Data science-driven smart inventory management system for manufacturers

About Webinar

Inventory management is not a new activity in the business world; however it has got tougher than ever before in the age of big data. In fact, we are on the verge of a major transformation in the way inventory is managed. With the availability of the huge amounts of data that is generated by smart products and connect systems, manufacturers are facing many new challenges that cannot be easily solved by paper invoices and even spreadsheet documents.

Data science can make a huge difference in inventory management system – more than you may think. Advanced machine learning techniques and optimization algorithms can help you make the most of available data and empower your people to respond to product demand faster.

Webinar agenda

• Traditional inventory management in manufacturing
• Limitations of traditional inventory management practices
• Problems manufacturers face with traditional inventory management
• What is Smart Inventory Management and how it overcomes the challenges that manufacturers face
• Real-life examples and use cases
• Core elements of this paradigm shift - Data Science and Machine Learning
• How to get started
• Q&A

Questions & Answers

The following are the answers to the questions that were asked during the live webinar.

Question 1: We use drop shippers for fulfillment. Can any forecasting be made in this case?

Answer 1: Yes definitely. As long as those drop shippers provide data access with information about finished goods once they are out of your vicinity. They may provide some data connectors like APIs and we can feed that data back to our forecasting models.

Question 2: How long will your POV program take to implement in a mid-sized manufacturing company?

Answer 2: Well, the typical PoV program falls between 3-6 weeks. However, this timeline would be defined once we gather preliminary details and define business problems. To know more about our POV program, please click here.

Question 3: Isn't RFID the gold standard when it comes to inventory management? Why do you think it belongs in the traditional/old fashioned way of doing inventory management?

Answer 3: Yes, RFID systems are definitely a great way to control and track goods or items. RFID also helps manufacturers to track their items throughout the supply chain from production to point-of-sale. Even though what Data Science and ML technologies have to offer would not take away the importance of RFID, these technologies moreover give you greater control and visibility into your futuristic needs. The smart inventory system will help you make smarter decisions.

Question 4: 100% of our inventory is our customers. We are a workshop that packs our customer’ products and stuff is moving in and out each day. Anyway, is it easy to keep track of this? We are constantly asked for inventory counts. We don’t have the manpower to keep track of each customer’s products.

Answer 4: I think this is very similar to the case study that we described in our webinar. When you have moving inventories at such a pace, you have to identify the avenues of real-time inventory updates back in your database. You should then have a backend interface that shows insightful reports and information that you can update back to your customers even if they have complex queries involved.

Question 5: Real-time inventory tracking involves specialized hardware. Which devices and software will your company supply?

Answer 5: Based on your needs, we can consult our hardware partner and identify the best fit. This may involve RFID readers, scanners, sensors, etc. When it comes to software, we are an end to end service provider starting from backend admin, APIs, database, to mobile apps and can help you host them on-premises or in the cloud.

Question 6: We are a small business and having a mid-size warehouse. Do we need to have a specialized technical person to manage this smart inventory?

Answer 6: No, not necessarily. It’s because as far as a Data Science solution is concerned, our experts will deploy it plus the models that we use are self-trained to evolve. So, you ideally don't have to hire a resource to maintain the solution.

Question 7: We have recently invested in the industry leading inventory management tool, which has capabilities of forecasting. How can you add value on top of it?

Answer 7: Well, those out-of-the-box forecasting models often miss to provide a 360-degree view impacting your inventory. For example, the shipping status coming out from FedEx APIs, whether conditions, etc. Neither they address all your unique use cases. So, if we can discuss them with us, we can have definite words on our Data Science offerings.

Question 8: How much do we have to invest to have a smart inventory system?

Answer 8: Well, there is no fixed-price model for that since we normally suggest clients to get engage with POV first and then figure out specific needs. This will help us identify the requirements and then after, we can comment on the cost.