sap apo re-tuning for a fortune 100 conglomerate - case study

Demand Planning LLC, 26 Henshaw Street, Woburn, MA 01801.

DEMAND MODEL TUNING AND SAP RE-IMPLEMENTATION

FOR INDUSTRIAL CONTROLS COMPANY

FEB 2016

Project Completed and Rolled Out 2014


Case Study – A Fortune 100 Conglomerate * For the reasons of privacy referred with a hypothetical name THERMAGIX

Introduction: Thermagix contacted Demand Planning LLC to discuss how they can improve the quality of their demand plans by leveraging the statistical features of SAP APO Demand Planning. We started this project keeping in mind this project will revolve around three building blocks; system diagnostics and recommendations to understand the lay of the land, exception planning to focus on the key 20% of the products which make up 80% of the revenue and finally the statistical modeling to capture the real value of the tool.

We started by interviewing the demand planning team and stakeholders involved in the entire Demand Consensus process. This gets us closer to the business so we could identify the right specifications for their business in the design of the system, planning area, planning book and finally training content.

APO System Diagnostics

Exception Management

Statistical Modeling

THERMAGIX


System Diagnostics: Planning Area Design:

The stacked design of the planning area posed some limitations. Planning levels and aggregations put the planners on crutches. There was just one statistical model used for 10,000+ SKUs. A generic model was run at the lowest level in weekly buckets producing the ubiquitous straight line.

Forecast Settings: Some important key figures that need to be in the planning area were missing. This disabled some statistical modeling options. It also made difficult for the planners to see the quality of the model and the forecast, calculations of a variety of useful diagnostic metrics.

With broad review and approval from the planning team, IT and senior management, the DPLLC team worked with the Thermagix team to re-design the system to make the needed system configuration changes to enable and facilitate statistical modeling and consensus forecasting.

Portfolio Management and Optimization: We strongly recommend planning by exception – this involves starting from the simple Pareto rule to analyze both products and customers to segment the analysis and review.

We divided Thermagix’s products into various segments based on Line of Businesses and other custom segmentation criteria. Following were some of the segmentation factors that threw a lot of light on the underlying products and SKUs.

"Mark & his team from Demand Planning LLC did a phenomenal job when they came onsite to help us with our statistical model tuning in SAP. We are now implementing the stat models in our forecasting process, and on our way to

increased forecast accuracy. Additionally, we did have some SAP APO system limitations and Mark still managed to find a work around solution to enable the

lifecycle planning process for our company. He is an expert in this field, and very resourceful. Our company will be using him for future logistics projects due

to the great success we’ve had!"

-- Sales Forecast Analyst, Ghirardelli Chocolates, CA.

THERMAGIX


Percentage distribution of all Thermagix’s products as defined in the project is given below –

We leveraged the master data structure to incorporate the segmentation logic so planners can use this as a selection criterion. This simplified the creation of selection id process and the sequencing of the background jobs.

Stat Model Tuning: Although Thermagix historical data exhibited visible patterns in trend and seasonality, the older stat forecast models were not able to capture these patterns. The resulting forecast generally was a straight-line across all future months – this is a common phenomenon we have observed across various clients.

Although, SAP offers the flexibility to model in both weekly and monthly view, only weekly view was utilized for modeling. Ideal set up will be to run both weekly and monthly models, weekly for the better splits and monthly which will project accurate long-term patterns.

THERMAGIX


This is a screenshot on the type of forecast that was produced before our stat model tuning project.

Below is screenshot of same product when forecasted using our new implementation and stat modeling in the monthly bucket.

Monthly model here captures the seasonal peaks accurately. This was a major improvement when it is compared to weekly models on the same product that were incorrectly defined.

THERMAGIX


Final Deliverables: • Aligned the business process to solution requirements, • Revised planning books to conform to DP Consensus Process which provided more clarity and

reduced clutter, facilitated consensus and reconciliation • Fined-tuned Statistical models keeping in mind Thermagix’s business. • Capability was provided to model in monthly buckets where different demand components were

captured in a more efficient manner. • Planners were trained to customize the models and track the forecast quality using various

forecast metrics available in APO • Introduced new key figures to evaluate quality of stat model and leverage all types of stat models • Right level of custom stat models and related sequential back ground jobs.

THERMAGIX

“"Demand Planning LLC has an amazing ability to morph into employees of the company and tackle challenges head on. As a team they delivered on their

commitments and institutionalized a new Statistical forecast process that fit within the business constraints.

Demand Planning LLC has an uncanny ability to spot problems before they occur and gave world class training in terms of demand planning and statistical

forecasting.

A Must Hire if you are struggling with end to end SAP/APO Statistical forecasting processes & methods."

-- Project Leader, Honeywell, Golden Valley, MN.

sap apo re-tuning for a fortune 100 conglomerate - case study

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