Are we ready? And if so what’s involved?

1. Slotting Machine Tools
2. Machine Learning Warehouse Slotting
3. Warehouse Slotting System
4. Free Warehouse Slotting Software

Fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick oper-ations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity. The usage of MBA and Machine Learning Techniques will help increase pick efficiency, pick accuracy, and space utilization for the warehouse. Efficient slotting will also result in improved direct labor productivity, reduced order shorts, improved ergonomics and ultimately, reduced costs and improved customer satisfaction (KOM International, 2011).

The evolution to using artificial intelligence and machines that learn in supply chain planning is inevitable. In fact, there are early examples of the potential of AI to improve both supply chain planner efficiencies and provide better or optimized supply chain decisions. The question is, are we, as a profession, ready to embrace Machine Learning? If so, what does that mean and how do we get there?

Machine learning is a type of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. Machine learning computer programs teach themselves to grow and change when exposed to new data.

The latest Gartner Hype Cycle, published in July 2016, shows Machine Learning approaching the Peak of Inflated Expectations. Gartner predicts that mainstream adoption of Machine Learning is at least five years away, potentially ten. Still typing “Machine Learning AND Supply Chain Planning” into Google delivers more 16,000 results in less than half a second. This is a topic that supply chain planning people are thinking, talking, and writing about. In reality, the supply chain planning mindshare spent on Machine Learning is miniscule compared to that spent on reducing costs, improving customer service, and driving new revenue. Type “Cost Savings AND Supply Chain Planning” into Google and you get 2.5 million results. One could argue that Machine Learning could contribute to meeting cost, revenue and customer service goals, but clearly there’s more focus today on basic supply chain planning capabilities like Demand Planning, Inventory Optimization, Sales & Operations Planning, and Supply Planning and Optimization.

One way to get started with Machine Learning is to look at your Demand Planning capabilities. For example, a “Best-Fit” forecasting algorithm automatically switches to the most appropriate forecasting method based on the latest demand information, ensuring you create the best forecast for every product at every stage of its life cycle. The algorithm evaluates forecast error each forecasting cycle and recommends or automatically selects the forecasting method that will produce the best forecast. “Best-Fit” forecasting is a basic form of Machine Learning.

Another example of today’s Machine Learning capabilities is found in software solutions that use algorithms 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 decisions.

One powerful example is the use of Multi-Echelon Inventory Optimization (MEIO) to automatically adjust inventory positions. The current inventory-to-sales ratio in the United States sits at 1.41, higher than any time since 2009 (right after the 2008 downturn). 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 based on outdated information fail to attack the underlying issue of stock-outs, a fundamental shift in fulfillment. MEIO automatically seeks the optimal balance of inventory at the right locations, and provides optimal inventory parameters and positions by stocking location to establish optimal buffer locations and quantities. Embracing MEIO can reduce total inventories by upwards of 30% 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 learn to crawl, then walk, then run. 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 Machine Learning solutions available today. Implementing algorithmic planning and optimization technologies today builds the kind of expertise and experience that will ease the adoption of advanced Machine Learning solutions in the future.

Are you considering Machine Learning solutions? If so, what steps are you taking to get there?

• SAP EWM Tutorial

• SAP EWM Useful Resources

• Selected Reading

A Warehouse process type is used to define the activity or movement of each process in a warehouse. It deals with each warehouse process like packing, good receipt, good issue and is assigned to each warehouse task document.

Example

For simple movements, warehouse process type defines the storage type and bin for the movement of goods. Warehouse process type is defined at the point of creation of the warehouse document or in separate step.

All warehouse process types are further grouped into warehouse process categories. Warehouse process categories are used to define the goods movement in a warehouse.

There are different warehouse process categories that are predefined in the EWM system −

Slotting Machine Tools

• Stock Removal
• Putaway
• Internal Movement
• Goods receipt posting
• Goods issue posting
• Physical inventory
• Cross Line putaway

Machine Learning Warehouse Slotting

How to Create a Warehouse Process Type for Picking?

To create a Warehouse Process Type for Picking, navigate to EWM → Cross Process Settings → Warehouse task → Define warehouse process type.

Warehouse Slotting System

You can then select warehouse process type to copy. Click on Copy button at the top.

In stock/putaway removal section enter the storage bin and type and click Enter.

Free Warehouse Slotting Software

To save the warehouse process type, click on the save icon.