Category Archives: Technology

Show Me The Money! (Supply Chain Cost Reduction Opportunities)

Show Me The Money!

Sorry to disappoint you, but this isn’t a post about Cuba Gooding Jr., whom all of you action fans will remember as recurring minor character Billy Colton in MacGyver near the end of the series.

Instead, this is a post about how you can Show Me The Money by applying the proper technology at the proper places and proper times in your supply chain to save big, even with rising material costs, inflation, and the global talent war.

The reality is that unless you are best-in-class, and the harsh reality is that, by definition, the vast majority of you are not, your supply chain is hemorrhaging cash. And in all likelihood, lots of cash. Where?, you ask. Everywhere!

Let’s take a simplified PC supply chain for example. Raw materials are mined and shipped to a processing plant where they are refined and shipped to base part manufacturers. These base parts (such as chips, wires, etc.) are then shipped to component manufacturers who produce circuit boards, hard drives, cables, etc. These base components are then shipped to an assembly plant where the PC is assembled. From the assembly plant it is shipped to a central distribution center where it is then shipped to either a regional distribution center, store, or your home, depending on the sophistication of the distribution center.

Furthermore, the specifics of your supply chain depend on who you choose to buy from, who your suppliers choose to buy from, who is chosen to handle your transportation requirements, and who you choose to sell to.

From this example, we derive the following fundamental sources of cost:

  • Labor (inc. raw material collection, processing, & subsequent part and component handling)
  • Parts (inc. design, component raw materials, & built in production operations)
  • Operations (inc. part production, handling, & overhead)
  • Transportation (inc. raw materials, parts, components, & finished product)
  • Buying (who you buy from, where, & when)
  • Selling (who you sell to, where, & when)

However, from a savings viewpoint, not all of these are equally important, since only some of these are really hemorrhaging cash, despite their absolute value on the cash flow statements.

  • Labor is more or less defined by market rates. Moreover, companies that pay more for more productive people often have a higher ROI per person than those that pay less.
  • Selling is marketing, materials, and labor. The first is generally not under your purview, and again the issue is not cost, but results; the second is covered by buying; and the third we just discussed.

This tells us that the fundamental sources of cost, and thus the fundamentally sources of unnecessary costs, ripe for saving, have to do with:

  • Parts
  • Operations
  • Transportation
  • Buying

And those of you reading regularly will know what the answers are.

But back to the point – how do you Show Me The Money? You use these solutions to identify where you are hemorrhaging cash, tackle the issues head on, and stop the leak. And then you point to the big, fat increase on the balance sheet as your doing. And that’s how you Show Me The Money!

It’s also why I keep talking about companies like the following:

  • Apriori, Akoya (acquired by I-Cubed), etc.
  • Informance (merged with QlickiT, acquired by Catalyst IT), Apexon (acquired and merged with Infostretch), etc.
  • CombineNet (acquired by Jaggaer), i2 (acquired by JDA, rebranded Blue Yonder after the acquisition thereof), etc.
  • Iasta (acquired by Selectica, merged with b-Pack, rebranded Determine, acquired by Corcentric), Procuri (acquired by Ariba, acquired by SAP), BIQ (acquired by Opera Solutions, rebranded ElectrifAI), etc.

They may be small, they may be new, but they are trying to build a solution that will help you find those savings leaks that you are not likely to find on your own. So keep reading!

Spend Analysis I: The Value Curve

Today I’d like to welcome Eric Strovink of BIQ (acquired by Opera Solutions, rebranded ElectrifAI) who, as I indicated in my There’s No Spend Analysis Without the Slice ‘N’ Dice post, is going to be authoring the first part of this series examining what is required for a true spend analysis system, spend analysis 2.0 if you are part of the 2.0 movement, as opposed to just a basic spend visibility system.

Spend Analysis has always suffered from what the late British humorist
Stephen Potter might have called the “So What Diathesis.” In other words,
now that you have your spending loaded and classified, what next? Well,
if you’ve never seen your purchasing data loaded into a spend analysis
system, you’re in for a treat, because you can find savings opportunities
just by drilling around. It’s often that easy — drill around; find
opportunities.

However, once the low-hanging fruit is harvested, which can take
anywhere from 6 to 12 months, the value of the spend analysis system
declines steeply — at which point Mr. Potter’s observation comes home
to roost. As illustrated below, there is a moment at which the cost of
the spend analysis system begins to exceed its ongoing value.

It is shortly after this time that (1) usage of the product drops to low
levels; (2) the rest of the organization begins to question the value of
the software; and (3) stakeholders come under pressure to justify continued
high expenditures.

That’s why it’s odd to hear people talk about “The Spending Cube” —
in capital letters — as though there were only one data cube ever
to be built. Actually, there are many different ways to look at spend,
and there’s lots of spend data that simply can’t be organized into a
single data cube anyway. How about a compliance cube, oriented around
invoice level data? A purchasing card cube, specific to p-card idiosyncrasies?
A T&E cube, built from travel agency data on “best price” versus
“actual price,” tracking employee travel and the reasons for the discrepancies?

In fact, it’s obvious to anyone who has worked with multiple datasets at
the A/P, PO, and invoice level that there are many, many different kinds
of data to analyze. Each dataset addresses more opportunity, and presents
another chance to apply a sophisticated analysis tool. Some of these
datasets aren’t “spending” datasets at all, but consist of demand-side
information — for example, cell phone or fleet vehicle usage records,
or operational data such as equipment recovery and maintenance logs.

If a spend analysis system makes it easy to load data and create new datasets,
which it should; and if the system supports as many datasets as you’d like,
as it ought; then there really isn’t any limit to how often the system can
be used, or to how many different kinds of data it can be applied. Which
means that a full-utilization spend analysis system value curve looks more
like this:

In other words, each use of the spend analysis system provides high
initial value, as well as residual value; but the system is used again
and again for new sets of data. The value of the spend analysis
software therefore remains high over time.

Next installment: The Psychology of Spend Analysis

Enterprise Manufacturing Intelligence

Informance (merged with QlickiT, acquired by Catalyst IT) just released their Enterprise Manufacturing Intelligence Solution for manufacturing companies eager to accelerate improvement initiatives, drive operating strategies, and obtain actionable insight for operational performance.

According to their press release, their EMI solution delivers the top-three critical capabilities required to drive better business decisions:

  • multi-site performance analysis
  • enterprise visibility of production financial performance
  • data aggregation from multiple plant facilities

The solution consists of two modules:

  • Informance Manufacturing Strategist
    • What if Scenario AnalysisEvaluate strategies and the impact on KPIs based on real time data.
    • Bi-Directional Information FlowAllows for the development of strategies and day-to-day operating tactics.
    • Real-Time Performance MonitoringA solid foundation for closed-loop process improvement.
  • Informance Enterprise Alerts
    • Proactive NotificationsAutomatic warnings if the enterprise is in danger of missing a metric at any level – facility, asset, or resource.
    • Dashboard MonitoringManage issues globally from a single access point.

According to Informance, this allows your enterprise to:

  • Unlock Capacity
  • Increase Productivity without additional Capital Investment
  • Reduce Inventory and Labor Costs
  • Decrease Working Capital

since it can now

  • accelerate, sustain, and benchmark operational performance initiatives such as lean manufacturing, Six Sigma, and TPM,
  • drive operating strategies at the executive level into execution tactics at the plant level, and
  • provide intelligence in the form of actionable insight from actual data.

So what is Enterprise Manufacturing Intelligence? According to Informance, it is a strategic decision support system providing real-time visibility and a consolidated view into your entire manufacturing operations with powerful analytics, exception-based alerting capabilities, and integration to enterprise systems to give corporate decision makers control over all aspects of your manufacturing operations.

Whether or not you choose to define Enterprise Manufacturing Intelligence, or EMI, this way is up to you. What I can tell you is that these capabilities are important, since inefficient operations can cost you a lot of money. That’s why I’ve invited Sudy Bharadwaj, CMO & VP of Solutions Consulting, formerly of Aberdeen, to explain to us precisely what Informance EMI is and how it can help your manufacturing organization, or your contract manufacturer, increase productivity and save money.

Forecasting

No doubt about it – despite being critical for effective business planning, accurate forecasting is complex and challenging and still remains elusive for many organizations. However, as the recent issue of APICS Magazine points out in their article “Outlook Warm and Sunny”, one can create good forecasts through the proper combination of judgmental and statistical methodologies and use them to identify new market opportunities, anticipate future demands, effectively schedule production, and reduce inventories.

What’s interesting about this article is that it is well known that neither technique on it’s own can be very effective. Most of us lack the ability to accurately judge future demand due to limitations in human cognitive abilities, the restricted amounts of information we have at our disposal, and unknown causal relationships. Similarly, statistical forecasts are limited with respect to the models they are based on. Although a statistical model is much more accurate than any intuitive model we could come up with, it is built on assumptions and causal relationships which may change over time. The best example of a statistical model gone bad is Nike’s $400M failure in 2000 due to demand forecasting software. Nike relied exclusively on automated forecasts without any judgmental checks, but the newly implemented models were not yet fine-tuned and accurate enough to be deployed in a fully automated mode.

The best forecasts are those that leverage the strengths of both judgmental methods and statistical methods. However, as the author points out, well-established rules must be followed in order to effectively combine these techniques.

The following table summarizes the strengths and weaknesses of each approach.

Judgmental Forecasts
Strengths Weaknesses
Responsive to latest environmental changes

Can include “inside” information

Can compensate for “one-time” or unusual events

 Human cognitive limitations.

Biases
Statistical Forecasts
Strengths Weaknesses
Objective

Consistent

Can process large amounts of data

Can compute many variables and complex relationships

 Slow to react to changing environments

Only as good as model formulation and available data

Can be costly to model “soft” information

Require technical understanding

According to the article, judgmental and statistical forecasts can be combined in different ways to take advantage of their individual strengths but the most popular method in practice appears to be the managerial adjustment of statistical forecasts where managers adjust the statistical forecast in a “managerial override”. Managerially adjusted forecasts can often improve forecast accuracy by including information not available to the statistical model. However, if performed incorrectly, adjustments can cause inaccuracy due to inherent human bias. Thus, established rules should be followed for effective adjustments.

The rules outlined by the article are the following:

Only practitioners with domain knowledge should adjust statistical forecasts.
Judgmental adjustment is more likely to improve accuracy when the adjustment is based on domain knowledge. Generally, only domain practitioners will be aware of the relevant contextual information that should be used to adjust a forecast.
Adjust statistical forecasts when there are known changes in the environment.
The adjustment should compensate for specific events not captured by the statistical model or time series. It should not be based just on intuition or bias.
Structure the judgmental adjustment process.
Use a documented or computationally consistent methodology. This will allow you to repeat successes and insure that failures are caught, corrected, and not repeated.
Document all judgmental adjustments made and measure forecast accuracy.
Records must be kept of all adjustments made, and the reasons therefore, and the results of the forecast must be measured so the process can be improved over time and the underlying statistical models updated when relevant observations are made.

When good, quantifiable, and historical data is available, reliance should be placed primarily on statistical forecasts. Only when the domain practitioners know of relevant contextual events or information not contained in the model should judgment be used to adjust the forecast.