Will this be the year we traverse the supply chain plateau? Part II

In yesterday’s post, we noted that five years ago we covered a piece by the Supply Chain Shaman who believed we had reached the supply chain plateau. And while SI did not agree, SI agreed that progress had completely stalled. And SI believed that the root cause of the issue was manpower capability. Precisely, the fact that most executives do not understand the supply chain from a holistic perspective, treating each step as its own function (and disassociating NPD/Design from Sourcing (a manufactured product) from Logistics and Distribution, when they all have to be examine and managed as part of an integrated supply chain. And the fact that neither do the function managers. Moreover, these function managers often do not even understand the best practices associated with their job.

And SI believed the root cause of this was a lack of education — most Supply Chain / Supply Management / Sourcing / Procurement / etc. managers don’t leave college or university with a solid supply chain background, as few institutions offer such programs, and they haven’t been properly trained. Add this to the fact that year over year training budgets are slashed and leaders are run ragged fighting fires and dealing with tactical issues instead of being given time to focus on long-term strategy, how the supply chain works, and how it should work for optimal performance and optimal corporate gain.

Now, it’s true that the education issue hasn’t improved much in the last few years, but what has improved is the technology to provide executives and function managers both with a more holistic view and guidance as to directions they can take. Modern cognitive technologies backed by machine learning and automated reasoning, which can process millions of data records in near real time, identify trends, identify outliers, identify normal behaviour, identify typical responses, and so on, can present executives and managers with holistic views that let them understand not only what their options are, but what impact it has on the immediate problem and the supply chain as a whole. Ripple effects through the organization and the chain can be predicted and an informed decision made with the known impacts in mind.

Companies will know not only the impact of a delayed payment, but the benefit of an early payment as well as the trade-offs between JIT delivery and maintaining raw material inventory or the benefits of combining volume with a single supplier for more cost-effective shipments from a closer supplier. And so on.

If we can’t fix the education, at least we can fix the holistic understanding of the impact of a decision. And while we don’t have systems for all situations yet, you can bet they are in development. Maybe 2020 will indeed bring 2020 vision to some supply chain areas!

Will this be the year we traverse the supply chain plateau? Part I

Five years ago today we commented on a piece by the Supply Chain Shaman who believed we had reached the supply chain plateau. While SI always believed there is innovation to come, the Shaman presented some pretty damning evidence. Analyzing the balance sheets of process companies over the course of a decade, she found that the average process manufacturing company has reached a plateau in supply chain performance. As she stated:

Growth has stalled. To compensate and stimulate revenue, the companies increased SG&A margin by 1%. However, the conditions were more complex; the average company, over the last ten years, experienced a decline of 1% in operating margin, and an increase in the days of inventory of 5%. While cycle times have improved, the majority of the progress has come from lengthening of days of payables and squeezing suppliers.

And it’s certainly the case that delaying payments and squeezing suppliers is NOT progress!

And while SI believed, at the time, that we had not reached the plateau, SI certainly believed that growth had stalled. But why?

The Shaman conjectured that while complexity has increased, many well-intentioned executives lack the understanding of the supply chain’s potential or how to manage the supply chain as a system. So, while individual projects are getting great results, departments as a whole are not performing as well, and being managed even worse. SI had to agree.

And while SI also had to agree with the Shaman that there is a discontinuity and we need to declare the APS and ERP systems of the 1990s obsolete and start again, SI did not believe it was the core problem. SI believed the core problem was manpower capability. Not only do most executives not understand the supply chain from a holistic perspective, treating each step as its own function (and disassociating NPD/Design from Sourcing (a manufactured product) from Logistics and Distribution, when they all have to be examine and managed as part of an integrated supply chain, but neither do the function managers. Moreover, these function managers often do not even understand the best practices associated with their job.

SI conjectured the manpower capability issue was a lack of education, and hasn’t changed it’s belief. But even though little has changed on this front, there is a light in the sky now … we can see the day when we cross the plateau and see the peak ahead. How?

Why Do We Still have First Generation ERP/Data Warehouse BI?

the doctor was recently asked by a senior consultant if a CFO was right when he said why should I use Spend Analysis if my ERP has BI functionality that allows me to do ‘any’ analytics and generate reports … and I only have one ERP instance as the company was relatively small (< 100M).

How is the CFO wrong? Can we even count the ways? First of all, let’s go back ten (long years) to when SI published this great post from the spend master himself, Eric Strovink on screwing up the screw-ups in BI where he noted that Baseline, in their efforts to defend AI, were simply pointing out more holes in the process. In this post, the spend master noted:

1. A central database won’t solve the analysis problem, and at the end of the day you’ll have just as many spreadsheets as before … which, as every CFO and CIO knows, is way too many.

2. Business analysts should be able to construct BI datasets on their own, as needed, from whatever data sources are useful/appropriate, and it shouldn’t be difficult for them to do so … but most BI tools only make it easy to construct datasets and reports on data in the ERP. And you NEVER, EVER, EVER have all the data you need in the ERP. Some is in the AP. Some is in the sourcing and procurement systems. Some is in the WIMS. And then there are market data feeds that can provide insight, not in the ERP.

3. While BI is said to be the cornerstone of a governance program, a governance and stewardship program doesn’t actually put any meat on the table … whereas modern spend analysis systems do.

4. While BI can support data integrity, it typically isn’t cleansing that’s the problem, it’s (1) the fixed organization of the data, which is guaranteed to be inappropriate for any analysis that hasn’t been anticipated a priori, (2) the ad hoc reporting on it, which has to be easy to accomplish, as opposed to requiring IT resources (see below), and (3) the fact that cleansing can’t be accomplished on-the-fly (as it should be) by the business analysts themselves.

5. BI systems are difficult to use and set up, it is difficult to create ad hoc reports, and it is impossible to change the dataset organization … especially compared to spend analysis.

And this doesn’t even begin to address the facts that

6. BI reports are pretty generic, and not fine tuned to Sourcing, Procurement, or Finance. Modern SA systems, built by Sourcing, Procurement, and Finance professionals, have out of the box reporting fine-tuned to the needs of sourcing, procurement, and finance professionals that report on spend by category, supplier and metrics by category and supplier with easy drill down and segmentation by department, category, etc.

7. BI engines work on one schema — the ERP schema. And this is not always appropriate for Sourcing and Procurement who need to manage by category. and then do what if analysis against different re-categorizations to try and find the best way to source and procure for the organization. Modern SA tools allow for the creation of different schemas, different cubes on those schemas, and different views on those cubs. Power not in the BI.

8. BI engines expect the data in the ERP. SA systems don’t. They can import data from multiple systems, flat files, market feeds, etc. — put it in private, or public workspaces, reclassify and modify and augment the data as needed, and create true intelligence on a category or a supplier — not just a summary of last year’s spend by product or supplier.

9. The ability of first (and even second generation) BI engines to create arbitrary reports is considerably overstated. Most of them limit the facts and dimensions that can be used in reports to those in defined tables, and limit the self-service reporting to modification of pre-defined templates. Not the freeform capability of a modern Tableau or Qlik solution, and definitely not the freeform capability in a best in class Spend Analysis solution that can allow any dimensions or facts to be used and reports and dashboards to be created using any defined report components in an easy drag and drop manner.

And so on. Hopefully by now you get the point — especially when there are SA solutions out there that start at only a few thousand a month and provide at least 10 times the value of that outdated BI solution the ERP company should be paying you to use. (The technical debt they owe you on this is huge!)

Scared of AI? Try Auto-Classify!

Last week we noted that if you were scared of AI (and rightfully so, as it tends to over-promise and under-deliver), you should start with Auto-Buy — specifically, auto-buy for certain tail-spend products and services where the platform can at least get you market average pricing on a product or service you’re likely overpaying by 15% or more on. The platform might not be able to match the best expert, but it can far surpass an average buyer, and paying market average is better than overpaying by 15%.

In this post we said that your spend generally breaks down into strategically sourced, bought from the GPO, catalog buy, and the rest falls into the tail. And the way to save significant money quickly and easy is to get the tail out of control … a large tail spend can be costing you 6% against the bottom line. That’s huge. And there’s only one way to get this under control. Auto-buy. And there’s only one way to do better — get the spend out of the tail into the other categories.

This is easier said then done. Tail spend might only be 20% of the spend by dollar, but it’s 80% to 95%+ of spend by volume — trying to classify each and every purchase to a strategically source, GPO, or catalog category it could fall into is a monumental task, and that’s why the tail spend stays high,

But it’s not a monumental task for AI. Remember, we can’t do millions of calculations a second – computers can. And when enough of these calculations are done, and correlated, computers can make assignments that, on average, greatly exceed the accuracy of an average buyer in significantly less time. Plus, the rare-misclassification will be found quickly by a human buyer and re-assigned to the right category — either the product has an equivalent in the GPO, or it doesn’t. Either it has an equivalent in the catalog, or it doesn’t. Or it fits the way the strategic buyers buy, or not. But in the first two cases in particular, the computer will be not only be able to identify the best matches with high accuracy, but even provide its reasoning.

So use the AI for what it’s good at — bulk computation and analysis. And be confident that while it will greatly reduce your tactical workload and make you more efficient, it won’t replace you — in fact, it will make you irreplaceable as you will be freed up to spend more time on the strategic, value generating work.