Manufacturers of products ranging from the highly complex to the most commodity-like are challenged daily with managing unpredictability in their supply chains.

Advanced supply chain techniques only go so far in mitigating uncertainty and risk; what’s often needed is a complete transformation of supply chain processes and systems. Demand management, sales and revenue forecasting,  Sales & Operations Planning and shared risk models including Vendor Managed Inventory (VMI) are predicated on accurate, descriptive data. What’s critical for any enterprise, regardless of manufacturing complexity of their products, is having a stable and secure system of record to base their supply chains strategies on.

In working with manufacturers on how to transform their complex enterprises into dynamics businesses, I’ve run into the following four lessons learned when it comes to making supply chains more accurate, efficient and focused on profitable long-term results.

1. The rigid taxonomies of previous generation Supply Chain Management (SCM) systems are becoming quickly antiquated as enterprises seek out more agility in their data structures.

We’re seeing this in our ongoing development efforts on the Microsoft Dynamics AX platform. Prospects and customers want greater flexibility in defining their data structures and in how they apply data within networks, both inside and outside the company.  The idea that best practices in SCM systems requires a highly structured, rigid taxonomy is giving way to more agile, network-based approach to knowledge capture and management through entire supply chains.

2. The enterprise is the network.

One of my favorite studies of SCM dynamics is based on a thorough analysis of the Toyota Production System (TPS) by researchers Dyer & Nobeoka titled,  “Creating and managing a high-performance knowledge-sharing network: The Toyota Case.” Of the many excellent insights this study provides, the most interesting is how the TPS framework create a willingness on the part of suppliers to contribute and share valuable information to alleviate potential supply chain-wide risk. This study is excellent in defining how Toyota consistently works to reduce the costs associated with finding and accessing the many different types of valuable knowledge they have, both implicit and explicit, throughout their supply chain.

3. Focusing on the leading indicators of supply chain collaboration and performance using real-time Analytics and Business Intelligence (BI) is becoming more commonplace.

This is where analytics and BI is getting significant traction across the manufacturers’ supply chains as enterprises are looking to trim back to only those metrics that measure collaborative, shared performance. The focus is now on leading indicators as manufacturers look to gain greater insight into potential future events. Lagging indicators have proven only marginally useful in anticipating stock-outs and errors in forecasts. This aspect of analytics and BI in supply chain management is also being driven by enterprises realizing that despite their best efforts, it is very difficult to create an optimized plan. Many are looking to create an event-driven SCM framework that can respond accurately and quickly across their entire series of product supply chains.

4. The best-performing event-based SCM networks today are more focused on enterprise-wide dynamic orchestration of complexity management and decision management systems and strategies.

At Cincom we’re seeing the emergence of Dynamic Complexity Management (DCM) and Dynamic Decision Management (DDM) as the unifiers of the enterprise systems, all focused on creating a more agile, flexible supply chain in the process.

Bottom line: The ability of any manufacturer to transform their complex enterprise into a dynamic business begins with a recognition of how critical it is to create an event-driven, highly flexible framework.  We’ve created one based on the decades of successful results our customers have accomplished. It’s called the Cincom Tranxform Framework.

Reference:

Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota Case. Strategic Management Journal, 21(3), 345-367.