The first point they make is that for most companies, Excel is not a viable S&OP tool.

• Anyone who has done extensive work in Excel knows that it is a very powerful tool that works best with smaller data sets.  Companies managing a complex supply chain soon find that they are stretching Excel’s boundaries to the breaking point.
• Modern S&OP process have a significant “what-if” component to them.  While it is easy to make high level changes to Excel, understanding what the real impact of these changes are, is simply not possible.  Consider a typical S&OP decision…what-if we were to add a promotion for this product line? A simple question, but a complex answer.  Sure, you can change the forecast quantities in Excel, and that can show change to revenue.  But what-if the product line is constrained?  What is the purchasing spend going to be to support this?  A simple Excel model simply cannot support this level of granularity.
• Excel doesn’t support alerts or lend itself to managing by exception.  Once your S&OP plan is set, you need to monitor performance against it.  Sure, you can see how you’ve performed at the next S&OP meeting, but isn’t that too late?

I’ve gone into some details about how Excel is not the right tool for S&OP (and other things) in other posts, if you are interested, you can find them here;

Is Excel the right tool for S&OP
How are spreadsheets like cockroaches
The endless debate: Is S&OP about technology
When spreadsheets go bad

So, if Excel is not the right tool for S&OP, what is?  Rather than call out a specific tool, let’s look at some criteria that you need to consider when looking for an S&OP application:

• I mentioned that Excel is not a good tool for S&OP is because it doesn’t support “what-if” analysis.  This is obviously something you need to look for in any tool for long range planning.  But simply allowing you to hive off a version of the plan and make a change is not enough.  The “what-if” analysis must provide detailed analysis as to what the impact of a given change will be throughout the supply chain.  A key supplier of low level components was just hit by a flood and won’t be shipping parts for six months – what impact will this have on my production?  What-if demand increases on one of my product lines?  What impact does this have on a constrained resource?… on my contract manufacturers?… on my suppliers?  Only a system with full supply chain analytics can address these questions.

• A related requirement is integration.  If you are going to provide answers to the “what-if” questions above, you will likely need to have detailed information from multiple manufacturing centers around the world. These centers in all likelihood are running a variety of disparate ERP systems, so your tool will need to integrate across all these systems and bring the data in to a single environment.

• As mentioned earlier, the ability to monitor performance against the plan and report on exceptions is key.  A S&OP plan is good.  A S&OP plan that you monitor and react to when underperforming is truly powerful.

There are several other factors that can play into your selection of a system for S&OP, but I think these are key ones.   What tools are you using for S&OP planning?  Are you evaluating new tools?  What factors are you considering?  Comment back and let us know!

What we haven’t seen is apps that are directed at manufacturing and the supply chain…yet. I saw an article in Industry Week several days ago talking about mobile technology being used in manufacturing.  Two apps are discussed in the article; one is an app that allows sales representatives to view pictures and specifications of complex products.  The other is a plant floor app that can be used for material flow analysis.   These are interesting and in some cases necessary, but there is another area where mobile apps can be really exciting.

Let’s play a little scenario out in our minds; imagine the typical in-person sales meeting.  You are visiting with a prospective client trying to get orders placed for one of your high-profit products. Your customer is interested but they have an immediate need which requires delivery inside of lead time.  To add to the stress, they will place the order if you can confirm it today; otherwise they will place the order with a competitor.  So how do you respond in this situation?  The optimist (or perhaps the cynic) might say that you take the order and pray that you can deliver.  The pessimist might say that you don’t take the order and hope that you can do business next time (because you haven’t antagonized the customer by not delivering on a promise).  Either way, you are put in a difficult situation because you don’t have the information you need at hand.

Now let’s imagine the same scenario but this time you are carrying a tablet connected to your supply chain software. Using the tablet, you create a scenario (a safe environment where you can do “what-if” analysis without impacting actual production) and simply add your customer’s order. The analytics in the supply chain software evaluate the new order in the context of the existing customer orders, and the current supply, and capacity information and then determines an available date. It turns out that the available date (when the order can be shipped) is acceptable to the customer and you can accept the order with confidence. You are happy, the customer is happy, and your supply chain is happy because you haven’t promised something that may not be delivered.  This is where the true power of mobile computing shines for manufacturing and the supply chain.

Manufacturing apps are here.  Those that embrace these new capabilities will have a distinct advantage over those are stuck in the old paradigms. But the journey is only beginning; how do you see mobile applications changing your job? Comment back and let us know!

Today, time is on the cutting edge. The ways leading companies manage time – in production, in new product development and introduction, in sales and distribution – represent the most powerful new sources of competitive advantage.

Unfortunately Stalk decided to name the book he co-wrote on the topic as “Competing Against Time” which isn’t the point, although the subheading “How time-based competition is reshaping global markets” rescues the concept, which is really about competing against the competition with time.  It is all about being more agile, more responsive, to real conditions. Stalk sets out some Rules of Response very clearly:

• The .05 to 5 Rule
  Across a spectrum of businesses, the amount of time required to execute a service or to order, manufacture, and deliver a product is far less than the actual time the service or product spends in the value-delivery system
• The 3/3 Rule
  During the 95 to 99.95 percent of the time a product or service is not receiving value while in the value-delivery system, the product or service is waiting. (Stalk breaks this out into 3 components of waiting, hence the 3/3.) The amount of time lost is affected very little by working harder. But working smarter has tremendous impact.
• The 1/4-2-20 Rule
  For every quartering of the time interval required to provide a service or product, the productivity of labor and of working capital can often double. These productivity gains result in as much as a 20 percent reduction in costs.
• The 3 x 2 Rule
  Companies that cut the time consumption of their value-delivery systems turn the basis of competitive advantage to their favor. Growth rates of three times the industry average with two times the industry profit margins are exciting – and achievable – targets.

All too often though people get the impression that these rules are only applicable in the short term.  They are not.  The issue of responsiveness in operations is driven by the latency of the information and the time it takes to respond. In other words, the time to detect that something of significance has happened and the time to respond to the change, or correct the discrepancy. Reducing either of these will have a dramatic effect on a company’s competitiveness, whether this is a short term detection of demand change that requires rescheduling manufacturing or a longer term change in technology that requires the purchase of new manufacturing capacity.

Terms such as VUCA – Volatility, Uncertainty, Complexity, and Ambiguity – or PDCA – Plan, Do, Check, Act – don’t excite me because they are focused on removing volatility and complexity, usually promoting ‘stability’ at the cost of responsiveness, whereas Stalk’s concepts are all about being responsive, being agile. To me this is the correct emphasis. While of course there is an overlap in that a decision or manufacturing process that is overly complex will result in longer lead times, it is the overall sentiment of complexity and volatility being ‘bad’ expressed in VUCA and PDCA with which I disagree.  As I wrote in a previous blog from the 2011 Gartner Supply Chain Conference:

I say embrace VUCA. Accept that it is the new norm. Resistance is futile.

Similarly, Deming’s idea of PDCA is all about process improvement, it is about ‘managing’ complexity and ensuring ‘consistent’ processes. Again, I am not saying that these are bad approaches in and of themselves, only that they are insufficient.  Knowing that you are performing a process consistently doesn’t mean that you are performing it well.  It is like assuming that if you throw everyone in jail who has committed a crime that we will live in a crime-free environment.

Far more interesting to me is the OODA – Observe, Orient, Decide, Act – idea from the US military strategist Colonel John Boyd.

The steps of the OODA loop are:

• Observation: the collection of data by means of the senses
• Orientation: the analysis and synthesis of data to form one’s current mental perspective
• Decision: the determination of a course of action based on one’s current mental perspective
• Action: the physical playing-out of decisions

While at first this may seem to be very similar to VUCA and PDCA, the key point to the OODA loop is that:

Time is the dominant parameter. The pilot who goes through the OODA cycle in the shortest time prevails because his opponent is caught responding to situations that have already changed.

In other words reduce the time to detect and the time to respond.  To put this into supply chain speak, it is all about:

• Visibility – having access to the state of the supply chain across a wide span of operations, especially in outsourced environments, in order to detect misalignments
• Alerting – knowing or calculating the impact of misalignments on key financial and operational metrics in order to understand the severity of the issue
• “What-If” – working with others in the supply chain to come up with alternatives and evaluating these quickly
• Collaboration – to reach a consensus on the best course of action that reduces risk while increasing performance

Another absolutely key concept expressed by Boyd is the need for ‘human judgment’, for the system to act as an organic whole to adapt to situations as they unfold at the location at which they unfold.  Having long chains of command that force front line people to get approval from HQ is antithical to this idea:

… large organizations such as corporations, governments, or militaries possessed a hierarchy of OODA loops at tactical, grand-tactical (operational art), and strategic levels. In addition, he stated that most effective organizations have a highly decentralized chain of command that utilizes objective-driven orders, or directive control, rather than method-driven orders in order to harness the mental capacity and creative abilities of individual commanders at each level. In 2003, this power to the edge concept took the form of a DOD publication “Power to the Edge: Command…Control…in the Information Age” by Dr. David S. Alberts and Richard E. Hayes. Boyd argued that such a structure creates a flexible “organic whole” that is quicker to adapt to rapidly changing situations. He noted, however, that any such highly decentralized organization would necessitate a high degree of mutual trust and a common outlook that came from prior shared experiences. Headquarters needs to know that the troops are perfectly capable of forming a good plan for taking a specific objective, and the troops need to know that Headquarters does not direct them to achieve certain objectives without good reason.

These are key concepts we at Kinaxis have been promoting for a long time.  Every second that we waste in making a decision is a minute less that we have available to actually respond to situation.  In sports, reaction time is a well recognized competitive advantage.  Reaction time is coupled with the ability to ‘read the game’ and, for example, to call audibles at the line of scrimmage in American football. (I have always felt more comfortable with ‘European’ sports, such as soccer, that are a lot less structured and orchestrated precisely because the players have a lot more decision making power.) So in the end perhaps Stalk’s title “Competing Against Time” was correct, but this is a process efficiency perspective.  I still prefer the OODA concept of competing with time because this is about process effectiveness.

Vinnie mentions the fact that Daimler AG has over 1,000 developers in R&D, and a few hundred IT people supporting their internal enterprise systems such as SAP.  Of course they still have lots of people designing cars, but it is the amount of software that is in a car that is amazing. We are all aware of the more obvious innovations such as OnStar, which is developing into a platform like any other such as iPad or Android, with the recent announcement at CES of a developer community.

But it is the stuff ‘under the hood’ that is truly revolutionary, especially for the supply chain.  We are only getting there, but stuff is happening, especially in the industrial world. We are getting ‘smart’ machines that can self-diagnose, determine the corrective action, start a case, determine inventory availability, and schedule a technician to make a physical change when the material is available.  But now that so much software is imbedded in systems, this may require a software patch to be developed and downloaded when available.

Of course this is an extension of concepts first described 10-15 years ago when RFID first became available, even as long ago as when bar codes were becoming standard.  But these technologies could only really expand the use of inventory data.  I don’t mean to belittle the value of inventory data.  Far from it since ultimately it is the availability of inventory on a store shelf or in a warehouse that triggers the need for replenishment to satisfy demand.  But ‘smart’ machines go so much further.  In addition, naturally there will be analytic apps that absorb the information coming from many ‘smart’ machines in order to detect trends, for example that a certain mechanical part is failing more frequently or after a certain length of use.

Naturally the ‘smart’ machine scenario I paint above is most applicable to the service supply chain, but with the increased technology content in nearly everything that is man-made, the ability for a company to change how their product behaves by simply downloading new software is an incredible change for both the users and manufacturers/designers of the equipment.  I am old enough to remember washing machines and dishwashers that had electro-mechanical controllers for different cycles, and the mechanical systems themselves were often designed specifically for the cycles offered in that model. Now the mechanics are nearly identical, with different cycles being driven by software downloaded onto identical controllers.  I can see the day when we will upgrade our appliances by simply downloading new software, assuming the mechanics hold up and the aesthetics are still acceptable.  But why not have a ‘smart’ skin with selectable color and/or pattern that the user can change at any time?

But, while I agree with Vinnie that this is a major trend, at the same time, as illustrated by OnStar recruiting an independent apps development community, ‘smart’ machines should really be viewed as simply another user experience, such as tablets. In fact, I don’t see any reason why a tablet couldn’t be imbedded in a car’s dash, or a dishwasher’s front panel. So in the end I think many of the other trends are still very relevant, especially big data and cloud. But this is ‘structured’ data. The ‘smart’ machines will be programmed to issue clear and consistent instructions.

So where does social fit into the mix?  Andrew McAfee wrote an interesting blog in 2010 titled “Did Garry Kasparov Stumble Into a New Business Process Model?” in which he explores the trends of machine intelligence. Machines are very good at doing repetitive things quickly, including ‘smart’ machines. As McAfee notes,

Kasparov notes that computers play chess not by simulating human reasoning, but instead by comparing all possible moves and their consequences — the resulting board positions, subsequently available countermoves, possible counter-countermoves, etc. — until time runs out and a decision is necessary. And time will always run out; there are 10^40 possible legal board positions and 10^120 possible games, so even today’s fastest computers can’t be exhaustive. But they can be thorough, precise, and consistent. They evaluate lots of options, compare them rigorously, and never ever overlook or forget anything that they’ve been programmed to take into account

In other words machines, even ‘smart’ machines struggle to deal with nuance and ambiguity, though of course the recent experiment run by IBM in which they pitted Watson against top Jeopardy winners shows that even in this realm computers are beginning to gain ground. However, as stated by Greg Lindsay in his blog titled “How I Beat IBM’s Watson at Jeopardy (3 Times!)”, Watson is beatable by competing in areas in which humans excel, namely ambiguity and nuance.

Binary relationships–countries and their capitals, for instance–would be easy for him to figure out, and he would beat me to the buzz every time. So I had to steer him into categories full of what I called “semantic difficulty”–where the clues’ wordplay would trip him up. I would have to out think him.

So I am confident that in supply chains we will forever need human judgment and what computers will bring is primarily speed.  It will be in the exchange of unstructured data and the exploration of nuance and ambiguity that social will play its biggest role within the supply chain. After all, most decisions made between two supply chain partners are about compromise in which there isn’t one right answer, even though that is a view many supply chain vendors have promoted over the years through the use of optimization.

In conclusion, I agree with Vinnie that ‘smart’ machines and imbedded apps are big news and a major trend in IT and user experience. But from a supply chain perspective my sense is that ‘smart’ machines will mostly be an additional contributor to big data, which in itself is big news in the supply chain.  But it will be the manner in which we exploit social concepts in the supply chain that will be a real breakthrough because so much of supply chain management is about risk mitigation across a range of possibilities in a very ambiguous world.

So, what does this have to do with supply chain you ask?  Many companies have major installation projects required to recognize revenue for their products; solar farms, cell phone towers, major equipment installation, etc.  They don’t receive full payment until the project is complete and running.  Typically, these companies will have a system that manages their supply chain and another system for project management. What is surprising is that these systems will often run independently of one another. If information flows between these systems, it is a manual update performed by the project manager or master scheduler depending of which way the information is flowing.

But isn’t there a relationship between the supply chain and the project that drives the requirements?  If a project moves out by six months, doesn’t that impact the supply chain?  If a major component on the critical path for the project is going to be late, doesn’t that impact the project? Project managers and supply chain managers are like those people in the commercial; walking along, doing their thing, eating their chocolate and peanut not realizing that there is a better way.

Let’s imagine the following scenario; you are a project manager managing the installation of a large piece of medical equipment at a hospital.  The contract has been signed, and the project plan has been approved.  But all is not rosy.  The schedule is aggressive and there are significant penalties if the project comes in late.

So, a few weeks have gone by, and things are progressing well; the supply chain has gone to work building the equipment and work has started at the hospital to prepare the site.  Then, one of the suppliers several levels down in the supply chain has pushed out their delivery date on a key component that goes into a critical piece of equipment needed for the project.  This lateness is propagated up through the supply chain and is now reflected in the delivery date of this assembly.  Traditionally, the project manager might never be made aware of this critical shortage, because traditionally, the project manager doesn’t have visibility into the supply chain.

In this new world, however, supply chain and project management are both part of the same system and are linked together. So, when the project manager comes in, he immediately gets an alert that the project is going to miss its date including the impact on revenues and additional penalty costs.  A few clicks later, the project manager sees that the lateness is due to a late assembly coming from the supply chain. With this information, he project manager can collaborate with the supply chain to resolve the issue.

Integrating supply chain with project management is like sticking chocolate in peanut butter.  Once you put it together, you realize that they are better together than on their own.   If you’ve been following posts in this blog about Control Tower, you may have realized that this is just one of the ways that Kinaxis is bringing various aspects of the company together into an integrated new way to solve problems across the enterprise.  However, you might not have thought about some of the practical applications of what we are proposing.   Do you manage projects as a regular part of your business?  How do you tie the project plan back to the realities of the supply chain?  Comment back and let us know!

As most know, Thailand has been hit hard by flooding during this year’s monsoon season. There has been a horrible human cost with hundreds dead, and millions of people affected. What many don’t know is that Thailand is a major manufacturer of hard drives and major brands like Seagate and Western Digital have factories there…thus the price increase for hard drives.  Not only is Thailand a primary producer of hard drives, they also produce components that go into hard drives, so even companies that don’t manufacture in Thailand are affected because they can’t get components.

As you would expect, the hard drive shortage is affecting upstream manufacturers too.  Many companies only have a few weeks of supply on hand and are seeing shipments impacted because they can’t get components.   Fortunately, it looks like the flood waters are starting to recede and some hard drive manufactures will start producing again this month, although forecasts show that high hard drive prices and shortages will continue well into 2012.

From a supply chain perspective, these events have again highlighted the need for companies to implement and maintain a supply chain risk management process. There is much content on risk management, but there are a few key points relevant to what we’ve seen recently;

• Identify key components and where they come from.  If you have multiple suppliers for these components, GREAT!  If these suppliers are in the same geographic region, however, you still can be at risk.
• For your key components, be sure to look at where your suppliers are getting their components from.  Thailand also has significant hard drive component manufacturers that supply hard drive manufacturers around the world.  Even if you source from different hard drive manufacturers in different regions, they all may get their components from a single supplier or region.
• Review your key components on a regular basis.  Many companies do this as part of their S&OP cycle as there is a logical affinity between risk management and S&OP.
• Make sure that you have the tools in place to be able to quickly assess and respond to a significant event like the Thailand flooding. ERP systems and Excel can’t do this fast enough to make a difference.  You need a tool that can model your entire supply chain, allow simulation and reporting to be able to get on top of a fast moving situation like this.

2011 has been another record year for major events with supply chain impact.  Japan is still recovering from the effects of the earthquake and tsunami.  The flooding in Thailand has impacted other manufacturing segments besides hard drives.  Companies operating on a global scale must look at how a regional event could impact their  supply chain.

For further reading, I (and others) have covered Supply Chain Risk Management in some detail over the past couple of years, including a white paper titled Essential Characteristics of a supply chain risk management strategy.  Has your company been impacted by the Japan earthquake or the Thailand floods?   Comment back and let us know

It has been said that Henry Ford used the two-bin system in the early production of his automobiles.  As you know, each bin had to be equal in size to hold the quantity used during the lead time of the part with a little extra for unforeseen emergencies.  When the first bin was emptied, a reorder card was sent to Purchasing to reorder another bins worth.

The two-bin system assumed, falsely, that all demand quantity was, for the most part, steady and continuous.  Because of this false assumption, there were many problems with stock shortages and inflated inventories.  There was also a problem where you had to stock lots and lots of different sized bins to operate such a system.

In the 1930’s some mathematicians worked to eliminate the “lots of different bins” problem by devising the Reorder Point quantity formula.  Here they said the reorder point quantity on hand was equal to the demand during lead time plus some added safety stock (RP = DLT + SS).  This formula allowed the two-bin system to still be used and stocked on any shelf without the bin problem but still falsely assumed all demand to be steady and continuous while still generating stock outs and inflating inventory levels.

Can you see the magic in the two-bin Reorder Point formula?  A lot of people can.  Reorder points are still used in many of the new sophisticated ERP software packages today.  Heck, I even read a published article where one author thought that MRP didn’t work for whatever reason and suggested we should do away with MRP in favor of a 3-Bin Kanban with two bins on the shop floor and one at the supplier.  After reading this I remembered the old saying that “Those ignorant of history are doomed to repeat it”.

By now you can figure out that I don’t believe in two-bin, reorder point magic, however I do believe in statistical forecast models.  I believe that all forecast models are wrong.  I don’t like to forecast at all but I realize that sometimes you just have to.  I’ve been trying to come up with a forecast model for the six numbers of the Powerball drawing for years.  When I come up with an accurate model, I’ll let you know.

Forecasts are simply guesses requiring constant vigilance and adjustments to history.

Two-bin reorder points assume parts are totally independent of each other so you have to forecast for every single part.  That’s a lot of guessing and adjustments to history which requires a lot of maintenance.

The MRP planning process was devised to avoid most of the smoke and mirrors hocus pocus by a dramatic reduction in the amount of forecasting (guessing) you have to do.  MRP has product structure.  The idea here is only to guess where you have to (Independent Demand) and derive (Dependent) demand from those fewer guesses via a product structure therefore requiring much less maintenance.

When you need reorder points for “C” items use the MRP generated Time Phased Order Point (TPOP) which utilizes independent, derived, unsteady and discontinuous demand, a future blog topic of mine.

Stepping back from the triad, and taking an objective view, the process and software pieces only really exist to make people more effective.  To go a step further, the process and software should exist to make the people work together more effectively.  You can think of this as the promise of collective intelligence of an enterprise.  In “executive speak”, you can think of this as fulfilling the promise of having the entire organization actually executing the strategy across all functions, on behalf of the organization as a whole.

Collapsing planning and execution (real-time response management to deviations from the plan) into a single integrated process and breaking down the silos between various functions, departments, and trading partners, are key tenets of an enterprise control tower solution.

Andy Coldrick, working with Dick Ling, advanced Dick’s original definition and process of S&OP in the 1990’s to introduce the concept of integrated reconciliation – the key to driving the cross-functional, collaborative process at the heart of successful S&OP.  In the past decade, they have advanced an implementation methodology to drive cross-functional collaboration to truly allow strategic and sales and operations planning to become an execution-driven activity.

Andy and I will be presenting a blueprint for enabling people to realize the promise of strategy execution on Tuesday, December 13^th.

Register now for the webcast: Orchestrating Success in Sales and Operations Planning by Driving Cross-functional Collaboration with RapidResponse Control Tower

In the late 1980’s I was working for a large government electronics manufacturer located in Scottsdale, Arizona.  This was the first time that I was on a complete MRPII (Manufacturing Resource Planning) implementation team.  My duty on this team was that of a Training Instructor, responsible for the development of the training materials and the facilitation of the courses.

During the initial training sessions I attended given by the software developers, one of the instructors introduced me to a field I had never heard of called fixed Manufacturing Lead Time Override.  He explained that it was to be used on an exception basis.  True manufacturing lead times were always to be used but on occasion, when some assemblies were bought outside or brought in from another division, you could temporarily override fixed and variable elements of true manufacturing lead times.

To my surprise, at completion of the implementation, fixed Manufacturing Lead Time Override was, in fact, the norm and not the exception.  All of the make parts were being planned utilizing fixed lead times.  This means that once you set a fixed manufacturing lead time, all of the manufacturing orders will use this one lead time.  If you have a 10 piece spares order, it will be back-scheduled with the same lead time offset as a 1,000 piece manufacturing order.  There is no variable element to give the 1,000 piece manufacturing order the correct amount of lead time it deserves.

When I questioned the use of Manufacturing Lead Time Override, I was told that the studies that were conducted found that the start dates of the assembly manufacturing orders were only off by about 2 – 3 manufacturing days and usually within the same week.  Besides, it takes a lot of work to come up with all of the move, queue, setup and runtimes and it is much easier to input one Manufacturing Lead Time Override.

I believe that there is a law of physics that states “The whole is equal to the sum of the parts”.  We aren’t just building and shipping assemblies, we are building an entire product.  If you have ten levels in a bill of material and you short all of the assembly orders by 2 – 3 manufacturing days, you are shortening the planned build schedule of the product by an entire manufacturing month along with scheduling the purchased material to support that product build by the similar amount.

The 2 – 3 day start date variation argument might hold water if you are just shipping spares assemblies but from a Master Production Scheduling viewpoint that is concerned with forecasting and shipping finished product it results in a crapshoot.  That’s right, MPS success then depends on the roll of the dice.  The manufacturing order quantities that closely align with fixed manufacturing lead times will succeed but the ones out of alignment will fail.  What makes it an even bigger crapshoot the fact that very seldom will you ever wind up with all of the manufactured order quantities for a finished product that align properly.  The odds against that go up as the number of levels in a bill of material increases.

Fixed manufacturing lead times appear to be the norm in just about every company I’ve come in contact with and so my question is:  Does the art of scheduling still exist?

Those senior schedulers were undervalued by their company but in my opinion, they were worth their weight in gold.  Who knows, if we redeveloped the art of scheduling, we might be mass producing aircraft as fast as they did back in WWII and maybe a 2012 Chevy Bel Air might cost $2500 again.

An early practitioner who became famous in our field, Ollie Wight, described a good, realistic schedule as basic and fundamental to the health and integrity of a good MRP planning process.  He also described the aftermath result of poor unrealistic scheduling as the informal systems of hotlists and shortage meetings engulfing the formal planning process.  The MRP planning process still exists, today, in its original form, as the foundation of our evolved MRP, Closed Loop MRP, MRPII and ERP systems.

Back in 1973, I was a young college graduate living in Columbus, Ohio.  I was very fortunate in my first job as a Materiel Controller (Buyer/Planner) to work with the first computerized MRP software package developed by IBM and APICS, the IBM PICS (Production and Inventory Control System) Package.  During those two years I learned a lot about the computerized MRP planning process and wondered how this complicated and detailed process was ever achieved prior to the invention of the computer.

IBM in cooperation with APICS modeled this software package from the most efficient manual planning processes of manufacturing companies such as Steelcase and Black and Decker.  These manual planning systems utilized Fixed Lead Times for purchased parts and a combination of fixed and variable lead time elements (fixed move, queue, setup and variable run time per unit) for the manufacturing lead times of make assemblies. Mfg. LT = Move + Queue + Setup + (Order Qty. * Run Time per Unit).

A week after being walked out the door due to the 1975 recession I found employment again in Columbus, as an Inventory Control Analyst (Shop Floor Scheduler) for a heavy equipment manufacturer of electric driven coal mining machinery.  This company was in business since the late 1800’s and it was there I found the manual planning process that preceded computerized MRP planning.  It was a complete manual MRP planning process utilizing Acme Visible index cards with lots of clerks running around posting entries to transactions.

I had to be totally retrained by some senior schedulers who wore green cellophane visors as headwear and striped long-sleeved shirts with arm bands.  I thought to myself that these were the type of schedulers who planned the production of the bombers of WWII and the 1957 Chevy Bel Air.  Wow!

There was one retraining session that will always remain in my mind.  The senior scheduler was judiciously studying one of his ledgers and said to me “Here, we have to create two manufacturing orders for this gearbox assembly, a 10 piece order for final assembly and a 5 piece order for a spares requirement.  They are both due on the same due date but we have to release the final assembly order on June 1^st and the spares order on June 15^th”.

I questioned him and asked since the orders were both for the same gearbox and due on the same date, why not set the release dates the same for both orders?  He stated that the orders were for different quantities and releasing them both on the same date would unnecessarily overload the broaching and Bridgeport machining operations.  He was doing CRP in his head!  He was also using the true manufacturing lead time elements of move, queue, setup and runtimes so the smaller quantity spares order could be released at a later date than the larger quantity production requirement.

The most important lesson I learned from the senior schedulers was the impact of the accuracy of the true manufacturing lead time elements to the success of the overall schedule.  The lead times elements had to be accurate with as little padding as possible.  Too much padding and you will overload limited resources and start the jobs too early along with bringing in purchased material too soon thus inflating inventory levels.  Too little padding or lack of lead time would release the jobs too late and not give the shop floor enough time to complete the orders on time.  Too little manufacturing lead times also schedules the purchased materials in too late and then when the informal shortage meetings are held you are in a constant expedite mode to move up the jobs on the shop floor and pull in the purchased material.

Stay tuned for part two tomorrow where I’ll discuss Manufacturing Lead Time Overrides.