Leveraging Big Data to Save on Supply Chain & Other Expenses
There’s almost always another way to reduce costs in the supply chain. Big data is the big buzzword right now, and it’s key to mastering an in-depth 360-degree view of your suppliers and procurement chain to ensure you’re getting the most bang for your buck and leaving less money on the table. While harnessing big data isn’t the only tool for maximizing supply chain efficiency, it has huge implications.
According to SCM World, 64% of supply chain executives recognize that big data analytics are important technology offering positive disruption.
What is big data? Definitions vary, but generally big data refers to a collection of data from both digital and traditional sources. It may include data from both within the company and outside sources, and it provides rich ongoing opportunities for discovery and analysis. The analytics which depend on big data are often lumped in with the “big data” term. That is, user behavior analytics and predictive analytics which are extracted from big data, are often themselves called big data.
The volume of information comprising big data can be divided into two general categories. Unstructured data is not easily organized or interpreted by traditional data models or databases. Multi-structured data can typically be derived from human-machine interactions. Think essay question versus multiple choice–what’s easier to incorporate into a database?
Why is big data so valued and so studied in the supply chain environment? The rapid modeling of huge volumes of data from multiple sources offers incredible opportunities for visibility and insights. Potential disrupters like volatile demand, supply chain risk, quality issues or vendor stability can be more easily identified and understood.
While the “big” in big data is a prime attraction, there’s more than size to the picture. Data also has to be of good quality and successfully integrated.
What defines quality data? It needs to be unambiguous, consistent and useful. Database merges, system integrations, and cloud integration processes often result in poor quality data because data fields are not compatible. Data cleansing can transform poor data into quality data. To support the generation of quality data, data quality rules should be adopted and integrated into all transactions and coding.
Integrated data is also crucial. “Data silo” is the expression for isolated data, and it’s not what you want. For example, marketing has a database on conversions or click-throughs; operations has a database tracing material sourcing; sales has a database on what customers are buying; purchasing has a database on the vendors it relies on. When these data sources are not integrated, their utility is tremendously compromised. When they are properly integrated, they contribute to a 360-degree view of company dynamics which can be harnessed for effective analytics.
Once you’ve got your big data under control, here’s how you can make effective use of it:
Supply chain visibility (SVC)
SVC is essential for cost reduction and maximum ROI. Supply chain visibility is the tracking of components, products or parts in transit to be tracked from the source supplier to their final destination. Effective SCV improves the supply chain by ensuring data is readily available to all stakeholders, including the customer. Consistent, clean, integrated data is essential input for procurement, supply chain, operational and analytical applications, as well as for a 360 view of raw materials, services, products, and suppliers.
A big data-fueled application for supplier relationship management streamlines the uncovering of hidden costs and duplications by consolidating all relationships and spending across your business. When these are revealed in analytics, they’re powerful negotiation tools.
Streamline supplier lifecycle management.
Accelerate and simplify processes such as supplier onboarding by automating your supplier relationship management (SRM) and workflows with a supplier lifecycle management application which relies on big data. You can save expenses by automating the management of your suppliers’ lifecycles, and the analysis and assessment of their information and performance.
Automate product data exchange (PDX)
As you exchange product data with trading partners, automation saves time and money and increases accuracy and efficiency. This can apply to:
- Approved Manufacturer Lists (AML)
- Engineering Change Requests (ECR)
- Engineering Change Orders (ECO)
- [Bill of Materials] (BOM)
- Deviations (concessions)