The company was using a computerized standard Reorder Point mainframe software system called MMIS (Materials Management Information System).  Yes, my job was to “Set em and Forget em” then deal with the aftermath (stock outs, inflated inventory) of thousands of parts, much like the manual standard reorder points I discussed in a past blog.

Our customer service goal that we strived for was 95 percent.  I was doing fairly well with my parts hovering around that goal so when one of the analysts responsible for the scheduling of the overhead and underground cable was tracking in at 80 percent, my boss, the Inventory Control Manager ,invited me into his office and assigned the cable scheduling to me.

He explained to me that although there are only 26 different cable part numbers, these were critical parts.  A stock out on cable meant that union workers went home until the cable came in.  He also noted that although we only use, at most, about $300,000 worth of cable per month, we have to maintain about $3,500,000 of cable inventory on hand to achieve the 95 percent customer service goal.  I thought to myself that this is typical when you use classic reorder points that falsely assume steady continuous demand.  That’s over ten times the actual inventory dollars that you need.

It was in May of my second year of employment with the company and my boss asked me if I could get the customer service level back up to at least 95 percent by the end of the year.  Being the cocky young puppy I was back in those days, I replied that “I don’t see any problem with 100 percent service level and an on hand inventory dollar reduction of at least a million dollars”.  To which he replied, “You’re on!  I’ll bet you a steak dinner on January 2^nd that you can’t do both.”  I felt that to be a pretty safe bet.

I realized that what I had to do with those 26 part numbers was to take them off of the standard reorder points and time phase them using a TPOP (Time Phased Order Point).  Time phasing is what standard reorder points are lacking.  TPOP has the capability to signal when reschedules are required to grind down your projected available to zero which indicates an exact match of supply and demand above safety stock.  Ollie Wight in his book, Production and Inventory Management in the Computer Age describes TPOP as “simply the MRP logic used for independent demand items.”

Please see below how I manually set up these 26 overhead and underground cables on TPOP.  I obtained  history from MMIS which had up to ten years of past history including seasonality.  This was a very good source that I used as input for my forecasting of Gross Requirements.  I would monitor the history of these and every two or three weeks I would readjust my forecast to current history.

The first month of June, inventory dollars rose from $3,500,000 to $3,750,000 but because of the rescheduling capability of TPOP, I was able to get the customer service level up to 100 percent.  I was able to maintain the 100 percent customer service level every month through December of that year.

Every month after June, inventory dollars did fall dramatically. At the end of November, I was well on my way to enjoying that steak since inventory dollars had dropped by about $2,000,000.  By the end of December of that year the inventory dollars plummeted from $3,500,000 in June to around $487,000.  That’s over a $3,000,000 reduction of unnecessary inventory.

The additional drop in inventory dollars was helped along by an ice storm that hit the El Paso, Texas area in early December.  The utility company in El Paso had called up my boss stating that they were in an emergency situation with lots of power lines down and they didn’t have enough overhead cable in stock to repair all of the downed lines.  They wondered if we could help them out with a loan of cable.

With my manual time phased dashboard capability, I had a complete view of the on hand cable I could relinquish immediately and was able to work with my cable suppliers to redirect cable commitment from my purchase orders to the El Paso utility’s requirements.  The Inventory Control Manager of the El Paso utility expressed awe and appreciation that we could cover their total emergency needs with on hand inventory and purchase order adjustments.

On the morning of January 2^nd of the following year, my boss walked into my cubicle and said:  “John the warehouse supervisor over at the cable storage facility called me and he was very shook up”.  I replied:  “What’s he got to be shook up about?  He’s got all the cable he needs with 100 percent customer service level.”  He then walked out of my cubicle saying nothing more except “Give him a call”.

Confused, I got right on the phone and called John at the warehouse.  I asked him what was he so shook up about.  He said:  “Ray, it’s the craziest darn thing I have ever seen.  I have all of the vendor’s cable delivery trucks lined up on one side of the road leading to the warehouse and on the other side of the road I have all of the crew’s construction trucks awaiting cable.  We have hardly any cable in the storage lot and I have guys on forklifts on the road transferring spools of cable directly from the delivery trucks to the construction crew trucks.”  He did admit, however, that he had all of the cable he needed.

I went over to see this spectacle and sure enough, three or four forklifts were busy transferring the large spools of cable from flatbed delivery trucks to the construction crew’s trucks.  It filled me with a great sense of accomplishment to see the fruits of my cubicle efforts in action.

Later that day, my boss took me to a great steakhouse for lunch and as the waiter was walking over to our table, he asked me how I would like my Rib-Eye?  I answered:  “medium-well please”.

The trade off is cost versus speed as to how to get the goods from Asia to North America: via air or sea.  In an ideal world, and especially for bulky items, freight by sea is the most cost effective option and is used as the default shipment option.  Shipments by sea can take around a month or even longer to reach the destination.  Therefore, there are times when shipments need to be expedited to meet demand and air freight must be used.  This is a classic supply chain problem: how to balance transportation costs against demand and supply.

Once the decision to put the materials on a boat is committed to, these materials are basically unavailable for a month or more.  This can be a real problem to have this in-transit inventory tied up when these materials are needed to fulfill shortages or customer demand.

Many ERP systems have modules available to help manage transportation, and there are also third party logistics applications or Transportation Management Systems (TMS) on the market that can be “bolted on” as well to assist with transportation management.

However, some companies struggle with the issue of transportation, primarily because there are multiple considerations:

1. Which orders should be expedited to use air shipment?
2. Is the cost of the expedited shipment worth it?
3. How do you effectively communicate to suppliers which orders to expedite?

Related to item 1, we need access to planning information to determine orders that potentially are late to demand.  Adding in item 2, we need to be able to evaluate the cost of expediting against the additional revenue that can be realized in the current period with the expedited order.  This implies that some sort of simulation capability needs to be present to model the change in the supply plan and how that will impact the fulfillment of demand.  Item 3 has to do with the actual execution, purchase orders may need to be updated or at a minimum the change in shipping method needs to be clearly communicated to the supplier.  The considerations or dimensions of the problem can be summarized as

• plan/simulate,
• calculate cost, and
• execute. 

Many of the third party or extended modules available on the market today can only handle one or two of the three considerations.  Therefore, the above considerations require a “one to many” solution. The ideal solution is to have an application that enables all three of the dimensions: determining what needs to be expedited and the resulting impact on revenue and customer service; calculating the cost and benefit; and effective communication to suppliers and execution.

I am curious if you have any insights as to how you have (or suggested approaches) solved this multi-dimensional problem of transportation management?

The entire Q&A along with PJ’s introduction and commentary on Kinaxis can be found here (free registration required).

PJ: Do you see a link between S&OP and multi-echelon inventory optimization (MEIO), and what do you offer in that regard?

TM: There is a wider link between S&OP and multi-tier visibility, planning, and control. S&OP has been traditionally seen as an internal process focused on getting consensus among different internal functions. But S&OP was conceived well before the advent of pervasive outsourcing and offshoring: a time when the manufacturing, let alone commodity management and procurement, were just ‘down the hall’—a time when most customers were in the same country and spoke the same language.

The intervening years haven’t reduced the level of visibility needed. In fact, offshoring has increased the need for multi-echelon visibility, while outsourcing has reduced multi-echelon visibility. So I am absolutely convinced that multi-echelon inventory visibility, and of course demand visibility, is closely linked to S&OP. How inventory levels will be set will depend on the contractual agreements between the original equipment manufacturer (OEM)/brand owner and the contract manufacturer. In addition, S&OP is often referred to as sales inventory operations planning (SIOP) and production, sales, inventory (PSI)—both cases emphasizing the inclusion of inventory management in the overall S&OP process.

RapidResponse provides ways to determine ideal inventory levels given the customer service level targets and historical demand and supply patterns. Scenarios can then be used to test the financial and operational consequences of changing safety stock or reorder point (ROP) values.

PJ: There is indeed a great deal of cross-functional cooperation and collaboration that is required for managing S&OP. How are companies enabling this, and are they doing it successfully?

TM: From a process perspective, it is difficult to get people to work across functional boundaries, let alone across organizational boundaries. In addition, technology is often a barrier when each function has its own data and systems for analysis. Not only are there arguments about the results, but also about the data. Another barrier is that often, particularly in outsourced environments, people don’t even know whom to call in another function or company to resolve an issue.

As discussed above, we enable cross-functional cooperation and collaboration by providing a solution that has a single data model, a single set of analytics, and a single UI. Naturally, we have a full security model and the ability to filter and aggregate the data to make it relevant to the person and their role. If anyone makes a data change that has a significant impact on someone else, the person affected will be alerted immediately and can then collaborate on resolving the issue. Naturally, more than two people can collaborate in a given scenario. Any changes are immediately visible to all participants in the scenario.

PJ: What is your take on the link between S&OP and collaborative planning, forecasting and replenishment (CPFR), and what are your customers doing in that regard?

TM: CPFR, in the strictest terms, has been a failure largely because it was a burdensome process and because of the expectation on the part of consumer packaged goods (CPG) manufacturing companies that it would improve the forecast accuracy of the retailers. If instead we take CPFR to simply mean a more collaborative and inclusive planning process between trading partners, then I would say it is on the rise. As already discussed, outsourcing of manufacturing has led to the need for CPFR between the OEM and the contract manufacturer by extending internal cross-functional cooperation and collaboration to contract manufacturers in particular.

PJ: If you had to name the top three priorities for a company looking to evolve their S&OP process, what would they be?

TM: I would say the following:

1. Do S&OP more frequently, preferably continuously.
2. Do S&OP collaboratively and consecutively, not sequentially through a traditional five-step process.
3. Understand that S&OP is one step in a planning continuum, and that all steps need to be synchronized constantly.

PJ: What role does exception management play, or should play, in S&OP?

TM: All planning, let alone S&OP, should be governed by the principle that execution against the plan should be monitored continuously. In addition, if there are major market shifts, a company must react quickly by regenerating the S&OP plan. But exception management is important not only in monitoring execution of the plan and market shifts, but also in detecting big changes in the plans being generated. But the exceptions need to be relevant to the person receiving the alert, and the supporting data needs to be packaged in a manner that is relevant to that person.

PJ: How and where do ‘what-if’ capabilities fit into the S&OP process? Is it a priority capability for an effective S&OP process?

TM: I don’t see how S&OP can be carried out effectively without strong ‘what-if’ capabilities. It is more than a priority—it is a core requirement! Humans are so much more creative than machines. What they need is a rapid way of testing alternatives and evaluating the consequences in a timely manner. If they have an effective manner to understand the effect of the decisions on financial and operational metrics, they will inevitably make the right decision, particularly when several alternatives can be compared side-by-side.

PJ: What is the role of master data management (MDM) in S&OP, and what is Kinaxis doing in that regard?

TM: Because of our long history in the outsource high-tech/electronics space, we have had to deal with MDM-like issues for a long time, particularly equivalent item numbering, including bill-of-materials (BOM) structures. We focus a lot of attention on data quality and have a number of workbooks that identify missing and incomplete data. Without a doubt, MDM systems do increase the quality of data, but I do not see any reason to delay the deployment of RapidResponse until the MDM system is in place.

Clearly, the business is being run with the existing data, and using the existing data in a more effective manner would only be beneficial. However, I would not recommend waiting for perfect data. If an MDM system or data warehouse already exists, we can integrate it.

PJ: Some S&OP/IBP players offer functionality (often via acquisitions) for DP, trade promotions, financial consolidation, strategic network optimization (SNO) (‘what-if’ simulations of networks), and even PLM capabilities for NPI/PPM. What is your plan of action for successfully competing with these much broader and strategic-level S&OP offerings?

TM: As stated above, we provide a single solution to satisfy many supply chain processes using a single data model, a single set of analytics, and a single UI. We believe we already have a broad and strategic-level offering. Our analytics either currently covers all of the capabilities you list above or will shortly.

We have a fundamental issue with the concept that S&OP can be satisfied effectively using an overlay solution that has a separate data model and analytics from other planning tools. How can NPI be separated from DP/forecasting, supply planning, and capacity planning? By extension, how can you finance these other functions? The relative importance of these adjacent capabilities will depend on the industry, and clearly we have broader coverage and deeper capabilities for the industries we focus on, namely high-tech/electronics, aerospace, industrial, and pharmaceutical.

PJ: Do you have any other observations and trends related to S&OP that haven’t been mentioned in the previous questions?

TM: Without a doubt, there is a trend to a broader description of S&OP, particularly in outsourced environments where the contract manufacturers, at the very least, need to be included in the S&OP process. But, this is not only broader in ‘geographical’ coverage, but also in departmental function and time horizon.

That’s it! Thanks for following along.

This got me to thinking about my experiences in the pharmaceutical industry, where product expiry and scrap due to expiry are a key planning issue. Could the methodologies we use to mitigate expiry be used when planning for obsolescence? While on the surface it would appear the two are unrelated, when looked at closer from the planning perspective, commonality begins to emerge. Both scenarios deal with supply that is unavailable (or degraded) for use after a period of time, both scenarios deal with material that must be scrapped and disposed of, and both can have severe impacts on inventory and the bottom line.

Expiry deals with product which has a defined life, usually due to efficacy and regulatory issues. Obsolescence is much more difficult to define in terms of a definitive lifecycle, but products have a finite lifecycle due to customer demands driven by technology and market changes. While obsolete products don’t necessarily result in product scrap and disposal, dealing with obsolete products can have a significant negative impact on margins and inventory levels, as well as cannibalize market share from newer products that replace them.

What if we used expiry planning tools to model obsolescence? If we assign the anticipated time to obsolescence to the minimum shelf life (time remaining to expiry for a product in the market), we can actually model and plan for the ‘expiry’ of our products. Armed with that knowledge, we can look at effects on margins, scrap, disposal costs, and inventory levels. Are those safety stock levels we are currently setting going to result in significant amounts of unusable inventory down the road? Are our minimum economic buy levels really that economical, or are we losing all the cost benefits in obsolete inventory losses? Are our projected margins going to hold, or will we see a drop as obsolete product factors come into play (price cuts to move inventory)?

In order to answer these important questions, we could use planning tools to effectively simulate some ‘what-if’ scenarios. We could play with ‘expiry’ to simulate multiple different scenarios, and look for best case scenarios to minimize scrap risk or margin loss. In order to perform an effective what-if simulation, we would want a tool to be able to handle complex (where a component determines expiry of a parent) as well as simple (the item itself determines expiry) expiry planning. This is because a component or components (a CPU or drive for instance), at several levels in the BoM or supply chain, could actually determine the life of the end product, and we would want to simulate the effects of these relationships.

A planning tool can help identify these ‘inflection points’ in the planning cycle, and thereby alert the planners to manage the supply chain in different ways to generate a better outcome for the business.

In summary, even though pharma and electronics are very different industries, there are elements of one which can be applied to the needs of another in new ways in order to improve business performance and planning.

More food for thought: What can supply chain optimization techniques in the electronics business teach pharma?

While trying to address a real issue that has huge financial impact, I believe Max’s suggested cure is worse than the original symptom because of the distortion to the forecast. Getting a forecast that is as accurate as possible is vitally important and we should not be adding distortions. I also think that using a ‘periods of cover’ inventory policy instead of a fixed quantity SS is a very good way of providing a more dynamic SS. In commenting on the use of ‘periods of cover’ I state that:

The policy may be to keep 3 weeks of demand in stock because that is the supply lead time to the stocking point.  If the forecasted weekly demand is 10, 20, 20, 30, 10, 10 then the SS would be 50, 70, 60, 50 over the next 4 week.  In other words the safety stock fluctuates with the anticipated demand.

I always find it difficult to separate out the topics of inventory policy and postponement. In fact I don’t see how one can separate out these topics because they are so tightly related. In addition, postponement is often thought of in terms of manufacturing or assembly postponement, but it is equally valid to think of inventory or distribution postponement.  Your ability to postpone either manufacture or distribution of FG determined by the markets order-to-delivery (OTD) lead time expectations and your supply chains supply lead time and agility.  If the market expects a 1 day OTD lead time and your supply lead time is three weeks, there is very little opportunity for postponement, and SS must be kept as FG at the most forward stocking location.  If on the other hand the OTD lead time expectation is one week, SS could be kept as FG at the factory.  If the OTD lead time expectation is five weeks, then the SS can be kept as raw materials before manufacturing or assembly.

But the discussion above is fairly simplistic and describes principles. Hewlett Packard (HP) adopted an approach, much of it pioneered by Hau Lee and Corey Billington in the 1990’s, that, in my opinion, makes them a leader in inventory management.  Currently they may not have the ‘cool’ products that Apple has, but they have some great supply chain ideas. They focus a lot on both demand segmentation and product segmentation. For product segmentation they use the terms ‘No Touch’ (completely outsourced), ‘Low Touch’ (sub-assemblies outsourced), and ‘High Touch’ (mostly insourced). Actually they have five classifications in total. While demand volatility is one of the factors considered in outsourcing decisions, outsourcing has more to do with IP and core competencies.

In a webinar titled “Using Segmentation Strategies for Better Demand and Supply Balancing in the Mid-Market” (registration required) Jeff Range of March Networks discusses a very similar concept. March uses the categories of ‘Runners’, ‘Repeaters’, and ‘Rogues’ to segment products, and the categories ‘Fixed’, ‘Flex’, and ‘Forecast’ to segment demand within each of these categories. Supply chain agility, or postponement, and inventory policy decisions are made for each of these intersections. This is very different from most approaches which tend to set inventory policy by product group only.

While slightly different, the diagram below, from a presentation made at Stanford University, shows how HP segments demand. For the ‘Lo scenario’ they put in place long term minimum cost contracts based upon regular and guaranteed quantities and a guaranteed, but longer, lead time. For the ‘Base scenario’ they put in place shorter term contracts that balance flexibility and price, definitely at a higher unit cost than the ‘Lo scenario’ and also possibly a more variable, but shorter, lead time. The ‘Hi scenario’ is essentially a spot buy, greatest flexibility (uncommitted) but also greatest cost and possibly the most variable lead time, which can also be offset with higher cost through the use of ‘expedited’ transport to reduce lead time even further.

What is hidden in all of this is postponement.  The presentation is title Procurement Risk Management (PRM). For the ‘No Touch’ products clearly procurement means of FG, for ‘Low Touch’ it means for sub-assemblies, and for ‘High Touch’ it means for components. Of course I am making broad generalizations and the decisions are more subtle than I describe. But for all these product classifications there is still the question of the difference between supply lead time and order-to-delivery lead time.  Postponement can only be used as long at the supply lead time from the point of postponement is shorter than the expected order-to-delivery lead time. If the expected order-to-delivery lead time is a lot shorter than the supply lead time, then the safety stock buffer has to be in FG. If the supply lead time is shorter than the order-to-delivery lead time, then the safety stock buffer has to be kept in the raw materials or components.

To get back to Max’s question, how should you calculate safety stock? Should you even have safety stock, or should this be dealt with in the manner that HP does? First of all I think phrasing the question in terms of FG doesn’t get to the heart of the issue because it all depends on the level of postponement. So let’s generalize the question to ask if this if forecast consumption is not more sensible than the use of safety stock at the point of postponement.

The most common way to calculate safety stock and the reorder point to satisfy a certain customer service level expectation, where Lead Time is the supply lead time to the buffer, is as follows:

1. Z: NORMSINV(Service level) , for example Z=1.64 for a 95% service level
2. Safety stock: {Z*SQRT(Avg. Lead Time * Standard Deviation of Demand^2 + Avg. Demand^2 * Standard Deviation of Lead Time^2)}
3. Re-order Point (ROP): Average Lead Time*Average Demand + Z*SQRT(Avg. Lead Time * Standard Deviation of Demand^2 + Avg. Demand^2 * Standard Deviation of Lead Time^2)

In other words, if demand is fairly stable (relative to volume) and the supply lead time is fairly stable, very little safety stock is required. This is where HP was really innovative. For the ‘Lo scenario’ they were able to reduce inventory throughout the supply chain by putting in long term contracts that reduced variability of supply and for the demand for which they could assume zero variability, therefore effectively reducing safety stock to zero, but taking a bit of a hit on ROP since their average supply lead time was a bit longer. On the other hand for the ‘base scenario’ they accepted greater variability but focused instead on reducing supply lead time, which reduces both safety stock and ROP. Of course this strategy works best for mature products with low demand variability relative to volume. For early stage NPI nearly all demand will be ‘Uncommitted.’  As far as I am aware HP still put in place long term contracts based upon their estimates of total available market and expectations of market share.

In summary, I think Max is bringing up the right question. But I think the answer lies in deep analysis of both demand and products to determine differentiated inventory policies – which includes postponement, SS, and ROP – to best arrive at what Hau Lee calls a Triple-A supply chain: Agile, Adaptable, and Aligned.

What do you think? Is this too complex? Should we instead just add some quantity to the demand as Max suggests? It would be great to hear from practitioners on how they are solving this issue.

The benefits of increased forecast accuracy include:

• Increased customer satisfaction due to reduced stock outs
• Reduced write-off and obsolescence costs due to unsold inventory
• Lower inventory carrying costs, as less buffer stock is required to cover missed forecasts

Significant benefits can be realized in a company’s top and bottom line results if a more accurate forecast generation process can be realized. One of the cardinal rules of forecasting is that a forecast becomes more inaccurate as it goes further out in time. Another is that a forecast becomes more inaccurate as the granularity is increased (ie. It is easier to get an accurate forecast at the business unit revenue level than it is to forecast the number of units of each SKU that make up the sales for that business unit).

This second rule can be better addressed by focusing not an individual SKUs sales history or possible future trends, but by looking at the ‘group’ this SKU(s) fall into and analyzing its history and trends. The attributes shared by these SKUs constitute their forecast group. The advantage of grouping forecast SKUs are many:

• A lot of market research is available at a higher level in the marketplace (at the channel product category level versus an SKU level). Market share analysis must be done at this level
• Aggregating SKUs reduces the month to month demand fluctuations seen at the SKU level, allowing for better data analysis for trend forecasting (reduces the noise)
• Revenue forecasting generally occurs at the product group level or higher. Since revenue projections are the most common driver of forecast models, a better alignment between forecasts at the SKU level and revenue forecasts can be achieved if there is a clearly understood link between the two
• A better alignment of revenue and SKU forecasts allows the business to more clearly drive production (also known as the sales versus production forecast). A poor alignment between the two can lead to missed sales numbers, increased inventory carrying costs, or stock outs.

The breakdown of the revenue forecast into a realistic SKU level forecast can be complex, and can vary greatly among different business units and markets. While these different processes can lead to a more accurate forecast based on local market conditions, it makes it very difficult to get a true picture of the alignment between the global revenue forecast and the individual unit forecasts, as the methods to get from one to the other can vary greatly.

This leads us to the recognition for the need of a formal, structured process for generating SKU unit forecasts from revenue forecasts that can generate a better forecast for the business as a whole. In order to allow the process to accommodate the differences between markets and regions, it must be have the functionality to allow for market specific processes, while maintaining a common underlying process that can tie back to the revenue forecast. The best way to achieve this goal is to use region and market independent product attributes, as they can ‘filter out’ the variations that can occur due to different business cultures. This also describes the best product attributes to select, as they should reinforce the underlying commonality of the SKUs being forecast. Market specific processes can then be applied to fine tune the forecast for a specific market or region.

In order to more accurately predict future demand at the SKU level, a tool is required can process large amounts of historical data and knowledge based assumptions quickly. This allows the analysts to perform what if simulations and determine the effects of various assumptions, as well as incorporate any trends that are evident in the marketplace. This leads to another “must have” feature: the ability for the analyst to easily and quickly incorporate trend simulations into the numbers. By using aggregated attributes on multiple levels and presenting the numbers in a percentage of total view, the analyst can quickly modify the splits at each attribute level to reflect known assumptions or model future trends, while keeping the totals within the revenue guidelines (the sum of the percentages must always equal 100). As well, by modifying percentages versus actual numbers at the lower attribute level, the overall revenue forecast can be kept consistent throughout the forecast process. The revised percentage splits can then be applied to the revenue numbers to break them down into a SKU level forecast that ties back to the revenue in an understandable way.

In summary, it is desirable to have a close correlation between the revenue forecast and the SKU level forecast. The best way to get there is to use product attributes, historical patterns, and the analyst’s expert knowledge to break the revenue forecast down to an accurate SKU level forecast. A tool that can support an underlying, formalized process with enough flexibility to allow for regional and market differences will deliver the optimum solution.  This tool must be intuitive to use and allow the analyst to quickly generate a valid forecast that supports the consensus revenue projections.

There is no question that the Pharma industry must deal with a unique combination of supply chain and regulatory issues to meet its obligations to consumers, supply chain partners and shareholders. For some biotech companies, availability of their products are literally a life saving necessity and disruptions in the supply chain can result in a form of supply triage that is uncomfortable to even consider.

Unlike other industries, the Pharma industry has traditionally treated supply availability as a prime factor, even to the extent that several months of safety stock was not considered excessive. While in the other industries, best in class performance inventory turnover performance is double digit, the Pharma companies seem satisfied with low single digits despite the impact on cash and working capital.

In an April 2010 article in Pharma Pro, entitled, “How to Unlock $43 Billion in Value by Improving Working Capital Management,” A.T. Kearney reported that an analysis of Pharmaceutical companies revealed an average inventory level of 170 days.

Supply availability though is just one key difference to other industries. In a number of the Bio-tech companies, they’ve had to invent equipment and manufacturing processes to produce the new wonder drugs. Early manufacturing is just targeted on producing enough to satisfy trials and is less focused on cost and efficiency. Unlike engineering changes in the high tech industry that change the content of a product, changes in the Pharma space usually target the cost and yield of the manufacturing process. To add to the complexity, these changes must satisfy the quality control requirements of regulatory agencies. The approval process can take months and uncertainty in the timing creates a variety of risk management issues. In addition to the regulatory control over manufacturing, there are different regulatory issues to face in each of the market regions that govern a wide variety of factors including packaging, labelling and shelf life requirements. Combine these factors with the extremely high margins on non generic drugs, and it’s no wonder that the Pharma industry has historically put little emphasis on minimizing the non-production aspects of the supply chain costs.

BUT despite the unique aspects of the Pharma industry, the bulk of the supply chain management issues are largely common to many other industries. Planning for raw materials, dealing with manufacturing constraints, establishing and monitoring distribution inventory levels, and dealing with significant market demand volatility, are just a few. It is important to note that the dynamics that have introduced unparalleled volatility in the high tech industry (short life cycles, internet purchasing behaviors, and intense price competition), have had less of an impact on Pharma, but other factors have achieved much the same result.

Increased global competition, economic conditions, and changes in both regulatory factors and insurance coverage have collectively produced higher levels of demand volatility. Without the same degree of supply chain focus that is present in the high tech space, the net result generates a degree of waste almost unfathomable by those outside the industry. The chief question is, “can you achieve an acceptable level of supply availability without the waste?” It’s understood that the definition of “acceptable” ranges from 100% availability for some life sustaining medications to more conservative fill rates for OTC generics. There are enough examples of supply chain mastery in the high tech and automotive industries to suggest the answer is an absolute yes.  The next question is “How?” 

Stay tuned for Part 2 of this discussion tomorrow.

… U.S. citizens are dramatically more likely to lack access to healthcare, but that spending per person is higher than it is in Britain by $7,538.

The article in The Guardian is based on a survey run by The Commonwealth Fund which shows a dramatically lower access to healthcare in the US; despite a much greater spend per person.

Click to view larger

Click to view larger

While the argument that the survey by The Commonwealth Fund focused on those in the US without access to healthcare and doesn’t measure the overall satisfaction with the US healthcare system has some merit, the survey still points to an ineffective and inefficient US healthcare system.

Last week I attended the Gartner Healthcare Exchange in Boston.  This was one of the most fulfilling and eye-opening days I have spent recently on a professional basis.  As Bob Ferrari comments,

…one of the most insightful portions of the program involved a lively panel discussion that included four industry executives representing Orlando Health, Dana Faber Cancer Institute, Cook Medical, and Broadline Group.  It was one of the better executive panel discussions I’ve observed this year.

Some startling statistics and observations came out of this discussion which I have not been able to verify.  But I will assume that they are directionally correct, if not absolutely correct.

• “Three breast implants go out by overnight Fedex, and two come back overnight Fedex.”
  Who pays for this?  Where is the incentive to reduce this cost?  Why were three implants required in the first place?  I am sure this was not emergency surgery, so why were the implants sent by overnight Fedex?  You can bet your bottom dollar that the operating room was scheduled some time ahead.
• “40% of nursing time is spent looking for items not on the shelf.”
  “15% of product expiring on hospital shelves.”
  Even if these numbers are off by a long way, and I have no reason to believe they are, these two statements side-by-side are a clear example of the wrong inventory at the wrong place at the wrong time.  Even if we halve these numbers and assume the reference to nursing looking for items is related to the manner in which the materials are organized and the reference to products expiring refers only to drugs, these numbers add up to huge inefficiencies in the supply chain.  While undoubtedly there are inefficiencies in grocery stores, there are examples from other industries, particularly grocery retail, that address many of these issues more effectively and more efficiently.
• “Hospitals think consignment stock is not a problem.  They should be charging for the storage space.”
  At the very least the cost of financing the inventory, not to mention the cost of scrapping the inventory that has expired, will be passed on to the hospitals and therefore to the patients.
• “Most people who take care of inventory in hospitals have no training in inventory management.  They are usually nurses who have been reassigned.”
  Let’s face it, there is a big difference in an out-of-stock condition in your local electronics store and in a hospital pharmacy.  But the nurses are already struggling to find items on the shelf, some of which has to do with inefficient stocking policies.  Would you get an inventory analyst to administer a morphine drip?

The 2 parts of the healthcare supply chain that distorts the system the most are the distributors and the payers, not necessarily in and of themselves, but because the demand signal is so distorted by these parties. But these are easy targets and my sense is they were being used to deflect from inefficiencies throughout the healthcare supply chain from manufacturers to distributors to retail pharmacies to HMO’s to hospitals to payers and whatever other actors there are in the supply chain, or, as Gartner prefers to call it, the value chain.  But I left the conference wondering who represented the patient?  No-one at the conference anyway.  The reason this is important is that Wal-Mart’s “everyday low prices” mantra is driven largely by consumers voting with their feet.  Without the “voice of the customer” being loud and clear it will be left to the government to mandate change in the healthcare supply chain through legislation.  Which is something no-one wants.

Having lived as an adult in six different countries with six different health systems, I can state categorically that while the issues of inefficiency in the healthcare supply chain are not restricted to the US, it is where the inefficiencies are most apparent.  While I hesitate to suggest a change, end-to-end visibility of the demand signal has been demonstrated to have a dramatic effect on behavior throughout the supply chain.  The famous MIT Beer Game, dating from the 1960’s, springs to mind.  So let’s get started and let’s use supply chain principals from other industries as guideposts.

Image via Wikipedia

Seeing jack-o-lanterns, skeletons and witches on front porches and store-front displays reminds us that—as difficult as it may be to believe–the holiday season and end of the year are right around the corner. And while that’s historically good news because holiday-season sales play a considerable role in many companies’ revenues, the sales season this year looks to be challenging.

One issue is that as orders fell during 2009, inventory levels for many companies stayed high. Since then, they have been able to deplete that surplus inventory, which is good. On the other hand, with that safety stock now either gone or very slim, these companies have become much more dependent on their suppliers’ responsiveness and overall performance.

The problem, as has been noted by both Bob Ferrari and Jim Fulcher, is that suppliers have had difficulty meeting their customers’ requirements this year. Texas Instruments, a supplier of several component chips for the DroidX, EVO and iPhone4 smart phones, is struggling to keep pace with customer demand. It is, however, far from being the only company with supply chain challenges. Mobile phone manufacturers themselves are responding to component shortages by switching to other components that may have more availability.

Furthermore, that situation isn’t just limited to the smart phone market. According to an MFG.com survey, 42 percent of small and medium-sized suppliers said they have received queries or work from larger companies that are urgently in need of assistance due to their own supply chain problems.

The second issue companies increasingly struggle with is demand uncertainty. Whether it’s toys, shoes or smart phones, no one really knows what future consumer spending will look like. In recent months consumers have shown a willingness—albeit somewhat guardedly at times—to spend. But with unemployment hovering around 10 percent and persistent fears of possible end-of-year layoffs, consumers may scale back their spending on holiday gifts this year.

The watchword in all of this then is “volatility.” That applies to suppliers’ ability to meet manufacturers’ demands as well as unpredictable consumer demand.

Addressing those market conditions requires companies to walk a tightrope of sorts. No one wants to lose possible sales and fail to meet customer expectations, so faced with uncertain demand and supplier volatility, the temptation is to build inventory levels. That way, there always is sufficient inventory on-hand to meet a potential sudden increase in demand. However, carrying that inventory presents drawbacks as well. Most notably, there is the high cost of carrying that surplus inventory—not to mention the possibility of being stuck with excess and obsolete inventories in case potential demand never materializes.

Use of S&OP helps alleviate these concerns because, as a cross-functional planning process, it helps organizations better identify and respond to demand so they can not only maximize opportunity—but also mitigate risk. That’s possible because S&OP helps users strike a balance between the sales and operations planning disciplines.

Key capabilities play an immense role. The ability to conduct what-if analyses–for instance—for both supply and demand is crucial, as is being able to evaluate analyses against both financial and operational targets. Secondly, event-driven S&OP is increasingly valued because it delivers the ability to actively monitor the current plan and notify the appropriate people when the plan is at risk. That, in turn, allows them to take immediate action to correct the course.

So, how is your S&OP process? Will you receive treats, or tricks, in the coming months?

I read a lot of books.    I typically have a few books “in the queue” but occasionally I’ll run out of reading material and have to wait for my next trip to the city to buy more.   When I travel, I’ll bring the book I’m currently reading and one or two more just in case I run out during the trip.  I’ll often re-read books I’ve enjoyed so I tend not to sell or give away books I’ve already read. As a result, my book shelves are stuffed way beyond their capacity.

So, when the Kindle came out a few years ago, I watched with interest.  There were still a few bugs to work out, so I waited.  The Kindle was also kind of expensive and…oh yeah…it wasn’t available to us cave-dwelling Canadians.  The Kindle 3 was announced in July.  It had some nice new features and a great price and was available north of the border, so I decided that I would treat myself for my birthday and ordered one on August 17th.

When I placed my order, Amazon couldn’t tell me when my new Kindle would ship.  Hmmm.   Yesterday, a couple of days into September, Amazon STILL couldn’t tell me when my Kindle will ship…not online anyway.  What’s going on?  Do they honestly not know when they can ship my product?  Is the date so bad that they are afraid I’ll go elsewhere?   Those among you that have been involved in supply chain, customer service or sales, know that the only thing worse than not shipping a product on the date a customer wants it is not being able to tell the customer when they can expect it.  

So what is the cause of the delay?  Bad Forecast?  Supply issues?  We just don’t know and likely won’t know until Amazon tells us (if they ever do). Regardless of the cause, what is stopping Amazon with providing me with a delivery date?  We all know that there are supply chain analytics available that can be used to identify an expected ship date.  Perhaps, being new to the brand owner /  manufacturing side of things, Amazon might not yet be aware of what is possible with good supply chain software.

Desperate for Amazon to tell me where the heck my Kindle is, I broke down and called customer service and finally got an answer that it should ship likely on September 10th.  Why that date wasn’t or couldn’t be provided online when I checked the status, I don’t know.

As someone who understands the complexity of the supply chain, I know that sometimes we can’t ship when a customer wants a product.  However, as a customer, I want to know when you CAN ship my product.  Amazon, you gotta give your customers any insight you can….and don’t make them work for it!