Inventory Management Review

A Case Study in Software Implementation

I recently had the pleasure of attending a guest lecture delivered by Optiant director of research, Ph.D. John Neale on the topic of multi-echelon inventory management software implementation.  Optiant is a supply chain software solutions provider for companies including Gillette and HP.  His discussion described how the implementation of Optiant's PowerChain® software suite helped to dramatically decrease inventory and increase service for a well-known manufacturer of consumer adhesives.  This post will give a history of the adhesive manufacturer's supply chain problems and the solution they chose.  The results of that solution will be explained with a brief lesson on multi-echelon inventory.

Supply Chain
The manufacturer being discussed operates primarily in North America.  Their supply chain has the following characteristics:

Raw Materials
First, they procure over 3000 raw materials from multiple sources and hold the raw materials at one of their two manufacturing sites until they are processed.  This is the first stage (echelon) where inventory is held.  After the raw materials are turned into finished goods, they are immediately shipped to a distribution center (DC).

Finished Goods
The firm has two DCs and over 1800 SKUs (stock keeping units.  This is basically how many different products and package variations they have on those products).  The DCs are the final stop for finished goods inventory before they are shipped to a myriad of retailers.  Of those retailers, Wal-Mart, Staples, Home Depot, and other mass market stores constituted one third of their overall volume.  The DCs also receive some finished goods which come from other manufacturers in the form of finished goods.  The DCs are the second stage (second echelon, thus making more than one echelon, hence the name, multi-echelon) where safety stock is held.

Old Inventory Policy
Prior to Optiant's consulting work and software implementation, the manufacturer had no real method for determining their safety stock. Essentially, they used a trial and error method where they would set a level and if they were stocking out too often, they would increase inventory.  When they stopped stocking out, they would scale back inventory.  Their inventory was also high because of expansion in their product line and high service levels demanded by stores like Wal-Mart.

The manufacturer lacked the expertise regarding how to set a safety stock level that optimized each local inventory stage, and additionally, they were without the experience necessary regarding how to optimize the overall system.  As John Neale put it, this is a problem because while safety stock formulas can be useful for local optimization, one point he made was,

"Don't just optimize things in isolation."

Upon realizing that there were better ways of doing things, they contacted Optiant.

Optiant
What Optiant did for them was more than just selling them a software package. Optiant spent many weeks learning about their supply chain constraints and gathering data, and then used PowerChain®  to optimize the safety stocks for each of the 1500 SKUs and each of their 3000 raw materials.

Data Requirements
Much of this data is demand data. In order to get a feel for demand, Optiant uses historical demand projections.  In order to figure out what kind of deviation there is on these projections, they look at historical projections and compare them with historical demand realizations.  As you can imagine, a lot of companies don't keep accurate records regarding this data.  The less a company has in the way of records, the less effective software initially is.  Keep this in mind before you bring in any consultants: start collecting data before they get there, so you're ready to roll once they're on the clock.

Optiant also required supplier data, including lead times, costs, and a bill of materials (list of parts required for each SKU).

Software
Once they have the data, they can start to use their software model.  I'm not clear on the math that runs the program other than that it uses algorithms to minimize holding costs while maintaining service level requirements.  I didn't bother asking for more detail, because what I understand is this: the software they have works and is based on the kind of framework that you would expect to come from someone with a Ph.D. from MIT, which is precisely what Optiant co-founder, Sean Willems, has.  The point is, the program is complex, but it is not baseless, and it is not a hoax.  It is exactly the kind of complex software I was referring to when I wrote about the kind of advantage that professional software can offer that Excel can't even come close to providing.

Results
Before you refuse to believe the software works without understanding every detail behind it, consider that Optiant's solution allowed this adhesive manufacturer to raise their service level while lowering safety stock value by over 20%.  First of all, 20% is a very large reduction in safety stock value on its own.  In addition to this, they were able to raise their service level while lowering safety stock.  At first glance, this seems too good to be true.  Normally, the way to raise a service level is by raising safety stock, not by lowering it.  Why is this case any different?

Multi-Echelon Inventory Management
This case is different because it is a multi-echelon inventory model.  What this means in this case is that they had the opportunity to hold inventory at two stages: the raw materials stage and the finished goods stages.

Balancing Raw Materials and Finished Goods
Remember, holding costs are a function that involves the value of the inventory and at the raw materials stage the value is considerably less expensive.  This means that if the adhesive manufacturer has short production lead times from raw materials to finished goods, which they do, then they can afford to hold large amounts of raw materials, small amounts of finished goods, and still be in a position to meet demand.  Thus, by reducing finished goods inventory and increasing raw materials inventory, they can increase service level because of their ability to quickly turn raw materials into finished goods, and they can reduce inventory costs because they are holding less finished goods.

Risk Pooling
The other reason they are able to reduce safety stock value while increasing service is because they have so many SKUs that all use the same basic raw materials.  The importance here is the inherent flexibility that raw materials when they can become a variety of different finished goods.  This allows them to keep materials raw for as long as possible, which reduces their vulnerability to fluctuations in demand.  The vulnerability to these fluctuations is limited because many of their glue product SKUs are essentially pooled as one product with an overall demand that is less likely to fluctuate as long as products are kept as raw materials that can be turned into any product once demand projections are closer to demand realizations.  To further illustrate this is an example from the MITSloan Management Review about apparel manufacturer Bennetton Group SpA and how they delay final goods production by keeping raw materials in a position ready to be turned into finished goods:

An inventory of undyed sweaters gets stockpiled in one location; coloring takes place only after specific orders have been received. This pooling of demand across geographical areas, and across colors, helps Benneton greatly reduce inventory risk while more effectively meeting customer demand.1

Another example cited in the article is how the house paint industry holds only base paints which colors are added into instead of holding onto hundreds of different colors at each retail location.

The effects of this are incredible because for the adhesive manufacturer, paint companies, and Benneton, the risk of each individual product in the product line can be vastly reduced by simply keeping finished goods as raw materials for as long as possible.

Paint companies no longer have to worry about having too much blue paint and not enough red paint.  Statistically, the variations in each type of paint will even out.  So if yellow doesn't sell as much as expected and green sells twice as much as expected, paint companies are still ok as long as they have the right amount of base paint.  Unfortunately for the consumer this makes it difficult to return paint.

Luckily for the adhesive manufacturer, risk pooling works.  So does the software Optiant creates.

Final Notes on PowerChain®
Originally, the adhesive manufacturer only hired Optiant so that Optiant could use to use their software to tell provide a report detailing how to optimize each of their 1800 SKUs and 3000 raw materials.  The CFO of the adhesive firm was so impressed with the forecasting abilities of the software that he eventually invested in a license of the software.  I'm not sure whether or not they worked with Optiant to adapt the PowerChain®  software to their other computer programs for automated entry of optimal safety stock into their other systems, although this is something Optiant does.

I'm not trying to suggest that you dive right into the investment of such software, although I can't imagine Optiant would mind, but hopefully this post has given you a better understanding of what inventory management software packages can do for you and what the implementation process entails.

1 Sunil Chopra & ManMohan Sodhi, "Avoiding Supply Chain Breakdown", MITSloan Management Review, Fall 2004, Vol. 46, No. 1

McDonald's, a guide to the benefits of JIT

Just-in-Time (JIT) inventory is the big thing right now in operations.  This, along with lean operations and six-sigma are the buzz words being talked most about.  But what exactly is the deal with JIT operations?

First of all, JIT is a form of providing supplies for customers, as the name suggests, just in time.  For example, Dell, whom I wrote about, has become famous for its JIT model which involves not even being in possession of the raw materials needed to fulfill an order until that order is placed and yet they are still capable of filling orders in a short period of time.

McDonald's is another example of a JIT system wherein McDonald's doesn't begin to cook (well, I should probably say reheat and assemble what may or may not be actual food) its orders until a customer has placed a specific order.

What used to be the case was McDonald's would pre-cook a batch of hamburgers and let them sit under heat lamps.  They would keep them for as long as possible and eventually discard what couldn't be sold.  The only way to get a fresh hamburger under the old system was to make a special order.  Now, due to more sophisticated burger-making technology (including a record-breaking bun toaster), McDonald's is able to make food fast enough to wait until it's been ordered.

What both of these firms do is they provide a customer with their order as fast as possible while having the finished product sitting in inventory for as short as possible.

What are the benefits for McDonald's?
The major benefits for McDonald's are better food at a lower cost.

Let's stop here for a second to drive home a very important point: Whenever you can implement something that allows you to raise quality AND lower costs, you should definitely look into implementing that practice.  Unless illegal, immoral, socially irresponsible, or likely to drive down demand (which is unlikely considering quality is being improved), you are probably going to want to implement this practice.  Back to McDonald's.

McDonald's has found something that allows them to improve quality and lower costs.  Let's take a look at how it does both.

Improved Quality
I think benefits of a better tasting burger should be fairly apparent.  Unless of course you prefer aged burgers, the fresher burger is going to be higher quality if made fresh just for you.

The less obvious benefit is the higher quality customer service that arises from the JIT burger assembly.  When McDonald's waits for you to order the burger, they do a few things to improve customer service.  First of all, when you place a special order, it doesn't send McDonald's into a panic that causes huge delays.

Now that McDonald's is in the practice of waiting until you order a burger until they make it, they don't freak out when they have to make a special order fresh just for you.  This higher quality customer service is subject to McDonald's ability to actually produce faster.  Without this ability, McDonald's ordering costs would be sky-high because the costs associated with ordering would be the loss of customers tired of ordering fast food that really isn't fast.

Second, JIT allows McDonald's to adapt to demand a little bit better.  Seemingly, lower inventory levels would cause McDonald's bigger problems in a higher demand because they wouldn't have their safety stock.  However, because they can produce burgers in a record time, they don't have to worry about their pre-made burger inventories running out in the middle of an exceptionally busy shift.

Lower Costs
The holding costs for burger parts (beef, cheese, buns, whatever other garbage they put on their burgers) are fairly high because of their spoilage costs.  Frozen ground beef that's good today might not be so good in a few months.  Once cooked, the same ground beef's spoilage rate shoots through the roof.  Instead of having a shelf life of months or weeks, the burger needs to be sold within 15 minutes or so.  The holding costs go from roughly 20% per week to 100% per hour.

In other words, under McDonald's old system, they produced at a level that gave them high inventories so that food would be available fast, which is the main benefit of fast food.  Unfortunately, food that was unsold after a short period of time was scrapped.  Food that was sold was forced to be sold at a higher price in order to absorb the scrap costs of unsold food.  Ultimately this meant higher costs for McDonald's.

For McDonald's, the benefits of JIT are fairly clear.  For Dell, it was the same way.  So what is it that both of these firms have in common, and ultimately, when is JIT a good system to implement?

Why JIT

Economic Order Quantity Savings
A large benefit of JIT is that it reduces the total cost of ordering and holding inventory.  Let's quickly recap three firms that have achieved this and how they did so.

Dell and McDonald's
High holding costs are the nature of the computer and fast food industries.  JIT system allowed them to exploit the savings that were realized by holding less inventory.

Wal-Mart
Instead of having particularly large holding costs, Wal-Mart recognized that they were in a position to make ordering costs very small.  Because of their importance to their suppliers, along with their software made affordable through economies of scale, Wal-Mart has made ordering a very small percent of their overall costs.  By lowering ordering costs, Wal-Mart has made ordering small batches with greater frequency a profitable reality.

High holding costs and low ordering costs are the factors that drive JIT.  Generally, it's the ability to lower ordering costs that make it a feasible solution.  McDonald's and Dell were both slaves to the high holding costs.  It was just the nature of their industry.  The solution for them was that while they couldn't lower holding costs, they could lower ordering costs.  Wal-Mart didn't even have particularly high holding costs, but they realized it would be profitable to lower ordering costs which led to high holding costs as a ratio of holding costs to ordering costs.

What McDonald's, Wal-Mart, and Dell have in common is very high holding costs in comparison to their ordering costs.  Ultimately, this, coupled with the ability to lower safety stock, is when JIT is effective.  EOQ determines how much you should order and there are two factors that drive economic order quantities down: low ordering costs and high holding costs.  Depending on the product and the industry, one or both of these qualities may exist in your operations.  If they do, JIT may be right for you.  Without the ability to make ordering costs low as a percentage of holding costs then there is no need for JIT.  In fact, the increased frequency in ordering will result in cost increases.

Safety Stock Reductions
The other aspect of JIT is the drastic reduction in safety stock.  My previous article on safety stock discussed the two reasons safety stock exists:  variability in demand and variability in lead times from suppliers (in McDonald's case, the supplier is the internal production process).

It is because of this variability that safety stock exists in the first place.  What JIT does is tries to reduce the lead times and variation in lead times in order to help reduce safety stock.  Let's revisit the safety stock formula to figure out why this is:

Safety Stock:  {Z*SQRT(Avg. Lead Time*Standard Deviation of Demand^2 + Avg. Demand*Standard Deviation of Lead Time^2}

The first term is Lead Time*Standard Deviation of Demand^2.  This is the inventory needed to account for fluctuations in demand during the lead time.  If lead time is shorter, which JIT tries to accomplish, then this part of the safety stock is smaller, this lowering safety stock inventory.

Wal-Mart and Dell accomplished this by using better software and communication with their suppliers.  McDonald's accomplished this by creating a system that allowed a faster burger production (remember, McDonald's lead times are internal).

The second term is Avg. Demand*Standard Deviation of Lead Time^2.  This is the inventory needed to fill demand because of lead time variance.  If lead time has no variance or is reduced then this term can be eliminated or at least reduced.  Again, this is what JIT try to accomplish.

Wal-Mart accomplishes this by demanding it, Dell by working with suppliers, and McDonald's by standardizing production.

In order to accomplish the tasks of shortening lead times and reducing their variances, a considerable amount of work needs to be done with suppliers/internal operations.  For some firms this is worth the trouble, for others, it is not.

Conclusively, there are two major parts to JIT inventory operations: lowering the ratio between ordering costs and holding costs and shortening lead times.  What results is a firm with such high holding costs that ordering very small batches very frequently is the most profitable solution.  This eliminates average inventory above the safety stock level.  Then, if lead times and lead time variability can be decreased, safety stock can be decreased.  The result is inventory coming in as it needs to come in.  In other words, it comes in just-in-time.

Dell Computers: A Case Study in Low Inventory

When managers discuss low inventory levels, Dell is invariably discussed. Hell, even I've mentioned Dell on this site. So why all the commotion? Has their low inventory REALLY helped out that much? In short, yes. This article is primarily going to discuss how much it helped. This article will not discuss how they achieved such high inventory turns using a state of the art just in time inventory system.

Reasoning behind need for lower inventory

The first thing that needs to be discussed is why low inventory has such a great effect on Dell's overall performance. The reason is quite simple: computers depreciate at a very high rate. Sitting in inventory, a computer loses a ton of value. 

As Dell's CEO, Kevin Rollins, put it in an interview with Fast Company: 

"The longer you keep it the faster it deteriorates -- you can literally see the stuff rot," he says. "Because of their short product lifecycles, computer components depreciate anywhere from a half to a full point a week. Cutting inventory is not just a nice thing to do. It's a financial imperative." 

We're going to assume that the depreciation is a full point per week (1%/week) and use that to determine how much money high inventory turns can save Dell. 

This means that for every 7 days a computer sits in Dell's warehouses, the computer loses 1% of its value. Ok, now that we know how much Dell loses for each day, let's take a look at some of Dell's data over the past 10 years that I pulled from www.themanufacturer.com 

What I got from this was the inventory turns. An inventory turn, as this website successfully describes it, is "cost of goods sold from the income statement divided by value of inventory from the balance sheet". Typically, this is turned into a value showing how many days worth of inventory a firm has by dividing inventory turnover by 365. I divided the inventory turnover by 52 in order to show how many weeks worth of inventory Dell holds. 

Here are the results:

Dell’s Inventory Turnover Data 

Year      Inventory Turnover         Week's Inventory

1992      4.79                                10.856
1993      5.16                                10.078
1994      9.4                                  5.532
1995      9.8                                  5.306
1996      24.2                                2.149
1997      41.7                                1.247
1998      52.40                               0.992
1999      52.40                               0.992
2000      51.4                                1.012
2001      63.50                              .819 

Key point to notice here is that Dell was carrying over 10 weeks worth of inventory in 1993. By 2001, Dell was carrying less than 1 week's worth of inventory. This essentially means that inventory used to sit around for 11 weeks and now it sits around for less than 1 week.

So what does this mean for Dell?

Remember, computers lose 1 percent of their value per week. This isn't like the canned food industry where managers can let their supplies sit around for months before anyone bats an eye. Computers aren’t canned goods, and as Kevin Rollins of Dell put it, computers “rot”. The longer a computer sits around, the less it is worth. 

That said, due to depreciation alone, in 1993 Dell was losing roughly 10% per computer just by allowing computers to sit around before they were sold. In 2001, Dell was losing less than a percent. Based on holding costs alone, Dell reduced costs by nearly 9%. 

Since 2001, Dell has continueed to lower inventory. Looking at their latest annual reports, day's inventory has dropped by approximately a day. 

Hopefully this article provided you with a practical example that demonstrates the positive effects lower inventory can have on a firm's overall costs. For more information regarding lawyers in the Texas area, check out Dallas Fort Worth trucking accident attorney. For more basic information regarding holding costs, please read A Simplified Look at the Pros and Cons of Inventory.

Notes From an Inventory Management Consulting Job: Part IV of IV

This is the final post of a series detailing a consulting job I recently completed.  This post discusses the pros and cons of employing Excel as the decision support system.

The spreadsheets built for this job are a very good example of an automated inventory tracking system that can be built for much less than the price of purchasing a “real” inventory management software package.  There are however some definite pros and cons to look at when building your own software package using excel.

The main pro is the price. Excel is a software package that just about every office already owns and with a little bit of know how, you can build whatever you want. In some cases you might now even need to have any clue in terms of writing code to use excel to get it to do what you want it to do. It’s also a nice package to use because everyone in the office should already know how to use it. 

The downside of using excel is that it might not be able to do everything you want it to do, nor may it do everything it should do. For example, in this project I encountered a problem with the inventory counting. The counts should really be updated every 6-8 weeks just to be safe. But when they are, the used materials that are subtracted out of them need to be cleared. Unfortunately this involves the inventory manager at the mailing room to update the values in excel. Really this isn’t a huge deal but ultimately it cuts down on the automation of the system. (As it turns out, I found a way to get around this about 2 weeks after I wrote this, but I can assure you, it was a serious hassle).

Another serious hassle is getting these spreadsheets onto the internet. After completing this consulting job, I was recommended by the firm I consulted to do a job almost identical to this job. The only notable difference is that I had to put the spreadsheets onto a webpage that gets updated daily. Although possible with excel, the interface is not as friendly as I have seen with other software packages. 

Another problem with excel for this project is that I ended up using it as a daily and weekly demand database when really, Excel is a terrible database system. What makes it so terrible is that it does close to nothing to verify that the data is complete. Ensuring the integrity of the demand data is very using Excel. 

Overall, considering I’m not a information system specialist and yet I did manage to build this system using excel, I would have to say that for low level jobs such as this one, excel is an excellent tool that is already at hand and ready to be utilized by those who know what they are doing with it. As for the integrity of the data, had I teamed up Access with Excel, I could have ensured the data’s integrity to a better degree. In the end, the company got a software package that meets their needs for a sliver of the price that it would cost to buy a “real” inventory management system. 

If you would like to see the spreadsheets I have made for the firm discussed in this series of articles, you can now download them.  Please leave a comment letting me know your thoughts.  I haven't received any from any users yet (thanks a lot guys), so don't be a jerk.  I'll be posting user instructions very soon.  I promise.  Sorry to all who left comments any haven't received them yet.

Notes From an Inventory Management Consulting Job: Part III of IV

3 of 4: Building the decision support system

Now that I have determined the Reorder Points, I needed to figure out how to help the managers determine that their inventory was at this level without having to count their inventory each day. This requires the following steps:

Step 1: Count the current inventory levels. This needs to be performed as a starting point. In this case it only took about 30 minutes to count it and enter the values into the database I built for them.

Step 2: I then entered these values into a column that was labeled as Most Recent Inventory Levels in an excel spreadsheet.

Step 3: I setup up a database in excel that keeps track of how many and of what type of kits are sent out on a daily basis. Data for this database needs to be entered daily.

Step 4: I took the daily data and summed it up so I now had total data for each type of kit.

Step 5: I determined which materials were associated with each type of kit and I set up Excel so that it automatically subtracts each days raw material usage out of the current inventory levels.

Now I had setup a system that automatically subtracts inventory as it is used up. This constantly accurate inventory level is compared to the Reorder Points and when the actual inventory level is less than the Reorder Point, the inventory manager is notified that it is time to reorder. This is all achieved by the inventory manager spending approximately 1 minute a day to enter in the day’s demand into the system.

While the system is setup to notify the manager, it is important that the data is as accurate as possible. I mentioned in the previous post that the demand data only contained 19 points. This is not exactly a huge sample size. To account for that, I have set up the system so that it constantly reevaluates the demand data.

This is achieved in a very simple fashion. The daily demand is kept track of as the inventory manager inputs it into the system. This demand is broken up into the two different types of kits. That data is then taken to a different spreadsheet which uses the recently entered demand data along with historical data and calculates the average demand, the standard deviation of demand, and the average ratios of the two types of kits.

The only thing left to consider now was how returned items affect inventory levels. Because of bad addresses, a small percent of the mail comes back to the mailing room. Lucky for them the more expensive parts of the kit can be re-used. Realizing this could throw off inventory levels, I altered the spreadsheet so that there was a sheet into which daily returns are to be entered. This data is used to add in additional inventory to the parts that can be reused.

On a normal consulting job I would now evaluate the Economic Order Quantity to really help keep inventories as low as possible, but quite frankly, I didn’t feel like it in this case. Ok, the truth is it wasn’t necessary. As stated earlier, the quantity discounts are too great to pass up. The ordering costs are high and the space used for inventory is going to waste otherwise. Finally, the inventory isn’t particularly expensive, so the money invested in them isn’t even really a waste of funds. The economic order quantity for them is as many as they can fit. 

This post concludes the step by step discussion of how the system was built. The next post will discuss the usefulness of Excel as the decision support system.

 

Notes From an Inventory Management Consulting Job: Part II of IV

2 of 4: Collecting the remaining data needed to determine Reorder Points 

To recap, at the end of the first day of consulting I had found out what went into the kits and the lead times for the materials. On day two it was my job to determine what the demand was and how much standard deviation was in that demand. Lucky for me the mail room manager keeps excellent records of this data. Not so lucky for me was that their demand recently increased by quite a sizeable amount due to a permanent increase in advertisement and that this demand was projected to remain high. This cut my useable demand data drastically.

Fortunately the manager was still able to supply me with 19 pieces of demand data. This data was enough for me to determine the average demand the mail room faces as well as the standard deviation in that demand. I then took that data and turned it into weekly figures and determined the average weekly demand and average weekly standard deviation. Although this weekly demand data is only based on 3 complete weeks worth of data, the data will be better in the future, as I’ll explain later.

As I mentioned earlier however, there are certain types of kits that require an additional booklet as well as an additional 30 pieces of paper. In order to determine the approximate demand for these materials, I determined the approximate percentage of kits that require these extra pieces and then I counted those pieces into the use of raw materials only as often as I projected they would be used. 

Now I had the information I needed to determine the Reorder Point. I had the average lead time, the average demand, and the standard deviation of the demand. The missing piece of this formula is the z value which comes from the service level. Service level and z values are explained in greater detail in a previous post which can still be found on this blog.

I multiplied the average lead time by the average demand together in order to get the point at which they should reorder assuming there is no deviation in demand. However, since there is always deviation from the average demand I also determined the safety stock.

To get the safety stock, I used the following formula:
 

z*Square Root of (Average Demand^2*Standard Deviation of Lead Time^2 + Average Lead Time*Standard Deviation of Demand^2)

I Then added the safety stock to the result  determined from multpiplying demand with average lead time and together they make the reorder point.  When current inventories reach this level, the managers need to reorder.

Notes From an Inventory Management Consulting Job: Part I of IV

This post is the 1st of 4 posts that provide detailed notes of an inventory management system design job and the building of a decision support system that helps managers determine when to reorder materials. 

I was recently hired to setup an entirely new inventory management system at a small mailing room in Los Angeles. Rather than keep what I learned to myself, I thought I’d share it with all of you. First I’ll give you some background information on the mailing room. 

The mailing room processes leads from a variety of different sources and sends each lead a mailing kit that has a few pieces of paper, a business reply envelope and a couple of booklets in it. This fairly small operation handles approximately 1500 leads a month and is run out of a vacant room in the office. The booklets, the business reply envelopes, and the large envelopes are all printed by another firm. The loose sheets of paper are printed in house and are printed on blank paper shipped from Staples. 

When I arrived at the mailing room I talked to the man in charge. While he had set up a very good system for keeping track of leads, when and where they came from, whether or not they were delivered successfully, and while he had setup a fairly automated mailing kit production process, his inventory tracking system was as archaic as his lead tracking was detailed. The ordering of his supplies was very simple. He would order as much as he had room for because the quantity discounts at the printing house were so great that ordering small amounts was not even an option. 

For him, the real question was when to order. Previously his policy regarding when to reorder was, “I dunno I reorder, when it looks like I’m running out.” As funny as this may seem, it is not the only company I have encountered that has this policy, and I can assure you, that is not a good thing. In this case, when I say it is not a good thing, I mean that in the sense that they ran out of supplies and now are facing two weeks of production with a missing part of their kit. I guess it looked like they had more inventory than they really did. 

Now I’ll discuss step by step exactly how I set up their system. 

The first step I took was to determine all of the individual components that are associated with the final product. It is a little bit more complex than what I mentioned earlier, so I’ll break it down in more detail. Each mailing kit involves the following raw materials: 

1 letterhead

Black Toner

2 blank pieces of paper

Color Toner

1 Booklet A

1 Booklet B

1 Booklet C

And sometimes the kits require an additional 30 pieces of blank paper AND

1 Booklet D 

The second step I took was to determine the lead times of each of these raw materials. Right now these are estimates. The manager in charge of ordering the materials has informed me that he does not know how long it takes for him to order and receive shipments on any of his supplies except paper and toner. His best estimate for the other supplies was that the lead time was approximately a week and a half.

That's about all the information they were able to give to me on the first day, so I think that's all I give you.  Part II, which will be published on July 28th will discuss the collection and analysis of the demand data.