How Can I Improve My Forecast Accuracy?

Imagine

Imagine that Amanda (a completely imaginary person) is a demand planner at Kool Komfort Foods (a completely imaginary company, also branded as K2), a nationwide producer of healthy comfort foods.  She got her bachelor’s in Mechanical Engineering about five years ago.  After a short stint in manufacturing process engineering in another industry, she got interested in the business side of things and moved into supply chain planning, starting in demand planning.  After taking a couple of on-line courses and getting an APICS certification, she seized on the opportunity to be a junior demand planner at K2.  Through her affinity for math and her attention to detail, Amanda earned a couple of promotions and is now a senior demand planner.  At present, she currently manages a couple of product lines, but has her sights set on becoming a demand planning manager and mentoring others.

Amanda has been using some of the common metrics for forecast accuracy, including MAPE (mean absolute percentage error) and weighted MAPE, but the statistical forecast doesn’t seem to improve and the qualitative inputs from marketing and sales are hit or miss.  Her colleague, Jamison, uses the standard deviation of forecast error to plan for safety stock, but there are still a lot of inventory shortages and overages.  

Amanda has heard her VP, Dmitry, present to other department heads how good the forecast is, but when he does that, he uses aggregate measures and struggles when he is asked to explain why order fill rate is not improving, if the forecast is so good.

Amanda wonders what is preventing her from getting better results at the product/DC level, where it counts.  She would love to have it at the product/customer or product/store level, but she knows that she will need better results at the product/DC level before she can do that.  She is running out of explanations for her boss and the supply planning team.  She has been using some basic forecasting techniques that she has programmed into Excel, like single and double exponential smoothing as well as moving average, and even linear regression.  She is sure the math is correct, but the results have been disappointing. 

Amanda’s company just bought a commercial forecasting package.  She was hoping that would help.  It is supposed to run a bunch of models and select the best one and optimize the parameters, but so far, the simpler models perform the best and are no better – and sometimes worse – than her Excel spreadsheet.

Amanda has been seeing a lot of posts on LinkedIn about “AI”.  She has been musing to herself about whether there is some magic bullet in that space that might deliver better results.  But, she hasn’t had time to learn much about the details of that kind of modeling.  In fact, she finds it all a bit overwhelming, with all of the hype around the topic.

And, anyway, forecasts will always be wrong, they will always change, and the demand planner will always take the blame.  Investments in forecasting will inevitably reach diminishing returns, but for every improvement in forecast accuracy, there are cascading benefits through the supply chain and improvements in customer service.  So, what can Amanda and her company do to make sure they are making the most of the opportunity to anticipate market requirements without overinvesting and losing focus on the crucial importance of developing an ever more responsive value network to meet constantly changing customer requirements?

Unfortunately, there really is no “silver bullet” for forecasting, no matter how many hyperbolic adjectives are used by a software firm in their pitch.  That is not to say that a software package can’t be useful, but you need to really understand what you need and why before you go shopping.  

Demand planning consists of both quantitative and a qualitative analysis.  Since the quantitative input can be formulated and automated (not that it’s easy or quick), it can be used for calculating and updating a probabilistic range for anticipated demand over time. 

A good quantitative forecast requires hard work and skilled analysis.  Creating the best possible quantitative forecast (without reaching diminishing returns) will provide a better foundation for, and even improve, qualitative input from marketing, sales, and others.

Profiling

One of the first things you need to do is understand the behavior of the data.  This requires profiling the demand by product and location (either shipping plant/DC or customer location – let’s call that a SKU for ease of reference) with respect to volume and variability in order to determine the appropriate modeling approach.  For example, a basic approach is as follows: 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 may be best suited for simple reorder point replenishment.
  • High volume, high variability SKU’s will be difficult to forecast and may require a sophisticated approach to safety stock planning.
  • Low volume, high variability SKU’s may require a thoughtful postponement approach, resulting in an assemble or make-to-order process.  
  • A more sophisticated approach would involve the use of a machine learning for classification that might find clusters of demand along more dimensions.

Profiling analysis can be complemented nicely by a Quantitative Reasonability Range Check (see below), which should be an on-going part of your forecasting process.

Once you have profiled the data, you can start to develop the quantitative forecast, but you will need to consider the questions:

  1. What is the appropriate level of aggregation for forecasting?
  2. What forecast lag should I use?
  3. How frequently should I forecast?
  4. What are the appropriate quantitative forecast models?
  5. How should I initialize the settings for model parameters?
  6. How should I consume the forecast?
  7. How will I compensate for demand that I couldn’t capture?
  8. What metrics should I use to measure forecast accuracy?

Let’s consider each of these questions, in turn.

A. Level of Aggregation

The point of this analysis is to determine which of the following approaches will provide you with the best results:

  • Forecasting at the lowest level and then aggregating up
  • Forecasting at a high level and just disaggregating down
  • Forecasting at a mid-level and aggregating up and, also, disaggregating down

B. Correct Lag

If you forecast today for the demand you expect tomorrow, you should be pretty accurate because you will have the most information possible, prior to actually receiving orders.  The problem with this is obvious.  You can’t to react to this forecast (which will change each day up until you start taking orders for the period you are forecasting) by redistributing or manufacturing product because that takes some time.

Since you cannot procure raw materials, manufacture, pack, or distribute instantly, the “lead time” for these activities needs to be taken into account.  So, you need to have a forecast lag.  For example, if you need a month to respond to a change in demand, then, you would need to forecast this month for next month.  You can continue to forecast next month’s demand as you move through this month, but it’s unlikely you will be able to react, so when you measure forecast accuracy, you need to measure it at the appropriate lag.

C. Frequency

Should you generate a new forecast every day? Every week?  Or, just once a month?  This largely depends on when you can get meaningful updates to your forecast inputs such as sales orders, shipment history, or updates to industry and any syndicated or customer data (whether leading or trailing indicators) that are used in your quantitative forecast.

D. Appropriate Forecasting Model(s)

So, what mathematical model should you use?  This is a key question, but as you can see, certainly not the only one.

The mathematical approach can depend on many factors, including, but not limited to, the following:

  • Profiling (discussed above)
  • Available and meaningful trailing and leading indicators
  • Amount of history needed for the model vs. history that’s still relevant
  • Forecasting a distribution of demand vs. forecasting the actual distribution 
  • Explainability vs. accuracy of the model
  • The appearance of accuracy vs. useful accuracy (overfitting a model to the past)
  • Treatment of qualitative data (e.g., geography, holiday weekends, home football game, etc.)

A skilled data scientist can be a huge help.  A plethora of techniques is available, but a powerful machine learning (or other) technique can be like a sharp power tool.  You need to know what you’re doing and how to avoid hurting yourself.

E. Initializing the Steady State Settings for Parameters

Failure to properly initialize the parameters of a statistical model can cause it to underachieve.  In the case of Holt-Winters 3 parameter smoothing, for example, the modeler needs to have control over how much history is used for initializing the parameters.  If too little history is used, then forecasts will likely be very unreliable. 

When it comes to machine learning, there are two kinds of parameters – hyperparameters and model parameters.  Training can optimize the model parameters, but knowledge, experience and care are required to select techniques that are likely to help and to set the hyper parameters for running models that will give you good results.

F. Forecast Consumption Rules

There are a few things to consider when you consume the forecast with orders.  For example, you might want to bring forward previously unfulfilled forecasts (or underconsumption) from the previous period(s), or there may be a business reason to simply treat consumption in each week or month in isolation.

You may want to calculate the forecast consumption more frequently than you generate a new forecast.

G. Compensating for Demand You Couldn’t Capture

This is a particular challenge in the retail and CPG industries.  In CPG, many orders from retail customers are placed and fulfilled on a “fill or kill” basis.  The CPG firm fulfills what it can with the inventory on hand and then cancels or “kills” the rest of the order.

In retail, a consumer may simply go to a competitor or order online if the slot for the product on the shelf in a given store is empty.

In either case, sales or shipment history will under-represent true demand for that period.  If you don’t accurately compensate for this, your history will likely drive your forecast model to under-forecast.

H. Metrics and Measurement

There are many measures of forecast accuracy that can be used.  A couple of key questions to answer include the following:

  1. Who is the audience and what is their interest?  Consider the sales organization which is interested in an aggregate measure of sales against their sales target, perhaps by sales group or geography.  On the other hand, customer service doesn’t really happen in aggregate.  If you want to have better customer service, you need to look at forecast accuracy at the SKU level.
  2. Are you measuring forecast error based on an assumed normal distribution that you have defined by projecting a mean and standard deviation?  Or, have you been able to use the actual distribution of forecast error, perhaps created through bootstrapping? 

Remember that you will need to measure forecast error at the correct lag.

Another thing you may need to keep in mind is that not everyone has been trained to understand forecast error and its interrelationship to inventory, safety stock, and fill rate.  You may have a bit of education to do from time to time, even for executives.

Price & Forecast

In most cases, demand is elastic with respect to price.  In other words, there is a relationship between what you charge for something and the demand for it.  This is why consumer packaged goods companies run promotions and fund promotions with retailers, and also, why retailers run their own promotions.  The goal is to grow sales without losing money and/or gain market share (possibly, incurring a short-term loss).  The overall goal is to increase gross margin in a given time period.  Many CPG companies make competing products – think of shampoo or beverages, or even automobiles or car batteries.  And, of course, retailers sell products from their CPG suppliers that compete for shelf space and share of wallet.  Many retailers even sell their own private label goods.  The trick is how to price competing products such that you gain sales and margin over the set of products.  

Just as in forecasting demand, there are both quantitative and qualitative approaches to optimizing pricing decisions which, then, in turn, need to be incorporated into the demand forecast.  The quantitative approach has two components:

  1. Using ML techniques to predict prie elasticity, considering history, future special events (home football game, holiday weekend, football team in playoffs, etc.), minimum and maximum demand, and perhaps other features.
  2. Optimizing the promotional offers so that margin is maximized.  For this, a mathematical optimization model may be best so that the total investment in promotional discount and allocations of that investment are respected, limits on cannibalization are enforced, and upper limits on demand are considered.

The Quantitative Reasonability Range Check

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?  A Quantitative Forecast Reasonability Range Check (or maybe QRC, for short) accomplishes this perfectly.  If the historical data is not very dense, then a “reasonability range” may need to be calculated through “bootstrapping”, a process of randomly sampling the history to create a more robust distribution.   Once you have this distribution, you can assign a probability to a future forecast and leverage that probability for safety stock planning as well.

At a minimum, a QRC must consider the following components:

  • Every level and combination of the product and geographical hierarchies
  • A quantitative forecast
  • An asymmetric prediction interval over time
  • Metrics for measuring how well a point forecast fits within the prediction interval
  • Tabular and graphical displays that are interactive, intuitive, always available, and current

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

Eliminate duplication.  When designing a QRC 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 QRC, so that planners can focus their energy on asking critical questions only about those cases.

Minimize human time and effort by automating the math.  Leverage automation and, potentially, even cloud computing power, to deliver results that are self-explanatory and always available, providing an immediately understood context that identifies invalid forecasts. 

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.

Reflect reality.  Calculations of upper and lower bounds of the prediction interval should reflect seasonality and cyclical demand in addition to month-to-month variations.  A crucial aspect of respecting reality involves calculating the reasonability range for future demand from what actually happened in the past so that you do not force assumptions of normality onto the prediction interval (this is why bootstrapping can be very helpful).  Among other things, this will allow you to predict the likelihood of over- and under-shipment.

Illustrate business performance, not just forecasting performance with prediction intervals.  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.

Summary

Demand planning is both quantitative and qualitative.  In this paper, we have touched on the high points of the best practices for building a good quantitative forecasting foundation for your demand planning process.  In our imaginary case, Amanda still has some work to do, some of which lies outside of her expertise.  She will need to articulate the case for making an investment to improve the quantitative forecast and building a better foundation for qualitative input and a consensus demand planning process.  A relatively small improvement in forecast accuracy can have significant positive bottom and top-line impact.  

Amanda needs to convince her management to invest in a consulting service that will deliver the math, without the hype, and within the context of experience, so that she can answer the key quantitative questions every demand planner faces:

  • What is the profile of my demand data?
  • What is the appropriate level of aggregation for forecasting?
  • What forecast lag should I use?
  • How frequently should I forecast?
  • What are the appropriate quantitative forecast models?
  • How should I initialize the settings for model parameters?
  • How should I consume the forecast?
  • How will I compensate for demand that I couldn’t capture?
  • What metrics should I use to measure forecast accuracy?

Does Your Demand Planning Process Include a “Quantitative Reasonability Range Check”?

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’ll call it a Quantitative Forecast Reasonability Range Check (or maybe QRC, for short).  It may be similar in some ways to analyzing “forecastability” or a “demand curve analysis”, but different in at least one important aspect – the “reasonability range” is calculated through bootstrapping (technically, you would be bootstrapping a confidence interval, but please allow me the liberty of a less technical name – “reasonability range”).  A QRC can be applied across industries, but it’s particularly relevant in consumer products.

At a minimum, QRC must consider the following components:

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

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

1.  Eliminate duplication.  When designing a QRC 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 QRRC, 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 QRC.

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!

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, high variability SKU’s may require a thoughtful postponement approach, resulting in an assemble-to-order process.  This profiling analysis is complemented nicely by a Quantitative Reasonability Range Check, 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
  • Frequency
  • Appropriate forecasting model(s)
  • Best settings for forecasting model parameters
  • Forecast consumption rules
  • Demand that you didn’t capture in sales or shipments (in the case of retail stockouts or fill/kill ordering in CPG)

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!

Do You Need a Network Design CoE?

shutterstock_148723100

Licensed through Shutterstock. Copyright: Sergey Nivens

Whether you formally create a center of excellence or not, an internal competence in value network strategy is essential.  Let’s look at a few of the reasons why.

Weak Network Design Limits Business Success

From an operational perspective, the greatest leverage for revenue, margin, and working capital lies in the structure of the supply chain or value network.*

It’s likely that more than half of the cost and capabilities of your value network remain cemented in its structure, limiting what you can achieve through process improvements or even world-class operating practices.

You can improve the performance of existing value networks through an analysis of their structural costs, constraints, and opportunities to address common maladies like these:

  • Overemphasis on a single factor.  For example, many companies have minimized manufacturing costs by moving production to China, only to find that the “hidden” cost associated with long lead times has hurt their overall business performance.
  • Incidental Growth.  Many value networks have never been “designed” in the first place.  Instead, their current configuration has resulted from neglect and from the impact from mergers and acquisitions.
  • One size fits all.  If a value network was not explicitly designed to support the business strategy, then it probably doesn’t.  For example, stable products may need to flow through a low-cost supply chain while seasonal and more volatile products, or higher value customers, require a more responsive path.

It’s Never One and Done

At the speed of business today, you must not only choose the structure of your value network and the flow of product through that network, you must continuously evaluate and evolve both.  

Your consideration of the following factors and their interaction should be ongoing:

  1. Number, location and size of factories and distribution centers
  2. Qualifications, number and locations of suppliers
  3. Location and size of inventory buffers
  4. The push/pull boundary
  5. Fulfillment paths for different types of orders, customers and channels
  6. Range of potential demand scenarios
  7. Primary and alternate modes of transportation
  8. Risk assessment and resiliency planning

The best path through your value network structure for each product, channel and/or customer segment combination can be different.  It can also change over the course of the product life-cycle.

In fact, the best value network structure for an individual product may itself be a portfolio of multiple supply chains.  For example, manufacturers sometimes combine a low-cost, long lead-time source in Asia with a higher cost, but more responsive, domestic source.

Focus on the Most Crucial Question – “Why?”

The dynamics of the marketplace mandate that your value network cannot be static, and the insights into why a certain network is best will enable you to monitor the business environment and adjust accordingly.

Strategic value network analysis must yield insight on why the proposed solution is optimal.  This will always be more important than the “optimal” recommendation.

In other words, the context is more important than the answer.

The Time Is Always Now

For all of these reasons, value network design is more than an ad hoc, one-time, or even periodic project.  At today’s speed of competitive global business, you must embrace value network design as an essential competency applied to a continuous process.

You may still want to engage experienced and talented consultants to assist you in this process from time to time, but the need for continuous evaluation and evolution of your value network means that delegating the process entirely to other parties will definitely cost you money and market share.  

Competence Requires Capability

Developing your own competence in network design will require that you have access to enabling software.  The best solution will be a platform that facilitates flexible modeling with powerful optimization, easy scenario analysis, intuitive visualization, and collaboration.  

The right solution will also connect to multiple source systems, while helping you cleanse and prepare data. 

Through your analysis, you may find that you need additional “apps” to optimize particular aspects of your value network such as multi-stage inventories, transportation routing, and supply risk.  So, apps like these should be available to you on the software platform to use or tailor as required.  

The best platform will also accelerate the development of your own additional proprietary apps (with or without help), giving you maximum competitive advantage.  

You need all of this in a ubiquitous, scalable and secure environment.  That’s why cloud computing has become such a valuable innovation.  

A Final Thought

I leave you with this final thought from Socrates:  “The shortest and surest way to live with honor in the world is to be in reality what we appear to be.”

 

*I prefer the term “value network” to “supply chain” because it more accurately describes the dynamic collection of suppliers, plants, outside processors, fulfillment centers, and so on, through which goods, currency and data flow along the path of least resistance (seeking the lowest price, shortest time, etc.) as value is exchanged and added to the product en route to the final customer.

The Value Network, Optimization & Intelligent Visibility

The supply chain is more properly designated a value network through which many supply chains can be traced.  Material, money and data pulse among links in the value network, following the path of least resistance, accelerated by digital technologies, including additive manufacturing, more secure IoT infrastructure, RPA, and, potentially, blockchain. 

If each node in the value network makes decisions in isolation, the total value in one or more supply chain paths becomes less than it could be.  

In the best of all possible worlds, each node would eliminate activities that do not add value to its own transformation process such that it can reap the highest possible margin, subject to maximizing and maintaining the total value proposition for a value network or at least a supply chain within a value network.  This is the best way to ensure long-term profitability, assuming a minimum level of parity in bargaining position among trading partners and in advantage among competitors.

Delivering insights to managers that allow them to react in relevant-time without compromising the value of the network (or a relevant portion of a network, since value networks interconnect to form an extended value web) remains a challenge.

The good news is that many analytical techniques and the mechanisms for delivering them in timely, distributed ways are becoming ubiquitous.  For example, optimization techniques and scenarios can provide insights into profitable ranges for decisions, marginal benefits of incremental resources, and robustness of plans, given uncertain inputs.

When these techniques are combined with intelligent visibility that allows you detect and diagnose anomalies in your supply chain, then everyone can make coordinated decisions as they execute.  

I will leave you with these words of irony from Dale Carnegie, “You make more friends by becoming interested in other people than by trying to interest other people in yourself.”

Thanks again for stopping by and have a wonderful weekend!

Resilience Versus Agility

Just a short thought as we move into this weekend . . .

Simple definitions of resiliency and agility as they relate to your value network might be as follows:

Resiliency:  The quality of your decisions and plans when their value is not significantly degraded by variability in demand and/or changes in your competitive and economic environment.

Agility:  The ability to adjust your plans and execution for maximum value by responding to the marketplace based on variability in demand and/or changes in your competitive and economic environment.

You can take an analytical approach that will make your plans and decisions resilient and also give you insights into what you need to do in order to be agile.

You need to know the appropriate analytical techniques and how to use them for these ends.

A capable and usable analytical platform can mean the difference between knowing what you should do and actually getting it done.

For example, scenario-based analysis is invaluable for understanding agility, while range-based optimization is crucial for resiliency.

Do you know how to apply these techniques?

Do you have the tools to do it continuously?

Can you create user and manager ready applications to support resiliency and agility?

Finally, I leave you with this thought from Curtis Jones:  “Life is our capital and we spend it every day.  The question is, what are we getting in return?”

Thanks for stopping by.  Have a wonderful weekend!

A Few Random Thoughts

This week, I was privileged to attend the INFORMS Analytics Conference in Huntington Beach, California and the IEG S&OP Conference in San Francisco.  I heard some insightful points and thought I would list a couple here (with appropriate attribution) along with a few thoughts of my own.  I hope that at least one strikes a chord with you.

If you use a heuristic to solve a problem with 100% complete and clean data, using a data model that exactly represents reality at any given moment, you still have an inexact answer.  But since such data and data models are rare (or nonexistent), even a pure optimization is still inexact and, in effect, a heuristic solution requiring both art and science on the part of the analyst. (Colin Kessinger, End-to-End Analytics)

If the purpose of Sales and Operations Planning is to make the best integrated decisions for running your business, then you will have a firm, published schedule and people will schedule other meetings (even customer meetings) around it. (Bob Ratay, SAP),

Key capabilities in an S&OP decision-making process are business agility, versatility, and elasticity.  (Olaf Gelhausen, Infineon)

S&OP is about a range, not “one-number” – one plan with a range and distribution of probabilities, but not one number. (Olaf Gelhausen, Infineon)

The best business decisions, even very qualitative ones such as those in the fashion industry are built on a foundation of rigorous data analysis and decision modeling, providing the qualitative decision-maker the largest head-start possible by reducing the “solution space” and delivering insight into the most sensitive tradeoffs.

Working with people is the hardest part of any business challenge – by comparison, the mathematics are relatively easy.

In business planning, longer term investment decisions require detailed scenario analysis.  Near term execution decisions require existential insight into the cash flow changes and their causes.  One might call the latter, “analytical awareness”.

Once sources have been qualified, sourcing decisions among sources (both near and far, “in” and “out”) should be cost-optimized and dynamic (Olaf Gelhausen, Infineon).

Thanks for dropping by Supply Chain ActionPlease feel free leave your random thoughts as a comment below or send them to me, and I’ll try to include them in an upcoming post.

Until next week, always choose life, light and love and don’t forget to laugh along the way.

Have a wonderful weekend!

Leading for Profit

My guess is that you may have already read the book, Islands of Profit in a Sea of Red Ink by Jonathan Byrnes.  He suspects that 40% of your business is unprofitable, but nobody knows because your metrics are aggregate and profitable transactions subsidize the rest.  Byrnes’ book is replete with insights for managing profitably, including:

 

 

  1. Identifying unprofitable business is job one. 
  2. The big question, then, is how to manage for profitability (or value) going forward. 
  3. In most companies, no one is responsible for managing the interaction of key tradeoffs to increase profitability to its full potential. 

Managing your company’s assets for increased profit is the essence of supply chain management.  To that end, product development, marketing and selling, forecasting, supply chain design, capacity and production planning, and sourcing must be orchestrated as interrelated processes.  This requires cross-functional coordination enabled by advanced analytics that explicitly and simultaneously evaluate many critical tradeoffs, giving you the head start you need to seize new areas of profitability.

I see three sequential priorities for transforming the supply chain management of an enterprise:

1)      Profitability profiling by product and customer, down even to the invoice level.  Combined with a pricing power/risk and margin leakage analysis, this identifies where prices can be immediately increased and where costs to serve some customers need to be slashed, yielding an immediate impact.

2)      Detailed analysis of the decisions that are driving the unprofitable or marginally profitable transactions

3)      Designing improved, integrated decision and planning processes as well as the tools to sustain the them, enabling the capture of new areas of profitability

For the weekend, I leave you to ponder this quote by Byrnes, “Middle management excellence is the key leverage point for great performance.”

Thanks for stopping by.  Have a wonderful weekend!

Applying Analytics and Supply Chain Tools to Healthcare

To say that understanding and managing a large, disaggregated system such as healthcare delivery with its multitude of individual parts, including patients with various medical conditions, physicians, clinics, hospitals, pharmacies, rehabilitation services, home nurses and many more is a daunting task would be an understatement.  Like other service or manufacturing systems, different stakeholders have different performance measures.

Patients want safe, effective care with low insurance premiums. 

Payers, usually not the patient, want low cost. 

Health care providers want efficiency.

The Institute of Medicine has identified six quality aims for the twenty-first century healthcare system:  safety, effectiveness, timeliness, patient-centeredness, efficiency, and equity.  Achieving these goals in a complex system will require an holistic understanding of the needs and performance measures 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 are summarized in the table below.

While the bulk of the work and benefits derived from the application of these tools will lie at the organization level, these tools are well-developed concepts that can be applied directly to healthcare systems, beginning at the environmental level and working back left down to the patient, where indicated by the check marks.

Industrial engineers and operations researchers use systems analysis tools to understand how complex systems operate, how well they perform, and how they can be improved.  For example, mathematical analyses of system operations include queuing theory which can be used to understand the flow of patients through a system, the average time patients spend in a system, or bottlenecks in the system.  Discrete event simulation is another tool that can aid in a more detailed examination of system characteristics and sensitivity to inputs and changes in the system.  Economic and econometric models, based on data-driven analysis, help identify causal relationships among system variables.  Supply chain management tools help forecast demand for services and relate that demand to available resources.  Longer term mismatches can be minimized through sales and operations planning, while short-term challenges are addressed with capacity planning and scheduling.  A few examples of specific challenges that can be addressed through systems analysis tools include the following:

  1. Staff scheduling
  2. Improving patient flow through rooms and other resources and elimination of wait time and waste in work flow
  3. Capacity management in hospitals
  4. Evaluation of blood supply networks
  5. Distributing and balancing consumable supplies
  6. Ensuring the availability of medical device kits
  7. Optimal allocation of funding

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 in order to apply them efficiently and effectively.  Many healthcare delivery organizations are beginning to build staff who have at least some familiarity with a few of these tools, particularly Lean thinking in process design and six-sigma in supply chain management.  There also instances where some of the tools under “Optimizing Results” are being applied.  But, it is clear that much more remains to be done and many healthcare providers will initially need to depend on resources external to their own organizations in order to leverage many of these tools.

Two notes of caution as we move forward:

  1. In our efforts to consider end-to-end processes and their inherent tradeoffs, we must ensure that we do not enforce a complex structure to the detriment of disruptive innovations that will lead to more efficient and effective health care as described by Christensen et. al. in The Innovator’s Prescription.
  2. We must also take care not to base our data analysis and decision models on faulty cost data or inadequate outcome data.  In most cases, neither reimbursements nor charges reflect costs and the measurement of outcomes is significantly underdeveloped.  Some of the tools outlined above will be helpful in addressing these challenges.

Thanks again for stopping by Supply Chain Action.  I leave you with a thought from Mother Teresa, “We can do no great things – only small things with great love.”

Have a wonderful weekend!

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