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12/04/2023
15 mins read

Best Practices for Data Management in Supply Chain

Data management in the supply chain

Data management is the process of getting, storing, organizing, and keeping track of the information that an organization makes and collects. This is very important for the supply chain because it helps make sure that information is correct, safe, and accessible to everyone who needs it.

Data management can provide businesses with some exceptional results:

Improving accuracy: Data management improves the efficiency of the supply chain by making sure that information about the goods and services delivered is correct and up-to-date. It also contributes to expense reduction by identifying incorrect orders or missed shipments.

Increasing efficiency: Stakeholders can make quick decisions with precise and available information, and then the supply chain can function more efficiently. Managing your data well can save you time when you are processing orders, keeping track of inventory, and managing deliveries.

The push decision-making process: When supply chain managers look at data from past shipments, they can find opportunities and find trends. This can assist them in avoiding disruptions and stock-outs, optimizing inventory levels, and lowering expenses.

Best practices for accuracy and efficiency

We cannot deny that data management improves productivity in the supply chain. Companies can improve their supply chain operations and gain a market advantage by investing in accurate and efficient practices like:

Define goals and plans for data management and use: This allows businesses to store only pertinent data and limit information interference.

Use descriptive folder names to help businesses store information in a way that makes it easy to find when needed.

Data security should be a top priority to stop data theft and information leaks, which can hurt customers, businesses, and other related things.

Allowing access lets information be shared with stakeholders and makes some important business information more secure.

Data warehouse cleaning: This enables information to be filtered, updated, and removed unnecessary information. Businesses should have a periodic cleaning plan to save resources and use data effectively.

Centralize your data in one location

First of all, we should clarify the question of what centralization data is.

A centralized database gathers, stores, and updates information in one place, but it can be accessed from more than one place. When data is centralized, every team can find data that is accurate and consistent, which improves productivity, collaboration, and the ability to make decisions.

Centralizing the data in a cloud-based platform or data warehouse allows businesses some of the below benefits:

Time savings: With all data saved in a single central location, we can minimize the time and effort required to discover and get the information required.

Better data management: When data is stored consistently, the risk of mistakes and inconsistencies goes down, which makes it easier for companies to manage their data assets.

Better data quality: When companies centralize their data, they can make sure that the information is correct, consistent, and up to date.

Better data security: It's easier to manage and protect data in a single central location than it is to keep an eye on it in a number of different places.

Improved data analysis: By centralizing data in one location, we can identify future trends and make objective judgments based on the data.

What metrics should your business consider when choosing a platform?

One of the best ways to put all of your data in one place is to use cloud-based platforms or data warehouses. Businesses have some trouble using data effectively, perhaps because of the warehouse, which is irrelevant to their data.

That is why it is critical to comprehend the various types of data stores and select the appropriate ones. How well an organization can store, access, and analyze data will depend on the platform or data warehouse that is chosen.

However, choosing a data warehouse is not a simple task; businesses need to consider some factors:

Scalability: The platform or data warehouse should be scalable, which means it should be able to accommodate data expansion over time. The platform should be able to handle greater data quantities and complexity while maintaining performance as the organization grows.

Security: Data security is a constant problem for businesses. Strong security measures should be put in place to protect data from unauthorized access, cyberattacks, and data breaches.

Integration: The platform or data warehouse should be able to combine data from different systems, both inside and outside the company. This gives the business a single picture of its data and allows it to spot trends and insights across several sources.

How do some pioneers successfully centralize their supply chain data?

Many companies have been able to centralize their supply chain data to improve the visibility, control, and efficiency of their supply chains. We can name some companies that succeed in centralizing data in their supply chain:

Unilever is a global company that makes consumer goods. To improve efficiency and sustainability, it has put all of its supply chain data in one place. Unilever's centralized system tracks its environmental footprint, optimizes transportation routes, and cuts waste.

Nestle is a big food and drink company that has put all of its supply chain data in one place to make it easier to see and keep track of what is going on in its supply chain. Nestle's centralized system uses data analytics to track supplier performance and keep an eye on inventory levels and production schedules.

Walmart is one of the best stores in the world, and it has successfully centralized its supply chain data to improve inventory management, cut costs, and improve the customer experience. Using real-time data analytics, Walmart's centralized system keeps track of inventory levels, checks on how well suppliers are doing, and improves logistics operations.

Implement data governance policies

Data governance is a set of principles, standards, and practices in one place. They apply to the data lifecycle (collecting, storing, utilizing, protecting, archiving, and deleting). Data management carries out all of these tasks in order to combine the data and use it for decision-making.

Data governance and data management have a close relationship because data governance ensures:

Accuracy of data: Data governance makes sure that data is correct, complete, and consistent, which improves the data's overall quality.

Consistency: The data governance principles tell us how to handle data based on rules and a certain format. This keeps system management from being random and inconsistent.

Compliance: Setting clear standards and procedures for data management can help streamline operations and reduce the time and effort needed to manage data.

With a planned approach to data management, a company can reduce the chance of mistakes, inconsistencies, and doing the same work twice.

Set clear data quality standards and guidelines

Businesses should first set clear standards and guidelines for data quality. This will help them manage data in a way that is consistent and works well. When setting the standard for data quality, we need to think about data definitions, formats, and validation rules, among other things.

Data definitions and formats will be the standard for the quality of your data, showing everyone in the organization what they should try to achieve. This involves determining how data should be collected and stored, such as the date and time format and whether or not phone numbers have spaces.

And validation rules help businesses figure out if the value attributes they need to meet a specific need are available. They will fix the bad data by setting up validation procedures to make sure everything is correct and save time.

Best practices of data governance policies and processes implemented in the supply chain

Policies and processes for data governance are needed to make sure that data across the supply chain is correct, consistent, and reliable. We cannot overstate the significance of the following data governance policies and procedures:

Data stewardship

It means making sure that data is correct, up-to-date, safe, and used in the right way to meet the needs of the supply chain. This is done throughout the whole process of the supply chain.

Data stewardship is a way for businesses to make sure their data is secure and of good quality. It also lets them look at their data to predict trends, measure performance, and make decisions. Suppliers, customers, and logistics providers, among others, need to work together and coordinate for data stewardship to work. This makes the supply chain more visible, cuts costs, and makes it more efficient.

Data lineage

It allows organizations to trace data back to its source and understand how it has been transformed or manipulated over time. Data lineage can help supply chain managers improve the way data is managed, tracked, and shared throughout the supply chain.

Data cataloging

Data cataloging is useful for supply chain managers because it lets them organize, categorize, and manage their data assets to make them easier to find, use, and use efficiently. A data catalog makes it easy for supply chain managers to find the data they need for their jobs without having to spend time looking for it.

They can also understand how data is used throughout the supply chain to avoid inefficiencies and potential dangers. Furthermore, supply chain managers may make quick decisions based on accurate and up-to-date data.

Data analytics tools in supply chains

Using data analytics tools in the supply chain brings supply chain managers a lot of benefits. We can mention some of them:

Identifying trends: By analyzing the collected data through tools, supply chain managers can track data in real time. They can track inventory levels, evaluate which item is the highest seller and the trend of selling goods, and then focus on which item has the greatest number of sales.

Forecasting the demand: Supply chain managers can use data analytics tools to make more accurate predictions about demand and supply when they know the lineage of the data. This can assist them in making more informed decisions regarding the time to order supplies, how much inventory to have on hand, and how much to produce.

Optimizing inventory levels: Using data analytics helps supply chain managers optimize inventory levels. Because of statistics and periodic reports, supply chain managers can easily manage goods in detail, making plans for inventory.

In addition, they can set inventory standards to avoid the case where the business has too much inventory or is out of stock, and then address solutions to solve them wisely.

There are numerous data analytics tools available, which businesses will utilize depending on their goals and the type of data they are dealing with. Here are some common data analytics tool examples:

Predictive analytics tools are software programs that use statistical algorithms, machine learning techniques, and data mining to look at past data and predict what will happen in the future. Some examples of predictive analytics tools include IBM SPSS, SAS, and Google Cloud Machine Learning Engine

Machine learning: Similar to predictive analytics tools, machine learning also uses algorithms and statistical models to forecast trends or patterns that can help businesses make data-driven decisions. Some examples of machine learning are TensorFlow, Microsoft Azure Machine, and LearningAmazon SageMaker.

Visualization tools are programs that turn text into tables with creative, clear charts and dashboards that can be as simple or as complex as you want. This makes it easier to understand and analyze. Some of them we can list are Tableau, Microsoft Excel, and Google Data Studio.

Data integration and data preparation in using data analytics tools effectively

Before we can explain how important data integration and data preparation are when using data analytics tools, we need to be clear on two things.

Data integration is the act of combining data from different sources into a centralized view that can be analyzed together. This can mean putting together information from different databases or file formats.

When data isn't properly integrated, it can be duplicated, inconsistent, or missing. This can lead to bad results and wrong conclusions.

On the other hand, data preparation includes tasks like removing duplicates, filling in missing information, and converting data into a format that can be used with the analytics tools being used. Properly preparing the data can help make sure that it is correct, consistent, and complete, which is what is needed to get reliable and useful insights.

Businesses can improve the quality of their data and make their data analytics tools work better by integrating and preparing their data in a transparent and careful way. This can drive better decision-making and improve operational efficiency and competitiveness in the marketplace.

Ensure data accuracy in the supply chain

One of the main goals of managing data in the supply chain is to make sure that the data is always correct. In reality, because supply chain organizations rely heavily on data, data accuracy impacts the overall operation of the supply chain.

The following are some of the reasons why data accuracy is critical in the supply chain:

Avoid supply chain disruption: Accurate data helps supply chain managers know clearly about inventory, sales trends, and statistics reports so that they can make plans and strategies to produce wisely. But if data is inaccurate, decision-making becomes difficult, leading to poor planning and execution.

Increased customer satisfaction: When supply chain managers have accurate data, they can make sure that products are delivered on time and in the right amount, which can make customers happier. On the other hand, wrong or missing data can lead to wrong or late orders and shipments, which will make customers less happy.

Prevent financial losses: Incorrect information can cause costs to go up because of overstocking, understocking, and the need for faster shipping. Managers of the supply chain can make better decisions about inventory levels, storage costs, and the need for fast shipping when they have accurate data.

Tips for ensuring data accuracies

Since the supply chain supervisor knows how important accurate data is, he or she should write down some tips for making sure data is correct and avoiding situations like those mentioned above.

Here are some tips for ensuring data accuracy:

Auditing data on a regular basis: This way, we can make sure that data is always up-to-date and filled out according to format and rules. We can also check the source of the data to see if it is trustworthy and reliable.

Error report processes: Establishing the error report system allows supply chain staff to avoid basic issues, including spelling mistakes, typos, and incorrect values when inputting data, and to require the solution to be found as soon as possible.

Profiling data: Make a list of profile data so that supply chain managers can learn more about the data and find any potential problems in it. This helps them greatly with data preparation when analyzing.

How can data cleansing and enrichment improve data accuracy?

Data cleansing and enrichment are two important steps that can make your data much more accurate.

Data cleansing is a way to find and fix errors, inconsistencies, and mistakes in data. This may involve removing duplicates, standardizing data formats, and correcting misspellings. By cleaning your data, you can get rid of any mistakes that could make your analyses less accurate.

Data enrichment, on the other hand, refers to enhancing your data by adding new information to it. By doing this, you can learn more about your target clients or market, which lets you come to clearer conclusions and improves your overall accuracy.

Invest in data security

Data security in the supply chain

Along with data accuracy, data security is also a key factor that chain managers always keep an eye on in the data management process. Since the supply chain involves sensitive information between stakeholders, such as manufacturers, suppliers, distributors, and retailers.

If this information gets out, it can have serious consequences for everyone involved. We can anticipate some bad results when leaking sensitive information:

Cyber attacks: Sensitive information can be customer information or financial information. If leaked to the outside, it can affect the company's reputation and cause a loss of trust among customers, in addition to losing competitiveness in the market. So, making sure information is correct is important because it helps build the company's reputation and customers' trust.

Data breaches: There are strict rules in many industries, like healthcare and banking, that require businesses to keep sensitive information safe. Companies that don't follow these standards could be fined or sued, and their operations could be slowed down or stopped.

Insider threats: An insider threat is anyone who has access to confidential information and could either on purpose or by accident give it to a third party. Insider risks can come from a variety of sources, including data access.

For example, employees, vendors, and suppliers can get their hands on sensitive information like sales data, customer information, and financial reports. They may disclose this information to third parties for personal reasons. This is seen as a problem with not giving everyone enough access rights guarantees.

Another example, which is similar to social engineering attacks, is that attackers can pretend to be IT support staff and ask employees for their login information.

Tips for securing your supply chain data

As was already said, supply chain managers should make sure their data is always safe so that information leaks don't cause unexpected results.

Here are some tips for them to take action:

Data Encryption: Supply chain managers should use strong encryption algorithms and secure key management techniques to encrypt all sensitive data, both in transit and at rest.

Access Controls: Implement and provide permission to have access to the appropriate people who require information. Passwords, multi-factor authentication, and smart cards are used to authenticate user identities.

Security Monitoring: Set up security monitoring systems on a regular basis to detect and respond to security risks in real-time. Monitor all data access and usage activities and notify security personnel of any strange activity so that the appropriate response can be implemented.

How can data privacy regulations impact data security in the supply chain?

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are known as data privacy regulations used to protect the privacy of individuals' personal information.

These regulations impose strict rules and requirements on companies that handle personal data, including those in the supply chain. Failure to comply with these regulations can result in significant fines and reputational damage.

Data privacy policies have a substantial impact on data security in the supply chain. Here are some examples of how these policies may affect data security:

Increased requirements for data security: Companies are required by privacy laws to put in place security measures like data encryption, access controls, and regular security audits to protect personal data.

Vendor management requirements: Companies must ensure that their vendors and suppliers follow data privacy standards in order to avoid private purposes for information leakage.

Subject rights to data: Individuals have the right to access, alter, and delete their personal data under data privacy laws. Supply chain companies must have policies in place to respond to these requests.

Increased chance of data breaches: Companies are required by privacy laws to tell authorities and people whose information has been compromised when a data breach happens. Failure to report a data breach can result in large fines and reputational harm.

Conclusion

It can be said that the field of supply chain management is large and complex, so data management is no longer a small problem but an urgent and important issue in management operations. physical.

This article has given you useful information about how important data management is and what tools and methods can help you manage your data in the best way possible.

As a software manufacturer, MLTech Soft, we understand the importance of data management and always make it the number one priority in our work. So, when we work with partners, not just companies in the supply chain, we always follow the laws and rules of information security.

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