Business Analytics Empowers by Removing Supply Chain Bottlenecks

Supply chain execution with multiple employees performing specific roles is becoming increasingly more difficult because of growing customer expectations, global operating systems, and pricing pressures. Although business analytics has made its presence felt, there are loopholes evident in almost every process. A network of low-cost sensors, two-way controllers, and machine-to-machine networks can add significant competitive advantages across supply chains.

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Analytical tools make it possible to dive deeper into supply chain data for better savings and efficiency. Deeper insights from production, sales, and logistics data facilitate better business decisions relating to critical supply chain functions, such as forecast optimization, demand planning, inventory optimization, and deployment strategies.

For better understanding the role of business analytics in removing supply chain bottlenecks, first let’s understand the challenges of supply chain:

  • Lack of synchronization between planning and execution
  • Lack of real-time data visibility
  • Irregular reviews of safety stock levels, causing frequent stock-outs and excess inventory
  • Price volatility and difficulty in de-risking
  • Lack of visibility of global supply chain and logistics processes
  • Managing demand volatility
  • Cost fluctuations

How does analytics tackle the challenges of supply chain?

  1. Improvessupply chain visibility via smarter logistics

Globalization has given rise to a host of suppliers, manufacturing units, and distribution locations. Growing product variance to suit the needs of globally dispersed customers necessitates close monitoring of regional supply chains. The efficiency of a supply chain can be improved by enhancing basic metrics and reporting, as these two provide the necessary data for performance improvement and ensure visibility in supply chain activities.

Advanced analytics-driven ‘control metrics’ can monitor real-time critical events and key performance indicators (KPIs) through multiple touch points. Along with predictive analytics, these metrics can provide valuable savings in freight optimization of customers. Organizations can improve supply chain responsiveness, optimize cost, and minimize customer impact.

  1. Manages volatility through demand and inventory management

Demand and inventory management have become a big challenge due to demand volatility resulting from globalization and changing product preferences. Customer industries such as retail, consumer goods, and automotive, need daily or real-time forecast to meet expectations. Advanced analytics can reveal anticipated demand accurately, monitor supply requirements, and plan inventory flow better.

Planning the flow of goods and services is important for customer-oriented organizations in order to optimize service levels, augment investment decisions, and improve supplier performance and quality controls. Advanced analytics can also facilitate programing of contextual exception handling alerts and multi-criteria inventory ranking.Companies can also runsimulation on best-fit solutions. 

  1. Checks cost fluctuations with optimized sourcing and logistics activities 

How analytics-driven intelligence can control cost:

  • Materials: Analytical tools can provide total component cost of products, thereby paving the way for purchase on a cost basis. As a result of complete information, organizations can reduce the quantum of material purchases via improvements in supply chain practices and better price negotiations.
  • Logistics: Analytics-driven planning activities, such as route optimization, load planning, fleet sizing, and freight cost reconciliation, can yield strong ROI and put an effective check on expenses growing due to expansion.
  • Sourcing: Managing a multitude of suppliers across the globe becomes challenging as a result of business expansion into new, volatile markets with diversified product portfolios. Moreover, considerable costs and potential risk are involved in signing up anew supplier. Advanced analytics programs generate real-time supplier performance data that can be used to improve sourcing strategy. Analytics assessespotential supplier risk through a combination of financial analysis and capability constraints—therefore, a strong fact-based selection process ensuesrather than being solely based on cost.

Supply chain analytics therefore plays a key role in improving supply chain performance through greater visibility, lower volatility, and fewer cost fluctuations.

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