As a Supply Chain Leader responsible for ensuring all key business processes deliver an effective inventory replenishment program, and ultimately, a strong In-Stock position to maximize sales opportunities: right product, right place, right time and right quantities becomes your daily modus operandi and words to live by.

Within a fast moving and demanding merchandise retail environment, supply chain teams deal with hundreds of vendors, thousands of SKUs and aggressive promotional strategies. These factors and many others will influence and work against you to achieve your in-stock goals. Ensuring product is available to support consumer demand becomes a complex, process and data-driven challenge. Strong supply chain analytics, framed with the right metrics, is paramount to determining root cause of in-stock failure – and more importantly developing action plans to recover. Reporting on the right metrics (Critical Success Factor and related Key Performance Indicator – CSF/KPI) can provide the early warning signals you need for proactive intervention.

Recovering from the Weekend
It is Monday morning and you are about to face the most challenging question of your busy work week: “how is Inventory?” The weekend weather was favourable and sales have exceeded forecast in most Categories. Inventory dollars have fallen below plan and in-stock suffered a 50 basis point drop.

Where do I start?
Here is where on-order visibility and detailed pipeline reporting can validate how soon you may recover. Since we are investigating at the Item or SKU level, visibility to on-order units and date stamped “hand-offs” in the supply chain can quickly give you an estimated in-store landing date. Common questions your analysis should target are:

1. Are there active orders for the item in the pipeline? (there should be open orders if this is a regularly forecasted item and part of the “core assortment”).
2. When was the item last ordered and what is the system Lead Time (PO Create to In-Store Receipt)?
3. Based on date and lead time, you should know at your finger tips when the next order should arrive at the stores.
4. What is the current delivery status? System Lead Time is your best case planned pipeline. Often actual Lead Time will vary due to numerous controllable and uncontrollable factors. Granularity and real time availability of data, driven by vendor EDI, will suddenly matter:

• Does Vendor have inventory – can they fill the order? Have they acknowledged the order, booked a Delivery or Pickup Appointment and sent us an Advanced Ship Notice (ASN)?
• Is freight “Pre-Paid” or “Collect”? Collect may add additional dwell time at your DC. If Pre-Paid – has the Vendor shipped?
• Is the order shipping to store from DC or is it a Direct-to-Store delivery? Does the DC have available inventory? What is DC’s fill rate trending at?

Common metrics used is this scenario will be On-Order Units, System Lead-Time vs. Actual Lead-Time, SKU Level WOS and Forecast Accuracy.

Dealing with Chronic Out of Stock
Chronic Out of Stock (class/SKU-level) is a major complaint from the Field. Below are two factors that can influence demand and drive in-stock down if not proactively addressed:

1. Lower Retail Price – the Buyer has received a lower cost. She has decided to pass this along to the consumer or has agreed to take lower margin and compete in the market at a lower price. Increase the forecast by applying a % lift as a forced forecast increment while the system “catches up”.
2. Vendor Shortage – your demand has exceeded what the Vendor is capable of producing or has started to erode their Safety Stock. Increase the level of collaborative forecasting with your Vendor, particularly on key items that are the anchors of your Assortment.

Other factors that can influence in-stock are “On-hand Accuracy” and “Run On Sale” (unplanned lift). Detailed and specific analytics measuring weekly on hand delta, four week rate of sale, inventory sell-thru% and In-Stock% among others will provide you with sufficient “exceptions” intelligence to implement corrective actions.

The Canary in a Coal Mine
It is important to understand that an out of stock analysis is a rear view diagnostic. In other words, when the out of stock is detected, lost sales have most likely already occurred. Your reporting should include metrics that provide leading indicators and exceptions-based metrics that issue early warning signs of potential stock-outs and allow you to apply necessary corrective action plans – the canary in the coal mine.