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What are the most important data sources for supply chain analytics?

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Supply chain analytics is the process of applying data-driven insights to optimize the performance and efficiency of the supply chain. It can help you reduce costs, improve customer service, mitigate risks, and enhance innovation. But to achieve these benefits, you need to have access to the right data sources. In this article, you will learn about the most important data sources for supply chain analytics and how to use them effectively.

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Maxine Yang, MS/MBA

Microsoft Global Director Planning and Data Analytics | AI datacenters | MIT Masters Engineering and Sloan MBA |…
Pedro Paulo Silva

Gerente de Consumer e Market Insights | Marketing | Planejamento e Performance | UFMG | Harvard HBX | ISIMA
Ms Yoko Corbbygold

Experienced professional in Business Development, Sales, and Marketing, renowned for exceptional results through deep…

1 Internal data

Internal data refers to the information that you collect and manage within your own organization. It includes data on your inventory, sales, orders, shipments, returns, costs, quality, and performance. Internal data is essential for supply chain analytics because it helps you monitor and improve your own operations, identify and solve problems, and benchmark your performance against your goals. You can use tools such as enterprise resource planning (ERP), warehouse management systems (WMS), and transportation management systems (TMS) to capture and store internal data.

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Maxine Yang, MS/MBA

Microsoft Global Director Planning and Data Analytics | AI datacenters | MIT Masters Engineering and Sloan MBA | Technology | Operations | Leader of Global Operations Program
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The most important way to determine your critical internal data is to envision what performance you want for your business, and work backwards. I have seen a lot of companies have too much data but not connected to desired performance, and spend too much time on too many metrics. I have also seen companies that lack access to critical fundamental data that will drive performance. Both can be solved by first defining the desired performance and the metrics to meet this performance. This is best done as a cross functional team so that everyone is aligned.

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Ms Yoko Corbbygold

Experienced professional in Business Development, Sales, and Marketing, renowned for exceptional results through deep expertise, creative thinking, and innovative strategies. Dedicated to driving substantial growth
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Some of the most important data sources for supply chain include: 1. Enterprise Resource Planning (ERP) systems: These integrate various business processes, including supply chain management, providing real-time data on inventory levels, orders, shipments, and more. 2. Internet of Things (IoT) devices: IoT sensors can be embedded in products, equipment, and vehicles to track their location, condition, and performance throughout the supply chain. 3. Supplier databases: These contain information about suppliers, including their capabilities, performance, and compliance with regulations, helping companies make informed sourcing decisions.

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Ms Yoko Corbbygold

Experienced professional in Business Development, Sales, and Marketing, renowned for exceptional results through deep expertise, creative thinking, and innovative strategies. Dedicated to driving substantial growth
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4. Transportation management systems (TMS): TMS solutions track the movement of goods, optimize routes, and manage carriers, providing visibility into transportation activities. 5. Warehouse management systems (WMS): WMS solutions monitor inventory levels, manage storage locations, and track order fulfillment within warehouses, improving efficiency and accuracy. 6. Customer relationship management (CRM) systems: CRM platforms capture customer demand and preferences, enabling companies to forecast sales and adjust production and inventory levels accordingly.

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2 External data

External data refers to the information that you obtain from sources outside your organization. It includes data on your customers, suppliers, competitors, markets, trends, and regulations. External data is vital for supply chain analytics because it helps you understand and anticipate the needs and preferences of your customers, evaluate and collaborate with your suppliers, analyze and benchmark your competitors, and adapt to the changing business environment. You can use tools such as customer relationship management (CRM), supplier relationship management (SRM), market research, and social media to collect and analyze external data.

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Maxine Yang, MS/MBA

Microsoft Global Director Planning and Data Analytics | AI datacenters | MIT Masters Engineering and Sloan MBA | Technology | Operations | Leader of Global Operations Program
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External data on the supplier side is dependent on relationships. When there is alignment, your system is sharing the forecast with your supplier, who can then immediately respond. Or if there is a supply disruption, suppliers immediately let you know so you can partner on it. When there is a disconnect, this causes a lag and the entire supply chain data flow is disrupted. The other piece of external data is the demand. No one has a crystal ball, so standard practice is for the demand planning team to hold a consensus meeting with key stakeholders (finance, sales/marketing, etc.) to align on the demand, which is then cascaded through the organization.

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3 Structured data

Structured data refers to the information that is organized and formatted in a predefined way, such as tables, charts, and databases. Structured data is easy to process and analyze with standard methods and tools, such as spreadsheets, queries, and reports. Structured data is useful for supply chain analytics because it provides clear and consistent metrics and indicators that can help you measure and compare your performance, efficiency, and quality. You can use tools such as business intelligence (BI), data warehousing, and dashboards to access and visualize structured data.

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4 Unstructured data

Unstructured data refers to the information that is not organized or formatted in a predefined way, such as text, images, videos, and audio. Unstructured data is difficult to process and analyze with standard methods and tools, but it can offer rich and valuable insights that structured data cannot capture. Unstructured data is important for supply chain analytics because it can help you understand and leverage the emotions, opinions, feedback, and behavior of your customers, suppliers, partners, and employees. You can use tools such as natural language processing (NLP), text mining, sentiment analysis, and machine learning to extract and interpret unstructured data.

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5 Real-time data

Real-time data refers to the information that is generated and updated continuously and instantly, such as sensors, RFID, GPS, and IoT. Real-time data is dynamic and timely, but it can also be volatile and overwhelming. Real-time data is critical for supply chain analytics because it can help you track and optimize your supply chain activities, respond to unexpected events, and create competitive advantages. You can use tools such as streaming analytics, cloud computing, and artificial intelligence to process and act on real-time data.

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6 Big data

Big data refers to the information that is characterized by high volume, velocity, variety, veracity, and value. Big data is complex and challenging, but it can also be powerful and transformative. Big data is essential for supply chain analytics because it can help you discover and exploit new opportunities, generate and test hypotheses, and create and deliver value. You can use tools such as data lakes, data mining, and predictive analytics to manage and leverage big data.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Pedro Paulo Silva

Gerente de Consumer e Market Insights | Marketing | Planejamento e Performance | UFMG | Harvard HBX | ISIMA
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Para a análise da cadeia de suprimentos algumas fontes de dados s?o importantes: - Dados do ERP: Dados de cadastro para rastrear e gerenciar produtos. - Dados fabril e de logística interna e de fornecedores: Informa??es sobre custos de frete, capacidade de produ??o, lead time, qualidade, perdas, custo, etc. - Dados de Sell Out e Sell In: Dados de vendas de produtos ou servi?os podem fornecer insights sobre previs?o de demanda e demais padr?es (sazonal, promo??o). - Dados da Base de Clientes: NPS, padr?es de compra, etc. Esse s?o alguns dados que podem ajudar nessa análise e que podem realimentar o sistema para melhores decis?es.

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Maycon Lisboa

Analytics Solution | Precision Technology | Manager of Analytics Solution LATAM at Agriculture segment
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Priorizar a otimiza??o de processos é crucial para identificar as fontes de dados mais relevantes. Ao enfrentar desafios específicos, como longos lead times em cadeias de equipamentos agrícolas ou de constru??o em compara??o com a cadeia automotiva, é essencial focar em dados relacionados à previs?o de demanda, condi??es de manufatura, logística, inventário, armazenamento e ciclo de vida do produto. Diante da complexidade na gest?o de fornecedores, customiza??o de produtos e avan?o tecnológico, afirmar a importancia relativa de cada fonte torna-se desafiador. Prefiro avaliar a eficiência de cada integra??o dessas fontes de dados para otimizar processos, em vez de determinar qual é a mais importante de forma absoluta.

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What are the most important data sources for supply chain analytics?

1

2

3

4

5

6

7

1 Internal data

2 External data

3 Structured data

4 Unstructured data

5 Real-time data

6 Big data

7 Here’s what else to consider

Business Administration

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What are the most important data sources for supply chain analytics?

1

2

3

4

5

6

7

1 Internal data

2 External data

3 Structured data

4 Unstructured data

5 Real-time data

6 Big data

7 Here’s what else to consider

Business Administration

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