First of all, apologies for abusing the title of Gabriel García Márquez’s well regarded book Love in the Time of Cholera. However, it is set in a time of great volatility veering on the apocalyptic which is appropriate to our current situation.

My starting point for this article is that, for the most part, we have had a relatively benign environment economically across the globe for the last 50 to 75 years or so with continuous growth. There have of course been significant disruptions due to wars, famines and epidemics such as Ebola, SARS, MERS and Avian Flu. With the exception of the 2009 Crash, they have generally been contained or limited to one region and economic activity on the whole continued without disruption.

In this highly competitive world with ever increasing focus on achieving higher service levels, lowering inventories and eliminating waste while reducing costs and maximising profit, it becomes essential that the distribution of goods through the supply chain must be equally effective and efficient and must be optimised to achieve business goals. Products must be available in the right amount, at the right time, at the right place!

A lot of companies use SAP's Advanced Planning & Optimisation (APO) to optimise their production plan in the mid to long term and some are on their IBP Supply journey. However, having an optimised production plan without an optimised distribution plan may not yield business benefits, especially when the supply chain is complex.

When it comes to a tool, the supply optimiser from SAP is the best in class supply chain network optimisation engine and is highly powerful if modelled in the right manner.

In this blog, I will cover various aspects of using Supply Optimiser (APO or IBP)  for distribution planning / deployment optimiser which can help businesses achieve full value out of their investment in this great tool! I will also cover a case study from one of our recent implementations.

Dear Network,

We hope everyone is well?

None of us would have imagined 2020 to be the year of a worldwide pandemic with Covid-19 (Coronavirus). There is still uncertainty about what will happen next. These are indeed unprecedented times and a time that will not be forgotten in our lifetime or for that matter, in history. We extend our great respect and heartfelt thanks to Doctors, Nurses, Healthcare staff, Ambulance drivers, Supermarket workers, Cleaners, organisations and everyone involved in protecting and saving our lives whilst keeping society going.

The answer, of course, is “yes”. OK, you are right: it is rather easy to cut down inventory cost!

High-tech discrete manufacturers are feeling a very real and uncomfortable truth; the world has changed and the majority of supply chains simply have not innovated quickly enough to succeed effectively.

Cloud based Advanced Planning technologies such as SAP IBP require data integration both into and out of ERP platforms, BI Systems and other 3^rd party systems. 

Data integration covers both Master Data and Transactional Data. Certain data sets such as BOM’s and Routings have more challenges due to the complexity of the underlying data.

Introduction

Moving holidays like Ramadan, Easter & Chinese New Year can cause planners an increase in manual effort to get the demand right, in the right place, at the right time.

Traditional seasonal forecasting models handle the seasonal patterns for fixed holiday periods like Christmas & New Year, however no out-of-box forecasting models are available in any demand planning system for moving holidays.

This blog intends to deep dive into the functionality within SAP IBP for Demand module to determine what system features are available to handling this challenge.

(6 minute read)

Extract, Load and Transform to get a Single Version of the Truth

Today, most companies find themselves swamped by data and they often struggle to find a comprehensive, solid and reliable set of data that they can trust, and they can use for data-driven decision making.

In many organisations, it is very common to use the statistical forecast as a baseline forecast.  Understanding and analysing of historical data are very important to generate a reliable statistical forecast. In most of the cases, demand planners rely on their team’s experience and conduct visual check (graphs) to understand historical sales pattern such as seasonality, trend, etc. SAP adds another feature in its SAP IBP software under Forecast Automation application, and through this application, demand planners can analyse historical demand data statistically without being too mathematical. In this blog, I will walk you through how it works and discuss some of its advantages and drawbacks of it.