Five reasons why APICS CPIM is a must for every ERP user and consultant

Business people on a meeting at the office

For the most part of my career, I have been known to be an active member of the APICS community. This means that, quite frequently, I interact with SCM practitioners and ERP consultants from different industries and with different professional backgrounds. During discussions, I am often asked what ways are best to acquire more in-depth-knowledge of the SCM/ERP domains.

Drawing from my 9 years of extensive, hands-on experience in the fields of Supply Chain Management and SAP ECC ERP implementation/support within the Pharmaceutical and FMCG industries, and a unique techno-functional skill set in SCM enabling technologies and Domain Expertise in the SAP PP/PP-PI module, I have compiled some advice for others.

When reflecting on numerous SAP ERP implementation/improvement projects, I keep falling back on the certainty and solidarity of the APICS certification: Certified in Production and Inventory Management (CPIM) which I believe was one of the main factors that led to my implementation success. Here are five reasons why I believe the APICS CPIM is a must for every ERP user and consultant:

  1. It harnesses your talents: It is widely believed that a lack in SCM talent is the reason behind many ERP implementation failures or less than optimal ERP performances – both the user/consultant sides. And while there is no one-size-fits-all kind of advice, the APICS CPIM certification has so many benefits to both users/consultants that I almost always advise people to pursue APICS CPIM because it is more about getting the best ROI of an ERP implementation.
  2. It follows a process-orientated approach: ERP commercial packages are all built to computerise the classical value chain activities of a company. These value chain activities are resembled in the modular structure that all commercial ERP packages follow. For example, business processes relating to Supply Chain Planning including, Sales and Operations Planning, Demand Management, Production Planning/Scheduling would be found under the Production Planning “PP/PP-PI” module in SAP ECC ERP. Likewise, other business process compromising a company’s value chain would be found as “canned” business processes across different modules of an ERP solution. The CPIM follows a process orientated approach to Supply Chain planning in a fashion that’s is almost identical to what is found in a SCM/Manufacturing Modules of and ERP package. This strategic fit between how ERP systems are structured and the process-oriented structure of the CPIM courseware is what makes CPIM the most powerful framework for SCM/ERP professionals in both user/consultant roles.

    cpimls2018-composite-plus-books

    Australasian Supply Chain Institute offers the CPIM Learning System for self study or together with Guided Learning sessions, available right across Australia

  3. It mirrors the same language as your ERP: The concepts and terminology of an SCM/Manufacturing module of an ERP system, such as MPS/MRP, BOM, phantom assemblies, time fences and forecast consumption techniques, just to name a few, that prove tricky for most users/consultants to grasp are explored in-depth in the CPIM courseware in an a clear and easy to follow approach with plenty of real life examples. This helps to better utilise system functionalities/features that are likely to be ignored due to the lack of underrating of such concepts.
  4. It builds confidence to apply a configuration effort: CPIM equips designees with knowledge that proves critical to guide system configuration efforts in the SCM area.
  5. It results in better, more streamlined implementations and a higher ROI for digital transformation efforts: Many companies the likes of BASF, DuPont and Intel have adopted APICS frameworks which helped them achieve organisational goals and increase the efficiency of their systems and people. It’s why over 110,000 other SCM practitioners around the world have attained the CPIM. Now it’s up to you. https://www.apics.org/apics-for-business/customer-stories

By Hatem Abu Nusair, M.Sc. Engineering, CPIM-F, CSCP-F, SAP Certified Application Associate, APICS Master Instructor

Haytem

Hatem is a Global Supply Chain Management & ERP Expert. He is currently the Production Planner at Tip Top, one of GWF’s divisions in Sydney, having moved from Jordan where he worked for a blue-chip international company that grew rapidly. Here, Hatem founded the Regional Middle East & North Africa (MENA) Supply Chain Department with the purpose of optimising Supply Chain performance across 13 subsidiaries through demand management and forecasting, capacity management, inventory control, and special projects, which entails: IT initiatives, ERP implementation, re-engineering of Supply Chain processes and other relevant matters.

Hatem is a qualified Industrial Engineer and a Master of Manufacturing Engineering candidate at UNSW. He is a Certified Fellow in Production and Inventory Management (CPIM-F) by APICS, a Certified Fellow Supply Chain Professional (CSCP-F) by APICS and a Certified Application Associate by SAP SE.

Hatem will be facilitator for Term 4 CPIM Part 2 Guided Learning for Australasian Supply Chain Institute where will be share his passion of streamlining supply chain processes, eliminating redundancies and utilising enabling technology to achieve operational goals with CPIM Part 2 students.

 

 

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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.