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.
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.
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:
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.
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:
With an intelligent supply chain, organizations can enjoy the following benefits:
The integrated data structure enables all the tools and processes for solving challenges across the supply chain:
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 |
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| 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 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 |
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| 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 |
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 |
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| 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.