Having worked with many clients over the years, it is striking how rarely a complete and consistent historic inventory dataset is received when embarking on a new supply chain consultancy project.
Usually sufficient sales and/or transport data is provided ensuring that the delivery/outbound logistics can be optimised, but inventory is more often a piecemeal collection of invoices; returns from customers; a guestimate of when the product that was delivered at such a time/date was actually loaded and therefore “left” the inventory system; fag packet calculations, and finally a snapshot of how much was in stock at any given moment of a random day. Add in different systems which at worst don’t talk to each other without a coded workaround from the IT department, or at best, talk to each other but update at different times, and one can have a perfect storm of duplicated items; stockouts yet somehow simultaneously over-full warehouses; out of season/obsolete items; and health and safety issues when things start getting left on shrink-wrapped pallets or cages in the aisles.
Most competent analysts can reverse engineer an estimate of product flow based on this usually disparate collection of reports, but there is a richness of information that is lost to the business for ever if snapshots are the only dataset considered. If inventory were held to be as important as sales data and transport and warehouse costs, rather than the very large millstone of capital-tied-up presented at the bottom of a financial report, dealt within meetings with a vague “we must address the stock levels …”; then businesses could transform their profitability and secure a platinum standard service reputation.
Historic inventory records enables an analytically forensic investigation of a huge financial chunk of any business and can enable information that helps:
- Measure seasonality – either climate related or commercial/religious holidays – as an index by product held on a daily, weekly, and monthly basis.
- Calculate dwell time and therefore, an associated cost of storage.
- Take the time to populate the volumetric fields in the WMS, because then various spreadsheet or system models can be created with actual cubic metres and it can be seen when, how and why the warehouse(s) burst.
- Slow, medium, and fast moving items can be defined, and layouts and picking processes optimised, to accommodate these.
- Negotiations are empowered by all this new found knowledge, leading to more favourable rates/prices because optimal stock levels & re-order quantities are known.
- SKUS that hang on the coat tails of another product can be identified, with judicious pattern recognition and/or coding, and inventory strategies adjusted accordingly.
- Supplier lead times can be brought into the mix.
- Forecasts can be run that are based on the budgeted company growth in sales, and with seasonality indices applied, suddenly it becomes clear when the warehouse(s) will burst in the next two/five/ten years so plans for additional space can be found and future proofed, without paying the price of firefighting to store the overflow. Or, flipside, it can provide evidence to downsize the depots or centralise some products but not others….
With the right analytical team by a Client’s side, there is almost no limit to the answers that a robust databank of historic inventory can provide, and with it, the transformation which becomes possible. Informed Inventory Management is practically a superpower.
THE POWER OF INVENTORY STRATEGY
The team at Ascali has developed a bespoke inventory model which, once populated, can give insight into current stockholdings by line, and can ascertain if a business is carrying the optimal amount of stock. Once this model is set up, the first question to ask the client is: “what is your current inventory strategy?”
If you were asked this question, would you be able to answer it?
An inventory strategy is, in simple terms, a set of rules around safety stock and re-order point levels, that considers supplier lead time, forecast/past demand, and minimum and/or optimum order quantities along with a few other variables that can be thrown into the mix. Thus, a simple answer to this question about inventory strategy might be “one weeks’ average demand plus the supplier lead time, two weeks, so we keep minimum stock levels of three weeks average weekly sales at all times, of all lines”.
An excellent inventory strategy would also consider the volatility of demand/supply and the impact on your business if it is out of stock. Yes, average demand might be 2.5 units per week, but if you sell zero for fifty weeks a year and 65 per week in those remaining two weeks, it wouldn’t be the best strategy to keep three units all year round, and you would still end up disappointing your customers. It’s a lose/lose. But imagine this over tens or hundreds of lines? The error is magnified, and the cost massively increased.
Optimal inventory strategy over the whole business, therefore, has got to be worth pursuing. Once intimate knowledge of your product lines has been provided from the aforementioned analysis, you can refine the inventory strategies for each line, if only by categorising it into a set of grades of importance to the perception of customer service, unique selling point, as well as the rate of sale.
For example; at first glance, it would seem to serve no purpose to fill a warehouse with widgets that take up space and sell on average, one unit per week, if one has no room left for the mega widgets that sell 100 units per week. But then again, what if that apparently slow-moving widget was absolutely critical to the nature of your business; its sales profile is in fact highly volatile, and to not have it in stock would be absolutely world-ending to your business, your brand image, and your reputation? Whereas the mega widgets are sold by many different businesses and don’t have such an impact on your reputation, and frankly it wouldn’t be terrible if you lost one sale or even one week’s sales of mega widgets. How then would this knowledge affect your inventory strategy on stockholding of the widget vs the mega widget?
With inventory strategy, clearly, it is not enough to question what it is, without also asking “why” and “how well does it serve the business”?
Note. The polar opposite of an effective inventory strategy is to discover that stockholding levels takes none of the above into account and are in fact a basic fixed number that came when setting up the system, and no one has ever questioned it before…
So get curious about the “what, why and how” behind stock levels, and potentially save the business an absolute fortune.
THE POWER OF INVENTORY RECONCILIATION…
It is often the case that a customer wishing to purchase a particular item, will find the shop they are in to be out of stock of the size/colour combination of the item of preference despite the store inventory system telling the sales assistant that one of those exact items is in stock, somewhere, within the physical boundaries of the store.
Of course, it is obvious what could have happened – a sticker was missing on an item, so someone just grabbed the similar looking “because it’s the same price so what’s the harm?” item to scan it out the building. Or maybe in pre-retail it was labelled accidentally with the wrong bar code. Or it got dropped into the wrong pick bin with a bunch of other items and booked in en-masse to the inventory during goods in. Or there’s been a theft that obviously the system isn’t aware of. Human error – whether malicious or carelessness – can undermine even the most robust of inventory systems.
Training, regular stock takes (or spot checks on bins), and increased surveillance in both public and employee areas can all help reconcile the physical stockholding to the system, and as you can probably tell from the previous section – when basing strategic company decisions on the outcomes of the analysis of inventory history, that history must be reliable otherwise expensive errors could be made.
HOW TO MAINTAIN INTEGRITY OF THE DATA?
Assuming the presence of reliable and accurate POS systems, and the physical inventory matches the system stock on the retail side, where is data integrity lost?
It usually (but not always) starts with the initial system set up. This can be a long and onerous task to repair once a badly initialised systems have been established, and generally involves hours of squinting at items and attempting to use brute force algorithms to group fields containing data that are only ever so slightly different from each other.
Most systems have a key identifier field which contains unique identifiers only, on which the whole table of data hinges. It can be supplier codes, or unique stock keeping units, to give two examples, and if the inventory is going to be an infinite pool of new widgets or suppliers or customers being added, then a wise system technician would choose a long code formed from an alphanumeric system – not unlike choosing a secure password – to ensure you don’t hit a wall in the number of variables that are available.
One thing to absolutely guard against, is manually typed user input. We often find that there can be several (almost) repeated entries for either the same product, supplier, customer or location, all because people will manually input data, and often small differences will arise. Sadly, a system attaches no meaning to the information – it is read literally and thus nuances that humans would spot, go unnoticed. The same can and does happen to stock items themselves; there was a client dataset recently which had a number of the same lines entered completely differently, and they had a warehouse littered with redundant stock that had gone under the radar, all because of poor data management.
A comma, or misplaced apostrophe, or a typo in initialisation – A.B. vs A. B. – can create havoc, but even more unfortunate is the data analyst who reconciles these errors in the field of the live data; if the intention were to look up one record/field against another and this key is lost by inadvertently editing the original data, then the door to meaningful analysis is locked forever. Always maintain the given value of each cell of original data, by adding in additional fields that are “cleaned” so the original can always be accessed; try to limit what the user can actually type – suggestions that autofill are always safer. Trailing and leading spaces should be rigorously removed. Never accidentally lock a reference cell; if sorting one column, the new reference must be kept exactly relevant (live) and not pointing at an outdated cell that is no longer in the right place.
Reconciling European data to UK can be tricky in the matter of decimals and commas , which adds further complexity. This leads to the final and most compelling part of analysis – sense check. Always sense check the “size” of the numbers and look for trends or anomalies that appear to have been created and then ask yourself if it seems “real”.
CONCLUSION
To experienced Supply Chain Consultants, inventory management, strategy and data handling is often the most under-valued part of logistics & Supply Chain optimisation tasks. Logistics, warehouse and transport costs may seem to offer more opportunities for cost reduction and streamlining, and absolutely they are a valuable source of opportunities to harvest. But hiding in plain sight is Inventory Management and all the benefits it can deliver.
Poor inventory management will lead to either a glut or a shortage of individual lines: the former increases costs of warehousing, and slows down the efficiency of put-away and picking, meanwhile a shortage can lead to rush jobs, expedited courier costs and at the same time, under utilised trucks where the daily routes are not run to capacity.
