OperationsFramework / modelModelAccessible

Supply chain analytics

When and how should supply chain analytics be applied?

AccessibleOperationalProgram / project2 min read

Contents

The analysis of material, information and process flow from suppliers through operations to customer delivery.

Supply chain analytics examines the activities required to create and deliver a product or service, from purchasing inputs through production and fulfilment to the customer. Its purpose is to improve inventory decisions, delivery reliability, resilience and cost while preserving the service customers expect.

When to use it

The method applies to services as well as products, but physical flows usually create the clearest need. Review a dynamic supply chain at least monthly. A stable network with few material changes may justify a six-month cycle, while operational alerts and critical risks should be monitored more frequently.

Typical questions include:

Which route should delivery vehicles use?

Where do delays or bottlenecks occur?

Does supplier or process performance fluctuate materially?

Which suppliers are least predictable, and what explains the variation?

Origins

Supply chain analytics evolved from operations research, logistics, inventory control, quality management and forecasting. Enterprise systems, barcode and RFID data, telematics, sensors and cloud platforms later made end-to-end measurement more practical. The field has no single inventor; it combines analytical methods with process knowledge and supply-network governance.

What it is

The analysis seeks opportunities to save cost, increase return, reduce risk and improve flow while ensuring customers receive the correct order when promised.

Why it matters

Managers need visibility between supplier purchase and customer receipt to price accurately, control working capital, protect margin and maintain service. A better-understood and more flexible network can anticipate disruptions, adapt to changing demand and identify where inventory or warehousing can be reduced without weakening resilience.

Forecasts and optimisation are useful only when connected to operational definitions. Record what constitutes an order, delay, defect, hand-off and completed delivery so that different systems describe the same process.

How to use it

Map the chain, establish decision questions and connect permitted data sources to each stage. Telematics can describe delivery vehicles; GPS can locate ships and lorries; RFID can track pallets or products; cameras can provide governed visual evidence; and operational systems can record orders, inventory, production and receipt.

Link those observations with metrics such as order fulfilment cycle time (OFCT), measured from confirmation to receipt, and delivery in full on time (DIFOT), which reflects the result the customer experiences. Techniques including Neural Network Analysis, Linear Programming and Monte Carlo Simulation can support route optimisation, bottleneck detection and risk analysis. Validate model outputs against constraints and frontline knowledge before changing the operation.

Practical example

A drinks manufacturer investigated shrinkage from production through distribution to retailers. It needed to locate product lost through breakage or theft so that packaging and controls could address the actual cause. Evidence from Sensor Data, Image Analytics, Interviews and Ethnography showed that the company-controlled chain was secure and most loss occurred through theft in supermarkets. Better tagging developed with retailers reduced shrinkage considerably.

Manufacturers also place sensors on equipment to observe wear, utilisation and operating condition. Condition evidence can support maintenance before failure, avoiding both breakdown and replacement based only on elapsed time. Similar data improve delivery visibility: customers can receive a narrower arrival window, track an order and sometimes see the assigned driver. These capabilities depend on reliable data, safe maintenance rules and proportionate privacy controls.

Top practical tip

Start with one material decision—such as inventory placement, route choice or shrinkage—and trace the minimum end-to-end data needed to improve it before investing in a large platform.

Top pitfall

Do not assume that more data automatically produce optimisation. Fragmented definitions, inaccessible supplier records and weak system integration can make the required investment larger than the value of the decision.