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Supply Chain Innovation: Living with SAP APO

By Jeff Bodenstab13 Jan 2015

“Nine out of ten companies regret their decision to implement SAP APO,
and Oracle planning is not much better.”

– Lora Cecere, founder & CEO of Supply Chain Insights

It’s unfortunate, but true. In last week’s post, several industry analysts explained how ERP supply chain planning (SCP) solutions are clearly deficient.

But if your company is heavily invested in an SCP expansion to an ERP system, there is a solution. This week we will discuss how companies can improve their supply chain planning performance using what Gartner calls “systems of differentiation”—add-ons to foundational platforms which are the planning repository for their enterprise supply chain. Cecere sums it up, “In companies with an SAP APO environment, companies should use SAP APO as a system of record and buy other optimization solutions that are industry-specific to improve decision support.”

Let’s look at an example of wound care products manufacturer Systagenix. They needed to do a better job of forecasting volatile demand across 100 countries than they could with their SAP APO system.  Alastair Mitchell, Systagenix’ supply chain general manager, described their challenge, “We were dedicating significant time and expense to adapt the system [SAP APO] to our needs and repeat the process every time we needed further changes. This wasn’t really a viable option given the dynamic nature of our global business and pace of innovation.”

Deploying a ToolsGroup system of differentiation for forecasting and demand management, Systagenix improved service levels to 99 percent at its 3PL distribution sites, while reducing inventory levels 15 percent. Creating a monthly global forecast used to take the two people an entire week in non-value-added transactional work. Now it is accomplished by one person in a single day, a 90% improvement in planner productivity.

The fundamental problem: Demand forecasting and supply chains are more challenging than when SAP APO was developed. Business complexity has grown, fueled by multi-channel marketing, demand shaping, and the impact of the Internet on buying. There are more products, shorter life spans, and an explosion of product options—adding complexity to the supply chain and increasing demand volatility.

SAP APO still applies a traditional “top-down” approach to forecasting based on aggregated data. When these high-level forecasts are split to an Item-Location level of detail for inventory and replenishment planning, crucial granular information about volatility and error is lost. For example, when one of our customers ran a benchmark study of their SAP APO system, splitting monthly data into weeks increased forecast error by more than 40 percent. Forecast error also increased by 40 percent when National/SKU aggregates were split into Ship-From detail.

Forecasting today requires understanding the demand signal at the Item-Location level in order to see customers trending up and down, regions growing or shrinking and SKUs exhibiting unusual behavior. The most important information about demand volatility is in this granular detail.

Demand modeling captures this detail by creating a baseline demand forecast, and then automatically adjusts the baseline by “sensing” stimuli and demand indicators at a detailed Channel-Item-Location level. It can also employ the power of machine learning to anticipate end-consumer market demand, modeling shifting demand from trade promotions, new product introductions, extreme seasonality, and product cannibalization.

Next week we will explore how machine learning can be a powerful new system of differentiation for demand forecasting and supply chain planning.

For a more detailed discussion on this topic, download a brief on Digital Transformation in Supply Chain Planning:

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