Data Management in the Supply Chain

Overview

Given the enormous challenges that current supply chains face both on the demand as well as the supply front, it has become all the more essential to take steps and adopt suitable measures to enhance visibility and transparency across the chain to enable risk mitigation and to become more resilient in the medium to long term.

Companies across all sectors related to both ‘Discretionary’ as well as ‘Non-Discretionary’ spending have realized the need for efficient data management across the chain.

Data Management

Data management comprises aspects related to data creation, data extraction, data transformation, data cleaning, data manipulation and analysis, visual analytics and reporting, performance monitoring, data dissemination, master data management, application of data science and operations research tools, supply chain analytics, data security and data governance.

Let’s dig deeper into some of these elements in brief. I shall touch upon five key areas.

Data Management Elements

Data Creation

Transactional and operational data spanning the entire supply chain – both upstream and downstream could be created in various ways. Company systems (standalone and ERP) as well as external systems (syndicated data) could be potential sources. Data obtained could be used for decision making directly or would need to be extracted, transformed and then loaded onto other systems for further processing. 

In the case of supply chains, relevant data would include but not limited to supplier data, inventory data (RM, WIP and FG), customer data, pricing data, logistics service provider data, point of sale data, distribution data etc. 

All of the above data sources and types would be governed primarily by the data architecture of an organization. Moreover, the data utility and value are correlated to the 3Vs – Volume, Velocity and Veracity of data flows across the internal and extended supply chain networks.

Master Data Management

Although this aspect of data management and control looks fairly well established and well defined, the core and critical challenges emanate from this foundational layer of data management.

If a company’s master data structures are not well defined and managed, it could lead to erroneous analysis, business interpretations and outputs that could negatively impact strategic, tactical and operational decisions. 

For example – an SKU that has been incorrectly grouped and classified in the inventory master could lead to excess stocks or stock-outs due to consequent demand and supply planning errors in the case of an automated system. 

Therefore, the central control of master data elements in the ERP system could be the first step to ensure that the subsequent journey and transformation of the core data points result in desirable decision making.

Data Storage

Data could be stored in data warehouses or data lakes or even in the cloud. Clearly defined procedures and policies related to data storage need to be followed so that there is uniformity across the organization’s departments, teams and business functions.

Data access too needs to be clearly defined and mapped in order to prevent any misuse. Several other forms of data storage are available. The key point is to enable uniform rules across the organization. 

Data Security and Cyber Risk

The Covid-19 pandemic has accelerated the discussion and deployment of various risk mapping and resiliency tools and technologies. Needless to say that cyber risk and supply chain data theft are serious concerns that companies with a local and global supply and demand footprint continue to grapple with.

Given this scenario, potential threats related to data secrecy, security and theft need to be mapped and corrective and preventive action plans need to be documented and shared across the organization through collaboration and on-going training and development. 

This area is by far the most complicated and requires the combined expertise of various private, governmental and non-governmental bodies to devise practical and workable policies and procedures. These would need to be audited from time to time and would need refinement to align with global trends and developments.

Data security and cyber risk plans should form an integral part of Business Continuity Plans (BCP).

Data Analytics and Optimization

There is sufficient expertise and knowledge available in this domain. Moreover, academic papers, case studies and literature discuss and highlight the applications and benefits that would arise from the adoption, deployment and use of various digitalization and automations tools, techniques and technologies across the end to end supply chain. 

I shall not get into the details of the scope and applications of current technologies impacting the supply and demand side of the chain. However, unless the previous elements related to data management and architecture are addressed, the use of a sophisticated tool or technology would have limited operational and financial benefits to say the least. 

Conclusion 

Everything that I have touched upon or covered in this article is well known and well documented. I still felt it would be necessary and pertinent to provide a quick recap and reiteration of critical areas related to data management that need to be prioritized so that supply chains can function smoothly and risks could be mitigated.

Finally, ‘Data Governance’ structures and mechanisms form the ‘core and the nerve centre’ and would set the tone and direction for frameworks, policies, procedures and processes related to data management as a whole.

It’s an ongoing journey. There is no start and end date for continual refinement and learning.