Supply chains today produce and consume humongous amounts of data daily. Percentage of companies adopting analytics to process such data has consequently ballooned from 17% in 2021 to over 50% in 2023 as per industry reports. Global retail giant Walmart estimates it saves over $1 billion annually through supply chain analytics.
However, Gartner also found that nearly half of analytics projects fail to demonstrate tangible results even as most business leaders rank it as a top 5 priority. This underscores gaps in realizing the purported benefits, due to ad-hoc implementations plagued by poor data, skill shortage and minimal cross-functional involvement.
This comprehensive guide prepares supply chain practitioners to adopt analytics effectively by covering:
- Criticality of analytics in modern demand-driven economy
- Techniques like predictive modeling and simulation used
- Mitigation strategies for commonly observed pitfalls
- Career roadmap spanning tools, certifications and specializations
- Future trends like AI/ML that will disrupt supply chain analytics
Equipped with this multi-dimensional perspective and tactical toolkit, current and aspiring professionals can transform supply chain performance significantly through analytics, while avoiding common mistakes.
Why Supply Chain Analytics is Indispensable Today
Global Supply Chains (GSC) form the backbone that delivers products and services that billions of consumers and businesses rely on daily. Their smooth functioning impacts national GDPs given that supply chain spends account for nearly 65% of global operating expenses.
This complex system integrally links hundreds of discrete entities like manufacturers, 3PLs, distributors, retailers spanning continents. The ability to gain end-to-end visibility across the sequence of plan, source, make, deliver and return depends on data and analytics.
Key aspects where analytics is indispensable include:
Intricate Global Interconnections
Globalization necessitates that corporations seamlessly integrate partners worldwide across multiple echelons. This intensifies the impact of any localized disruption.
For example, a 2021 fire accident destroyed a Renesas Electronics chip plant in Japan, crippling production in automakers like GM, Ford and Toyota. Why? Renesas manufactured over 30% of semiconductors used in advanced auto electronics.
Such cascading supply shortages demonstrate the fragility arising from hyper-optimization of supply networks chasing cost efficiency. Analytics provides the means to model risks and alternate scenarios for business continuity.
Omni-Channel Customer Expectations
Today‘s consumers demand uniform brand experience and real-time order visibility across online and brick-and-mortar channels. Meeting such high expectations depend on analytics to synchronize forecasts, inventory and logistics across channels.
For instance, apparel retailer H&M saw a 20% revenue uptick after implementing analytics to harmonize inventory levels across 350 US stores with its ecommerce channel. This allowed delivering on customer expectations seamlessly via their channel of choice.
Industry 4.0 Manufacturing
Smart manufacturing powered by IIoT, robots, 3D printing promises mass personalization and lot sizes of one. But optimally scheduling such flexible production necessitates analytics on equipment effectiveness, real-time demand signals and logistics connectivity.
Siemens enabled analytics on operational data captured via IoT sensors has shown potential to reduce manufacturing costs by 10-20% as per expert estimates. Predictive maintenance powered by such data could contribute nearly half of these savings.
Thus analytics is pivotal in linking virtual and physical elements to realize Industry 4.0 goals.
Key Techniques Powering Supply Chain Analytics
Prescriptive, predictive and descriptive techniques analyzed below enable organizations to translate supply chain data into insights for fact-based decisions.
Predictive Modeling
Predictive modeling harnesses statistical and machine learning algorithms to identify trends and patterns within data to make reasoned forecasts. In supply chain context, it focuses on modeling demand, pricing, inventory requirements, transportation delays, equipment failures etc.
For example, aluminum producer RioTinto fed aerial images of its mining pits into neural networks to predict daily output 6 weeks in advance with 80% accuracy. Such precision demand forecasting led optimized production planning and logistics.
Retailers like Staples use predictive models considering product attributes, price elasticity, promotions calendar and customer demographics to forecast sales by channel. This allows aligning staff and inventory optimally.
Prescriptive Modeling
Prescriptive analytics complements predictive estimates by recommending decision options aligned to business strategy. It allows modeling diverse scenarios through ‘what-if‘ analysis to showcase impact on KPIs – cost, service levels, sustainability etc.
For instance, a freight company predicting a 20% volume surge due to upcoming festival demand would use prescriptive models to evaluate options like – leasing added trucks vs outsourcing overflow loads vs increasing shipment rates. Comparing trade-offs quantitatively focuses strategic planning.
Energy firm Shell uses such optimization modeling to guide billion dollar asset investment decisions on expanding refineries, pipeline infrastructure and retail outlets globally.
Reinforcement Learning
This cutting-edge machine learning technique empowers algorithms to learn improved decision policies iteratively by exploring varied scenarios in a simulation, evaluating outcomes and reinforcing actions that optimize goals.
Owing to such self-learning capabilities from continual experimentation, reinforcement learning is gaining adoption for automated warehouse robots, dynamic pricing engines, vehicle routing software etc.
For example, Google‘s Waymo automated trucks learn nuanced behaviors like maintaining safe distance from vehicles ahead and avoiding potholes through repeated simulations equivalent to over 10 million miles of driving. Such learned policies minimize road risks.
Simulation
Discrete event simulation provides a low-risk means to evaluate the holistic impact of supply chain transformation initiatives through a virtual replica or digital twin. Detailed modeling of entities, process logic, queues, resources allows mimicking end-to-end interactions.
AP Moller-Maersk used simulation to validate a new warehouse operating model encompassing receipt, putaway, replenishment and picking processes balanced by workforce allocation and material handling equipment. Post implementation, the redesigned warehouse achieved over 20% productivity increase.
Data captured from such digital twins also fuels predictive maintenance, capacity planning and other analytics use cases – delivering multiplier ROI.
Pitfalls to Avoid with Supply Chain Analytics
While an array of potent analytical approaches hold promise, realizing measurable operational impact requires carefully navigating common pitfalls that plague 70% of analytics projects as below:
Inadequate Data Volume or Quality
Analytics models are only as good as the data used to train them. Low data volume leads to inaccurate or biased models that fail to generalize. Common issues include:
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Irregular data collection or inconsistencies between partners, channels
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Inaccurate forecasting histories retained across regime changes like pandemic disruption
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Sparse yield data from suppliers hampering quality analytics
Lack of Skills and Solution Mindset
Managing analytics projects requires navigating stakeholder needs, data infrastructure complexities and conveying ROI convincingly. Lack of such well-rounded competencies result in:
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Poor problem definition – effort wasted in analyzing irrelevant use cases
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Technical myopia – accuracy maximized but operationalization ignored
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Black box systems – lack explainability hampers adoption
Change Management Challenges
Transitioning enterprise users from experience/intuition-led decisions to analytics-based recommendations requires thoughtful enablement to prevent:
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Lack of buy-in due to low understanding of model logic
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User inertia hampering analytics tool adoption at operational level
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Sub-optimal models being ignored without feedback loops
Proactively defining mitigation strategies across above dimensions accelerates analytics ROI.
Launching a Supply Chain Analytics Career
The supply chain analytics job market is red hot with average salaries soaring above $140,000 for senior roles as per Indeed. Both early career and mid-level switchers have ample open doors provided fundamental and specialized skills are demonstrated.
Typical entry routes include:
Graduate Degrees
Master‘s programs in supply chain management, business analytics or data science serve as proven springboards into analytical roles. Curriculum spanning optimization, ERP systems, delivery logistics, manufacturing processes and database/BI tools equips fresh grads with both technical and business context.
Carnegie Mellon University offers a MS in Business Analytics with a Supply Chain focus covering analytics applied to global procurement, Smart Grid analysis, dynamic pricing and omnichannel optimization.
Analytics Certifications
For experienced functional specialists keen to transition into analytical roles, professional certifications signal analytical thinking while demonstrating technology toolkit depth via practical projects.
Options like IIBA‘s CCBA (Certified Business Analytics Associate), Six Sigma Green Belt, CCA Spark and Hadoop certify expertise across statistics, BI tools, big data and machine learning while aligning to business priorities.
Specialization
Progressing into senior analytics leadership mandates deep specialization either across industry sub-verticals like Automotive, Retail, Healthcare etc. or across analytical domains like Inventory Optimization, Predictive Maintenance, Network Design etc.
For instance, holding predictive analytics expertise specifically applied to patient length of stay forecasts and hospital utilization planning separates one as a healthcare operations analytics specialist demanded by providers like Mayo Clinic and Cleveland Clinic.
Supply Chain Analytics Careers and Skills
Here are some typical supply chain focused analytics roles and desired skillsets:
| Role | Day to Day Activities | Skills Needed |
|---|---|---|
| Inventory Planner | Statistical demand forecasts, multi-echelon inventory optimization, determining safety stock | Time series analysis, machine learning, optimization |
| Transportation Analyst | Logistic network design, shipment consolidation analysis, routing guide analysis | Operations research, graph theory, geospatial analytics |
| Sourcing Analyst | Spend classification, tail spend analysis, price diagnostic, cost modeling | Data wrangling, cost modeling, visualization |
| Production Planning Analyst | Production schedule optimization, predictive quality/yield analytics, equipment effectiveness | Optimization, predictive modeling, IoT data analytics |
Clearly both domain knowledge and technical competency is highly valued to unlock supply chain insights.
Software Platforms Enabling Supply Chain Analytics
Specialist supply chain software tools provide extensive analytical modeling baked into planning engines while integrating datasets across value chain partners.
| System | Key Analytics Capabilities | Companies Using It |
|---|---|---|
| Blue Yonder | AI/ML-based forecasting and optimization for S&OP, inventory and transportation | Starbucks, PepsiCo, Colgate |
| Kinaxis | Prescriptive analytics and rapid scenario modeling for demand/supply planning | Unilever, Boeing, Nestle |
| SAP IBP | Predictive analytics for procurement spend classification and anomaly detection | Lenovo, Applied Materials, Clorox |
While software capabilities continue to evolve rapidly, ultimately deriving value relies on disciplined deployment driven by cross-functional analytics talent.
Future Outlook Amid New Opportunities
Today supply chain data availability, analytical skills democratization via self-service BI tools and cloud-based scalable computing power have removed all technological constraints related to analytics adoption. The onus lies on supply chain leaders to operationalize these for purposeful performance improvement.
Upcoming opportunities include:
Digital Twin Modelling – High fidelity virtual replicas of logistics assets like warehouses, fleets and distribution centers embed real world physics into simulations to assess endless what-if scenarios
Blockchain Enablement – Blockchains through crypto transactional records and IoT sensory data feeds significantly improve integrity of supply chain data, notably across extra-enterprise ecosystems
Continuous Intelligence – Automating repeatable analytics tasks while dynamically adapting predictive models and prescriptive recommendations using AutoML and MLOps
Harnessing these innovations calls for championing pilots driven by cross-domain teams spanning strategy, analytics, engineering and operations. As macroeconomic volatility, geopolitical risks and competitive intensity continue to escalate, such symbiotic partnerships hold the key to supply chain resilience and excellence.
