The Time-to-Action Dilemma



dreamstime_m_26639042If you can’t answer these 3 questions in less than 10 minute
s
(and I suspect that you can’t), then your supply chain is not the lever it could be to
 drive more revenue with better margin and less working capital:
1) What are inventory turns by product category (e.g. finished goods, WIP, raw materials, ABC category, etc.)?  How are they trending?  Why?
2) What is the inventory coverageWhat will projected inventory be at by the start of a promotion or season.  Within sourcing, manufacturing or distribution constraints, what options do I have if my demand spikes or tanks?
3) What proportion (and how many) of your customer orders (or margin or revenue) shipped at 99% on-time and in-full?  How many at 98%? And so on . . . Do you understand the drivers?

The slack time that global competition is allowing you to have between planning and execution is collapsing at an accelerating rate.

You need to know the “What?” and the “Why? so you can determine what to do before it’s too late.  

You need to answer the questions that your ERP and APS can’t so your supply chain makes your business more valuable.

Since supply chain decisions are all about managing interrelated goals and trade-offs, data may need to come from various ERP systems, OMS, APS, WMS, MES, and more, so unless you have a platform that consolidates and blends data from end-to-end at every level of granularity and along all dimensions, you will always be reinventing the wheel when it comes to finding and collecting the data for decision support.  It will always take too long.  It will always be too late.

You need the kind of platform that will deliver diagnostic insights so that you can know not just what, but why.  And, once you know what is happening and why, you need to know what to do — your next best action, or at least viable options and their risks . . . and you need that information in context and “in the moment”.

In short, you need to detect opportunities and challenges in your execution and decision-making, diagnose the causes, and direct the next best action in a way that brings execution and decision-making together.

If you don’t have all three now – Detect, Diagnose and Direct – in a way that covers your end-to-end value network, you need to explore how you can get there.

As we approach the weekend, I’ll leave you with this thought to ponder:  Leadership comes from a commitment to something greater than yourself that compels maximum contribution, whether that is leading, following, or just getting out of the way.”

The Value Network, Optimization & Intelligent Visibility

The supply chain is more properly designated a value network through which many supply chains can be tracedMaterial, money and data pulse among links in the value network, following the path of least resistance.

If each node in the value network makes decisions in isolation, the potential grows for the total value in one or more supply chain paths to be 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!

Supply Chain Action Blog

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.

If each node in the value network makes decisions in isolation, the potential grows for the total value in one or more supply chain paths to be 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…

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“Moneyball” and Your Business

MV5BMjAxOTU3Mzc1M15BMl5BanBnXkFtZTcwMzk1ODUzNg@@__V1__SY317_CR0,0,214,317_It’s baseball season again!  A while back, the film “Moneyball” showed us how the Oakland A’s built a super-competitive sports franchise on analytics, essentially “competing on analytics”, within relevant business parameters of a major league baseball franchise.  The “Moneyball” saga and other examples of premier organizations competing on analytics were featured in the January 2006 Harvard Business Review article, “Competing on Analytics” (reprint R0601H) by Thomas Davenport, who also authored the book by the same name.

The noted German doctor, pathologist, biologist, and politician, Rudolph Ludwig Karl Virchow called the task of science “to stake out the limits of the knowable.”  We might paraphrase Rudolph Virchow and say that the task of analytics is to enable you to stake out everything that you can possibly know from your data.

That’s what competing on analytics really means.

In your business, you strive to make the highest quality decisions today about how to run your business tomorrow with the uncertainty that tomorrow brings.  That means you have to know everything you possibly can know today.  In an effort to do this, many companies have invested, or are considering an investment, in supply chain intelligence or analytics software.  Yet, many companies who have made huge investments know only a fraction of what they should know from their ERP and other systems while they are mired in long, costly projects that are rapidly losing momentum and delivering little or no value.

Take operational excellence as an example.

Are you able to see a bottleneck build in your order-to-cash process at exactly the step or steps where it is occurring, immediately comprehending the impact because you are seeing hard data in an intelligent context?

What about visibility of supply chain performance?

Can you see that what proportion of your perfect order performance is being caused by days of supply which has been recently impacted by changes in customer order request dates or forecast error?

If operational excellence or supply chain visibility and performance sit high on your list of priorities, your wish list should include the following:

  • Pre-built connectors to your ERP system from a secure, scalable, speedy cloud platform for immediate plug-in and start-up
  • Fast harmonization across multiple ERP instances or data models
  • Comprehensive, domain-specific (supply chain and maybe industry) interrelated metrics that focus new light on the levers for revenue, margin and working capital
  • Simple, but powerful, self-service configuration beyond out-of-the-box metrics
  • Root cause analysis
  • Role-based views with collaboration
  • (Almost) zero learning curve
  • A continuous stream of new value-added services (e.g. what-if scenario analysis, predictive and prescriptive analytics, etc.) based the fact that your provider is now the secure custodian of your enterprise data

Are you competing on analytics?

Are you making use of all of the data available to support better decisions in less time?

Can you instantly see what’s inhibiting your revenue, margin and working capital goals across the entire business in a context?

Do you leverage analytics in the “cloud”?

As always, thanks for stopping by and having a quick read.  I hope you found this both helpful and thought-provoking.

As we enter this weekend, I leave you with one more thought that relates to “business intelligence” — this time, from Socrates:  “The wisest man is he who knows his own ignorance.

Do you know yours?  Do I know mine?

Have a wonderful weekend!

What are “Analytics”?

“Analytics” is one of those business buzz words formed by transforming an adjective into a noun. 

So forceful and habitual is such misuse of language that one might call it a compulsion among business analysts and writers.

The term “analytics” commonly refers to software tools that can be used to organize, report, and sometimes visualize data in attempt to lend meaning for decision-makers.  These capabilities have been advanced in recent years so that many types of graphical displays can be readily employed to expose data and try to make information from it.   “Analytics” has been used to refer to a very broad array of software applications.  Numerous industry analysts have attempted to segment these applications in various ways.  “Analytics” refers to so many kinds of applications that it is useful to establish some broad categories.

A simple, though imperfect, scheme such as the following may be the most useful where the potential value that can be achieved through each category increases from #1 through #4.

Reports – repetitively run displays of pre-aggregated and sorted information with limited or no user interactivity.

Dashboards – frequently updated displays of performance metrics which can be displayed graphically.  They are ideally tailored to the needs of a given role.  Dashboards support the measurement of performance, based on pre-aggregated data with some user selection and drill-down capability.  Hierarchies of metrics have been created that attempt to facilitate a correlation between responsibility and performance indicators.  The most common such model is the Supply Chain Operations Reference Model (SCOR Model) that was created and is maintained by the Supply Chain Council.

Data Analysis Tools – interactive software applications that enable data analysts to dynamically aggregate, sort, plot, and otherwise explore data, based on metadata.  Significant advancements have been made in recent years to dramatically expand the options for visualizing data and accelerating the speed at which these tools can generate results.

Decision Support/Management Science Tools – simulation, optimization, and other approaches to multi-criteria decisions which require the application of statistics and mathematical modeling and solving.

Let’s focus on Decision Support/Management Science Tools, the category with the most potential for adding value to strategic (high value) decision-making in a sustained fashion. 

So, then, if that is what analytics are, do they enable higher quality decisions in less time, and if so, to what extent are those better decisions in less time driving cash flow and value for their business?  These are critically important questions because improved, integrated decision-making that is based in facts and adjusted for risk drives the bottom line.

Execution is good, but operational execution under a poor decision set is like going fast in the wrong direction.  It is bad, but perhaps not immediately fatal.  Poor decisions will put a business under very quickly.

Enabling higher quality decisions in less time depends on the decision-maker, but it can also depend on the tools employed and the skills of the analysts using the tools. 

The main activities in using these tools involve the following:

  1. Sifting through the oceans of data that exist in today’s corporate information systems
  2. Synthesizing the relevant data into information (a thoughtful data model within an analytical application is helpful, but not sufficient)
  3. Presenting it in such a way so that a responsible manager can combine it with experience and quickly know how to make a better decision

Obtaining a valuable result requires careful preparation and skilled interaction, asking the right questions initially and throughout the above activities.

Some of the questions that need to be asked before the data can be synthesized into information in a useful way are represented by those given below:

  1. What is the business goal?
  2. What decisions are required to reach the goal?
  3. What are the upper and lower bounds of each decision? (Which outcomes are unlivable?)
  4. How sensitive is one decision to the outcome of other, interdependent decisions?
  5. What risks are associated with a given decision outcome?
  6. Will a given decision today impact the options for the same decision tomorrow?
  7. What assumptions are implicitly driven by insufficient data?
  8. How reliable is the data upon which the decision is based?
    • Is it accurate?
    • How much of the data has been driven by one-time events that are not repeatable?
    • What data is missing?
    • Is the data at the right level of detail?
    • How might the real environment in which the decision is to be implemented be different from that implied by the data and model (i.e. an abstraction of reality)?
    • How can the differences between reality and its abstraction be reconciled so that the results of the model are useful?

Ask the right questions.

Know the relative importance of each.

Understand which techniques to apply in order to prioritize, analyze and synthesize the data into useful information that enables faster, better decisions.

We often think of change when a new calendar year rolls around.  Since this is my first post of the new year, I”ll leave you with one of my favorite quotes on change.  Leo Tolstoy:  “Everybody thinks of changing humanity, and nobody thinks of changing himself.”

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!

Analytics vs. Humalytics

I have a background in operations research and analysis so, as you might expect, I am biased toward optimization and other types of analytical models for supply chain planning and operational decision-making.   Of course, you know the obvious and running challenges that users of these models face:

  1. The data inputs for such a model are never free of defects
  2. The data model that serves as the basis for a decision model is always deficient as a representation of reality
  3. As soon a model is run, the constantly evolving reality increasingly deviates from the basis of the model

Still, models and tools that help decision-makers integrate many complex, interrelated trade-offs can enable significantly better decisions.

But, what if we could outperform very large complex periodic decision models through a sort of “existential optimization” or as a former colleague of mine put it, “humalytics“?

Here is the question expressed more fully:

If decision-makers within procurement, manufacturing and distribution and sales had the “right time” information about tradeoffs and how their individual contributions were affecting their performance and that of the enterprise, could they collectively outperform a comprehensive optimization/decision model that is run periodically (e.g. monthly/quarterly) in the same way that market-based economies easily outperform centrally planned economies?

I would call this approach “humalytics” (borrowed from a former colleague, Russell Halper), leveraging a network of the most powerful analytical engines – the human brain, empowered with quantified analytical inputs that are updated in “real-time” or as close to that as required.  In this way, the manager can combine these analytics with factors that might not be included in a decision model from their experience and knowledge of the business to constantly make the best decisions with regard to replenishment and fulfillment through “humalytics”, resulting in constantly increasing value of the organization.

In other words, decision-maker would have instant, always-on access to both performance metrics and the tradeoffs that affect them.  For example, a customer service manager might see a useful visualization of actual total cost of fulfillment (cost of inventory and cost of disservice) and the key drivers such as actual fill rates and inventory turns as they are happening, summarized in the most meaningful way, so that the responsible human can make the most informed “humalytical” decisions.

Up until now, the answer has been negative for at least two reasons:

A. Established corporate norms and culture in which middle management (and maybe sometimes even senior management) strive diligently for the status quo.

B. Lack of timely and complete information and analytics that would enable decision-makers to act as responsible, accountable agents within an organization, the same way that entrepreneurs act within a market economy.

With your indulgence, I’m going to deal with these in reverse order.

A few software companies have been hacking away at obstacle B.”, and we may be approaching a tipping point where the challenge of accurate, transparent information and relevant, timely analytics can be delivered in near real-time, even on mobile devices, allowing the human decision-makers to constantly adjust their actions to deliver continuously improved performance.  This is what I am calling “humalytics”.

But the network of human decision-makers with descriptive metrics is not enough.  Critical insights into tradeoffs and metrics come through analytical models, particularly, optimization models.  So, two things are necessary:

1. Faster optimization and other analytical modeling techniques from which the essential information is delivered in “right time” to each decision-maker

2. An empowered network of (human) decision-makers who understand the quantitative analytics that are delivered to them and who have a solid understanding of the business and their part in it

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. (ftp://ftp.deas.harvard.edu/techreports/tr-06-11.pdf)  Maybe, we don’t need robots.  Maybe we just need empowered decision-makers who not only engage in Sales and Operations Planning (or, if you will, Integrated Business Planning), but integrated business thinking and acting on an hourly (or right time) basis.

What think you?

More on this topic in a later post.  But, if you think this might make sense for your business, or if you are working on implementing this approach, I’d be very interested to learn your perspective and how you are moving forward.

I leave you with these words from Leo Tolstoy, “There is no greatness where there is no simplicity, goodness, and truth.”

I’m off for a little vacation.  Have a wonderful weekend!

Part 3 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.  The first post in this series introduced the topic of overcoming the challenges to calculating the return for an investment in an analytical supply chain software application.  This post deals with the second of four challenges.

Part 3 – Second Challenge – Business analysis skills are lacking – “We are looking for the vendor to tell us!”

Can the Vendor Help?

After all, since the software vendor is proposing the solution, shouldn’t the vendor know how it will affect your company?  The vendor probably does have some useful information about whether the decision to purchase will be of some benefit.  They will be able to tell you in general what business symptoms can be affected.  They may even have survey data that show how other companies in your industry, or at least in other industries, have reported benefits.  They should have anecdotal evidence of how some existing customer plans to benefit or has benefited from investing in their approach or solution.

There are a couple of problems with the vendor’s input. First, the vendor cannot be objective. The vendor’s business is on the line.  It is probably a fierce competitor and its representatives may be under pressure to make this deal happen.  Second, directional information, surveys, and anecdotes may or may not be reasonable predictors of how your company will fare.

The current state of your business processes and how they are performing is pivotal to the potential return.

What Should You Do?

This reaction “We are looking for the vendor to tell us!”, is similar to the first challenge and reaction,“We need to know now!”  This second challenge is less driven by time than by the perception that the skill to perform the cause and effect analysis, data gathering, and statistical analysis does not reside within your company.  But, it is important for you to be able to understand, monitor and control the process, even if you use an outside consultant. Following these steps will help you do just that:

1. Identify and quantify undesirable symptoms.

2. Perform cause and effect analysis to find possible root causes.

3. Gather data by reason code (in order to prioritize root causes).

4. Quantify and analyze root causes (Pareto analysis).

5. Estimate the positive impact of your investment decision (e.g. a new supply chain management tool) on your root causes.

6. Extrapolate this to the positive impact on the undesirable symptoms.

7. Perform sensitivity analysis around your estimate in step 5 by varying the estimate and repeating step 6. This will give you a sense for the range of possible outcomes that is reasonable.

Your success at steps 1 through 7 will be most likely if you follow two additional guidelines:

1. “Time box” the analysis to a minimum of 2 weeks and a maximum of 30 days. These time frames are really only a guide to represent the order of magnitude for the minimum and maximum time frames.

2. Assign a primary internal resource for each area of analysis you undertake.

Once again, I’m grateful that you took a moment to read Supply Chain Action.  As a final thought, I remind you of the familiar words to ponder, “Luck is that point in life where opportunity and preparation meet.”

 

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