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What is data governance, and how does it impact global supply chains?

In this modern era of liberalization and globalization, platform ecosystems have received significant attention as a critical business decision making concept. Sustainable business growth is achievable through the demand and supply of content amongst platform participants. The content helps in driving business in the right direction and facilitates the day-to-day information about progress and operations to stakeholders at different levels. To gain cost advantage and exponential market share, manufacturing and logistics organizations implement various initiatives such as outsourced manufacturing, hence involving more participants who would potentially touch the critical assets in the information management systems. These initiatives are useful in a stable environment, but they could make a supply chain a lot more vulnerable to various types of disruptions caused by data misuse or abuse, privacy issues, and revenue sharing. 

What is Data Governance?

Data governance is the process of managing the flow of data across various business units and or participants touching the digital platform. Governance focuses on data integrity and security based on standards defined by an organization. Inefficient data compliance and governance could often lead to inaccurate information being stored in sales, logistics, and customer service portals. This could further create discrepancies across business intelligence and reporting initiatives taken by an organization. 

The goal of governance is to break silos in information sharing across the organization. Enterprises often feel the urge of data governance in the following instances: when looking to expand their business in the competitive market and address legal and regulatory demands while ensuring standard operating procedures are not interrupted. 

Importance of Data Strategy and Governance in Supply Chain

A standard supply chain process revolves around the following key activities: design, planning, raw goods sourcing, production, logistics, financial planning, and reconciliation. As goods and services are continually evolving, large amounts of physical assets and data are exchanged and generated by entities operating outside an enterprise. Inefficient data governance could potentially lead to lost assets, inadequate outputs and business forecasts, and increased man-hours, hence imposing long term cost burdens on revenue.

Types of structured and unstructured data across the supply chain
Supply chain data volume and velocity versus variety. Image source: Forbes

Furthermore, as the supply chain grows, the possibility of sharing information with other unauthorized networks also increases exponentially. As a result, plans can leak to an irresponsible party, and, thus, competitors could easily imitate an innovation. Therefore organizations need to manage their data with a robust security infrastructure, which is critical to ensure that data are secured and privacy protected. Organizations should focus on defining a data strategy that enables internal and external participants to view data relevant to their tasks.

The majority of supply chain organizations fall in one of the following buckets while identifying their IT strategy.

Types of supply chain IT organizations
Image source: Suuchi Inc.

Effective data strategy will help decision-makers optimize their business decisions and ensure business processes are aligned. Data governance helps expand the scope of data beyond the traditional supply chain systems and provides an opportunity to better understand the user behavior of supply chain participants. Let’s take a deeper dive into each of the supply chain processes to understand how data can help attain operational advances.

Design

The use of data management tools facilitates the design release and distributes design data to multiple manufacturing sites, further allowing to manage changes digitally in a closed loop. An efficient data strategy during the design phase could reduce 15-20% in product cost. The ability to track configurations of the part and bill of material can help aggregate demand planning to allow organizations to be informed about what to order and, most importantly, when to order. 

Sourcing

Most brands today rely on historical purchase order data and annual supplier performance while analyzing the sourcing health. With a well-defined data strategy, sourcing activities can be tracked in real-time just by the supplier’s day-to-day contributions. Analyzing the number of rejections for a sample could directly impact a supplier’s quality ratings. Overlaying macroeconomic variables on sourcing activities could help firms mitigate the risk and better manage the demand. More than 30% of brands do not know how to react in these unprecedented times and reduce risk. Effective data strategy could help almost 20% of emerging brands to sustain and revamp their business. 

Production

Performance analysis is an integral step in the product lifecycle. The ability to track metrics that help improve the production schedule can help organizations attain 33% cost benefits. An example of a metric that business can track is comparing lead times against sales revenues. This metric could potentially help firms to plan better to meet the demands. Companies can also leverage production data to support sustainability commitments. They can measure the reduction of their carbon footprint by tracking the amount of water and energy consumption per production cycle. Additionally, analyzing the health of tools used for production can help manufacturing units to perform root cause analysis to reduce the number of defective units.

Warehousing and Distribution

Gone are the days when supply chains were agnostic, and companies made large investments in warehousing initiatives. With the boom of technology and the ability to analyze supply chain information, a lot of focus has been on enhancing the agility and imposing minimum costs on warehousing. Ability to get an overview of different inventory types — available inventory, usable inventory, and deadstock — help firms better plan the market needs and save inventory and warehousing costs. With appropriate data strategies in place, supply chain firms can potentially reduce their liability costs by over 20% over three years.

Conclusion

2020 has been a historic year for supply chains across hundreds of industries. Unforeseen disruptions have raised a question amongst professionals and key decision-makers worldwide, making them eager to implement digital strategies to optimize their operations. Limited interactions amongst supply chain participants have opened the door for the opportunity to adopt a data-driven operations model that facilitates push and pull of information. Business executives need to understand how data strategy can help achieve individual goals across each business unit. It’s high time to think about mitigating risks from the bullwhip effect. Digitization helps, but data strategy helps one analyze “what went right and what went wrong,” providing an opportunity to put the right foot forward towards achieving business goals. There are two important rules for a successful data strategy: Rule 1 – Set and review your data strategy, rule 2 – never forget rule 1. 

Learn more about the types of structured and unstructured data that flows through the supply chain and the importance of a system to collect and evaluate data sets accurately.

Written by Ketan Anand

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