What is Supply Chain Analytics?
Supply Chain Analytics aims at enhancing the manufacturing operational efficiency and overall operational effectiveness by enabling informed data-driven decisions across the factory floor. With valid Supply Chain Analytics, manufacturers now have real-time accurate data to make strategic, operational, and tactical decisions. It also encompasses the complete supply value chain right from sourcing, manufacturing, distribution, and logistics.
Typical Supply Chain Challenges
Some of the most common supply chain challenges that most manufacturers face are:
- Limited synchronization between planning and execution.
- Lack of real-time data visibility across the factory floor along with multiple versions of truth and views of this data across all businesses and channels.
- Regular stock-out issues causing fluctuating inventory levels or excess inventory.
- Absence of flexibility in the network and distribution footprint, which makes it difficult to prioritize between the cost to serve and customer service levels.
- Price volatility and production line imbalance and suboptimal batch sizes, creating asset underutilization.
Value Delivered by Supply Chain Analytics
A recent study by Gartner revealed that 29% of organizations achieved high levels of ROI by using analytics, compared with only 4% that achieved no ROI.
Supply Chain Analytics can help manufacturers overcome the above challenges with a clearer, more connected, and holistic view of the entire supply chain, involving minimum effort and investment.
It provides great value to them by:
• Enabling better sourcing decisions based on supplier performance.
• Pointing out possible supply chain risk across the current and future production scale
• Reducing future disruptions by analyzing exact root causes of past events.
• Effectively discovering product development opportunities based on order data.
• Analyzing how product/ service design changes impact production costs.
Supply Chain Analytics- Opportunities
1. Sales, Inventory and Operations Planning-
Usually, sales and inventory planning is the most data-driven process in the supply chain as these require a wide range of inputs from Enterprise Resource Planning (ERP) and SCM planning tools. With Supply Chain Analytics, manufacturers have greater potential to redefine the planning process, by effectively using new internal and external data sources, thus making real-time demand and supply shaping a reality.
Most often data on procurement volumes and suppliers are only gathered for some specific activities in the sourcing process. However, using accurate supply data manufacturers can also influence the classic spend analysis and annual supplier performance review. Also, supply processes can be analyzed in real-time to identify deviations from normal delivery patterns.
Big data and analytics are the greatest tools for manufacturers to help streamline factory operations with minimal time and effort. For example, energy-intensive production runs can be scheduled to utilize fluctuating electricity prices. Also, data on manufacturing parameters, including assembly operations or dimensional differences between parts, can be also analyzed to arrive at the root-cause analysis of defects.
Most of the time the Logistics function is cost-centric and organizations focus on advanced technologies that provide a competitive advantage. Warehousing also has witnessed many advances using available ERP data. New technologies, data sources, and analytical techniques are creating new opportunities in warehousing. New 3D modeling technologies can also help to optimize warehouse design and simulate new configurations of existing warehouse space to further improve storage efficiency and picking productivity.
Several truck operators are already making use of Analytics to improve their operations. From using fuel consumption analytics to improve driving efficiency in deploying GPS technologies to reduce waiting times by allocating warehouse bays in real-time, there are several benefits. Courier companies also use real-time routing of deliveries to customers based on their truck’s geo-location and traffic data.
Key Features of Effective Supply Chain Analytics
The Analytics solution has to be able to access unstructured data from various sources across the supply chain stream without any bottlenecks in connectivity.
The Solution has to focus on improving collaboration with suppliers, partners, and vendors by leveraging cloud-based commerce networks to enable multi-enterprise collaboration and engagement.
The Solution must be smart enough to protect its systems from cyber-intrusions and hacks, which should be an enterprise-wide concern.
4. Cognitively enabled
By leveraging relevant AI platforms, supply chains are able to collate, co-ordinate, and enable decision making across the value chain. Most of the supply chain is automated and self-learning.
Analytics capabilities must be scaled with data in real-time. These insights need to be comprehensive and quick without creating any latency in the value stream.
Role of Artificial Intelligence in Supply Chain Analytics
Adding Artificial Intelligence (AI) to Supply Chain Analytics creates unprecedented opportunities for manufacturers through real-time tangible benefits. Recent research by McKinsey shows that 61% of organizations report decreased costs and 53% report increased revenues as a direct result of introducing AI into their supply chains.
AI helps create end-to-end supply chain visibility, provides actional analytical insights, reduces manual grunt work and facilities informed decisions for manufacturers. Here is an interesting read on how you can future-proof your Supply Chain with the Power of Artificial Intelligence.