The year was 2009 and change was coming! For nearly a decade prior, major innovations utilizing artificial intelligence were at the forefront of a technological revolution striving to automate our world. These milestone achievements in technology were accomplished by teaming process knowledge and subject matter expertise with software developers to create targeted applications to improve efficiency, reduce errors, and automate processes. This was one of the pathways to full automation, and it was a successful avenue that produced technologies such as the autopilot software the airline industry uses today as well as a plethora of military and government related technologies for defense application purposes–among many others.

However, a shift occurred around 2010 that changed the way process automation would be pursued, and it delayed the path to full automation for industry functions, especially those requiring analytical human logic and intervention. By year two of the recession, companies started to cut back on costs. They restructured their workforces by removing many “non-essential” employees to include subject matter experts, process gurus, and continuous improvement practitioners. In addition, organizations also phased out pension plans, force-retired long tenured employees (or offered an appealing retirement package), leaned out their existing analytical-focused positions, and scaled back on benefits–reducing way more than just company loyalty. Consequently, process knowledge and subject matter expertise were significantly diminished, if not fully eliminated. Organizations began believing that this valuable logic, experience, and insight could now be found externally through the “fresh sets of eyes” of new hires, promoting hourly and/or floor level employees into analytical roles, and incorporating machine learning. 

As a result, companies moved towards top-down software applications while they centralized a large portion of their data analysis activities. The previously successful function level, bottom-up approach to software development–where the people who understood the work collaborated with the software engineers and programmers for automation purposes–was all but reliced. Organizations chose short term cost cutting returns over long term success.

Here are a couple examples of this “shift” observed during our team’s time in industry: 

By 2011, one Fortune 500 automotive manufacturer laid off the majority of their material planners and forced the existing production schedulers to take over the workload with limited training and process knowledge sharing. This decision failed so miserably that the company was forced to refill the planning positions a couple years later, but these new planners were external hires who needed to be trained and required a significant onboarding period to learn and develop in the midst of an ongoing recession and minimal process support from the organization (because the company laid off their subject matter experts two years prior). 

Two other Fortune 500 manufacturers in the appliance and consumer goods industries decided that promoting floor level hourly employees into planning and scheduling positions was a quick fix to resolve the process knowledge gap and save money; and now, across all sites, approximately 60-80% of these analytical positions are filled with employees who may or may not have the skillset, education, training, and/or analytical prowess to perform the work effectively. However, due to these inefficiencies, these departments have grown to nearly twice the size they were ten years ago, yet they continue to struggle to be successful (as seen during the pandemic). 

Computational technology also made a shift in 2010. Instead of continuing to pair process knowledge and subject matter expertise with software development for targeted automation solution creation, software engineering shifted to a more singular existence which led to the rapid mainstream emergence and growth of machine learning. A key theory behind the growing dependence on machine learning was vaulted into place because of the cutbacks organizations implemented during the recession. The Oxford dictionary defines machine learning as “the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.” However, as current Industry 4.0 initiatives are becoming a top priority for most companies, and process automation being an important element of Industry 4.0, machine learning has had a difficult time keeping up with expectations–especially due to timeline requirements.

In April of 2018, Elon Musk said on Twitter, “Yes, excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated.” Machine learning was a primary aspect to Tesla’s automation plan, and it proved to be a difficult one with less than optimal results. This is because the whole premise behind machine learning revolves around a computer “using algorithms and statistical models to analyze and draw inferences from patterns in data.” Well, what happens when the quality of data is poor, the process is not well defined, the process knowledge provided to the computer is limited (no situational awareness), and/or the implementation of machine learning is complex and painfully slow? Sounds like a recipe for failure, and organizations as a whole have invested trillions into the technology without seeing the return on investment they were hoping for.

Make no mistake, the Perfect Planner Team believes machine learning is a pivotal component to artificial intelligence and process automation, and it has a dual purpose. First, machine learning is powerful when a process cannot be or has not been fully established with what is known about it today. A couple great innovations that have been created due to this purpose are facial recognition technology and planet hunting telescopes. Secondly, machine learning is a complement to the automation process at the end of the journey when sustainment and continuous improvement activities are the focus. However, there are three fundamental steps that should come before artificial intelligence and machine learning are applied to the software development process (which our team considers the fourth step). These steps are what we call the Straight to Execution Approach, and they provide the foundational process automation elements required to transition smoothly into artificial intelligence and machine learning pairing, which ultimately results in a quick and effective automation outcome.

THE STRAIGHT TO EXECUTION APPROACH

The Straight to Execution Approach is a four step process to full automation designed by the Perfect Planner Team, but it is founded on the principles and theories of how software was developed before 2010. It has been proven highly successful in multiple Fortune 500 companies. For instance, one Fortune 500 appliance manufacturer used this approach over the course of two years after the pandemic impacted supply chain efficiency and operational resources (especially manpower). Over 30 process automation solutions were created in 2020 and 2021, and these organization-approved best practices affected eight core functions in three departments. Four of these functions were automated between 60% and 70%, and the results were achieved utilizing only the first three steps listed below–machine learning and robotics were not applied. However, the first three steps in the Straight to Execution Approach have now paved the way for full automation success when the time is right.

Step 1: A Process Must Come First

A function-specific, comprehensive process including all tasks and actions required for the job–prioritized by criticality–is necessary to standardize the role across industries. Knowing that the very best software has well-defined requirements, these requirements need to be provided first (i.e. the process). In the past, a significant part of a software budget went to understanding and defining the requirements.

As stated previously, for the past 10 to 12 years companies have essentially purged process knowledge and subject matter expertise. In their place, machine learning was inserted with varying results. Our belief is that the normalization of work across industries leading to the full automation of functions cannot occur without well-defined and proven processes. As the old Six Sigma adage asserts, “Good processes equal good results!”

Step 2: Data Accuracy & Situational Awareness are Essential

The difficult-to-find-and-quantify process knowledge extends further than defining the requirements and standardizing the process. There has to be data integrity for either a human or a computer to do the job without error. Using machine learning without data accuracy will undoubtedly hinder automation progress. In addition, from our team’s extensive experience with enterprise system data, only 50-60% of situational elements can typically be seen in the data, so the data gap experienced by machine learning algorithms creates a diminished environment for it to assess and apply situational awareness logic to the calculated results—also impacting accuracy. The only way we have found to consistently overcome the data integrity and situational awareness conundrum is through coded logic, and we believe this is the “missing link” to full automation.

Step 3: Make the Output Structured & Easy to Navigate

The most straightforward and simplistic way to instruct someone on what needs to be accomplished is a checklist. If steps one and two have occurred with a high level of confidence, then this step is considered the “making it easy” step. For full automation to be a success, the user output needs to be ranked, listed, and provided to the human or computer in an executable form. Regardless of who or what completes the work, the presentation of executables needs to be linear, structured, and easy to navigate.

Step 4: Apply AI & Machine Learning for Full Automation & Further Refinement

Now that the function has experienced up to 75% automation from the successful completion of steps one through three, the time is right to apply artificial intelligence and machine learning to finish the full automation journey.

Author: Thomas Beil

Publication Date: March 15, 2022

© Copyright 2023 Perfect Planner LLC. All rights reserved.

The post Why Machine Learning is NOT the First Step to Full Automation appeared first on Perfect Planner.

In the ever-changing global supply chain environment, part shortages have become the “new normal,” as cliche as that term is since COVID-19. The truth is that pandemics and recessions do not necessarily create the part shortages as much as they magnify existing process gaps within material planning which resulted in shortages. Where this is most noticeable in industry today is with purchased parts, and the critical issues that arise with them can cost companies millions of dollars in lost revenue, expedites, and productivity–among other negative impacts.

Although the past couple of years make it feel as though organizations are fighting a losing battle with purchased parts, this is a defeated position to take, and the Perfect Planner Team does not believe it is accurate. There are several strategies or tips that can be utilized right now to improve the part shortages businesses are experiencing. By implementing even a couple of the recommendations below, an improvement in shortages can be seen in as little as a week.

Tip 1: Long Term Visibility Is Critical

When looking at the material planning function, one of the most glaring process gaps for many organizations is the lack of long term visibility to supply and projected inventory balances. Lead times or firm periods are often much further out into the future than what planners typically analyze and review on a daily basis. After witnessing planning in five Fortune 500 companies, it became apparent to the Perfect Planner Team that most planners managing purchase parts were only looking out two to ten business days. The modern supply chain has significantly longer lead time than it did 15 years ago with more vendors being located overseas as well as a large contingency of domestic suppliers relying on internationally sourced raw materials. In today’s world, lead times can be anywhere from 14 days all the way up to 70 plus, and the antiquated, short term planning practices of the past are no longer effective. Long term visibility is necessary to manage the supply of purchased parts. Our team’s recommendation is to alter the planning reports and/or balance sheets to extend as far into the future as the supplier lead times, and if the planning team is not including supply elements into those balances (i.e. purchase orders, releases, etc.), then they need to be added. 

A good example of the consequences of not using this tip was seen at a Fortune 500 appliance manufacturer. Their planning tool only took into account on hand inventory levels, in-transit quantities, and demand. Since the average days on hand at the sites was anywhere from two to seven days, depending on the part, their visibility was approximately a week out. As a result, the appliance manufacturer had a track record of struggling with part shortages, and it became exponentially worse during the pandemic. Upon reviewing a year of their expedited shipment data and historical reports, it was concluded that over 70% of part shortages could have been seen three weeks or more in advance of the impact date if their planning teams had long term visibility and their balance sheets included supply elements.

Tip 2: Chase Past Due Purchase Orders Before They Impact You

Past due purchase orders or releases can decimate supply chains, and chasing past due deliveries is often one area planners either prefer not to prioritize, or they don’t have the bandwidth to address. However, resolving past dues without a doubt reduces shortages. From the Perfect Planner Team’s time implementing and using SAP and Oracle, we noticed certain inventory balance calculations within the system did not make much sense from a material planning perspective. For instance, most MRP/ERP systems view past due quantities as good supply elements, and they include them in the inventory balance calculations even though these orders have not arrived and are not available stock. To compound this issue, situations like these cover up shortages in system generated reports and functions (i.e. Collective Access in SAP). This process gap forces the planner to run more reports and perform analysis–or be required to have “super user” system experience to set the appropriate parameters in the system. 

In our team’s opinion, this is a calculation error on behalf of the system developers who most likely never sat in the planning role. To piggy-back off of the first tip’s recommendation (and after all supply elements are included in the planning reports’ balance calculations), we advocate removing all past due deliveries from those calculations. The benefit of this action allows planners to quantify and pinpoint if and when a part shortage would occur because the order did not show up. This also reinforces the need to chase down those past dues and resolve them.

 Tip 3: Perform Root Cause Analysis to Stop a Revolving Door

Understanding why a shortage occurred–and more specifically what the reasons were behind the “why”–is pivotal to keep a shortage from happening again. Similar to chasing past due orders (one root cause reason), planners often do not have the desire or the time to devote to root cause analysis, mainly due to the lengthy process required by running additional reports and navigating through multiple screens essentially looking for a “needle in a haystack.” However, the Perfect Planner Team would submit that this is definitely a value added exercise in material planning, and it is backed by multiple studies.  

One of these studies came from a Fortune 100 diversified manufacturing and technology company. After reviewing 18 months worth of part shortages and expedite data, the research and analysis concluded that upwards of 50% of impacted part numbers had already experienced a shortage and/or expedite event within the previous six months. These were recurring issues, and a prior shortage event’s “quick fix” or perceived resolution did not address the actual underlying reason(s) because the boomerang came back! Our recommendation would be to start with the obvious and more likely root causes, such as past due purchase orders, scrap or inventory adjustment events, and demand variances. Although there could be dozens of root cause reasons, becoming efficient and proficient with a few, and then expanding to others, is an effective path. 

Tip 4: Ensure Safety Stock is Adequate

From decades of industry learnings the Perfect Planner Team has cultivated in inventory management and material planning, we recognize that not much confounds planners and supply chain leaders more than safety stock. It is a critical piece to the inventory protection model, and reviewing it is frequently not prioritized to the level it needs to be within a planning team’s responsibilities. The consensus across industries is that companies feel they have too much of what they don’t need and not enough of what they do. For the “not enough of what they do” portion of that feeling, part shortages are usually the outcome.

A Fortune 500 automotive manufacturer recognized the importance of implementing a new dynamic safety stock formula as well as recalculating and entering the results into the system every month. With the new safety stock levels identified, an analysis exercise was conducted on previous shortage situations. The question was simple: “If the safety stocks would have been set to the new formula’s recommendations, how many shortages would have been avoided due to the additional inventory protection?” Expectedly, the data showed that nearly 30% of the shortages would have been prevented.

Our recommendation is to find and implement a dynamic safety stock formula while adding periodic safety stock updates to the standard work of the planning teams. Although there are many safety stock formula options and services, our team wrote another article on this topic, and it includes an industry-proven safety stock formula that can be used and modified for an organization’s needs. The article can be found here.

Tip 5: Know When to Seek Help

Knowing when to ask for help and escalating issues is often a lifelong journey. The Perfect Planner Team realizes this, but we would assert that resolution to a problem sometimes requires the intervention of others. If tips one through four have not resolved a planning issue, or if there is something keeping a planner or supply chain leader from utilizing any of those recommendations, then it is time to request support. This can be found internally within a company, such as a manager, process expert, or purchasing/procurement leader, or it can be located from an external source, such as a consultant or solution service provider. 

Our team offers a free consultation service, and our passion is to help companies bridge the process knowledge gap, resolve complex operational issues, improve the effectiveness of processes, and create efficiencies through targeted automation solutions. This is especially true for material planning. If you would like to connect with us on this article or any other topic, send us an email at info@perfectplanner.io or give us a call. You can also follow us on LinkedIn.

Author: Thomas Beil

Publication Date: March 11, 2022

© Copyright 2023 Perfect Planner LLC. All rights reserved.

The post Five Essential Tips You Can Use Right Now to Avoid Part Shortages appeared first on Perfect Planner.