What are the key factors to consider when choosing a data analytics platform for supply chain optimization?

In selecting a data analytics platform for supply chain optimization, consider scalability, seamless data integration, real-time processing, advanced analytics, intuitive visualization, predictive and prescriptive capabilities, supply chain-specific features, data security, ease of use, and cost-effectiveness. Prioritize platforms that accommodate diverse data sources, offer user-friendly interfaces, ensure data protection, and provide a favorable return on investment.

Analytical platforms often use their demo process to "wow" prospective customers with technical jargon and features. At Curtis&Co, we encourage our clients to take a step back and think about the purpose of the technology. Ground yourself in the question, "will investing in this technology enable me and my team to have the right materials, at the right time, in the right quantities". Then try to answer the question using criteria such as integration capabilities, scalability, reporting features, machine learning functionality, data security requirements, and support services.

When selecting a data analytics platform for supply chain optimisation, several key factors, should be considered first being data integration As in how easy is it to integrate the data coming from various systems like ERP, IOT devices, sensors, etc Second being scalability, the platform should have the ability to handle large volumes of data and scale as the business grows Third advanced analytics capabilities look for a platform that offers advanced and lat analytics, including predictive and prescriptive analytic identify pattern trends and optimise opportunity within supply chain Fourth real time visibility, the platform should have a dashboard which could provide you with the real time data updates around supply chain operations

Firstly, the platform must possess robust data integration capabilities, facilitating seamless amalgamation with diverse data sources like ERP systems, IoT devices, and external data feeds. This ensures comprehensive data collection across the supply chain landscape and next important part is Scalability and Data Security this is non-negotiable. Next vendor reputation and support are critical determinants. Opting for a reputable vendor with a proven track record in supply chain analytics ensures reliable support, expertise, and access to training resources.

The sheer volume of data generated by supply chains can be overwhelming. When selecting a data analytics platform, ensure it can handle your current data volume and scale as your business grows. The platform should be capable of processing and analyzing large datasets quickly, without compromising on the accuracy or reliability of the insights provided. This scalability is vital for adapting to market changes and expanding operations without the need for constant system upgrades or migrations. Additionally, consider the platform's flexibility in integrating with existing systems and its ability to accommodate future data sources and formats.

1. Choose a data analytics platform that seamlessly integrates with existing systems like ERP and CRM software. 2. Flexible integration capabilities reduce complexity and time required for implementation. 3. Smooth data flow across different areas of your business enables a unified view of the supply chain. 4. Integration ease enhances cohesion and informed decision-making in supply chain management. 5. Selecting a platform with seamless integration ensures efficient utilization of data analytics for business optimization.

One thing I have found is that the products can have different id's and categories from node to node, or company to company. It helps to have a platform that can filter and display information based upon how the prior leg, current leg, and future leg organizes it, to help facilitate any conversations along the chain. Also if you have multiple vendors it helps having the multiple hierarchies incase you need to manage quality overall for a product and for each provider.

A integra??o de dados de diferentes fontes é essencial para obter uma vis?o completa da cadeia de suprimentos. uma solu??o SAAS como o Microsoft Fabric oferece conectores integrados que permitem a conex?o com várias fontes de dados, como ERP, CRM, sistemas de gerenciamento de inventário e outros, facilitando a consolida??o dos dados em um único ambiente

Real-time analytics is a game-changer for businesses in today’s fast-paced world. By analyzing data as it is generated, organizations can gain valuable insights and make informed decisions in real-time. With real-time analytics, data visualizations reflect changes that occur within the business in real-time. This means that dashboards are current at any moment, enabling quick and smooth decision-making without delay.

An example of the aspect of real-time analysis in supply chain optimization relates to the management of inventory levels. A data analytics platform with real-time analysis capabilities can continuously monitor inventory levels across multiple locations. By instantly detecting low stock levels or unexpected surpluses, the platform enables immediate adjustments in procurement and distribution strategies. This ensures that optimal inventory levels are maintained, preventing stockouts and excess inventory costs. Moreover, real-time analysis allows for swift identification and response to fluctuations in demand, enabling proactive adjustments to production schedules and supply chain logistics in real time.

1. Real-time data analysis provides immediate insights for swift action in response to supply chain dynamics. 2. Essential for managing inventory levels, forecasting demand, and responding to disruptions effectively. 3. Enables operational agility and improves supply chain responsiveness. 4. Timely insights from real-time analytics enhance decision-making and performance optimization. 5. Platform offering real-time analysis is crucial for maintaining competitiveness and efficiency in supply chain management.

Supply chain optimisation is all about real-time analysis. As fast as you can analyse how your inventory levels are maintained, exactly which product is being picked up from which place, what's the forecasted demand for next month etc. These metrics help you to maintain your inventory and deliver products within the committed time. It will help you to reduce time , effort and the expense for the supply chain operation and effectively increase the profit. Some of the examples of amazing supply chain management are- Blinkit, Zepto ect. who are using real-time analysis to deliver the groceries at minimum time possible and maintaining the inventory at the same time.

I would put this aspect of Data Analytic tool as the top one for supply chain domain. In today's world of nezt day, same day delivery, every minute counts. User gets anxious if order gets late and they are not informed. Hence it is crucial that the tool delivers real time refresh of the reports in order to identify any issues

As vezes um plataforma em que a informa??o é clara e direta, em respostas importantes às tratativas cotidianas, é mais relevante do que as que apresentam análises e gráficos completos. Velocidade e eficácia s?o as palavras chave

Some times the user base for reporting matters as well. If you are building a report for 1,000 people of all different skill sets and experience then it is worth it to try and be simpler, and have some "what to do" indications for new or infrequent users. On the other hand a product that will be used daily by a few strategists you want to pack with information and not as much clutter.

Os varejo possuem necessidades e maturidade de opera??es mto diferentes. Com alguns, conseguimos analisar informa??es de planograma, elasticidade de pre?o, giro de estoque, on shelf availability, ... para outros, análises de sellout por categorias e cestas de produto respondem às principais dúvidas. Idealmente personalizar informa??es em uma base de dados rica temos solu??es de pedidos e reposi??o, S&OP, gest?o de estoques, com importantes kpi's e alertas em tempo hábil para mitigar rupturas

Look for a platform that offers advanced predictive analytics to forecast demand, optimize inventory levels, and minimize stockouts and excess inventory. Assess the platform's ability to utilize optimization algorithms for aspects like route planning, supplier selection, and risk mitigation.

When it comes to security, this is the most important feature that we need to integrate with our data. We need to encrypt the data and control the access to only authorised users. Because once your data is leaked , you may lose all your previous historical data which is very important to understand your past performance and improve upon it. If your data goes to the wrong hands it may create a huge problem as in today's time there are numerous unethical activities that can be done using personal data.

I frequently need to manage multiple levels of who has access to reporting. It is relatively easy to deal with when you need to set levels at management hierarchy. Sometimes it gets a little trickier when dealing with strategic levels or co-leadership when one side can't see hr issues. One thing that helps is to tie access to the job role with a 3rd party solution, so that anyone coming into a role get their access once they get the job role, they don't have to actively reach out for access on an individual basis.

When selecting a data analytics platform for supply chain optimization, prioritize seamless integration with existing systems, robust data management, and scalability. The platform should offer comprehensive descriptive, predictive, and prescriptive analytics capabilities. An intuitive user interface, customization options, and mobile compatibility enhance usability. Real-time data processing and streaming analytics are crucial for timely decision-making. Effective collaboration tools and seamless integration with other business systems are also important. Choose a platform backed by strong vendor support and an active user community. Consider both the total cost of ownership and the potential return on investment.

We need data security, flexibility of integration, data visualization, way to manage unstructured data, storage and predictive analysis. Another important factor is support structure post integration by the vendor company and it's expertise area. What kind of innovation the platform is continuously bringing in industry to be considered.

Data security is essential in our digital age, guarding sensitive information from breaches, ensuring privacy, building trust with clients, reducing financial risks for businesses, and guaranteeing the accuracy of information used for critical decisions.