Integrated Analytics: The Key to Supply Chain Efficiency

The Right Tools for Optimizing Your Supply Chain
The Right Tools for Optimizing Your Supply Chain

Businesses are always on the lookout for ways to improve their margins. One effective method is optimization of the entire operations. However, those that use data from transactional systems and siloed supply chain tools for decision-making find these to be difficult to use. Firms would be wise to use solutions that gather and analyze supply chain data under one roof to precisely draw insights for business decisions.

Why are transactional systems hard to use as tools for supply chain optimization?

One of the main reasons why the transactional systems in enterprise resource planning (ERP), warehouse management (WMS), transportation management (TMS), manufacturing execution (MES), and others are not apt tools for optimizing supply chains is that they are designed for internal operations (such as enabling and storing all transactions), not external collaboration. 

ERP systems, for example, integrate and automate the data and processes of different departments within an organization, such as accounting, human resources, production, and sales. However, they do not provide much visibility or coordination across the multiple organizations involved in a supply chain, such as suppliers, distributors, retailers, and customers. ERP systems are also focused on historical data and status rather than future scenarios and planning. They can tell what has happened and what is happening, but not what will happen or what should happen.

Table 1. An illustration of how an optimized supply chain looks like, with lower operational costs as an ultimate measure of success

Improved Total Cost of Ownership (TCO) Just-in-Time Supply Chain Cycle Time Days of Inventory on Hand (DOH) Fill Rate On-Time Delivery (OTD) Rate
Costs of purchasing, operating, integrating into existing systems, maintaining, and repairing tools are minimized. Retail sales trigger automated replenishment orders to manufacturers so that they can restock products almost as soon as they sell them. To balance the risk of having idle stock and the risk of being understocked, a good DOH for consumer-packaged goods (CPG) companies is between 35 and 50 days, though factors such as company size and industry may influence this metric. An average company achieves 85% – 95% fill rate, but top-notch firms strive for somewhere between 97% and 99%. A rate of 95% and above is deemed exceptional.


Integrated supply chain analytics (ISCA), on the other hand, is designed to optimize the flow of materials, information, and money across the entire supply chain network. Let’s take a closer look at how being powered by the right data reduces supply chain management (SCM) costs and, ultimately, drive a positive impact to the bottom line.

One integrated data structure makes analytics efficient

Having one analytics framework is not merely sufficient to serve the data needs of every point in the supply chain, but it is actually key to achieving efficiency. That is, we avoid the needless redundancies and potential inconsistencies and system incompatibilities that siloed data systems bring.

digital supply networks

A diagram visualizing how one digital core serves all
the digital supply networks in the supply chain

At the core of an ISCA platform are facilities that fulfill four fundamental tasks:

  • Collect and integrate data. Store and prepare data for use across the supply chain. The tools for fulfilling this task include data lakes, ERP, TMS, WMS, MES, and programs for master data management.
  • Analyze. Apply data science, AI, and machine learning to identify patterns and trends in the data as well as to accelerate analytics.
  • Derive insights. Present analytics findings through enterprise reporting, bespoke dashboards, and data visualization.
  • Generate. Provide accurate demand forecasts, recommendations for system enhancements, and other output that could guide decision-making. 

These four fundamental tasks are what makes all other specific analytics tasks fulfillable, be they tasks for managing transportation fleets, determining overall equipment effectiveness (OEE), and optimizing inventory. This is how supply chain systems can be described to be integrated with the ISCA’s core. 

Intelligent supply chain

In the ISCA’s integrated data structure, the supply chain systems are also integrated with one another on the cloud through real-time connectivity and advanced analytics to form an intelligent supply chain. This enables real-time performance monitoring and reporting, what-if scenario-based capabilities, and sustainability reporting.

The following components are needed to make a supply chain intelligent:

  • Real-time visibility: Utilize technologies like internet-of-things (IoT) sensors, RFID tags, and GPS tracking to achieve real-time visibility across your supply chain.
  • Automation and robotics: Integrate robotics and robotic process automation (RPA) in your supply chain operations to enhance efficiency and productivity. Robotic systems support warehouse operations, order picking, and packaging, while RPA can handle repetitive tasks like data entry or order processing.
  • Advanced analytics and intelligent insights: Harness advanced analytics as well as AI and machine learning (ML) techniques to process extensive supply chain data and produce valuable insights. For example, predictive insights can be used to forecast demand patterns, optimize inventory levels, streamline production schedules, and enhance overall supply chain efficiency.
  • Collaboration with suppliers: Cultivate stronger collaboration with suppliers by integrating their systems and data with yours. Through shared information on demand forecasts, inventory levels, and production schedules, demand and supply chain planning become more efficient. Coordination improves, lead times shorten, and overall supply chain visibility becomes more enhanced.
  • Resilience and risk management: Utilize intelligent supply chain technologies to bolster risk management and fortify resilience. This encompasses using predictive analytics to detect potential disruptions and putting contingency plans into action.

With an intelligent supply chain, organizations can enjoy the following benefits:

  • Improved flexibility and responsiveness: The enhanced collaboration with suppliers enables organizations to coordinate with them on revamping production, fulfillment, and other strategies when such changes are called for.
  • Opportunity to expand operations into new regions or scale up activities: The insights shared by the intelligent supply chain could point to significant and highly profitable growth opportunities.
  • More effective demand and supply chain planning: Better accuracy in predicting demand, inventory levels, and other crucial factors result in maximized returns.
  • Reduced inventory and transportation costs: Advanced analytics could determine the most optimal inventory levels and delivery routes.
  • Shortened order-to-delivery lead times: Insights lead to efficiency gains in every link in the supply chain that all add up into an optimized system.

The right tools for every job across the supply chain

The integrated data structure enables all the tools and processes for solving challenges across the supply chain: 

Transportation analytics

Transportation analytics optimizes the transport process from beginning to end. It addresses challenges such as having to untangle complex global transportation networks and factoring in volatility in fuel prices. For the latter in particular, Lingaro’s transportation cost analyzer and budgeting process automation helped reduce transportation costs between 8% – 12% for a large CPG company.

Table 2. An overview of Lingaro’s solutions in transportation analytics

Service End-to-End Transport Process Optimization Transport Cost Optimization Shipping Life Cycle Tracking
Subtasks Involved
  • Network modeling and optimization
  • Planning and execution improvement
  • Performance improvement and monitoring
  • Optimization in third-party logistics (3PL) management 
  • Overall transport cost optimization
  • Generation of insights into cost components
  • Budgeting process improvement (forecast and actuals/accruals)
  • Ocean transport management
  • Real-time shipment visibility
  • Tracking of shipping process
  • Monitoring KPIs (OTD, ETA vs ATA)
  • Demurrage and detention cost optimization
Example of Value Delivered Near-real-time visibility of global transportation network  Better vehicle utilization rate versus market benchmarks Track and trace cost reduction


Manufacturing analytics

Manufacturing analytics integrates data from IoT sensors and other various systems to reduce costs, optimize production, and increase operational efficiency. To illustrate, intelligent factories utilize predictive algorithms that enable predictive maintenance. For one of Lingaro’s clients, predictive maintenance increased equipment uptime by 10% and slashed maintenance costs by 23%.

Table 3. An overview of Lingaro’s solutions in manufacturing analytics

Service Intelligent Factory Control Towers and Monitoring Product Optimization
Subtasks Involved
  • Predictive maintenance and IoT data
  • Asset and equipment management
  • Factory modeling and digital twin
  • Safety and quality monitoring
  • OEE and factory productivity
  • Manufacturing cost analytics
  • Machine capacity and utilization
  • Deep-dive analytics
  • Process mining
  • Pattern sensing and demand volume prediction
Example of Value Delivered Reduced equipment downtime through effective equipment management Greater visibility that lead to improved operational efficiency Faster time to market as a significant competitive advantage


The right insights for every role in the supply chain

All the tools included in ISCA generates insights in natural language. Powered by sophisticated AI/ML modeling, this feature considers the persona or role a data user plays in the organization and reframes the tools they use accordingly. To illustrate, a factory floor manager and a quality controller might use the same manufacturing digital twin, but what the twin will highlight for the floor manager might be OEE, while what it will highlight for the controller might be first-time quality and rejection rate.

Contextualized highlights such as these plus persona-specific insights make the tools more useful to all users. This dramatically increases adoption, which, in turn, increases data literacy and democratization. With more users finding, accessing, understanding, and making use of the data given to them, they create business value that far exceeds the value of the initial investment in analytics solutions.

Table 4. An overview of Lingaro’s natural-language insights framework where analytics solutions generate contextualized insights in natural language

Step taken Capture Diagnose Predict Prescribe
Question Posed What happened? What are the details? What will happen and why? What to do next?

AI Used
  • Anomaly detection
  • Importance sorting
  • Trend analysis
  • Natural language querying (NLQ)
  • Contextualized insights
  • User feedback collection
  • Predictive AI
  • Explainer
  • Scenario simulation optimization
  • Prescriptive AI
Value Delivered Faster automated monitoring of events Quicker access to relevant data and reduced search time Predicting decisions for future events with understanding of root causes Preventive actions to avoid negative impact on the business

Firms must use the right tool for the job. ERP tools and systems have their own role to play in the business, but when it comes to optimizing supply chains, they must use SCM tools and systems that best serve their needs and are integrated under one platform.

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