Does Your Demand Planning Process Include a “Quantitative Sanity Range Evaluation”?

There is a process that should be part of both your demand planning and your sales and operations planning.  The concept is simple – how do you find the critical few forecasts that require attention, so that planner brainpower is expended on making a difference and not hunting for a place to make a difference?  I have heard this process called a “Forecast Reality Check” and a “Forecast Reasonability Check”.  Just to be difficult, I’ll call it a Quantitative Sanity Range Evaluation (I have my own reasons.)  It may be similar in some ways to analyzing “forecastability” or a “demand curve analysis”, but different in at least one important aspect – the “sanity range” is calculated through bootstrapping (technically, you would be bootstrapping a confidence interval, but please allow me the liberty of a less technical name – “sanity range”).  A QSRE can be applied across industries, but it’s particularly relevant in consumer products, where I have seen a version of this implemented first hand by Allan Gray, a really smart gentleman – back when I worked with him for End-to-End Analytics – just so you know I didn’t think this all up on my own!

At a minimum, QSRE must consider the following components:

  1. Every level and combination of the product and geographical hierarchies
  2. A quality quantitative forecast
  3. A sanity range out through time
  4. Metrics for measuring how well a point forecast fits within the sanity range
  5. Tabular and graphical displays that are interactive, intuitive, always available, and current.

If you are going to attempt to establish a QSRE, then I would suggest five best practices:

1.  Eliminate duplication.  When designing a QSRE process (and supporting tools), it is instructive to consider the principles of Occam’s razor as a guide:

– The principle of plurality – Plurality should not be used without necessity

– The principle of parsimony – It is pointless to do with more what can be done with less

These two principles of Occam’s razor are useful because the goal is simply to flag unreasonable forecasts that do not pass a QSRE, so that planners can focus their energy on asking critical questions only about those cases.

2. Minimize human time and effort by maximizing the power of cloud computing.  Leverage the fast, ubiquitous computing power of the cloud to deliver results that are self-explanatory and always available everywhere, providing an immediately understood context that identifies invalid forecasts. 

3. Eliminate inconsistent judgments By following #1 and #2 above, you avoid inconsistent judgments that vary from planner to planner, from product family to product family, or from region to region.

4. Reflect reality.  Calculations of upper and lower bounds of the sanity range should reflect the fact that uncertainty grows with each extension of a forecast into a future time period.  For example, the upper and lower limits of the sanity range for one period into the future should usually be narrower than the limits for two or three periods into the future.  These, in turn, should be narrower than the limits calculated for more distant future periods.  Respecting reality also means capturing seasonality and cyclical demand in addition to month-to-month variations.  A crucial aspect of respecting reality involves calculating the sanity range for future demand from what actually happened in the past so that you do not force assumptions of normality onto the sanity range (this is why bootstrapping is essential).  Among other things, this will allow you to predict the likelihood of over- and under-shipment.

5. Illustrate business performance, not just forecasting performance with sanity ranges.  The range should be applied, not only from time-period to time period, but also cumulatively across periods such as months or quarters in the fiscal year.

If you are engaged in demand planning or sales and operations planning, I welcome to know your thoughts on performing a QSRE.

Thanks again for stopping by Supply Chain Action.  As we leave the work week and recharge for the next, I leave you with the words of John Ruskin:

“When skill and love work together, expect a masterpiece.”

Have a wonderful weekend!

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Part 4 (Final) – A New Supply Chain Planning Paradigm for the Digital Value Network?

In Part 1, I introduced the 3-D Cycle that integrates the 3 dimensions of orchestrating a value network.  In Part 2, I defined the 3-D Cycle in more detail.  In Part 3 I explored the some of the challenges and industry imperatives, drawing on some examples from retail (thought I’d pick one of the more challenging industries).  In this final Part 4, I’ll explore what the 3-D Cycle looks like in terms of specific examples and architecture.

Detect, Diagnose and Direct with Speed, Precision & Advanced Analytics

Detection of incidental challenges (e.g. demand is surging or falling based on local demographics, a shipment is about to arrive late, a vendor is behind on production, etc.) in your value network can be significantly automated to take place in almost real-time, or at least, in relevant time.   Detection of systemic challenges will be a bit more gradual and is based on the metrics that matter to your business, such as customer service, days of supply, etc., but it is the speed and, therefore, the scope, that is now possible that drives better visibility through detection.

 

Diagnosing the causes of incidental problems is only limited by the organization and detail of your transactional data.  Diagnosing systemic challenges requires a hierarchy of metrics with respect to cause and effect (such as the SCOR® model).  Certainly, diagnosis can now happen with new speed, but it is the combination of speed and precision that makes a new level of understanding possible through diagnosis.

 

With a clean, complete, synchronized data set that is always available and always current, as well as a proactive view of what is happening and why, you need to direct the next best action while it still matters.  You need to optimize your trade-offs and perform scenario and sensitivity analysis.

The chart, below, shows both incidental/operational and systemic/strategic examples for all three dimensions of the 3-D Cycle.

 

 

Speed in detection, speed and precision in diagnosis, and the culmination of speed, precision and advanced analytics in decision-making give you the power to transpose the performance of your value network to levels not previously possible.  Much of the entire 3-D Cycle and the prerequisite data synchronization can be, and will be, automated by industry leaders.  Just how “autonomous” those decisions become remains to be seen.

 

Fortunately, you don’t need “Skynet”, but a faster and better 3-D Cycle is fundamental to your journey toward the digital transformation of your value network.

 

The basic ideas of detecting, diagnosing and directing are not novel to supply chain professionals and other business executives.   However, the level of transparency, speed, precision and advanced analytics that are now available mandate a new approach and promise dramatic results.  Some will gradually evolve toward a better, faster 3-D cycle.  The greatest rewards will accrue to enterprises that climb each hill with a vision of the pinnacle, adjusting as they learn.  These organizations will attract more revenue and investment.  Companies that don’t capitalize on the possibilities will be relegated to hoping for acquisition by those that do.

 

Admittedly, I’m pretty bad at communicating graphically, but I’ve attempted to draft a couple rudimentary visuals of what the architecture to support a state-of-the-art 3-D Cycle could look like (below), as a vehicle for facilitating discussion.  I do realize that the divisions I’m showing between Cloud, IoT, Extended Apps, and ERP are somewhat arbitrary and definitely fluid.

 

 

 

 

 

So, I imagine that I’m at least partly wrong, and could be completely wrong-headed . . . but, then again, maybe not.  I will say this:  The convergence of cloud business intelligence (BI) technology and traditional advanced planning solutions supports my point, and that is definitely happening.  Cloud BI solutions (e.g. Aera, Birst, Board) incorporate at least some machine learning (ML) algorithms for prediction, while Oracle, Microsoft, IBM, and SAP are all making ML available in their portfolios, adjacent to their BI applications.  Logility recently purchased Halo which embeds ML.  Most importantly, Oracle and SAP have built their cloud supply chain planning solutions with embedded BI, really making an effort toward a faster, better 3-D Cycle.

 

So, the future would appear to be now.  If that’s true, you have to ask yourself whether your current paradigm for value network planning will guide you to competitive advantage or leave you hoping that someone else will ask you to the dance.

 

I’ll leave you with this thought of my own:  You can only live today once.

 

Thanks for stopping buy.

 

Part 3 – A New Supply Chain Planning Paradigm for the Digital Value Network?

In Part 1, I introduced the 3-D Cycle that integrates the 3 dimensions of orchestrating a value network.  In Part 2, I defined the 3-D Cycle in more detail.  In this post, Part 3, I’ll explore the some of the challenges and industry imperatives, drawing on some examples from retail (thought I’d pick one of the more challenging industries).  Next and finally, in Part 4, I’ll explore what the 3-D Cycle looks like in terms of specific examples and architecture.

Data Is the Double-edged Sword

The universe of data is exploding exponentially from growing connections among organizations, people and things, creating the need for an ever-accelerating 3-D Cycle.  This is especially relevant for retailers, and it presents both a challenge and an opportunity for competing with your digital value network in the global digital economy.

 

For example, redesigned, retail supply chains, enabled with analytics and augmented reality (AR), are not only meeting, but raising consumer expectations.

 

Amazon’s re-imagination of retail means that competitors must now think in terms of many-to-many flows of information, product, and cash along the path of least resistance for the consumer (and not just to and from their own locations).  This kind of value network strategy goes beyond determining where to put a warehouse and to which stores it should ship.  Competing in today’s multi-channel world can mean inventing new ways to do business, even in the challenging fashion space – and if it is happening in fashion, it would be naive to think rising consumer expectations can be ignored in other retail segments, or even other industries.  Consider a few retail examples:

Zara leverages advanced analytics, not only to sense trends, but also to optimize pricing and operations in their vertically integrated supply chain.

Stitch Fix is changing the shopping model completely, providing more service with less infrastructure.

Zolando has been so successful in creating a rapid response supply chain that they are now providing services to other retailers.

Nordstrom, of all organizations, is opening “inventoryless” stores.

Walmart has been on an incredible acquisition and partnership spree, recently buying Flipkart and, as early as two years ago, partnering with JD.com.  And, then, there is the success of Walmart.com.

Target is redesigning the way their DC’s work, creating a flow-through operation with smaller replenishment quantities.

 

Yet, many companies are choking on their own ERP data, as they struggle to make decisions on incomplete, incorrect and disparate data.  So, while the need for the 3-D Cycle to keep pace grows more intense, some organizations struggle to do anything but watch.  The winners will be those who can capitalize on the opportunities that the data explosion affords by making better decisions faster through advanced analytics.

 

The time required just to collect, clean, transform and synchronize data for analysis remains fundamental barrier to better detection, diagnosis and decisions in the value network.  A consolidated data store that can connect to source systems and on which data can be consolidated, programmatically “wrangled”, and updated into a supra data set forms a solid foundation on which to build better detection, diagnosis, and decision logic that can execute in “relevant time”.  This can seem like an almost insurmountable challenge, but it is not only doable with today’s technology, it’s becoming imperative.  And, it’s now possible to work off of a virtual supra data set, but that’s a discussion for another day.

 

Thanks for stopping by.  I’ll leave you with this quote from the book, Hit Refresh (a read I thoroughly enjoyed), by Satya Nadella, CEO of Microsoft:

 

“Success can cause people to unlearn the habits that made them successful in the first place.”

Part 2 – A New Supply Chain Planning Paradigm for the Digital Value Network?

In Part 1, I introduced the 3-D Cycle that integrates the 3 dimensions of orchestrating a value network.  In this post, I’ll define the 3-D Cycle in more detail.  Later, in Part 3, I’ll explore the some of the challenges and industry imperatives, drawing on some examples from retail (thought I’d pick one of the more challenging industries).  Finally, in Part 4, I’ll explore what the 3-D Cycle looks like in terms of specific examples and architecture.

Detect, Diagnose, Direct

The work of managing the value network has always been to make the best plan, monitor issues, and respond effectively and efficiently.  However, since reality begins to diverge almost immediately from even the best plans, perhaps the most vital challenges in orchestrating a value network are monitoring and responding.

 

In fact, every plan is really just a response to the latest challenges and their causes.

 

So, if we focus on monitoring and responding, we are covering all the bases of what planners and executives do all day . . . every day.

 

Monitoring involves detecting and diagnosing those issues which require a response.  Responding is really directing the next best action.  That’s why we can think in terms of the “Detect, Diagnose, Direct Cycle”:

 

  1. Detect (and/or anticipate) market requirements and the challenges in meeting them
  2. Diagnose the causes of the challenges, both incidental and systemic
  3. Direct the next best action within the constraints of time and cost

 

The 3-D Cycle used to take a month, in cases where it was even possible.  Digitization – increased computing power, more analytical software, the availability of data – has made it possible in a week.  Routine, narrowly defined, short-term changes are now addressed even more quickly under a steady state – and a lot of controlled automation is not only possible in this case, but obligatory through robotic process automation (RPA).  However, no business remains in a steady state, and changes from that state require critical decisions which add or destroy significant value.   

 

You will need to excel at managing and accelerating the 3-D Cycle, if you want to win in digital economy.

 

There is no industry where mastering this Cycle is more challenging than in retail, but the principles apply across most industries.

Thanks for stopping by.  Next up is Part 3.  I’ll leave you with this thought from Cicero to ponder:

 

A thankful heart is not only the greatest virtue, but the parent of all other virtues.

Part 1 – A New Supply Chain Planning Paradigm for the Digital Value Network?

Photo licensed through iStockphoto

The strength of any chain is defined by its weakest link.  A supply chain, or as I prefer to say, a value network, is similarly constrained.  By orchestrating the flow of material, information and cash through your value network, you can prevent negative business impact from weak links by detecting anomalies, diagnosing their causes, and directing the next best action before there is a serious business impact.  Do you need some kind of self-aware artificial intelligence to make this work?  Let’s think about that for a minute.

 

 

 

Photo licensed through Shutterstock

 

 

There is a lot of buzz about the “autonomous” supply chain these days.  The subject came up at a conference I attended where the theme was the supply chain of 2030.  But, before we turn out the lights and lock the door to a fully automated, self-aware, supply chain “Skynet”, let’s take a moment and put this idea into some perspective.

 

 

 

The Driverless Car Analogy

I’ve heard the driverless vehicle used as an analogy for the autonomous supply chain.  However, orchestrating the value network where goods, information and currency pulse freely, fast, and securely between facilities, organizations, and even consumers, following the path of least resistance (aka the digital supply chain), may prove to be even more complex than driving a vehicle.  Digital technologies, such as additive manufacturing, blockchain, and more secure IoT infrastructure, advance the freedom, speed and security of these flows.  As these technologies makes more automation possible, as well as a kind of “autonomy”, the difficulty and importance of guiding these flows becomes ever more crucial.  

 

Most sixteen-year-old adolescents can successfully drive a car, but you may not want to entrust your global value network to them.

 

 

Before you can have an autonomous supply chain, you need to accelerate the Detect, Diagnose, Direct Cycle – let’s call it the 3-D Cycle, for short, not just because it’s alliterated, but because each “D” is one of three key dimensions of orchestrating your value network.  In fact, as you accelerate the 3-D Cycle, you will learn just how much automation and autonomy makes sense.

 

That’s Part 1 – the teaser.  Soon to be followed by Part 2 where I take a closer look at the 3-D Cycle.

 

Thanks for stopping by.  I’ll leave you with this bit of verse (public domain) to ponder from the great Emily Dickinson:

 

Hope is the thing with feathers  
That perches in the soul,  
And sings the tune without the words,  
And never stops at all,  
   
And sweetest in the gale is heard;          
And sore must be the storm  
That could abash the little bird  
That kept so many warm.  
   
I’ve heard it in the chillest land,  
And on the strangest sea;         
Yet, never, in extremity,  
It asked a crumb of me.

The Potential for Proven Analytics and Planning Tools in Healthcare Delivery

I’ve spent time in a hospital.  I was well cared for, but I didn’t like it, and I worried about the cost and how well I would be able to recover (pretty well, so far!)  Also, my daughter is a doctor (obviously takes after her mom!), so healthcare is obviously an area of high interest for me.

To say that managing a large, disaggregated system such as healthcare delivery with its multitude of individual parts, including patients, physicians, clinics, hospitals, pharmacies, rehabilitation services, home nurses, and more is a daunting task would be an understatement.

Like other service or manufacturing systems, different stakeholders have different goals, making the task even more challenging.

Patients want safe, effective care with low insurance premiums. 

Payers, usually not the patient, want low cost. 

Health care providers want improved outcomes, but also efficiency.

The Institute of Medicine has identified six quality aims for twenty-first century healthcare:  safety, effectiveness, timeliness, patient-centeredness, efficiency, and equity.  Achieving these goals in a complex system will require an holistic understanding of the needs and goals of all stakeholders and simultaneously optimizing the tradeoffs among them.

This, in turn, cannot be achieved without leveraging the tools that have been developed in other industries.  These have been well-known and are summarized in the table below.

While the bulk of the work and benefits related to these tools will lie at the organization level, such techniques can be applied directly to healthcare systems, beginning at the environmental level and working back left down to the patient, as indicated by the check marks in the table.

A few examples of specific challenges that can be addressed through systems analysis and planning solutions include the following:

1 – Optimal allocation of funding

2 – Improving patient flow through rooms and other resources

3 – Capacity management and planning

4 – Staff scheduling

5 – Forecasting, distributing and balancing inventories, both medical/surgical and pharmaceuticals

6 – Evaluation of blood supply networks

Expanding on example #5 (above), supply chain management solutions help forecast demand for services and supplies and plan to meet the demand with people, equipment and inventory.  Longer term mismatches can be minimized through sales and operations planning, while short-term challenges are addressed with inventory rebalancing, and scheduling.

Systems analysis techniques have been developed over many years and are based on a large body of knowledge.  These types of analytical approaches, while very powerful, require appropriate tools and expertise to apply them efficiently and effectively.  Many healthcare delivery organizations have invested in staff who have experience with some of these tools, including lean thinking in process design and six-sigma in supply chain management.  There are also instances where some of the techniques under “Optimizing Results” are being applied, as well as predictive modeling and artificial intelligence.  But, more remains to be done, even in the crucial, but less hyped, areas like inventory management.  Some healthcare providers may initially need to depend on resources external to their own organizations as they build their internal capabilities.

I leave you with a thought for the weekend – “Life is full of tradeoffs.  Choose wisely!”

Forecasting vs. Demand Planning

Often, the terms, “forecasting” and “demand planning”, are used interchangeably. 

The fact that one concept is a subset of the other obscures the distinction. 

Forecasting is the process of mathematically predicting a future event.

As a component of demand planning, forecasting is necessary, but not sufficient.

Demand planning is that process by which a business anticipates market requirements.  

This certainly involves both quantitative and qualitative forecasting.  But, demand planning requires holistic process that includes the following steps:

1.      Profiling SKU’s with respect to volume and variability in order to determine the appropriate treatment:

For example, high volume, low variability SKU’s will be easy to mathematically forecast and may be suited for lean replenishment techniques.  Low volume, low variability items maybe best suited for simple re-order point.  High volume, high variability will be difficult to forecast and may require a sophisticated approach to safety stock planning.  Low volume, low variability SKU’s may require a thoughtful postponement approach, resulting in an assemble-to-order process.  This analysis is complemented nicely by a Quantitative Sanity Range Evaluation, which should be an on-going part of your forecasting process.

2.       Validating of qualitative forecasts from among functional groups such as sales, marketing, and finance
3.       Estimation of the magnitude of previously unmet demand
4.       Predicting underlying causal factors where necessary and appropriate through predictive analytics
5.       Development of the quantitative forecast including the determination of the following:

  • Level of aggregation
  • Correct lag
  • Appropriate forecasting model(s)
  • Best settings for forecasting model parameters
  • Deducting relevant consumption of forecast

6.      Rationalization of qualitative and quantitative forecasts and development of a consensus expectation
7.      Planning for the commercialization of new products
8.      Calculating the impact of special promotions
9.      Coordinating of demand shaping requirements with promotional activity
10.    Determination of the range and the confidence level of the expected demand
11.    Collaborating with customers on future requirements
12.    Monitoring the actual sales and adjusting the demand plan for promotions and new product introductions
13.    Identification of sources of forecast inaccuracies (e.g. sales or customer forecast bias, a change in the data that requires a different forecasting model or a different setting on an existing forecast model, a promotion or new product introduction that greatly exceeded or failed to meet expectations).

The proficiency with which an organization can anticipate market requirements has a direct and significant impact on revenue, margin and working capital, and potentially market share.  However, as an organization invests in demand planning, the gains tend to be significant in the beginning of the effort but diminishing returns are reached much more quickly than in many other process improvement efforts.

This irony should not disguise the fact that significant ongoing effort is required simply to maintain a high level of performance in demand planning, once it is achieved.

It may make sense to periodically undertake an exercise to (see #1 above) in order to determine if the results are reasonable, whether or not the inputs are properly being collected and integrated, and the potential for additional added value through improved analysis, additional collaboration, or other means.

I’ll leave you once again with a thought for the weekend – this time from Ralph Waldo Emerson:

“You cannot do a kindness too soon, for you never know how soon it will be too late.”

Thanks for stopping by and have a wonderful weekend!

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