Supply chains produce large volumes of data that businesses must transform into actionable insights to enhance chain performance. Supply chain management is crucial as developing and maintaining these chains improves the efficiency and optimization of the company, increasing profitability, and reducing costs. The same principles apply to data management which is crucial to address supply chain pain points at the strategic and operational levels.
The phrase “data is the new oil” is overused and cliché, yet it could not be closer to reality in today’s modern world. The question is how to make this plethora of data work for your company.
This article will discuss everything you need to know about Data Supply Chain. Particularly:
- Data Supply Chain Definition
- How Big Data Analytics is Revolutionizing Supply Chain
- Analytics in Supply Chain Examples
- Supply Chain Data Best Practices
Data Supply Chain Definition
A supply chain is widely defined as the procedures involved in manufacturing and delivering a good or service. This definition includes the businesses, individuals, resources, and data required to get a product into the hands of consumers. Supply chain management has made recent headlines with the bottleneck of consumer goods clogging ports and railways. Solving these issues has become increasingly important for the survival of businesses.
However, the data supply chain is more specific. The data supply chain comprises the technological steps and human processes that support the flow of data through an organization. This process goes from its raw state through transformation and integration to the point of consumption or supply chain data analysis.
How Big Data Analytics is Revolutionizing the Supply Chain
Organizations are challenged more than ever by the data streams that enter from multiple routes at an increasing rate. Being overloaded with data might make it difficult for a business to keep up with data inputs and gain insightful data. Enter the art of data analysis supply chain management.
Data analytics in the supply chain can provide visibility for data scientists and supply chain managers and deeper insights throughout the supply chain.
Analytics in Supply Chain Examples
You can use analytics in supply chains in a few different ways to better understand and optimize the supply chain of your organization. Here are just a few ways analytics can be applied to supply chains:
Simulated Scenario Analysis
Identifying probable business scenarios, then developing suitable tactics to manage a scenario is a key component of data analytics for the supply chain.
Prescriptive analytics modeling enables small businesses to obtain results comparable to those of large corporations, without the high cost of a multidisciplinary team. Optimization-based scenario planning can run through numerous simulations and find the best solutions to various what-if supply chain questions. It uses a type of stochastic modeling where the output of one set of decisions serves as the input of the next set.
First on the list of how to use analytics in the supply chain is Capacity Planning. You may match manufacturing and procurement capacity to sales demand through capacity planning.
Even though this might seem difficult, it can easily be accomplished through prescriptive analytics. You start by creating a mathematical model of your company’s utilization of resources through a rigid analytics platform. Using this method you can uncover the answers to questions like what capacity planning is optimal for your firm by analyzing your data utilizing robust solver software.
Another example of the use of supply chain management analytics is optimization.
The term “optimization” describes the application of prescriptive analytics to find the best answer to a business issue.
The answer is in the use of prescriptive analytics to optimize inventory. As is always the case when using prescriptive analytics, the first step is to model a business’ operations precisely. After validating the model, solver-based nonlinear optimization software identifies the ideal inventory strategy for handling complex and contradictory demands typical of omnichannel retailing. This validation ensures it accurately reflects current business realities and the effects of internal and external factors.
Supply Chain Data Best Practices
Companies can benefit greatly from data-driven supply chains by having a thorough picture of their supply chain performance. But, it requires careful execution. So, here are a few best practices for developing data-driven supply chains to help you optimize your supply chain.
Enhance the Use of Internal Data
Making the most of a firm’s data about its supply chain is the first step in utilizing data to enhance a supply chain and optimize operations.
When beginning to improve their supply chains, leaders generally start by looking for data from sources outside the firm. Most companies are unaware of the readily available data they already possess and fail to fully utilize this resource. Therefore, supply chain managers and leaders should inquire with other departments about the data they have connected to the supply chain and leverage this information to make improvements.
Integrate External Data
Supply chain leaders should first enhance their internal data utilization before bringing in external data. However, once they have optimized their internal data, supply chain leaders should integrate relevant external data. For example, customer purchase information may also assist them in improving their supply chain.
Since the pandemic, consumer behavior has become much more unpredictable, making it difficult to plan for demand and thereby rendering external data crucial.
Automate the Collection of Data
Automated processes can easily increase the ease and availability of data to supply chain leaders
Leaders in the supply chain and other stakeholders should identify all the information that is useful in guiding their decisions. Then, they can employ software to automate the processes of data collection, management, and analysis.
Supply chain managers can use a cloud data warehouse to store data from a variety of tools. The management and analytics teams can then determine the relevance, and importance of the data, and extract meaning to make optimized business decisions.
Big data is becoming a more important component of a cost-effective supply chain. Collecting and analyzing vast volumes of data is common practice to increase sales. Building quick and effective data supply chains is an efficient method to keep up with data demand as the world enters this new era of data and optimize data’s value and influence on business.
Satori provides easily integrated and agile data governance. Any sensitive data you have within your data supply chain can be easily protected and continuously discovered across a multitude of databases, warehouses, and stores, without writing additional code.