From “Rules of Thumb” to Non-Linear Algorithmic Optimization

From “Rules of Thumb” to Non-Linear Algorithmic Optimization


From “Rules of Thumb” to Non-Linear Algorithmic OptimizationBack in the mid 1990’s when I first entered the Supply Chain field as a freshly minted MBA from the Eli Broad Graduate School of Management at Michigan State University, I was full of “state of the art” knowledge from visionary teachers like Don Bowersox, David Closs, Steven Melnyk and others. I was excited to start my career and apply my knowledge to improve supply chain operations. However, I quickly came to the realization that most companies were not ready to adopt forward thinking supply chain capabilities. For example, one company I worked for in the late 1990s had a legacy, blue-screen ERP solution augmented by spreadsheets and homegrown databases on which to manage fairly complex supply chain operations.

Still full of hope, I quickly realized that building a business case for my grand vision would be an uphill climb. While head deep in this struggle to build a business case for supply chain process, structure and technology improvements I came across a little blue book edited and compiled by Dick Morreale and published by the Council of Logistics Management Southern California Roundtable. The little book, “Logistics Rules of Thumb II,” contained around 120 rules of thumb covering all aspects of supply chain operations (more commonly known back then simply as Logistics). I used the many nuggets of information from this book to determine my company’s inventory performance against accepted standards and to alter inventory rules in our legacy system. I also used material from the book to help build a proposal to acquire and implement a supply chain planning solution.

By the end of the 1990s, Mr. Morreale’s “Rules of Thumb” was in its fifth edition and had expanded to more than 500 ‘rules of thumb,’ facts and definitions covering 250 pages. Mr. Morreale’s book was used by many supply chain practitioners to gauge their supply chain operations and build improvement justifications and for that, we all thank you!

Today, with the availability of powerful solutions using algorithmic optimization, machine learning, and running on lightning fast computer systems, using rules of thumb to determine the best way to manage your supply chain is archaic. However, many supply chain teams are still stuck with systems with the limited capabilities of those that we had in the 1990s. Companies still use rules of thumb like ‘days of supply’ to manage inventory when it has been proven that reductions of up to 30% in overall inventory can be achieved through the adoption of inventory optimization. Multi-echelon Inventory Optimization (MEIO), the most advanced form of inventory optimization, creates time phased safety stock targets, driven by a future forecast, that satisfy customer needs at lowest total supply chain cost. It is literally having your cake and eating it too!

MEIO can provide the answers to questions such as:

  • How much inventory is necessary to support your service level objectives, while handling demand uncertainty and supplier volatility?
  • How much buffer stock is required and where should it be placed?
  • What should the total inventory investment be, including safety stock and holding costs?
  • How much more inventory will we need to hold to support a new customer’s business?

Many organizations buffer inventory at the finished goods stage or spread it across multiple layers of the supply chain network. Often, this ‘peanut butter’ approach is considered by many to be the safe inventory deployment option. However, spreading finished goods inventory throughout the network leads to too much inventory in some places and not enough in others causing stock-outs, cross shipments, and obsolescence. By contrast, multi-echelon inventory optimization models and analyzes all stages of the multi-echelon supply chain using available information regarding costs, demand signals, supply volatility and sourcing. With a powerful analytics engine, MEIO can identify the many causes of inventory and take into account the interdependencies among suppliers, production stages, distribution centers, warehouses and customers, as well as the cost of all materials as they flow through the supply chain. The result is a single, accurate view of the company’s supply chain network shared by all stakeholders, from operations to sales, from procurement to logistics.

Whether your supply chain consists of three stages or thirty-three, whether your scope is regional or global, even if your inventory plans extend to multiple enterprises including suppliers and customers, MEIO can transform your inventory from a source of excess cost to a competitive advantage. Now, you can determine the optimal inventory required to successfully support your business plan, rather than attempt a top-down approach to derive an inventory budget. Instead of double- or triple-buffering safety stocks, your company can achieve the highest possible service levels with the minimum strategic investment in inventory.

Looking back, Logility offered the first commercially available Multi-Echelon Inventory Optimization solution to the market in the early 2000s. Over the last two decades we have continued to innovate and incorporate new artificial intelligence techniques and machine learning to help augment and automate the routine planning activities; to deliver real-time scenarios that ensure your plans are focused to satisfy the objectives of the business and to give the user confidence to  handle the challenges your customers present.

Although early forms of inventory optimization were available back in the late 1990s, computing power was a limiting factor and most companies were just not ready from a supply chain maturity standpoint to adopt a solution that used non-linear algorithmic optimization. Today, the use of algorithms in supply chain planning has become much more mainstream. In fact, in the latest Gartner Hype Cycle for Supply Chain Planning Technologies Gartner predicts that MEIO will reach mainstream adoption within the next 2 to 5 years. I sure wish I could have had a MEIO solution to optimize inventory when I was leading the supply chain for a $3B consumer packaged goods company back in the late 1990s.

Is your company still stuck using rules of thumb, error prone spreadsheets, and inflexible ERP systems to manage your quickly changing, high-speed supply chain? If so, it might be time to leap out of the 1990s and consider multi-echelon inventory optimization.

Hank Canitz
Hank Canitz

Product Marketing Director Hank brings more than 25 years of experience building high performance supply chains. This experience includes evaluating, selecting, implementing, using and marketing supply chain technology. Hank’s graduate degree in SCM from Michigan State, numerous SCM certifications, diverse experience as a supply chain practitioner and experience in senior marketing roles with leading supply chain solution providers helps him to bring a unique perspective on supply chain best practices and supporting technology to the Voyager Blog.

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