Procon Pumps Uses Smart Demand Planner to Keep Business Flowing

Introduction:
Procon, an industry leading pump manufacturer, uses Smart IP&O’s demand planning and inventory optimization modules from Smart Software to make sure they have the products their customers need, when they need them.  You might not have heard of their products, but if you’ve ever eaten at McDonalds or sipped a coffee at Starbucks, you have been served by Procon.  Procon’s broad portfolio of over 7,000 SKUs is supplied to more than 70 countries worldwide through their direct sales channel and an extensive distributor network.  Procon operates manufacturing facilities in the US, Mexico, Ireland, and through a licensed manufacturing partner in Japan.  We spoke with Procon’s Shankar Suman, Director of Sales, and Emer Horan, Global Supply Chain Manager, to learn more.

The Challenge
If Procon cannot ship a required product, their customers cannot ship theirs.  Accurate forecasting is a key driver of supply chain success and customer satisfaction. Procon’s monthly planning establishes the consensus demand plan that drives procurement, production, and stocking policies.  But they found they had a gap between sales and procurement, which historically led to missed deliveries and excess inventory.  What Procon needed was a robust demand forecasting and inventory optimization tool that was easy to use, enabled collaborative planning with their sales team and partners, and integrated with their  ERP system to drive procurement and production planning.

The Solution:
They found this in Smart IP&O,  web-based platform for statistical forecasting, demand planning, and inventory optimization.

  • Shankar Suman cited a broad mix of capabilities that convinced them to utilize Smart. Chief among them were:
  •   Smart Demand Planner supports the easy, orchestrated flow of information that yields an accurate consensus plan.  Presenting performance history and statistical forecast by product, territory, and partner, SDP provide the sales team with perspective that they can complement – adjusting for expected opportunities or demand shifts.
  • Forecast accuracy. Smart is an industry leader in statistical analytics, leveraging innovations developed over its forty-plus year history.  This combined with robust forecast vs. actuals analysis helps Procon continually improve the quality of their forecasts.
  • Transparent connectivity with Procon’s enterprise software, Epicor Kinetic. Daily sales and shipment data are automatically pulled into the Smart platform, fueling Smart’s forecasting engine, and results are easily pushed back to the ERP (MRP) via an API based integration to drive ordering and production planning.

Results:
Emer Horan explained how this plays out over the course of each month.   Emer provides forecasts for each of their five sales managers, they meet to compare statistical and sales forecasts, and agree on a revised 12-month consensus plan.  The sales managers have a good sense for the top accounts that represent 80% of revenue, often including direct input from customers themselves, and the statistical forecast fills in the gaps.  Next month they use the forecast vs. actual analytics to help improve accuracy, then repeat the process.

“Our sales team is incentivized to maintain and improve sales forecast accuracy,” said Emer, “and we have the tools to help them succeed.  This not only ensures optimal inventory levels but also contributes to improved on-time delivery and higher customer satisfaction.”

“Our journey with Smart Software has been quite remarkable,” added Shankar. “We began with an initial idea of the functionality and interface, and it has continually evolved from there. The Smart team has shown tremendous support and patience with our scope changes, delivering the product exactly the way we needed and wanted it.  We have been using Smart for over three years now, and this journey is ongoing. We continue to receive excellent support from the Smart team and truly enjoy working with them.”

 

 

Why Inventory Planning Shouldn’t Rely Exclusively on Simple Rules of Thumb

For too many companies, a critical piece of data fact-finding ― the measurement of demand uncertainty ― is handled by simple but inaccurate rules of thumb.  For example, demand planners will often compute safety stock by a user-defined multiple of the forecast or historical average.  Or they may configure their ERP to order more when on hand inventory gets to 2 x the average demand over the lead time for important items and 1.5 x for less important ones. This is a huge mistake with costly consequences.

The choice of multiple ends up being a guessing game.  This is because no human being can compute exactly how much inventory to stock considering all the uncertainties.  Multiples of the average lead time demand are simple to use but you can never know whether the multiple used is too large or too small until it is too late.  And once you know, all the information has changed, so you must guess again and then wait and see how the latest guess turns out.  With each new day, you have new demand, new details on lead times, and the costs may have changed.  Yesterday’s guess, no more matter how educated is no longer relevant today.  Proper inventory planning should be void of inventory and forecast guesswork.  Decisions must be made with incomplete information but guessing is not the way to go.

Knowing how much to buffer requires a fact-based statistical analysis that can accurately answer questions such as:

  • How much extra stock is needed to improve service levels by 5%
  • What the hit to on-time delivery will be if inventory is reduced by 5%
  • What service level target is most profitable.
  • How will the stockout risk be impacted by the random lead times we face.

Intuition can’t answer these questions, doesn’t scale across thousands of parts, and is often wrong.  Data, probability math and modern software are much more effective. Winging it is not the path to sustained excellence.

 

Using Key Performance Predictions to Plan Stocking Policies

I can’t imagine being an inventory planner in spare parts, distribution, or manufacturing and having to create safety stock levels, reorder points, and order suggestions without using key performance predictions of service levels, fill rates, and inventory costs:

Using Key Performance Predictions to Plan Stocking Policies Iventory

Smart’s Inventory Optimization solution generates out-of-the-box key performance predictions that dynamically simulate how your current stocking policies will perform against possible future demands.  It reports on how often you’ll stock out, the size of the stockouts, the value of your inventory, holding costs, and more.  It lets you proactively identify problems before they occur so you can take corrective action in the short term. You can create what-if scenarios by setting targeted service levels and modifying lead times so you an see the predicted impact of these changes before committing to it.

For example,

  • You can see if a proposed move from the current service level of 90% to a targeted service level of 97% is financially advantageous
  • You can automatically identify if a different service level target is even more profitable to your business that the proposed target.
  • You can see exactly how much you’ll need to increase your reorder points to accommodate a longer lead time.

 

If you aren’t equipping planners with the right tools, they’ll be forced to set stocking policies, safety stock levels, and create demand forecasts in Excel or with outdated ERP functionality.   Not knowing how policies are predicted to perform will leave your company ill equipped to properly allocate inventory.  Contact us today to learn how we can help!

 

What is Inventory Planning? A Brief Dictionary of Inventory-Related Terms

Inventory Control concerns the management of physical goods, focusing on an accurate and up-to-the-minute count of every item in inventory and where it is located, as well as efficient retrieval of items. Relevant technologies include computer databases, barcoding, Radio Frequency Identification (RFID), and the use of robots for retrieval.

Inventory Management aims to execute the inventory policy defined by the company. Inventory Management is often accomplished using Enterprise Resource Planning (ERP) systems, which generate purchase orders, production orders, and reporting that details current inventory on hand, incoming, and up for order.

Inventory Planning sets operational policy details, such as item-specific reorder points and order quantities, and predicts future demand and supplier lead times. Important components of an inventory planning process include what-if scenarios for netting out on-hand inventory, analyzing how changes to demand, lead times, and stocking policies will impact ordering, as well as managing exceptions and contingencies.

Inventory Optimization utilizes an analytical process that computes values for inventory planning parameters (e.g., reorder points and order quantities) that optimize a numerical goal or “objective function” without violating a numerical constraint. For instance, an objective function might be to achieve the lowest possible inventory operating cost (defined as the sum of inventory holding costs, ordering costs, and shortage costs), and the constraint might be to achieve a fill rate of at least 90%. Using a mathematical model of the inventory system and probability forecasts of item demand, inventory optimization can quickly and automatically suggest how to best manage thousands of inventory items.

Explaining What “Service Level” Means in Your Inventory Optimization Software

Customers often ask us why a stocking recommendation is “so high.” Here is a question we received recently:

During our last team meeting, we found a few items with abnormal gaps between our current ROP and the Smart-suggested ROP at a 99% service level. The concern is that the system indicates that the reorder point will have to increase substantially to achieve a 99% service level. Would you please help us understand the calculation?

When we reviewed the data, it was clear to the customer that the Smart-calculated ROP was indeed correct.  We concluded (1) what they really wanted was a much lower service level target and (2) we had not done a good explaining what was really meant by “service level.” 

So, what does a “99% service level” really mean? 

When it pertains to the target that you enter in your inventory optimization software, it means that the stocking level for the item in question will have a 99% chance of being able to fill whatever the customer needs right away.  For instance, if you have 50 units in stock, there is a 99% chance that the next demand will fall somewhere in the range of 0 to 50 units.

What our customer meant was that 99% of the time a customer placed an order, it was delivered in full within whatever lead time the customer was quoted.  In other words, not necessarily right away but when promised.  

Obviously, the more time you give yourself to deliver to a customer the higher your service level will be. But that distinction is often not explicitly understood when new users of inventory optimization software are conducting what-if scenarios at different service levels.  And that can lead to considerable confusion.  Computing service levels based on immediate stock availability is a higher standard: harder to meet but much more competitive.

Our manufacturing customers often quote service levels based on lead times to their customers, so it isn’t essential for them to deliver immediately from the shelf. In contrast, our customers in the distribution, Maintenance Repair and Operations (MRO), and spare parts spaces, must normally ship same day or within 24 hours.  For them it is a competitive necessity to ship right away and do so in full.

When inputting target service levels using your inventory optimization software, keep this distinction in mind.  Choose the service level based on the percentage of the time you want to ship inventory in full, right away from the shelf.