Let’s face it – industry analysts, big-time consultants, and your peers are all talking about technology, digital transformation, and the future of the supply chain. It can seem like a lot of noise given the day-to-day pressure you feel while working to ensure that inventory is on-hand and positioned where it is supposed to be. With all you have on your plate, it can be easy to miss the signs that a change is needed (until it is too late). Here are a few things to keep your eye on.

#1 Manually intensive end-to-end forecasting and planning process.

Your team spends countless hours finessing your forecast with manual overrides and you still cannot exceed 60% forecast accuracy. And the number is even worse for items with intermittent demand.

This signals you may be ready for a fresh look at how you are forecasting. Your supply chain software should help you forecast over a wide array of items (fast movers, intermittent, slow movers), across the entire network (even 2 or 3 tier), and provide target inventory levels (safety stock, reorder point, cycle stock). Reducing manual processes could help you achieve the service levels you desire and provide a continuous feedback loop for incremental improvement.

Key takeaway: Look at how much time is spent implementing manual overrides and other forms of intervention to arrive at your forecast.

#2 Forecasts are only in monthly buckets, so daily/weekly trends are lost.

Is your system capable of analyzing data at the weekly or daily level, or at the “Ship To” location? With the right technology, you may discover micro-trends that occur within the month and be better able to capture that demand and produce demonstrably improved forecast accuracy. This is especially powerful if done automatically at the “Ship To” location level.

Favorable customer outcomes increase in direct proportion to your ability to sense demand at a granular level.

Key takeaway: How limited are your timeframes to analyze and act on data?

#3 “Best Fit” forecasting turns out to be “whatever fits”.

Forecasting based on best fit from a limited selection of formulas is prone to overfitting. It does not really model demand. It simply jumps from model to model, using whatever worked last time, without correctly understanding demand drivers.

Consider statistical “order-line” based modeling to help understand the drivers of volatility, both natural (trends, seasonality, causal factors, and lumpiness) and organizational (demand shaping promotions, new products, forecast bias, and the bullwhip effect) to provide you with a reliable forecast.

Key takeaway: How much of your forecasts are at the order-line/How many models do you need to utilize to get the “best-fit”?

#4 You are unable to make an effective inventory to service trade-offs.

Do you fully know what investment is required to hit the target service level your executive team desires? Are you stuck with a “one size fits all” inventory policy across all SKUs and locations, stocking product that sells once a year when you could use the capital (or space) for products that sell thousands of units per month.

You ought to be able to perform “what-if” scenarios to evaluate multiple inventory optimization and choose your inventory sweet spot. Imagine what the value of getting a detailed breakdown of inventory targets, safety stock, and recommended service levels by individual item-warehouse combinations would be for your team and quality of work-life.

Key takeaway: how much of your planning is “one size fits all”?

#5 Cannot handle multi-sourcing and simultaneous sourcing.

Do products outsourced from multiple locations force you to manually split aggregated forecasts and layout separate inventory plans to meet the target service level? This is probably not bad for a few items but can be a serious problem for thousands of item location combinations which in turn can have millions of components that go into them.

Multi-sourcing allows you to deal with products that must be sourced from multiple suppliers. You set the percentages sourced from each supplier and the system calculates the total inventory needed at an aggregated level to achieve your desired service level and roll out replenishment orders for each supplier. Plus, it understands that your suppliers have different lead times.

Key takeaway: How many items are multi-sourced?

Summary:

Answering “yes” to any of these challenges should result in a review of your overall goals and how to address them.  The next question is whether you can get it done with your current solution or require an upgrade. Gartner has evaluated 22 supply chain providers across a variety of use cases to help you find the answer.  Gartner’s 2022 Magic Quadrant for Supply Chain Planning Solutions report available compliments of Arkieva.  Click here to download.  We hope you enjoy it.