• What are the types of data analytics applicable in supply chain?
Descriptive analytics – Visibility and a single source of truth across the supply chain, for both internal and external systems and data is provided.
Predictive analysis – Organizations can solve problems and collaborate for maximum business value. Helps businesses collaborate with logistic partners to reduce time and effort in mitigating disruptions.
Cognitive analysis – Helps an organization answer complex questions in natural language — in the way a person or team of people might respond to a question. It assists companies to think through a complex problem or issue.
Applying cognitive technologies – Supply chain analytics is a foundation for applying cognitive technologies, such as artificial intelligence (AI), to the supply chain process. Cognitive technologies understand reason, learn and interact like a human, but at enormous capacity and speed. This advanced form of supply chain analytics is ushering in a new era of supply chain optimization. It can automatically sift through large amounts of data to help an organization improve forecasting, identify inefficiencies, respond better to customer needs, drive innovation and pursue breakthrough ideas.
• Top 10 solutions to leverage in supply chain data analytics
- Intellestra by Voxware – Distribution operation managers are empowered by Intellestra by Voxware and executives to anticipate future supply chain requirements. It’s sophisticated algorithms aggregate and analyze data to present current and past activity, predict future events, and produce prescriptive analysis to inform decisions.
- KPMG third party intelligence – Complex challenges are addressed by intelligence engine KPMG spectrum which cannot be solved by people alone. Their Third Party Intelligence solution makes it possible for organizations to pinpoint vulnerability in the supply chain and take action before disruptions occur. Identify the threats and take corrective action in plenty of time with KPMG Spectrum Third Party Intelligence.
- PeopleSoft supply chain analytics – It gives organizations the real-time information needed to manage minute-to-minute operational performance. Track profitable products, investigate production problems, identify product quality issues, and track operational performance to keep costs low and improve customer satisfaction with PeopleSoft Supply Chain Analytics.
- Birst supply chain analytics – A global leader in business intelligence and analytics for the enterprise, supply chain analytics tool for managing each step of the supply chain from one view is offered by Birst. Collaborate across the supply chain and gain real-time visibility with Birst.
- Deloitte supply chain solutions – Known for helping clients and identify unrealized opportunities and illuminating new ways to adapt to change. Their suite of supply chain analytics solutions delivers smarter insight with analytics perspectives and solutions to help supply chain leaders put their data to work to solve challenges in commodity volatility, demand forecasting, and supplier-specific issues.
- Tableau – Tableau helps organizations see and understand data. Their solution delivers supply chain analytics in a visual environment that is accessible to everyone while supporting advanced modeling and forecasting.
- JDA Supply chain solutions – A leading provider of seamless supply chain planning and execution solutions also covers organizations from planning to delivery. Their end-to-end retail and supply chain planning and execution technologies is designed for tackling the challenges posed by today’s global, consumer-driven marketplace. Their analytics-driven insights help supply chains develop actionable plans for more profitable growth.
- Neubrain Supply Chain and logistics analytics software – They are the experts in budgeting, forecasting, and business analytics. Their supply chain and logistics analytics software delivers analytics for supply chain professions and helps them gain insights into customers’ demands.
- Halo supply chain analytics and business intelligence software – Halo BI offers analytics for supply chain planning and data discovery that is enterprise scaled, rapidly implemented, and data secured. With Halo, supply chain managers and executives can analyze, decide, and plan faster than ever before.
- TARGIT decision suite – A business intelligence and analytics company that helps users to quickly transform observations into actionable insights. Their Decision Suite takes supply chains to the next level with end-to-end visibility and tailored analytics for procurement and logistics managers.
• Role of business analyst in supply chain management
The business analyst is basically analyzing the business or logistics. He or she is liable for directing the whole life pattern of an item, including buy, appropriation, inner allocation, discharge, and last leeway of assets. He/She enables their businesses to upgrade execution by deciding and lessening wasteful aspects that may devour benefits. Streamlining tasks and distinguishing the most ideal approach to utilize the organization`s assets to accomplish its objectives is done by strategic investors. Business analysts and strategic experts as a rule work in an assortment of ventures inside the coordination division. Organizations of different types procure full-time coordination investigators to work in each industry, for the most part during daytime business hours. Analyzing the business is the primary job in an organization’s business offices, both freelance and group situations, and ultimately report to the coordinator. The industry also requires supply chain business analyst to visit multiple locations, businesses, or suppliers to coordinate operations for faster business movement.
• Steps involved in using data analytics for supply chain management
Inventory predictions – Opportunities need to be capitalized as soon as they present themselves. But predicting sales trends and inventory fluctuations requires rich data and intelligent predictive analytics.
Product quality and temperature control – Many industries, such as food, agriculture, pharmaceuticals, and chemical processing chains need close monitoring and control specific elements in the supply chain. Even a slight change of a few degrees in temperature can impact the quality of the product – or even make it completely unusable.
The solution is called cold chain monitoring technology, which supports temperature-sensitive product logistics through data logging. Managers can monitor temperature fluctuations in real-time and adjust cooling or heating systems accordingly during packaging, shipping, and delivery. Big data systems can also help to prevent potential disruptions based on variable data, such as weather changes or traffic delays. This creates a comprehensive control system for effective supply management from start to finish while reducing waste and preventing product issues.
Order fulfillment and real-time tracking – Order can be fulfilled and traced efficiently which is essential, both for business productivity and customer satisfaction. Amazon has changed the game by offering incredibly short delivery times along with alerts for estimated drop-off times and minute-by-minute tracking.
Big data can allow businesses in all industries to offer similar experiences for their customers and clients. Up-to-date shipping information can also help to cut costs with delivery fleet management by optimizing route deployment, delivery schedules, and item location. UPS utilizes supply chain data analysis through every step of their shipping process. Radars and sensors capture data as packages move through the supply chain. Big data systems then optimize the deliverer’s routes to ensure that packages arrive by the expected date. Overall, this has helped UPS to save 1.6 million gallons of gasoline in their trucks every year – cutting down delivery costs significantly.
Machine maintenance – Big data systems combined with IoT devices sends alerts of any issues or irregularities in machinery. Sensors can be used to track production, predict issues, and also notify when scheduled maintenance is needed to keep the machinery running in tip-top shape. This helps to reduce overall costs in two ways: first, it cuts down repair costs significantly and eliminates unscheduled downtimes. Secondly, predictive maintenance technology supports efficient production. All in all, a McKinsey study found that big data and predictive maintenance technology can cut machine downtime by 50% and even extend machine life by up to 40%.
• Conclusion – What does the future of supply chain look like with data analytics?
- Artificial intelligence and machine learning – Are the forefront of emerging solutions. The areas where these could be game changers are in-demand forecasting, production planning and predictive maintenance.
Machine learning takes historical shipment data and converts it into a forecast. These forecasts can gauge seasonal fluctuations in demand, and predictions are provided at a product, store, or facility level, for any time frame ranging from daily to monthly and even beyond. AI and machine learning benefit warehousing and transportation, as their capacity to improve order deliveries and service increases with automation. For instance, using AI, a company can determine optimal routes for fulfilling orders promptly.
Knowing future demands allows businesses to plan production and predictive maintenance to a far more reliable degree than was possible before. Lead times can be drastically shortened so that customers receive their deliveries quickly. Using predictive analytics, businesses can know in advance when and if a component in the system needs repairs and create alternate production schedules to compensate for it.
- Internet of Things – The more devices can talk to each other, the more communication can flow between them, the entire network can operate with efficiency. The use of advanced robotics combined with big data gives companies a comprehensive and accurate view of the real-time status of their supply chain networks, partners, and shipments. Since IoT connectivity brings in data, it allows processes across the entire supply chain to be highly optimised. Many companies already use sensors to keep track of their containers or shipments, which gives them the ability to quickly respond to issues on the ground, and forecast and even prevent problems from arising in the future.
- IoT allows continuous monitoringof critical equipment so that the state of every asset in the supply chain is known.
- It improves inventory practices, allowing managers to check inventory levels at any timeand prevent stockouts.
- It allows transparent marketingto the extent that brands can even let their customers know where their products are sourced from and how they are acting ethically and responsibly.
From optimising asset utilisation to improving overall supply chain performance, IoT can greatly increase the reliability of supply networks.
- Blockchain – A supply chain can only be considered robust when all the parties in the network are known and trusted. There might be too many players to keep track of, varying levels of accepted standards involved, and it might be simply impossible to maintain a central repository of the thousands—if not millions—of records generated with every transaction. The adoption of blockchain technology in supply chains can counter these challenges. All the entities in this network can know where any transaction originated from. This could entail anything from recording the source of raw materials to payments made to vendors to copyrights of assets. Since it’s not possible to erase a transaction within a blockchain, the only way a change can be made to it is by adding a new record, thus increasing the transparency of every transactionand ensuring the security of the entire operation. The shared ledger holds the same version of truth for everyone—all the players benefit from it, as the ledger is updated and validated instantaneously, as soon as a transaction occurs and is added. Blockchain can thus address issues in the areas of counterfeiting, enhance traceability, and provide more effective security.
- Advanced analytics – Advanced analytics gives companies the ability to work with processes in real time, or near real time. There is direct relevance in areas like dynamic pricing and replenishment. What in-depth analytics allows business leaders to do is extract data from existing conditions and imagine future scenarios, thus allowing them to design more profitable processesand create highly efficient supply chains. Due to the stringent quality standards fast becoming the norm in the functioning of supply chain networks, the ability to track products, optimise transportation, and even analyse returns on products and routes are important reasons for integrating advanced analytics into supply chain management platforms. The biggest advantage of using analytics is that it forms a cohesive link between planning and execution, making supply chains both agile and highly responsive. It allows managers to set standardised processes and benchmarks, utilise assets to their optimum levels, and eliminate waste. What this translates into for supply chain leaders is that locked capital is freed up, cash flow increases, and margins are improved.