reduce travel and fuel
• Minimize time - Faster deliveries
• Meet constraints - Time windows, capacity, hours
• Adapt dynamically - Real-time adjustments

Business Objectives

Cost Reduction

Fuel, vehicle, driver

Time Efficiency

Faster completion

Customer Sat

Reliable delivery

Environment

Reduce emissions

Utilization

Max vehicle use

Problem Complexity

Route optimization is NP-hard (TSP/VRP variants)

Constraints to Consider:

• Vehicle capacity

• Time windows

• Driver hours

• Traffic conditions

• Road restrictions

• Customer preferences

Analytics Methods

Exact Algorithms

Optimal for small problems

Heuristics

Fast for large problems

Metaheuristics

Genetic, simulated annealing

Reinforcement Learning

Adaptive real-time

Key Performance Indicators

Distance

Time

Fuel

On-Time

Utilization