Supply Chain Analytics

AI-Based Supply Chain Analytics

Supply chain management (SCM) is the process and activity of sourcing the raw materials or components an enterprise needs to create a product or service and deliver that product or service to customers. Supply Chain Analytics attempts to constantly measure, monitor and centrally control or link the production, shipment, and distribution of a product. With an effective Supply Chain Analytics, companies will be able to cut excess costs and deliver products to the consumer faster. The analytics specializes in keeping a tighter control on the visibility of suppliers, partners, internal inventories, internal production, distribution, sales, and the inventories of direct suppliers and vendors.

Critical Industrial Challenges

Are you looking to enhance your customer experience?
Whenever a customer invests their time and effort to go through a pool of competitive product options and place the order, customers expect that the right product, in its right quantity and size, reaches to the designated address at the fastest time on the right location. Customer experience is tarnished if there is any deviation from this whatsoever.
Are you struggling with high operating costs?
Minimizing the time product takes to reach the end client from the source(supplier) can significantly decrease the purchasing cost because fast-moving goods have minimal inventory costs.
Are you constantly facing on-time delivery issues?
Customers expect to receive the correct product and its quantity to be delivered on time. With just-in-time offerings, it is only becoming more and more challenging to rely on partners and stakeholders outside the organization to meet on-time delivery promises. The supply chain management can help in avoiding such bottlenecks and ensure customers get their products in the promised time frame
Do your Shareholders expect better Financial positions of your company?
Supply chain management decreases the use of large fixed assets such as plants, warehouses, and transportation, essentially diminishing costs. Businesses rely on effective supply chain management because it helps control and decreases supply chain expenditures.

Benefits of Supply Chain Analytics

01.
Reduced Inventory Cost & Higher Profit Margins
Any on-going business can increase profits without increasing sales. This is achieved by various optimizations in the way business operations are performed and optimized. For example, by implementing intelligent data-driven supply chain analytics, the business will get significant visibility of demand and supply thereby decreasing the inventory costs, cost of shipping, storing and retrieving them.
02.
Improve accuracy in planning
Supply chain analytics enables businesses to predict future demand by precisely analyzing historical transactional data and real-time data. This enables an organization to follow a competent decision-making system regarding inventory management and production by analyzing market trends and changing demands.
03.
Reduced Supply Chain Cost & Market Volatility
Supply chain analytics holds the key to mitigate several business complexities. It has the potential to enhance the end-to-end performance of the supply chain in terms of financial, operational, as well as managerial aspects. Additionally, supply chain analytics can act as a guide for companies to understand the impact of indirect supply chain cost and market volatility.
04.
Accurate Demand Forecasting & Higher Customer Satisfaction
Supply Chain Analytics Demand forecasting engine ensures that all your forecasts related to the demand from the customer along with the time, date and quantity are accurate. This means that your products, service, and employees can be allocated to address customer needs as soon as they arise. This results in a higher capability to handle customer's demands while achieving rich customer satisfaction and predictable business growth.

How Synctactic.AI Supports Supply Chain Analytics?

Supply Chain Analytics is helping to improve operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational and tactical levels. Synctactic.AI Machine learning models are reliable in dealing with large dynamic data sets related to supply chain analytics. Synctactic.AI Machine learning models is a cutting edge tech which gets seamlessly integrated into the warehouse automation and supply chain planning. Procurement operations also benefit from the adaptability of machine learning when attempting to deal with the constantly changing market of customer demand.

Machine Learning-based Supply Chain Analytics

Synctactic.AI is an advanced form of supply chain analytics that 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.

Prescriptive, Descriptive and Predictive Analytics based Supply Chain Analytics

Synctactic.AI Supply Chain Analytics helps companies to add value by gaining a clearer, more efficient picture of their entire demand, supply and logistics operation. Supply chain Analytics is significantly promising because it predicts the events related to upcoming demands by leveraging predictive analytics for forecasting future demand. At the same time, Supply chain analytics leverages the power of descriptive and prescriptive analytics by using their proprietary AI tools like Sync discover, Sync learn, Sync data, Sync analyze.

Model-Driven Algorithm

Synctactic.AI Supply Chain Analytics engine plays a critical role in Supply Chain Planning (SCP) to help with forecasting within inventory, demand, and supply. When applied correctly through the available data and model-driven SCM work tools, machine learning engines remarkably enhance the agility and optimization of supply chain decision-making.

Data-Driven Supply Chain Analytics

The Supply Chain Analytics factors the best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of numerous operational and transactional stored big data sets. Such a deep and data-driven capability would optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.

To immediately leverage this power of AI on your existing data and system please connect with one of the data scientist members of the Syntactic.AI team

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