What are the current trends and challenges in data analytics for supply management?

From my perspective, the concept of data integration in supply chain management is not just a trend, but a pivotal transformation. The ability to seamlessly merge diverse data sets enables organizations to make more informed decisions, optimizing their operations and responding agilely to market changes. Companies that excel in data integration often outpace their competitors by understanding customer needs and market dynamics more deeply. However, it's crucial to navigate the complexities of data compatibility, security, and analysis with strategic precision, ensuring that the integrated data serves as a robust foundation for decision-making.

Integrating data from various sources and systems can provide a comprehensive view of the supply chain, allowing for optimization of operations, identification of areas for improvement, and mitigation of risks. Companies must invest in data tools that can integrate data from multiple sources and ensure data quality, consistency, and security. They should also establish clear policies and procedures for data governance to ensure that data is managed effectively and securely throughout its lifecycle. Data integration is a crucial trend in data analytics for supply management. Organizations that adopt it can gain a competitive advantage by making data-driven decisions that optimize supply chain operations and improve customer satisfaction.

Uma das principais tendências na gest?o de suprimentos é a integra??o de dados de várias fontes para criar uma vis?o holística da cadeia de suprimentos. Utilizar tecnologias como IoT, blockchain e sistemas ERP para consolidar dados de fornecedores, produ??o, logística e vendas. A integra??o de dados permite uma análise mais precisa e abrangente, facilitando a identifica??o de ineficiências e oportunidades de melhoria.

Linkedin asked me to provide a perspective since apparently, according to AI, I'm an expert even though I'm not interested in the topic or know anything about it. Anyways, my general perspective is keep doing the things that are working and stop doing the things that aren't working. Your welcome.

For business intelligence, you have some options here - 1. Pay your ERP vendor for their BI tool (RIP wallet) 2. See if you can build custom reports out of the ERP system and then ingest those through a data pipeline 3. Assuming API exists, work with the vendor and some technical resources to navigate the ERP data model. Ideally your business stakeholder would also attend these to make sense of what sort of sample data these tables have. (2) could involve an API, or maybe even a scheduled job to dump to an S3 bucket. Depends on your data freshness needs!

In my view, data visualization stands as a critical element in modern supply management, transcending traditional reporting methods.By converting complex data into intuitive and interactive visual formats, organizations gain a powerful tool for quick and effective decision-making. From my experience, the use of dashboards and infographics not only streamlines the communication of key metrics but also fosters a data-driven culture within the organization. However, it's vital to approach data visualization with a keen eye for design and accuracy, ensuring that these visual representations are both aesthetically appealing and, more importantly, accurately reflective of the underlying data.

Data visualization is a critical trend in supply management data analytics. It aids in the transformation of complicated data into clear and actionable insights, making it easier to comprehend and discuss. Dashboards, charts, maps, and graphs can be created by supply chain managers to highlight critical performance indicators, metrics, benchmarks, or scenarios. It does, however, necessitate selecting the appropriate kind, form, and layout of the visualization, as well as assuring data quality, relevance, and clarity. Overall, data visualization has the potential to improve supply chain efficiency, save costs, and increase customer happiness.

Before visualizing anything, work on modifying raw supply chain data from bronze -> gold. If you have a plethora of NULL values, you probably shouldn't be working on data visualization just yet. There could be business processes that are inherently broken causing data quality issues. Assuming your data quality is in check, the visualization piece comes naturally - what are the biggest pain points we're solving for - mock those up as a wireframe or proof of concept (LucidChart, Excel), pitch to your business stakeholder, and then let them drive. Keep doing this over and over (depending on urgency) until you reach a set of reqs for a minimum viable product (MVP)! Build that into your BI tool of choice (Tableau, PowerBI, Quicksight)

From my experience, embracing data-driven decision making in supply management transforms not just the operational strategies, but the entire organizational ethos. I believe that when you base your decisions on concrete data, it shifts the focus from speculative to strategic thinking. This approach can revolutionize how you manage inventory, negotiate with suppliers, and meet customer demands. However, the key challenge lies in integrating a data-centric mindset throughout the organization, which involves training, cultural change, and occasionally, overcoming resistance to new methods of decision-making.

Data-driven decision-making, adds a level of objectivity to any end-to-end process. In the case of Supply Chain, practitioners & managers can use data to make strategic and operational decisions. This approach improves efficiency, effectiveness, and market competitiveness by optimizing sourcing and procurement strategies, managing inventory and logistics costs, improving supplier relationships and collaboration, and responding to customer needs. But this approach requires everyone in the organization to have a data-driven culture and mindset.

A tomada de decis?o baseada em dados está se tornando a norma na gest?o de suprimentos. Utilizar análise preditiva e prescritiva para prever demandas, otimizar inventários e planejar a produ??o. Implementar algoritmos de machine learning para analisar grandes volumes de dados e fornecer insights acionáveis. Essa abordagem data-driven melhora a precis?o das decis?es e aumenta a eficiência operacional.

I believe that in today's data-centric business environment, data literacy is not just an asset but a necessity for supply chain management. From my perspective, the ability to comprehend and manipulate data effectively is as crucial as financial acumen was in the past. In my experience, teams that possess strong data literacy skills are more adept at identifying trends, forecasting needs, and making proactive decisions. To overcome the barriers in acquiring these skills, it is imperative to invest in comprehensive training programs and collaborate with data experts. This not only bridges the knowledge gap but also fosters a culture of continuous learning and adaptation.

In my experience, one of the main challenges in data analytics, across all industries, is data literacy. Understanding the source, format, intent and structure of data sets is critical at every stage of a data analytics project. This impacts everything from the selection of tools, to data points to capture. With AI and generative AI, we are quickly moving in a direction where this will be less of a challenge, allowing a wider range of technical and non-technical people to interact with data.

Data quality is the biggest challenge with the highest impact that I have seen - it is a struggle for many businesses, and becomes the hurdle for startups to get their innovative software in to businesses and add value. It is an unsexy part of data analytics, often assigned to lower level teams and not given enough resources and priority, with change management aspects constantly underestimated. Companies that give this the attention it deserves can reap the benefits of fast adoption of new processes and technology - it is worth the focus, but it is far from easy.

Cada vez mais temos acesso a informa??es vindas de diferentes fontes e sistemas, mais recente temos a utiliza??o de IA para otimizar essas informa??es, porém sem que tenhamos confian?a e certeza na qualidade dessas informa??es prejudicamos as tomadas de decis?es. é necessário avaliar toda a infraestrutura da organiza??o e a capacidade de absorver novos sistemas e ferramentas de análises de dados, garantindo 100% de compatibilidade. Um trabalho feito a "quatro m?os", que n?o depende apenas da área de suprimentos.

Ensuring data quality in supply management is akin to laying a strong foundation for a building. It's essential for the stability and reliability of any data-driven strategy. I believe that without high-quality data, decision-making becomes a hazardous guesswork, potentially leading to costly mistakes. In my experience, the implementation of stringent data quality standards and practices is not just a technical necessity but a strategic imperative. It involves meticulous attention to detail - from defining data requirements to regular auditing and cleansing. This rigorous approach not only enhances the accuracy of analyses but also reinforces the confidence of decision-makers in the data they rely upon.

Data integrity , reliability and quality are the three biggest challenges along with biases that can impact the desired output . Data collection and cleaning processes needs to be the major focus before diving deep into data to get meaningful insight .

I see the biggest challenge being analysts’ unfamiliarity within the industry they’re working. Even more so when no one on the team has experience with the nuances of the data. It’s so evident in healthcare data when analysts come from other industries and treat all data the same.

Challenge 4: lack of Data vision / Data instrumentation before making any business plan/feature launch . Many a times I have noticed that there is no data to track the feature. This can be due to variety of reasons the primary one's is the failure of the Product/Program Manager/Business Owner to involve analytics during planning stage which leads to incomplete/incorrect Impact evaluation and incomplete data instrumentation. Involving Analytics right from the planning stage can helps in outlining the best strategy for tracking and impact evaluation of the program and also helps to raise alarms / issues in development phase where data instrumentation errors can be fixed saving a lot of time and resources for the business

A seguran?a dos dados é uma preocupa??o crescente, especialmente com o aumento do uso de tecnologias digitais e a integra??o de dados de várias fontes. Proteger os dados contra acessos n?o autorizados, viola??es e ataques cibernéticos é crucial. Implementar medidas de seguran?a robustas, como criptografia, controle de acesso e monitoramento contínuo, é essencial para proteger as informa??es sensíveis da cadeia de suprimentos.