Recently, I had the pleasure to have a couple of conversations with Ajit Singh, the Founder and Director of Algorhythm, a company in Pune, India that has significant expertise in Optimization and Supply Chain Modeling. The have their own optimization engine, a set of front-ends for different types of supply chain models that can be used by anyone with modeling skills, and significant experience in helping large global multi-nationals with significant supply chain network design and optimization problems. Basically, they’re India’s CombineNet, but with a slight distinction – every model they build, including custom models, can be executed and modified completely by the client through an extension of their easy-to-use windows-based front end – you are not tied to their services. In comparison, although CombineNet has done a great job over the past few years of actually building stand-alone products and interfaces, it’s still often the case that custom models are only available through their services model.
Algorhythm has the capabilities to attack both strategic and tactical supply chain problems from an optimization and simulation perspective. They have sophisticated models for strategic planning that include inventory optimization, distribution network design, manufacturing network design and for tactical execution that include production planning, logistics planning, and supply network execution.
They also have specialized solutions for oil, steel, and packaging as well as having a considerable amount of experience in creating models for manufacturers and distributors. Major clients include Unilever (Hindustan Unilever, Unilever Plc. UK, and Unilever China), Thyssen Krupp, Hindustan Petroleum, and Parle Products among dozens of others. Their manufacturing and distribution network design models often save their clients 3-5%. Remember that we’re talking production models here – not sourcing models, so this is actually quite good. In terms of efficiency, their production planning and scheduling models often halve throughput time and inventory carrying requirements – which is also very good. Furthermore, we’re not talking small models here – Parle, for example, ships 50K trucks per year per SKU from hundreds of factories to thousands of wholesalers.
It’s quite easy to build a model in their products, which they call Prorhythm (for production-planning based models), Netrhythm (for network-planning based models), and Logrhythm (for logistics planning models), and which run on top of their Xtra Sensory optimization engine. They’ve thought through what the model is, what the core elements are that make it up are, what the costs are, and what measures you might want to optimize. Building a model is simply defining all the relevant entities (which are factories, lines, outputs, inputs, etc. in production planning), the associated costs (material, labor, overhead, etc.), the measure(s) you want to optimize (cost, throughput, etc.) and their priority / weighting if multiple, and the constraints. It assumes all relationships between related entities are valid unless you specify them as invalid (and permits groupings for easy constraint definition). It also groups constraints in a “constraint file” so you can easily run the same model against different constraint sets. Basically, it’s built to build models the way the doctor would build it.
Since there is no “one” optimal solution when you’re optimizing against multiple objectives, as it’s almost always impossible to precisely normalize each measure to a uniformly distributed 0-1 interval that can then be weighted according to the weights you want, they also support simulation. You can tell the optimizer to construct a set number of models equally distributed around the desired optimization point and it will automatically create and run all of the variants which you can then compare to see how slight changes impact solutions and goals.
It’s a great offering, and the people are quite knowledgeable. If you have a tough optimization problem, be sure to check them out. They might surprise you.