1 month, 11 days ago

Data Estimation – Stockpile Reports

Link: http://rvsoapbox.blogspot.com/2022/07/data-estimation-stockpile-reports.html

My attention has been drawn to a company called Stockpile Reports, which provides a data estimation service for piles of material. As I understand it, the calculations are based on visual images of a pile of material, possibly obtained using either a drone or a handmobile phone app, from which a detailed 3D model of the pile is produced, allowing the volume, weight or value of the material to be estimated.

I don’t have any information about how good these estimates are, but they must be a lot more accurate than simply gauging the height of the pile, and I guess they are probably good enough for the organizations that use the service. In this blogpost, I want to make some observations about this service and its potential value to user organizations, as well as some general thoughts about aggregation, variety and governance

The top benefit promised by Stockpile Reports is to eliminate painful write-offs. Because inventory is shown as an asset in a company’s accounts, and companies don’t like having to make large adjustments when these asset values turn out to be grossly inaccurate.

But this makes it seem as if the primary motivation for accurate inventory data is financial accounting. While I understand that many execs may be concerned about this, especially CFOs, financial accounting is a largely retrospective process. Whereas operational excellence and other forms of strategic advantage have a more immediate need for accurate inventory data.

And this is not just about the relative importance of financial reporting versus operational excellence etc. It is also about top-down versus bottom-up. Because a write-off essentially means that the total figure is incorrect. This plays into a top-down view of data quality and trustworthy data. Stockpile provide a service whose primary purpose appears to be to reassure investors that the financial accounts are correct.

This leads me to talk about aggregation. Top-down aggregation is about calculating a number, from a given body of data, for a given purpose. For example, the purpose might be financial accounting, management accounting, or billing. (Where billing essentially means aggregating everything that has happened during the past month in order to determine how much the customer owes us.) With some exceptions (such as Activity-Based Costing), accounting is largely premised on reducing everything to a standard set of numbers.

So this implies three capabilities. 

  1. The capability of producing the correct number 
  2. The capability of inspecting the source data and calculation in order to explain or verify the number. (For example, the reason for itemized billing is to justify the number at the bottom of the invoice.) 
  3. The capability of preserving the source data and calculation, so that inspection and audit will be possible into the future. (Also mechanisms to allow external parties to trust the integrity of the numbers.) 

All of these capabilities are simplified if we can eliminate as much variation as possible. If Stockpile’s drones are solely assessing the size of different heaps of stuff in order to aggregate them, then the fact that the heaps are all different is simply an annoying difficulty that the technology is designed to solve. Thus the variation between the piles is smoothed over.

Some degree of standardization may also be needed to support and enable Statistical Process Control. 

But what if the differences between the heaps are interesting and strategically significant? Now we start to look at the data in a different way – no longer top-down aggregation but something else. The Stockpile Reports claim is that estimating the size of the piles more frequently produces more accurate data. The immediate benefit is more consistent data and fewer nasty surprises. Presumably there is also a feedback loop, so that when a pile of gravel is loaded onto trucks, we can see if the quantity on the trucks matches the quantity we thought we had in the pile. So this is what might generate greater accuracy (over time), assuming other conditions remains unaltered. 

Where is the variety in this business problem?

  • Different shapes and sizes of pile
  • Vegetation or other extraneous material on top of the pile
  • Different kinds of material – e.g. gravel versus sand
  • Lack of consistency in the material – e.g. the quality of the gravel may depend on the quality of the original rock, plus the operational characteristics of the extraction machinery. Even within a single pile, the gravel may not be completely homogeneous.
  • Weather. Presumably wet gravel is heavier than dry gravel.
  • Control. What difference does it make if the pile of gravel is at your customer’s or supplier’s site rather than your own? How transparent are inventory levels across organizational boundaries? Do companies really want to share this information, or might they have reasons for hiding or distorting the true state of their inventory? 

These are all factors that may need to be considered when estimating the pile. But there is a more fundamental question, which is about the meaning and use of the number that is produced by this estimation process, given the variety of industry operating contexts – so I wonder whether there is a universal understanding of what counts as a pile. When estimating the size of a pile of gravel, does it matter what the gravel is going to be used for? What about the end-to-end journey of the gravel: if a pile of gravel is moved to a new location, is it still the same pile?

Depending on the frequency of viewing the pile, it may be possible to track changes in the shape of the pile, and this might provide information about stock movements (including unauthorized ones). For example, it may be possible to infer something about the movement vehicle (truck, wheelbarrow) from the difference made to the shape of the pile. For some materials, there may also be natural processes that change the volume of the pile over time – for example organic material may dry out or decay.

But this discussion of variety brings us to an important question – variety for whom. If some degree of requisite agility is needed to provide requisite variety, where does this agility need to be situated? How much does Stockpile Reports know (need to know) about its customer’s context of use? 

There may be variation within a single customer, to provide economies of scale and scope (to those customers), and to allow these organizations to operate with requisite agility in relation to a dynamic demand. Stockpile Reports might also wish to manage variation across different customers. For example, there may be some value in aggregating or comparing data from different customers – for example, calibrating the estimates of similar materials, allowing a new customer to get up to speed more quickly. 

There may be some value to both Stockpile Reports and its customers from closer coordination and mutual agility. But this raises some important questions. Firstly, how this value is shared between them. Secondly what kind of mutual trust does this require. And therefore how this coordination is to be governed.