Will the Evolution of Machine Learning Systems Lead to Self- Thinking Supply Chains?

Gartner found that companies need an SCP SOR to provide a strong foundational planning layer, to build more differentiated and innovative capabilities like automated operations and machine learning capabilities.

Once a company develops integrated operating capabilities supported by an SCP SOR, some believe the path to AI and Machine Learning is through climbing the levels of analytics maturity. Gartner has proposed five stages of analytics maturity:

1. Descriptive – Determining what happened through reports, KPIs, etc.

2. Diagnostic – Discovering why it happened through Root Cause Analysis, 6-Sigma Tools, etc.

3. Predictive – Understanding what is likely to happen through forecasts, what-if scenarios, etc.

4. Prescriptive–Determining the optimal response through algorithmic optimization including automating this response.

5. Cognitive–Developing new insights or recommended actions based on artificial intelligence and machine learning.

What capabilities might you investigate to climb the ladder of analytics maturity and start your journey towards transforming to a more automated and optimized supply chain? The logical place to start is to build algorithmic optimization capabilities to continually analyze the state of your supply chain and recommend or automatically execute plans to meet customer requirements. Optimization driven by algorithmic planning is an early form of machine learning that relies on a set of provided information (supply chain facilities and capacities, transportation lanes and capacities, customer service requirements, profit requirements, etc.) to automatically make optimal recommendations.

One powerful example of algorithmic optimization is Multi-Echelon Inventory Optimization (MEIO) used to analyze and optimize inventory positions. The current inventory-to-sales ratio in the United States sits at 1.38 (See Figure 1). One cause of this glut of inventory is the emergence of omni channel retailing. The “Amazon effect” of free and fast shipping, easy returns, and everyday low prices has changed customer expectations, and the way products get into the hands of the consumer. It’s a struggle to keep up with the mind-bending rate of change that prevents companies from having the right inventory positioned at the right location to service customers. Companies that respond by increasing buffer inventory by channel, based on outdated information fail to attack the underlying issue of stock-outs, a fundamental shift in fulfillment. Multi-echelon Inventory Optimization automatically seeks the optimal balance of inventory at the right locations and provides inventory parameters and positions by stocking location to establish optimal buffer locations and quantities. Embracing MEIO can reduce total inventories by upwards of 30 percent while maintaining or improving customer fill rates. MEIO is an example of basic Machine Learning that is available today.

Attaining the full benefits of machine learning will be an evolutionary process. We must build a strong foundation of integrated supply chain planning capabilities first, and then work to move up the analytics maturity curve. The introduction of machine learning into most supply chain organizations will take years, but that shouldn’t stop supply chain professionals from planning for the future or taking advantage of some of the basic machine learning solutions available today. Implementing algorithmic optimization technologies today builds the kind of expertise and experience that will ease the adoption of advanced artificial intelligence solutions in the future.

Headquartered in Atlanta, Logility is a provider of collaborative supply chain optimization and advanced retail planning solutions that helps small and large enterprises, and Fortune 500 companies.

See Also : Top Machine Learning Companies