A look at Supply Chain Analysis

What is Supply Chain Analysis?

Supply Chain Analysis is the systematic examination of procedures such as storage, distribution, and transportation of goods within the supply chain. Supply chain analytics helps make sense of these procedures by uncovering patterns and generating insights that can improve quality, delivery, customer experience—and ultimately, profitability.

Why Supply Chain Analysis is Important

Conducting supply chain analysis periodically helps avoid disruptions in production, sales, and distribution of goods, ensuring that product quality is maintained. It also detects potential problems at an early stage, lowering repair and maintenance costs. The rate of production becomes faster and smoother.

Features of Effective Supply Chain Analysis:

An effective supply chain analysis is conducted when the following features are combined in the desired manner:

All Components are Connected Seamlessly:

All components of the supply chain are connected seamlessly with an order management system to ensure smooth order processing and fulfilment of goods to be delivered or picked.

Collaborative Approach to Enhance Coordination:

A collaborative approach allows firms to build resilient and responsive supply chains by fostering transparent communication and aligning goals with everyone involved.

Awareness to Protect Against Digital Threats:

Stringent cybersecurity measures and effective training against social engineering need to be implemented to protect logistics companies’ digitized systems from cyber-attacks such as data theft and system hacking.

Comprehensive Strategy to Cover All Critical Aspects:

Based on the data gathered from the measures mentioned above, a well-rounded supply chain management strategy can be developed that eliminates negatives and improves crucial aspects.

Types of Supply Chain Analytics:

There are different types of supply chain analytics for various purposes. We will go through each of them:

Predictive Analytics:

Predictive analytics is the process of leveraging data, statistical analysis, and machine learning techniques by logistics companies to identify and mitigate the likelihood of future outcomes, such as seasonal demand fluctuations and potential failures, to ensure smooth supply chain operations.

Prescriptive Analytics:

Prescriptive analytics uses the data gathered to optimize a company’s decision-making process by providing recommended actions, such as optimising price points based on changing market conditions and other useful insights from historical data.

Cognitive Analytics:

Cognitive analytics involves using analytics, machine learning, AI, and other technologies by logistics companies to process large volumes of data produced by supply chains and extract the most accurate answers. This helps in maintaining stock levels in real-time and managing production and inventories automatically, keeping the entire network running smoothly.

How to Conduct Supply Chain Analysis:

Effective analysis is essential for the smooth functioning of logistics and can include several steps, which are described below:

Scope and Objectives:

The logistics team initially sets up a list of the scope and objectives to be met for the smooth running of the supply chain.

Data Collection:

Both primary and secondary data are gathered by collaborating with various departments and from relevant sources, including suppliers, transportation partners, and internal systems such as the enterprise resource planning (ERP) system or a transportation management system (TMS), inventory records, transportation data, and supplier performance reports. IT teams can help extract information from each system, while inventory managers and procurement officers can provide inventory and supplier data.

Supply Chain Mapping:

Supply chain mapping (SCM) is the process of documenting the flow of materials, information, and money in the supply chain. This mapped representation of the supply chain operations helps identify patterns and trends and pinpoints areas where inefficiencies may exist.

Performance Metrics:

The five most common metrics used in supply chain management are perfect order rate, supply chain costs, order fill rate, cash-to-cash cycle time, and inventory turnover. Performance benchmarking, conducted by comparing the company’s supply chain performance with industry standards, helps companies stay ahead of their competition and ensures that their systems do not become obsolete.

Risk Assessment:

An assessment of the supply chain is conducted to identify potential risks using techniques such as ‘root cause analysis’ to address the core issues, leading to a smoother supply chain and improved customer satisfaction.

Cost Analysis:

An analysis of cost components is conducted, and areas where cost reduction is possible without sacrificing quality are identified. Strategies like inventory right-sizing and supplier consolidation are implemented.

Challenges of Supply Chain Analysis:

Supply Chain Analysis comes with its set of challenges that need to be regularly addressed, including:

Data Availability and Quality:

Access to large amounts of data with sufficient accuracy can be difficult due to its vastness and high maintenance, requiring modifications to aspects of supply chain analysis to produce meaningful results.

Multiple Sales Channels:

Companies deliver their products to sales channels such as online stores, mobile apps, and physical stores, which operate differently. To accommodate their specific requirements for smooth logistics, adjustments are made to the supply chain accordingly.

Organizational Change:

Supply chain analysis often leads to the implementation of new changes in operations, causing division among team members. Unfamiliarity with the new additions is a core issue, and training members on them boosts the supply chain’s functionality.