Review of SAP, Oracle / JD Edwards, NetSuite ERP for electronics manufacturers

By Staff

Below, I highlight fundamental differences in the functional approach of demand management programs in SAP ECC 6.0, Oracle / JD Edwards and NetSuite.

The core demand management functionality inherent in both SAP and Oracle / JD Edwards was designed 20 years ago.

The pressing issue for today’s electronics manufacturers is integrating CRM and MRP functionality.

Core functionality of tier-1 and -2 ERP have been around for a long time. Obviously, the Internet and mobile technology we take for granted today was in its infancy when their code was developed.

Plus, computing has become more powerful, making it possible to run larger more integrated business processes in an ERP system a fraction of the time doing the same would have taken way back in 1994.

The possibility of real time collaboration with customers and field sales had not had the impact that sales mobility and CRM integration has had today.

Traditional ERP
JD Edwards and SAP ECC create demand plans based on a best-fit forecast of sales history as the basis of the production plan.

The core business process for SAP / JD Edwards looks like this:

Sales quote => Sales order document => Sales order transaction file => Aggregated sales history file => Forecast engine=> Best fit statistical forecast => Demand plan worksheet => MRP


Using SAP or JD Edwards, a quote is issued to the customer and some of the quotes are converted to sales orders. Records of sales orders are stored in a transaction file where they are processed through to shipment. The sales transaction file is then extracted into a sales history file.

The sales history file can then be used as input to the ERP’s built-in forecasting engine, which, using a best-fit algorithm the forecast engine creates a forecast for the product, need date, and, if desired, shipping point.

Or, it can be exported to an external forecasting engine and then reimported as a forecast file in ERP.

What happens next varies a lot from business to business but the gist of it is the best-fit forecast needs to be reviewed and ratified in some form before it is used as input to MRP. Many newer businesses are doing this process in parallel to reviewing the opportunity pipeline in their CRM systems.


Reality for most discrete manufacturers is weighted CRM data rarely conveys entire demand picture in planning horizons outside customer lead-times.


The sales team is game to this process and immediately sees the benefit and risk of biasing the demand plan separately from their own sales pipeline review. They usually also complain that reviewing the demand plan is redundant to the process sales management uses to manage and report on their bookings forecast. [Get list of 6 quality electronics services providers in 3 easy steps]


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SAP and Oracle JD Edwards would both need to re-write their core programs to provide real integration simplicity between CRM and MRP functions and may not ever recover the costs in a cloud-driven reduction of their revenue models.

In contrast to the multi-step history extraction process used in traditional ERP, information stored in the CRM module of NetSuite is available for the creation of demand plans.

CRM opportunity management => Demand management => MRP


This gives the demand planner direct access to chose to plan for the opportunities in CRM or use the historic data in the CRM to create a best fit forecast and this approach does not need to rely on statistical forecasts based on history.

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The inherent error of forecast is replaced by summarizing real demand.

The best system would time execution of manufacturing to align with orders so electronics products are ready when and where customer need products to be.

Because only ‘one version of the truth’ exists the gaming that goes on between tracking leads against sales quotas and what sales tells the supply chain team is removed. This is a 21st century supply chain solution and a game changer for ERP.

Dangers of making data more complex
SAP and Oracle have CRM offerings, but neither is integrated into planning. Both have similar modules to handle integrated cross-enterprise planning. SAP has advanced planning and optimization (APO – recently renamed supply chain management) that can bring information from CRM and SAP ECC 6.0 into a planning space, but it is a more complex and far more expensive approach to demand planning.

Similarly, Oracle purchased Demantra to add advanced supply chain planning (ASCP) to its product portfolio. Similar to APO, the ASCP approach can combine various inputs into a plan and then pass that information back to core ERP for detailed explosion.

Canada-based Kinaxis offers a planning tool that is competitive with SCM and ASCP. But, in these solutions, data becomes more complex, and separate master data management functions became necessary to combine and manage all of the disparate data driving greater cost and complexity.

Cost models must change
Oracle and SAP have been known to use a ‘pay as you grow’ model for their ASCP or APO capabilities, respectively. And while capabilities are immense, Oracle with advanced supply chain and SAP (now with HANA) are for creating rapid planning runs for the extended value chain.

Changes to the core ERP functionality would need to be made backward compatible to the customer base and purchase orders. With the disruption to cost model for ERP that cloud computing is bringing, it is highly unlikely that such an investment can be made cost-effectively.

NetSuite’s demand planning and integration of data falls short for manufacturing business requirements requiring managing multiple forecast inputs woven into a single demand plan.

The reality for most discrete manufacturers is that weighted CRM data alone rarely conveys the entire demand picture in the planning horizon outside of the customer lead-time.

This is more an issue in the beginning of a month (or quarter) when top-down and bottom-up demand pictures differ the most. Typically, a business may have several inputs to the demand plan that include marketing forecasts and, in some cases, channel partner and OEM customer forecasts.

A better system would enable the demand planner to maintain and combine these different forecast types with a clear audit trail back to the source data in an easy to use upload interface.

OEMs and their contract electronics provider partners often get wrapped up in a extra effort by trying to manage a longer term forecast with the same granularity of shorter-term ones. The end or quarter hockey stick shipping charts often found in many contract electronics providers speaks to this challenge.

To try to apply weighted CRM data for longer term forecasting could be alarmingly inaccurate.

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Where the advanced planning systems really shine is in handling more complex scenarios involving managing demand that comes from multiple sources: CRM, marketing forecast, order backlog…

The capability of an advanced planning system becomes more apparent under use cases where the demand planner wants to consume the forecast: meaning, monitor and update the forecast to account for actual orders v planned demand in-between plan iterations.

Simplicity and the cloud
When thinking about choosing the right ERP system, pay careful attention to what you are asking the system to do. The implications are huge in terms of cost and complexity when a simple approach to demand planning is ‘good enough’.

The solution you need at a few hundred million in sales may not be a smaller version of what you’ll want at $1B+. One thing that may be certain is more and more people are looking to the cloud for the answer to these complex requirements.

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