Overcoming 5 Major Supply Chain Challenges with Big Data Analytics
Even if your organization is among the 83 percent who have yet to leverage big data analytics for supply chain management, you’re probably at least aware that mastering big data analytics will be a key enabler for supply chain and procurement executives in the years to come.
Big data enables you to quickly model massive volumes of structured and unstructured data from multiple sources. For supply chain management, this can help increase visibility and provide deeper insights into the entire supply chain. Leveraging big data, your supply chain organizations can improve your response to volatile demand or supply chain risk, for example, and reduce the concerns related to the issue at hand. It will also be crucial for you to evolve your role from transactional facilitator to trusted business advisor.
Leveraging master data management (MDM) at the scale of big data ensures that high quality and accurate data is driving your insights. MDM technology helps you explore the hidden relationships and gain insights that weren’t possible in the past.
Here are some examples how big data relationship management provides opportunities along the supply chain:
Spend Matters recently published 5 data-driven supply chain challenges for 2016. Prioritizing the development of a big data analytics strategy will help your organization overcome these supply chain challenges:
1. Better Predict Customer Needs and Wishes
Over 90 percent of dissatisfied customers will not do business with a brand that failed to meet their expectations (Source: customerthink.com). In the age of the customer, offering the right product, to the right person at the right time and place is key to gaining (or retaining) customer satisfaction and loyalty. Smart organizations will leverage big data to get a full 360-degree view of your customer to better predict customer needs, understand personal preferences, and create a unique brand experience.
2. Improve Supply Chain Efficiency
Cost efficiency, cost reduction, and spend analytics will continue as top business priorities in supply chain management. Embedding big data analytics in operations leads to a 2.6x improvement in supply chain efficiency of 10 percent or greater, according to Accenture.
3. Better Assess Supply Chain Risk
Sixty-one percent of companies regarded as leaders in supply chain management consider supply chain risk management very important. Those same leaders also recognize the need for capabilities that provide greater visibility and predictability across their supply chains (Source: Accenture). Big data can help assess the likelihood of a problem and its potential impact, and support techniques to identify supply chain risk. Combining the analysis of historical data, risk mapping, and scenario planning can enable a risk management approach for early warning.
4. Improve Supply Chain Traceability
Traceability is often directly linked to supply chain risk. For 30 percent of companies, traceability and environmental concerns continue as the biggest issues to watch for (Source: Ethical Corporation). Traceability and recalls are by nature data-intensive. Big data has the potential to provide improved traceability performance; it can also reduce the thousands of hours involved with accessing, integrating, and managing product databases that capture products that should be recalled or retrofitted.
5. Agility - Improve Reaction Time and Order-to-Cycle Delivery Times
Ninety percent of companies say that agility and speed are important or very important to their business (Source: SCM World). The ability to quickly and flexibly meet customer fulfillment objectives is rated the second most important driver of competitive advantage across all industries. Embedding big data analytics in operations can have an impact on organizations’ reaction time to supply chain issues (41 percent) and can lead to a 4.25x improvement in order-to-cycle delivery times, according to Accenture.
Sources: