Computerization, the Internet and automation ushered in Industry 3.0, revolutionizing the way we work. Now there’s IoT, cloud computing, 3D printing, autonomous vehicles, mobile, social and Big Data. Welcome, Industry 4.0. In the not-to-distant future …
Supply chain planners will sit in control rooms with a real-time view of inventory at all nodes of the supply chain. They’ll release line-level production plans to machines, which will start up at the command of the control center without human intervention. Auto-palletized finished goods will be placed in automated racking systems through an AGV/automated guided vehicle. Goods will be retrieved automatically and loaded onto autonomous vehicles. The Big Data analytical mart will continuously ingest sensor data and feed it into a predictive maintenance model. Machine learning will augment the integrated business planning process, and new data patterns will change statistical forecasting models automatically to continually improve forecast accuracy.
No doubt, the supply chain landscape is transforming rapidly. It takes less than three months for a data scientist to build and deploy a custom optimization model written in R to solve real problems and lower total supply chain costs; however, operationalizing and integrating the model with existing ERP and other data sources requires a “system” or “platform.”
With the advent of digital supply chains, an APS will become a competitive advantage. Unfortunately, current systems haven’t kept up with the digital revolution. Chances are your APS is simply a “planning system of record” without a brain (or with one that’s suboptimal).
Most APS implementations took a “one size fits all” approach. But because no two businesses are alike, they required complex work-arounds and manual exception management. Not surprisingly, these multimillion-dollar investments were only moderately successful. The one- to three-year implementation and stabilization period—an eternity in the go-go-go digitized world—led to adoption issues and slow ROI.
The real value of analytics, operation research (OR) and statistics was hardly realized in the last APS implementation wave—ironic, considering that OR and statistics are core subjects in the supply chain management field. Every legitimate supply chain practitioner knows the theory, but has yet to see its application in real life. Clearly, there’s still enormous value from advanced planning systems to be unlocked.
It’s time for an “advanced,” cloud-based APS that:
- Includes fully integrated key value drivers like forecast accuracy improvement, inventory reduction and risk vs. return trade-off
- Receives social and IOT data, and perform advanced machine learning and optimization algorithms
- Provides plug-and-play connectors to standard ERPs like SAP, Microsoft and Oracle, and CRMs like SFDC
- Fosters seamless collaboration with external and internal stakeholders
- Delivers advanced visualization and reporting with templated reports for S&OP
From industry research and our experience, efficient supply chain planning and execution delivers measurable financial benefits, including:
- 10-15% supply chain cost savings
- 5-10% inventory cost savings
- 2-3% margin improvements
- 2-5% service level improvements
The CIOs and CEOs around the world are wrestling with a million-dollar question: “Should we decommission our current non-value-adding APS and move to a relevant business decision platform?” My unqualified answer is “Yes.” Leading firms like J&J have already made this decision. And while it may seem painful now, this will soon be the differentiator between leaders and laggards.
In my next blog, I’ll discuss how your organization can prepare for this transformational decision.