Supply Chain Planning & Optimisation Projections for 2018 – Back to the Future

By Henry Canitz, Director of Product Marketing and Business DevelopmentPicture1

I get a kick reading “prediction” articles both prior to the year start and then again after the year is complete. When it comes to predicting the very dynamic supply chain management industry, those after year reviews can be quite amusing. For even more fun look back 10 or more years to see where we all thought the industry would be. Therefore, it is with a bit of trepidation that I toss my hat in the arena and make my 2018 supply chain planning and optimization predictions.

From all indications, 2018 should be a very interesting year for supply chains and supply chain practitioners. I think there are a few advanced capabilities that will grow in importance but I also believe there is a growing awareness that to benefit from advanced planning and optimization capabilities a company needs to build a firm foundation. Companies need a robust integrated and highly functional supply chain platform that is operated by highly trained supply chain professionals. In a way, we need to go back to fundamentals to move forward to the future.

The Rise of Cloud Deployment and Heightened Security

The large number of major data breaches in 2017 has been the focus of many C-level meetings. In an interesting reversal, SaaS (software-as-a-service) solutions are now viewed by most as the least risky deployment option and this will increase in the year ahead. 2018 will bring a renewed emphasis on security for supply chain facilities due to the growing awareness that data breaches can originate through almost any type of connected system and the fact that more facilities are being opened in unstable geographies.

The Shift to Continuous Planning

The pace of the supply chain is increasingly driven by ever-growing customer expectations (Amazon Effect) making end of the day, week, or month periodic planning processes, while still important, no longer sufficient for today’s operations. The concept of continuous planning where planners address opportunities and disruptions as they happen will continue to gain ground in 2018. These efforts could be part of a Sales and Operations Execution (S&OE) process or tightly tied to building more robust digital planning and optimization capabilities. To facilitate continuous planning process companies will start to move towards cross-functional teams working in a control-room type environment to address global disruptions and opportunities using advanced planning and optimization capabilities.

Digitisation

You can’t open a recent supply chain periodical today without seeing something about artificial intelligence (AI). Advanced analytics, machine learning, algorithmic planning, and AI will all continue to capture a significant slice of attention in 2018. In the year ahead, this will require supply chains to shift how they operate. With digitization of the supply chain, the role of the planner will become one of solving problems using advanced analytics to make business decisions not just supply chain decisions.

The Internet of Things (IoT) will continue to drive supply chain execution innovation as companies find new opportunities in the wealth of data available. For many, the current state includes difficulties interfacing IoT data in a high quality, repeatable fashion. Most companies just aren’t at the point where consuming this firehose of data is feasible.

Supply Chain Data Quality and Ownership

Supply Chain Master Data Management (MDM) is quickly becoming a critical foundational requirement and I expect to see more interest in this area in 2018. Much of the data used for supply chain planning and execution comes from outside of a company’s ERP systems. Ask yourself, where is supply chain data maintained at your company today? The answer might surprise you. Effective supply chain planning and optimization requires high quality and consistent data and the ability to easily and quickly maintain and update that data. Inconsistent and poor quality data will degrade confidence in recommendations. One of my mentors once told me that, “One awe S#?* wipes out 1000 Attaboys (or Girls)”. One piece of bad data that tarnishes a recommendation will be difficult to overcome. To take full advantage of IoT data, supply chain organizations will need to invest in their Supply Chain Master Data Management capabilities and platforms.

Talent GapPicture12

You continually hear from hiring managers that there is a “War for Talent” driving increased salaries, benefits, and turnover rates for supply chain professionals. This will not change anytime soon. Actually, with a shrinking baby-boomer workforce the war for talent is only going to heat up in 2018 (read more here: The Talent Gap and here: Imagine 2030: Supply Chain Talent). Companies will need to find additional ways to attract and retain talent like rotational programs, clear-cut career paths, advanced degree support, and support for professional training. Another way is to provide advanced supply chain platforms that allow team members to work on more value-added activities. Yes, the ability to hire and retain talent could be another way to justify an investment in new supply chain planning and optimization capabilities.

Taking S&OP to the Next Level

I have personally seen the significant benefits of a well-run S&OP process and I know other practitioners have as well. I may be going out on a limb here but I think 2018 will be the year of renewed efforts around putting advanced S&OP capabilities in place including the ability to;

  • Optimize the end-to-end supply chain based on constraints and business objectives (minimize cost, maximize profits, meet customer service levels, etc.)
  • Analyze the impacts of product-lifecycle decisions, especially new product introductions
  • Align and synchronise strategic, tactical and operational planning
  • Collaboratively plan with partners and customers

A year from now I am sure we will all get a good laugh by revisiting this piece, but I am hopeful that a least of few of my predictions will hold true. Here’s to a happy and successful 2018.

About the author 

Hank Canitz PictureHenry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.

Advertisements

Machine Learning in Supply Chain Planning

Machine Learning in Supply Chain Planning - square

Machine Learning in Supply Chain Planning

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

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?

 

About the author 

Hank Canitz PictureHenry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.