Master data is an important pre-requisite for achieving good planning results. Like any other system, a successful SAP Advanced Planning & Optimisation (APO) implementation relies on having good quality master data. When projects go-live we always get good results but over a period of time, the quality of results tend to deteriorate due to poor master data hygiene. This is especially true when the SKU churn is significant and the supply chain networks are large.

In this blog, I will outline the features of the Master Data Check Tool developed by Olivehorse.

In any organisation, it is vital to maintain the demand and supply equilibrium. This objective drives the planning community to work on producing the right product mix and keeping optimal inventory levels whilst maintaining customer service levels - all of course within financial boundaries! 

Demand Planners are expected to forecast more Product/Location combinations, but it would be unrealistic to expect equal focus and importance to be placed on every item. We therefore need a tool which helps focus their attention and prioritise their work load to add real value to the forecast. Standard Supply Chain practice centres around ABC-XYZ analysis, the ABC part of which is based on the Pareto principle, i.e. a limited number of tasks produce a significant overall effect (explained further later).

The ABC dimension is usually based on value (margin or revenue) or volume of the sales, whereas the XYZ dimension looks at the forecastability of the demand, i.e. how hard is it to predict future sales from the variability previous sales? The use of segmentation allows for different forecasting procedures to be applied to different segments of ABC-XYZ, moving away from the ‘one size fits all’ mentality to forecasting. This can drive better forecast accuracy once the optimal forecast profiles have been assigned based on the segmentation.

In this blog I am going to explain how ABC-XYZ functionality in the Demand Planning (DP) module of SAP's Advanced Planning and Optimisation (APO) application would help leverage this.

Business Drivers

In the many SAP Advanced Planning & Optimisation (APO) Supply Network Planning (SNP) implementations that Olivehorse has been involved with over the years, we have identified a common mid-term production planning issue – especially in the consumer products and food industries.

The standard APO planning solution will plan to demand, however, such a plan will usually not be efficient to produce due to cost of changeovers and reduced capacity utilisation etc. There is therefore, a requirement to group production orders of different SKUs together into the same period to ensure that a more feasible and efficient plan is created in the mid-term. The standard solution in APO, whether it be Heuristics, Capable-to-Match or Optimiser, does not support the concept of Group Planning.

Forecast Accuracy defines how accurate the forecast works against the actual sales and is usually defined in percentage terms as;

Forecast Accuracy = 1 – Forecast Error

Forecast Error determines the deviation between the forecast and the actual demand/sales.

As Evan Esar’s saying goes "An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today".

So, to be able to react quickly, it is very important to understand why this deviation has occurred. As there are various Error Measures available in software tools such as SAP APO or SAP IBP, it is vital to understand which error measure is applicable under what circumstances.

When scheduling production order sequences in a manufacturing process, there are times when capacity losses are incurred when transitioning between orders. This can be due to changes in materials, cleaning between production, tooling changes etc. This loss in capacity can be referred to as ‘Change Over’ or ‘Setup’.

By reducing changeover times, you can increase the efficiency of the manufacturing asset and the productive manufacturing time.

A common way to represent change over losses is via a ‘Setup Matrix’. Here the transitions between different characteristics of the products being manufactured are represented.

A high-level insight on Resource Networks was provided in one of our earlier blogs entitled “Semi-Finished Goods Scheduling within SAP APO”. In this blog, I intend to provide a more detailed insight on how Resource Networks can be utilised in both SAP APO Planning and Scheduling scenarios.

A Resource Network is best described by the physical connection between Resources of different production lines. It models the flow of materials through these lines from one Resource to another. Some scenarios include:

• Scheduling across the allowable connections between packaging lines and semi-finished goods lines
• Recognising the prioritisation across multiple semi-finished goods lines feeding into a packaging line
• Providing flexibility to switch between alternative input resources in the case of capacity constraints

Supply chain planning experts Olivehorse and Every Angle are developing a common SAP APO reporting module in Every Angle's business analytics tool. The solution will be launched and presented later this year in a joint event, hosted in Amsterdam.

SAP APO (Advanced Planning and Optimization) is at the planning heart of SAP Supply Chain Management (SCM). It dates back to 1998 and over the years has gained in maturity, now forming the backbone of many SCM processes. However, APO was never designed with reporting in mind. This has led many companies to create bespoke reporting solutions dependent upon BI, ABAP and Microsoft Excel. Often though, the process to create reports is cumbersome and costly.

Supply Planning is an important function in every industry which tries to achieve a balance between customer service, inventory targets and resource utilization. A supply planner’s primary role is to deliver a tactical plan which meets the demand on time, covers safety stock targets at the distribution centres in the supply chain network while also ensuring that resources in the factories are utilized efficiently. A supply planner basically works on integrating the manufacturing and sales side of business and aims to optimize both ends.

The Manufacturing or Production function always prefers to produce according to cost effective lot sizes, minimal changeovers, production cycles and sometimes other constraints like labour or semi-finished material. On the other hand, Sales or Customer Fulfilment functions have different priorities which mainly include meeting customer demand in full and on time, and meeting inventory targets without going below or above targets significantly. These two sides of business often contradict each other and achieving a plan which has been optimized from both angles is challenging.

Usually, a medium term tactical supply plan is generated periodically (weekly, fortnightly or monthly). This plan is normally capacity constrained and contains decisions regarding production, sourcing and distribution of products through the supply chain.

A lot of companies who purchased  SAP’s Supply Chain Management (SCM) application use the Advanced Planning and Optimisation (APO) Supply Network Planning (SNP) Optimizer as a tool to generate the capacity constrained mid-term plan. It is a powerful tool which delivers a plan that respects resource contraints and optimizes the supply chain within a given cost framework. If implemented correctly, it can result in big cost savings for any organization. In this blog, I will try to cover several aspects related to the SNP Optimizer.

Short-term production scheduling is a key business process that ensures efficient use of scarce manufacturing resources, alignment of raw materials and flexibility when tackling manufacturing challenges. Whilst scheduling of Finished Goods (FG) is common place in SAP Advanced Planning & Optimisation (APO) Production Planning/Detailed Scheduling (PP/DS) implementations, an area often overlooked is the scheduling of Intermediates or Semi-Finished Goods (SFG) production.

Quite often this is a manual process, or is controlled by spreadsheets and/or third party/non-SAP systems which makes it challenging to manage the alignment between the SFG and FG schedule.

This blog is intended to highlight the main challenges and importance of the SFG scheduling process and present an overview of the capability within APO PP/DS that can be used to deal with these challenges.

The release of SAP IBP 6.0, just around the corner, will contain the first release of SAP Integrated Business Planning for Supply and Response.  This will be a landmark in the development of the IBP, introducing order level planning capability to the suite.   As a result SAP customers will, for the first time, be offered real choices in how they develop their planning application capability.  However with choice comes uncertainty, this is especially the case for those who currently run SCM-APO and have developed their supply chain planning processes around this tool set.  These organisations should not be alarmed, SAP has committed support of SCM-APO as a component of business suite until at least 2025. However customers should not expect widespread innovation or enhancement.

SAP’s vision for IBP however is far broader than delivering a turbo charged version of APO running on HANA.  When complete the toolset will, for the first time, provide the basis to integrate strategic, tactical and operational planning, facilitating “full business planning”.  IBP is constructed of five modules, two of which; IBP for Sales and Operations and Supply Chain Control Tower, provide end to end planning and monitoring capability. These tools are aimed at supporting management decision making and control, setting the direction for operational plans and supply chain execution. Unsurprisingly, these are also the two modules that SAP focused on first when developing the product.

IBP for Sales and Operations and Supply Chain Control Tower provide end to end planning and monitoring capability