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Improving Demand Planning and Inventory Optimization inside SAP ERP, Part Two: Forecasting


This is the second in a four-part blog series exploring GIB’s Solutions.

Nobody has a crystal ball, but with emerging algorithms and predictive analytics we are getting closer to more accurately predicting the future. When it comes to inventory optimization, planning ahead for what’s around the corner is more than a novelty, but an invaluable business advantage that can make the difference between a profit and a loss.

Insight Brings Major Impact

As we approach year’s end and reflect on sales and budgets, many of us will ponder how business could have fared with a bit more insight. What if you’d known what was coming next? What if you could predict what’s ahead even ten percent better? What kind of impact would that make on your business? Accurately predicting demand not only improves the bottom line, but helps companies anticipate and avoid outages, stop overstocking and carry less inventory along the way.

Unfortunately, most Demand Planners using SAP ERP are still struggling with accuracy. Some use manual spreadsheets while others complete the arduous process of exporting data outside SAP for analysis. In both scenarios, the process is inefficient and frustrating, and businesses continue to run the risk of stock outs or carrying excess inventory.

Better Planning For the Year Ahead

While 2018 may not be the year we invent time travel, seeing into the future of demand is getting closer to reality with GIB Forecasting (DCF). In as little as six weeks, we can implement this SAP ERP add-in, allowing you to run multiple forecasting algorithms simultaneously to determine the best fit by SKU.

Grace Kennedy’s Demand Planner Damion Davy uses G.I.B to forecast demand for their broad line of food and beverage items:

“With G.I.B Forecasting we have been able to improve our forecast accuracy which has directly lead to carry less inventory, improving our inventory performance by 20% while maintaining our high service level.”

It’s time to stop asking ‘what if’ and start looking ahead.  Consider what better demand planning could do for your organization, and reach out to us at for a one-on-one consultation.

In part three of this blog series, we’ll look at how supply and demand simulations can help cut costs and boost productivity.

Get To Know GIB, Part 1: Improving Demand Planning and Inventory Optimization inside SAP ERP




This is the first in a four-part blog series exploring GIB’s Solutions.

After 22 years of implementing SAP ERP solutions for our customers, we’ve seen every scenario under the sun. In the process of solving our customers’ business requirements and supporting their growing enterprises, common threads stand out and patterns emerge. As we assisted our customers with managing inventory, production, purchasing and forecasting, we saw a clear need to improve performance, efficiency and drive cost savings.

In 2014 we polled 100 SAP users to see if their supply chains were optimized and how they were performing demand planning. The results were stark; 85 percent were not optimized, and of those, 65 percent were performing demand planning using manual spreadsheets. In this day and age, with so much advanced technology at our fingertips, manually exporting data out of a core ERP system for planning, analysis and decision making is not only tedious, it’s ridiculous!

A Solution Inside SAP ERP

Closing, these troublesome gaps in functionality, G.I.B is an SAP partner that provides solutions that solve these supply chain challenges in core ERP, and has seen broad adoption in the German and greater European markets. Seeing the company’s satisfied customer base, built on over 300 successful implementations, we knew they were onto something, and chose to introduce GIB’s tools to the US market.

Stellar Results, Stronger Supply Chains

Since bringing GIB to the US, we’ve implemented over two dozen solutions, and seen an equally strong reception amongst our American clients.


In part two of this blog series, we’ll explore looking into the future with demand forecasting.