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Supply Chain Analytics:

Why it matters:

Transform data into real-time, predictive insights Commodity volatility, changing demand forecasts, and supplier-specific challenges have affected nearly every organization—including those with the leading managed supply chains in the world. Even top supply chain performers have faced embarrassing stock-outs during periods of unanticipated demand in recent years. A big reason for this kind of underperformance is the fact that supply chain visibility and analytical models are typically grounded in hindsight. Making decisions based only on what happened in the past no longer provides competitive advantage.

Potential benefits:

Insights that make a difference

  • Use historical enterprise data to feed predictive models that support more informed decisions
  • Identify hidden inefficiencies to capture greater cost savings
  • Use risk modeling to conduct “pre-mortems” around significant investments and decisions
  • Link supply chain models to customer and pricing analytics to clarify the whole profitability picture, not just the parts and pieces.

What to do now:

a. Treasure hunts

Leaders in supply chain performance often use “treasure hunts” to mine data for hidden opportunities. But before you start down that path, you may need to do a bit of data silo busting. That means making sure the information required to drive analytics insights is accessible.

b. Make more connections

Focusing on any single link in the supply chain will not deliver the value you’re looking for. High performance requires connecting supply chain forecasting and modeling tools to distribution models, pricing models, and even tax strategies. Only then can you dive deep into specific improvement opportunities such as promotion planning, inventory management, and channel management. The more specific the better.

Areas of focus

Forward Logistics Operations Analytics

Inventory Management

Identify available inventory and keep a tab to plan for additional inventory if needed.

Delivery (On Time & Accurately)

Deliver goods on time to the customer with what was ordered.

Reverse Logistics Operations Analytics

Disposition cycle time

Cycle times can be an important measure of reverse logistics. The more standardized and streamlined the processes are, the shorter the cycle time should be.

Asset Recovery

What percentage of product that moves to the reverse statistics system is reclaimed and resold? How much value is recaptured?

Remanufacturing/ Refurbishment

This metric tracks the percentage of product in the reverse logistics stream that is remanufactured/ refurbished in an appropriate manner.

Salvage

How much product and other materials are moved to landfills, incinerated, or disposed of as waste? The objective is to minimize product in the waste streams.

Maximize Value for assets

Is the firm maximizing the profitability of product that did not sell well or has been returned by consumers?

Cost computation

A cost-per-touch type of metric can be readily computed by dividing total facility costs per month by the number of items processed. This is also a valuable way to compare the efficiencies of different facilities.

Distance traveled

Tracking average distance traveled per item is not nearly as simple as determining per-item-handling cost. Generally speaking, the fewer miles that can be put on an item in the reverse logistics network, the better.

Total Cost of Ownership

What is the total cost of ownership related to originally acquiring the product, reselling it, bringing it back as a return, and moving it through a secondary market or placing it in a landfill?

Product & Process Quality Analytics

Manufacturing Quality Analytics

To provide early visibility to quality trends and issues with KPIs, dashboard, alerts via desktop or mobile apps, including traceability of defects back to source - suppliers, plants, work centers, production runs, etc.

Comprehensive Data Integration

From diverse data sources – both structured and unstructured.

Best Practices

To turn analytics insights into action, avoid common mistakes, achieve faster adoption and improve time to value.

Supply chain analytics offers the capability to enable increased automation efficiencies and business intelligence for facilitating more proactive, effective decision making. Businesses can in turn leverage greater economies of scale while more effectively serving the unique and localized market needs of customers.

Analytics insights can help positively impact top and bottom-line business growth specifically through improvements across the whole supply chain:

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Design

Make better informed, more cost-effective design decisions with time/cost analytics and simulation.

Planning

Prevent overstocking and understocking, achieve higher sales and increase customer satisfaction using demand sensing and forecasting techniques.

Sourcing

Optimize procurement and reduce costs using sourcing analytics for commodity pricing, risk management, spend, supplier performance management and total cost of ownership.

Operations

Optimize operations, improve process quality and prevent breakdowns with predictive analytics. Streamline & Optimize network flows, reduce costs, maximize value and improve flexibility.

Service

Improve customer service and loyalty with IoT, machine learning and predictive analytics, detection of quality issues via warranty claims analysis, and service network and resource optimization.