AI-ML power Supply chain Management: A story

Speedo is a supply-chain enablement company in a small town.

Suresh is the owner of this company.

He wants to use a technology solution to manage his inventory management + Supply Chain work through which all his customers get benefited

Their profit margin is also dependent on his end to end delivery mangemrnt capability

Inventory plays a key role in every retail and e-commerce company.

This aspect of running a trading business significantly influences cash flow, financial performance, and growth possibilities.

That’s why more and more retail and e-commerce companies decide to opt for inventory management using technology esp AI-ML.

For perishable products, an exceedingly high inventory turnover both in stores and in the supplying distribution centers is a must. This means that the supply chain is extremely sensitive to quality issues, delivery problems, or abrupt peaks in demand. In cases where store requirements outpace available inventory, rapid returns are of the vital for the business.

Our client Suresh knows this weak link for his businessline from his experience.?

Suresh wants to focus on this issue step by step.

The first focus area is Product tracking:

Most of the time is consumed in the end-to-end delivery chain in checking for the product. How can we optimize this quest? By employing technology.

Focusing mainly–on where the products from each category are and should be stored.

Every packet should be checked for compliance with the order, decreasing the order mix-up issue.

This step of the inventory management further rises customer satisfaction

Optimizing inventory management:

Suresh is soliciting support to deal with these questions, because these question drives significant operational challenges

• Where are the products stored and how?
• What’s our picking and packing strategy?
• How does shipping function in our business?
• How many employees are engaged in formulating orders?

Stock anticipating:

Strong supply chains are essential to maintain steady product availability

AI-ML can help us to predict requested stock levels that will match the number of orders. Historical data and predictive analytics enables us a lot. ML algorithms can interpret our past orders and assess future sales levels.

Predictive analytics can figure out when extra supplies will be required at specified times of the year.

When we order too many products that we can’t sell, they start accumulating up in our warehouse. If these products have a short expiration date or become swiftly obsolete, they become unsellable. They turn into dead stock. Stock predicting can help you avoid this complication.

Improving user satisfaction:

When our warehouse operates precisely, our supply chain is impressive, and there are no order mix-ups, our customers are assured.

They get their products on time, and they receive definitely what they requested. This seemingly evident aspect of running a retail company can undoubtedly reform the user experience

Improving customer satisfaction:

AI_ML can be adopted to cut down transport and warehousing costs by slashing inventory to a lean but healthy level, and can anticipate demand in the near future, allowing for stock to be acquired in time for sales. This enhances customer delivery times and soon or later enhances customer satisfaction.

How can we help Suresh to manage this Value stream?

Suresh has secured massive amounts of data on transactions and interactions with consumers, both on- and offline. By employing this massive data, he wants to promote decision making.

He was thinking about how to supplement outdated ways of working by using technologies like statistical analysis and rules-based heuristics

He wants to strengthen the Demand forecasting solution.

Suresh wants to invest money in Machine learning solution that empowers him to capture the impact of recurring sales patterns, their own internal business decisions, and external factors on demand for more accurate, granular, and automatic short- and long-term demand forecasts.

He has called for his team members contribution in

• Capture persisting demand patterns created by weekdays and seasons,
• Anticipate the impact of promotions, price changes, and other internal business decisions,
• Anticipate the impact of local footfall, events, weather, and other external factors, and
• Observe when unknown factors may be impacting demand.

What do you think we can do more to help Suresh?