I would like to expand on a couple of these points, focusing on optimizing the supply chain through the use of collaboration, simulation, and scenario comparison utilizing modeling tools.

The supply chain network is composed of many different components acting in concert to deliver the required goods and services at the right time. The better these interdependent relationships are understood, the better the supply chain can be optimized. This raises the question how best to optimize such a complex set of data points.

In order to get an accurate simulation, you must be able to obtain accurate data about each node in the network, and then utilize software to model it. These nodes can include, but are not limited to:

• manufacturing plants,
• supplier plants,
• sub contractor plants,
• warehouses, and
• transportation routes.

This requirement would suggest that any simulation model must be able to incorporate multiple disparate data sources in a relatively easy and timely manner. Important data to obtain would be:

• lead times,
• inventory levels,
• in transit times,
• associated costs,
• quality levels, and
• service levels.

There are many others that could also be included to give a more accurate picture of the network.

Once this data has been obtained (which may require some effort, especially with off shore suppliers), the next question then becomes, what do we do with the data? This is where the requirement for multiple scenarios becomes critical.

Because these networks can be extremely complex with many factors influencing outcomes, we must have the ability to compare many different scenarios in order to determine a path forward. One scenario may give us a lower overall cost, but poorer customer service. Another may result in cheaper raw material costs, but lower quality levels and increased transportation costs. All these outcomes must be weighted in order to determine the most optimal design, -this requires some way to be able to compare multiple scenarios in a clear and efficient manner.

The next step would then be to examine each scenario, and determine if the best parts of each can be combined into one ‘super’ scenario. As an example, one scenario may have a supplier with lower cost, but higher transportation costs. What if sourcing is split between a low cost/high transportation supplier and a higher cost/low transportation supplier? By managing the sourcing between the 2, an optimal outcome may be achieved (happy medium). This can only be determined by developing a complex model with multi-sourcing.

If you take this case and apply it across the network, the number and complexity of the inter-relationships can soon become mind boggling. This is why a multi scenario engine which can rapidly calculate the various outputs of the model from its inputs is so vital. In order to implement the optimal supply chain network, it must first be designed as a whole to deliver the optimal results.

Once the supply chain network has been successfully modeled, a powerful tool is now available for what if analysis. The maximal use of this tool can be realized by incorporating continuous improvement into the business development cycle and optimizing the network on a regular basis. But in order to make this an effective tool for continuous improvement, a plan must be generated quickly and easily analyzed, as time and resource constraints limit the amount of time to turn around the results and push improvements to the network. This is why speed and performance matter when implementing complex modeling solutions.

Because the only way to realize the benefits of an optimal network model is to implement it, collaboration among all the players in the network (inside and outside the enterprise), is vital. This leads us to another key criteria for a simulation system - the ability of many users to access and collaborate on the development of the network model.

Once the simulation, model, and collaboration pieces are in place, it raises the question: Can this environment be used to model other complex systems as well as the supply chain? What about the ‘sales chain’, the complex relationship between you and your customers? What about the complex network of relationships that exist between an enterprise’s internal resources (employees) and the enterprise? Financial models? As can be seen, there are a myriad number of networks in the modern world that can be modeled for the purposes of increasing efficiency, and thereby reducing costs or improving service levels.

The same keynote also described the foundation that today’s supply chains are built on. In particular, there were two analogies that stood out and challenged the audience to think about their own supply chains. The first analogy was having your supply chain foundation built on technology that resembles that of a “picket fence.” In this case, there are gaps where key information may “slip through the cracks” and it becomes difficult to do any meaningful supply chain analysis. If you use Excel this may sound familiar. Supply chain analysis is difficult because those that need to participate are looking at different data, missing data, and there are gaps in the analytics. If your supply chain analytics are built around Excel spreadsheets then you may be bound by the “picket fence.” Excel may be sufficient in an environment where there is little “VUCA,” but when managing today’s supply chain challenges, Excel is not a scalable solution.

The second analogy of interest used to describe the technology a supply chain foundation may be built on was that of “bricks and mortar.” In this case, the analytics required to respond to “VUCA” are just not flexible enough. Old processes that are cast in stone, long analytic run times, and user interfaces that are far from being user friendly are just a few reasons to describe this technology as “bricks and mortar.” If you are trying to get your supply chain analytics from your ERP system, which for the most part is the transaction system, this may sound familiar. In keeping with the analogy it would be like getting blood from a stone. This is the reason many people continue to turn to Excel for supply chain analytics but soon run into the “picket fence” problems.

This is where the “T” in VUCAT comes from, Technology that does not support today’s “VUCA”.

So what is one to do with “VUCAT”? Some analysts were talking about “best of breed.” This may mean abandoning your Excel spreadsheets or getting rid of ERP components that are either difficult to deploy or are not doing the job. In these cases “VUCAT” is actually a verb. For example, if somebody asks, “What are we going to do with all these Excel spreadsheets?” You might say, “We just have to VUCAT and replace them.”  Look for this new word in the next APICS dictionary

If you are going to replace the “picket fences” or “bricks and mortar,” what characteristics will allow you to manage “VUCA”? Here are three key requirements for today’s supply chain analytics;

• “Know sooner” by being able to continuously monitor plan versus actual and alert participants not only of the event but of the consequences.
• “Collaborate now” by automatically identifying not only the consequences of change but those individuals impacted by it and thtat can contribute to its resolution.
• “Respond immediately” by evaluating any number of resolution scenarios in seconds with anyone in any place and compare alternative actions against operational and financial metrics.

This means you can embrace “VUCA” and create a competitive advantage by being a “first mover.”

Did you hear the term “VUCA” used at the Gartner conference or anywhere else? What is your take? Is it a new more descriptive term for today’s supply chain or more of the same?

More importantly, the ash cloud itself presented significant business ramifications for companies around the world. I believe we will study the volcano’s eruption—and, consequently, the disruptions to supply chains around the world—for years to come because the impact was both so widespread and pronounced.

A recent BusinessWeek article described how automotive manufacturer Nissan Motorwas forced to shut down three auto assembly lines in Japan because the factories ran out of tire-pressure sensors when a plane carrying a shipment from a supplier in Ireland was grounded.

I’ll wager that you expect disruptions from hurricanes and possibly tropical storms, and maybe even a blizzard across the Great Plains. Since those events are likely, it’s smart to create contingency plans that account for alternate transportation routes or even modes. Furthermore, you may even have contracts in place with suppliers for alternate parts, and perhaps even contracts with alternate suppliers for necessary parts or components. But sometimes an unexpected event—like a volcano eruption—disrupts the supply chain. The question then becomes: How will your company respond to this unanticipated event?

Obviously, the first challenge is to realize that an event of some type has occurred or is about to occur. But even more significant, is the response. How quickly your company responds and just what that response is, may have substantial impact on the company’s performance and, possibly, its bottom line.

That’s why it’s critical to have tools and processes in place to respond quickly to unanticipated events that aren’t covered by a mitigation strategy.

• These tools must deliver visibility across the supply chain and provide alerts when an event is imminent, they must also include analytics so users can understand the importance of the event and the impact it will have.
• Secondly, the tools must allow users to collaboratively simulate possible solutions, such as splitting orders, expediting orders and finding alternate sources.
• The next capability may well be the most important. Once simulations are created, they must be compared and contrasted to determine which one best meets corporate goals and objectives. Using a multi-scenario scorecard allows users to compare the possible solutions and measure the impact of each potential resolution on key corporate metrics.

Consider these two possible solutions to a critical parts shortage….

The first solution is to use existing inventory and split apart orders. Customers will not receive their full order, but they will at least receive part of it. A second possible solution is to expedite shipment of parts from an alternate supplier to your facilities, fill the orders, and then expedite shipment to your customers.

The first possible solution results in a decrease in on-time delivery and, potentially, a decrease in revenue for the quarter. The second, however, will result in an increase in cost of goods sold and a corresponding decrease in margin.  How do you know which route to take?  These results, compared against a given target and appropriately weighted, provide an overall score for each solution. Thus, an analyst can then use those scores to determine which scenario best suits corporate objectives.   How you make those quick decisions and how well they align with corporate objectives can make or break a company’s bottom line.

Let me know what you think; either about responding to unanticipated supply chain events or photos of lightning in clouds of volcanic ash.

Wednesday, November 17, 2010 at 11:00hrs New York (16:00hrs London, 17:00hrs Paris)

with presenters:

• Dr. Hau Lee, Thoma Professor of Operations Information and Technology,  Stanford Graduate School of Business
• David Smoley, Senior Vice President and Chief Information Officer, Flextronics International
• John Sicard, EVP Marketing, Development and Service Operations, Kinaxis

Register today!

Here is a little more about the session…

Translating business drivers into technology requirements: How to support today’s S&OP and SCM landscape

As a leading EMS provider, Flextronics is no stranger to supply chain pressures. Short product life cycles, demand volatility, supply shortages – all of which must be managed within a supply chain that crosses organizations, geography and time zones. As a result, there is a tremendous need for supply chain agility. One of the most fruitful ways to improve agility is to reduce decision latency throughout the multiple layers of the supply chain and throughout the various stages of the S&OP process. From a technology perspective, this has created an urgent demand for:

• Visibility – across data sources, systems, and organizations
• Collaboration – between functions and trading partners, often across three tiers of the supply chain
• Identification & Alerting – easy and early identification of people who are impacted or can provide insight into decisions
• ERP agnostic analytics – for rapid evaluation of decision alternatives across partners using different systems within the multi-tier supply chain

Of course this uncertainty is not restricted to the US economy, which is the point Tom Friedman makes by focusing on the German economy and how it relates to economic recovery in Europe.  In fact he points to three influences that will need to be reversed if the US and EU economies are to recover soon:

The first big structural problem is America’s. We’ve just ended more than a decade of debt-fueled growth during which we borrowed money from China to give ourselves a tax cut and more entitlements but did nothing to curtail spending or make long-term investments in new growth engines.

Second, America’s solvency inflection point is coinciding with a technological one. Thanks to Internet diffusion, the rise of cloud computing, social networking and the shift from laptops and desktops to hand-held iPads and iPhones, technology is destroying older, less skilled jobs that paid a decent wage at a faster pace than ever while spinning off more new skilled jobs that pay a decent wage but require more education than ever.

But the global economy needs a healthy Europe as well, and the third structural challenge we face is that the European Union, a huge market, is facing what the former U.S. ambassador to Germany, John Kornblum, calls its first “existential crisis.” For the first time, he noted, the E.U. “saw the possibility of collapse.” Germany has made clear that if the eurozone is to continue, it will be on the German work ethic not the Greek one. Will its euro-partners be able to raise their games? Uncertain.

Commenting on Bernacke’s statements, Jeannine Aversa of Associated Press writes that

Consumers have cut spending. Businesses, uncertain about the strength of their own sales or the economic recovery, are sitting on cash, reluctant to beef up hiring and expand operations. A stalled housing market, near double-digit unemployment and an edgy Wall Street shaken by Europe’s debt crisis are other factors playing into the economic slowdown.

OK, OK, so there is lots of economic uncertainty.  What do we do about it?  During my time as a management consultant I learned a fundamental truth: Analyzing a situation is fairly easy, defining a future state is a lot harder, but the really hard part is defining the path to achieve the future state. Not being an economist I can comment little on the efficacy of Tom Friedman’s suggestions for recovery, nor on Ben Bernacke’s for that matter.  My guess is that most of the readers of this blog fall into this category too.  Clearly we all want the same future state of a revived world economy and we are all too aware of the current state of the economy.  Of course we all have our opinions on the path to recovery, which we can express in elections, but for the most part actually pulling the levers of the economy is not something which is in our control.

Which leaves us all feeling “really unusually uncertain”.

While we may not be able to effect change to the national or global economy, we do have some level of control over the economic performance of the companies for which we work.  As I commented in a previous blog titled “Why S&OP? Why now?”, this is where I see sales and operations planning (S&OP) playing a big role.  But for S&OP to be effective it must provide ways for people to evaluate and understand uncertainty.  There are 4 fundamental capabilities that are required to achieve this:

1. Capture of assumptions made about the future state for knowledge sharing and control
2. Facilitated collaboration across functional boundaries to get buy-in and inputs from multiple parties
3. Super-fast “what-if” analytics that allow organizations to evaluate and compare multiple scenarios in order to maximize performance and to mitigate any identified risks
4. Continuous plan performance management so that deviations are detected early and course corrections can be made quickly

The last point about performance management is often overlooked.  The more uncertain the future, the less likely it is that your plans will be achieved.  It doesn’t help much if at the end of the month you determine that the plan wasn’t achieved.  In a more stable economy this might have been sufficient.  In today’s volatile economy (which is the root cause of our uncertainty) it is really important to monitor performance continuously and to course correct as quickly as possible when significant deviations are detected.

However, what makes this all possible is super-fast “what-if” analytics.  Uncertainty is risk.  Without a mechanism to evaluate many alternative scenarios, your ability to evaluate and understand risk is reduced greatly.  Do you think Excel is up to this?  Do you think this can be achieved without any technology?

As most who closely follow SAP know, SAP has been talking about in-memory databases for several years. To those that don’t follow the exciting world of database technology, you might even think SAP invented in-memory databases! The truth is that in-memory databases have been around for longer than most realize. Check out this Wikipedia page and you’ll get a sense for the dozens of innovators that led the way well before SAP stepped onto the field. While you won’t see Kinaxis technology mentioned in the mix, we’ve been at it for longer than most—perhaps even the longest. Despite our laser-like focus in this area, our senior architects continue to admit there’s still room for improvement and are in tireless pursuit of it. However, to borrow from the barber’s famous line from one of my favorite Leone spaghetti westerns (“My Name is Nobody”) – they would also state — Faster than ‘us’, Nobody!

While I wasn’t present to witness it, our development of in-memory technology began 25 years ago with a hand full of brilliant engineers in a basement who founded their own company, Cadence Computer. Their goal was simple: to invent something meaningful and technologically amazing. One of our Chief Architects, Jim Crozman, had an idea to run ERP in-memory—motivated by improving upon a then 30-plus hour run. As you might imagine, finding a machine to run the software that was in his head at that time proved to be impossible, so Jim, along with a small group of talented engineers, did the only thing they could think to do: They invented and constructed a specialized computer (the size of two refrigerators), which would become a dedicated in-memory database appliance—likely a world first. They would call it SP1018. We all know how technologists love a good acronym with some numbers attached to it! At that time, 4MB of RAM took an 8×10 circuit card—and wasn’t cheap! They were packaged into modules with a custom bit slice MRP processing engine capable of 10M instructions per second that could process data in memory at its peak speed. Program and temporary working memory were in their own storage blocks, so the main memory space and bandwidth was reserved for the database. Up to 16 of those processing/memory modules were clustered with a high speed backbone to form single MIMD processing system that could do an MRP “what-if” simulation for a large dataset in minutes. We would go on to sell this computer to GE, at that time an IBM 3090 showcase center. The IBM 3090 had a whopping 192MB of RAM, and sitting next to it, our appliance with 384MB of RAM. IBM’s ERP analytics ran in over three hours, while our appliance replicated the same analytics in approximately three minutes.

Computer architecture and speed has evolved greatly since those trailblazing days. Inexpensive multi-core systems with big on-chip caches are capable of tens of billions of instructions per second. No need for custom hardware today! Speaking of on-chip caches, understanding and leveraging this resource has become the key to maximizing speed and throughput. Memory architecture remains 10 times slower than processor speed, so understanding how machines retrieve data and the treatment of that data within the core is fundamental to in-memory database design. It takes the same amount of time to retrieve 1 byte of data as it does 1 block of data. This makes locality of reference a very important system design criteria, minimizing memory access cycles to get the data you need for processing. Data organization and keeping data in a compact form (e.g. eliminating duplication) and with optimal direct relationships and clustering makes for optimal processing speed (minimize memory access cycles).

At this year’s SAPPHIRE Conference, SAP explained how it has chosen a hybrid row/column orientation as the construct to store in-memory relational data. Indeed, columnar orientation helps with data locality and compaction of a column of data (obvious), and is most effective in circumstances where the use cases are driven by querying and reporting against a database that does not change or grow rapidly or often. Dennis Moore says it best in his recent blog:

“There are many limitations to a columnar main-memory database when used in update-intensive applications. Many SAP applications are update-intensive. There are techniques that can be used to make a hybrid database combining columnar approach for reading with a row-oriented approach for updates, using a synchronization method to move data from row to column, but that introduces latency between writing and reading, plus it requires a lot of CPU and memory to support the hybrid approach and all the processing between them.”

The challenges associated with columnar orientation will be felt most when attempting to drive performance of complex in-memory analytics. By analytics, I don’t mean complicated SQL statements, compound or otherwise. Rather, I refer to compute-intensive specialized functions, like ATP/CTP, netting, etc. That is calculating consequence to input events based upon a model of the business, particularly the supply chain. Columnar organization solves issues for a small subset of problems but makes most usages of the data much worse. Processing usually involves a significant subset of the fields on a small related set of records at a time. Since a single record’s data is spread across different areas of memory by a columnar organization, it causes a bottleneck between memory->cache->processor. A single processor cache line ends up with a single piece of useful information and multiple cache lines are then needed to get just one record’s data. For example, ATP for a single order needs a subset of demand, supply, order policies, constraints, BOMs, allocations, inventory, etc. Perhaps this is the main reason why the PhD students at the Hasso Plattner Institute of Design at Stanford reported only achieving a 10x improvement for their ATP analytic prototype using HassoDB, significantly slower than their raw query performance ratios.

Millisecond query results are at most half of the equation—and definitely the easiest half. Don’t get me wrong, faster BI reports are great. If you’ve been waiting a few minutes for a report, and you can now get it in seconds, that’s real value. The trick is to go beyond “what is” and “what was” analysis, and add “what will be if” analysis. If done correctly, in-memory analytics can achieve astounding speeds as well. For example, the Kinaxis in-memory engine processes analytics (e.g. ATP), from a standing start (worst-case scenario) with datasets consisting of 1 million part records, generating 2 million planned order recommendations following the creation and processing of 27 million dependent demand records in 37 seconds, while handicapping the processor to a single core. Further, eight different users can simultaneously request the same complete calculations on eight what-if scenarios of the data and still get their independent answers in less than 60 seconds. No need for ”version copy commands.” My personal favorite performance test done in our labs involves proving that the more users logged into to the system, the less time it takes for them to receive their results (i.e. average time per request goes down). As impressive as these benchmarking numbers are, these tests do not represent typical user interaction (i.e. batching full spectrum analytic runs). If done correctly, massive in-memory databases with intensely complex analytics can scale to thousands of users on a single instance (think TCO here), each capable of running their own simulations—change anything, anytime, and simultaneously compare the results of multiple scenarios in seconds.    

RapidResponse simultaneously measuring eight scenarios for a user using weighted scorecard

All this speed and scale becomes valuable when businesses can bring about new and improved processes capable of delivering breakthrough performance improvements. With collaboration gaining traction as the new supply chain optimizer, companies are driving innovation toward this area and testing in-memory databases in new ways. For example, not only is it important to monitor changes in the supply chain and the potential risk/opportunity they create, companies now want to know “who” is impacted, “who” needs to know, and “who” needs to collaborate. While this seems like an obvious value proposition, the science involved in delivering this on a real-time basis is staggering.

I’m happy to see SAP draw such attention to the merits of in-memory databases. It serves to validate 25 years of our heritage, our focused research and development, and surely validates the investments made by some of SAP’s largest customers (Honeywell, Jabil, Raytheon, Lockheed Martin, RIM, Nikon, Flextronics, Deere, and many more) to leverage RapidResponse. Whether related to Sales and Operations Planning, Demand Management, Constrained Supply Allocation, Multi-Enterprise Supply Chain Modeling, Clear-to-Build, Inventory Liability Reduction, What-if Simulation, Engineering Change Management, etc., these great companies are experiencing and benefiting from the speed of in-memory technology today.

Why wait?

A big limitation of Excel in my view is that it cannot mimic the analytics in the company’s source ERP system.  Why is this important?  If someone is using Excel to make business decisions without all the capabilities the ERP source system has, then they may not be making the right decisions.  How can you make planning decisions if your spreadsheet doesn’t take into consideration functionality like sourcing rules, constraints and order priorities?

A company’s supply chain map is very complex, typically there are internal manufacturing data sources, external manufacturing data sources, inventory site data, etc.  It is possible to get data from multiple sources into Excel, but the big challenge is that the data is not always the same from each source system, so many organizations may have multiple spreadsheets to perform the same function/analysis.  But can any of those spreadsheets be truly accurate if they don’t show a true picture of the whole supply chain?

It is almost impossible to control the integrity of spreadsheet data and access to the spreadsheet.  With multiple people accessing the spreadsheet and no security, how can anyone have any confidence in the data?   In addition, most spreadsheets need to be reviewed by many people which typically requires pushing the spreadsheet around.  Without system standard security, data integrity could be an issue and auditing who made changes could be an issue.   How can there be a high level of confidence in the data and subsequent business decisions made?

I have known many supply chain companies who do make critical business decisions based off of spreadsheets.  For example, one company would use spreadsheets to analyze big order drop ins.  If they had a big order drop in they would use their spreadsheet(s) to determine the effect on their business and when they could commit to the customer to deliver the order.  This would typically require multiple spreadsheets getting data from multiple sources, tons of manipulation, trying to tie data together, and many different users from the organization looking at their piece, which would take several days and by then the data had changed and the end user would only have a 50% confidence level in the answer back to their customer.  This can be crippling if your products are very expensive like in the aerospace industry where the products are multi-million dollar and the customer is the government who may impose penalties if orders aren’t delivered when promised.

You need to:

• get all the supply chain data in one place for visibility (with frequent data refreshes),
• mimic the source system analytics,
• have all the system standard security functionality and
• output data in a familiar “Excel-like” format.

True nirvana is: one source of the truth, multiple users having access at the same time, data integrity, “what-if” capability with the power and flexibility of “Excel-like” outputs.

Financial services and telecommunication companies have pioneered the use of business intelligence (BI) solutions to enable them to analyze massive amounts of data they have accumulated over the years. As a result, considerable insight was gained from data mining and data analysis and thus, the need for BI capabilities grew in the ’80s and ’90s in other industries as well. But despite being a topic explored and written about extensively, there has been only a moderate uptake and mediocre results. Why?

Pure business intelligence (BI) tools suffer from two major drawbacks that prevent them from providing greater value and therefore obtaining greater adoption: They cannot identify causality and, as a consequence, they cannot provide a prediction of future performance.

In the past 5 years, the interest, and indeed the need, for real-time access to operational data has increased dramatically. The promise of real-time operational BI that goes beyond the capturing of static data snapshots and enables users to identify and analyze risks and events, is of major interest to supply chain management (SCM) managers. Driven to improve operations performance, supply chain managers know that better information about their operations and processes lead to better decisions and better supply chain performance.

What do you say when the CEO is asking whether the company will hit its revenue targets for the current reporting period? Can you tell the CEO instantly which customers may be facing late delivery, and which orders may not ship and why? Can you tell the CEO what is causing the late deliveries and how the company could get back on track? You should, because it is in these answers where the business value lies.

We just posted a paper that highlights what’s at the heart of evolving business intelligence into business value. Download it today.

The first step in adopting this philosophy is having a corporate strategy around your supply chain.  Because a company’s supply chain can be their competitive differentiator, it is important to have some strategic vision on how that supply chain will work.  Once a company does have a strategy, key metrics should be defined that can be measured and tracked to determine whether the company is executing appropriately against that strategy.

So, how do you track metrics against your supply chain?  The author states that “tracking and measurement falls into 2 general categories:  Abundance of Data – Dearth of Analysis and Stove Pipe City”.  I would argue that while some companies have an abundance of data, they don’t always have the right data.  Many manufacturing companies are now outsourcing at least some of their supply chain, if not all of it, and most of those companies have no visibility into their supplier data.  So how could they make strategic decisions about their supply chain if they don’t even know everything that is happening in their supply chain?  Companies need to be able to consolidate all of their supplier data to allow them to have true visibility into their supply chain and make better business decisions.  Then that data can be used in analysis of the supply chain and tracked against key metrics.

The premise of “Stove Pipe City” is that not all of all of the supply chain data resides in one place and therefore different constituencies are making different decisions based on which data they are looking at.  I agree with the author, it is important to make sure all of the data resides in one place so true analysis of the business can occur. 

When defining the metrics to track against the supply chain strategy, it is also important to define any business tolerances that may be important (i.e., orders shipping late to #1 customer) and have an ability to set alerts (based on consolidated supply chain data) to ensure that the appropriate person take action on a particular metric or business situation that could be urgent.  What is the point of tracking metrics if no one takes appropriate action to ensure the metrics are meeting the strategy?

In addition, the ability to simulate demand, supply or a variety of other factors against the full supply chain data is valuable as well.  With the volatility in the manufacturing marketplace right now, it is critical to understand how changes in the supply chain may affect the business and the overall supply chain strategy.  If your #1 customer called and said “please ship us 300 extra units of product B” and one of the metrics you track is your ability to satisfy #1 customer, how long will it take you to know when you can ship them the 300 units?  If it takes too long, the customer may get product elsewhere, so the ability to simulate changes in demand or supply is critical in our changing world.

The author also states  that the  “keys for a successful and comprehensive analytics program in the supply chain starts with these key ingredients:  Support, Vision, Inputs and Accountability”.    I agree with those ingredients but would add an ingredient called “Simulation”.   As stated above, it is important to be able to simulate changes in your supply chain in order to make better business decisions, maximize profit and increase customer satisfaction. 

I believe that a defined supply chain strategy that maps to metrics with the appropriate data captured and a tool to analyze and simulate changes is really the key to success in analyzing  your supply chain.

It’s amazing to see how many large multi-billion dollar operations still find themselves caught in the vicious cycle of widely using Excel spreadsheets. Some of them, recognizing that Excel is not the right tool and was not developed to be used in place of ERP systems look for alternatives that will enable them to become mere spectators in the planning and execution process.

I wonder how much thought companies typically give on deciding the amount of automation they want in their decision making process and how much is appropriate for the types of decisions that will be impacted by the new system.

I came across this Map of Technologies for Decision-Making, used by Professors Dwight Gertz and Tom Davenport at Babson College in Massachusetts. The picture proposes 2 dimensions that companies can use to organize their thinking about decision-making automation: the chart shows complexity of work in one axes and level of collaboration required in the other. This map can be used to analyze whether an organization is using or selecting the right technology and the right process given the nature of the decision they expect to support.

I believe that even in cases where the conclusion is that the process can be fully automated, companies need to be careful when planning for radical switches from fully manual to fully automated, for two reasons. First, it may require considerable change management effort to make everyone comfortable with all the calculations running behind the scenes; in some cases, a phased approach allows for increased confidence and hence better support to the project and better tool adoption (if you are not careful everyone will go back to the spreadsheets!). Secondly, even when you do a good job in the planning stage you should still expect to have to accommodate changes to the tool design during implementation: you’ll most likely see things differently and identify improvements. In addition, you need to ensure internal knowledge and documentation of how the analytics work so that, over time, as the business environment changes you know where to go and which setting to tweak in order to keep your system running up to date. Ideally, you’ll build in a process that would sense and identify the need for process reviews.  And you need to make the reviews when alerted, otherwise you may experience a version of the subprime mortgage fiasco, if you ignore the signs.

In any case, deciding what and how to change is a highly collaborative and judgmental decision, so don’t expect to find a tool to automate that one!