Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:

  • What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
  • How much to Buy? -- what are the most and least popular items at any given time interval
  • When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.

Product mix, supplier mix, turnover and restocking time (and distance) each contribute to the complexity of supply chain planning and management. When you add in shipping logistics, weather and other factors, these add even more to complicate the predictability of the supply chain.

Companies have invested in sophisticated ERP and supply planning software which rely on contextualized business configuration to function effectively. (SAP needs to understand the entire model of your business processes before it can automate it, and yet this exercise does nothing to help optimize it for any of the above mentioned conditions.

There may be built-in optimization features, but they too, have their limitations. Consider
  • The simple Min-Max planning is a common supply planning method that requires the minimum and maximum quantities to be specified by the user.
  • The re-order point planning method, where the safety stock needs to be specified.
  • The demand forecasting model, of course, is a very big part of supply planning.
In this webinar, we will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning. We will demonstrate the tangible value and ROI that can be realized by updating the various supply planning assumptions on a frequent basis.
  • Minimum and Maximum quantities per SKU
  • Safety Stock per SKU
  • Supplier lead time per SKU
In addition we will look at how a Graph database will add value by capturing state and giving better insight into the inventory flows, alternate paths and choke point identification. And if you don’t know what a graph database platform is, don't worry, we'll explain that too.
Jeff Morris
Head of Product Marketing, Neo4j

Jeff Morris brings a world of marketing experience to Neo4j as head of product marketing. At Neo4j, he leads product, solutions, customer and partner marketing. He's anxious and excited to expose enterprise IT organizations to the world of graphs. He believes data connections and relationships are the secret sauce for the next generation of revenue-generating applications. If your organization is thinking about building smart shopping carts, traversing social relationships, interconnecting things on the Internet, finding unusual behavioral patterns or entering the world of artificial intelligence, machine learning or predictive analytics, then Jeff would like you to consider Neo4j, the original graph database, for those projects.

CTO, DZee Solutions Inc.

Ashwin Pingali, PhD, is the CTO for DZee Solutions and Apps Consultants, companies focused on improving business operations through big data, AI and graph technologies.

Using a blend of process workflow expertise and thought leadership in data science, AI, Deep Learning and Cognitive Computing, Ashwin enables clients to improve their actionable decision making and predictive capability using innovative technologies like Spark and Neo4j.
Data Science Lead, Expero

Dr. G loves data. His favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.