SAC brings your supply chain to life with visualisations and insights

Many businesses we come across already use SAC for financial planning, but few leverage its capabilities to support supply chain. Through linking SAC to supply chain planning tools, Olivehorse has created solutions to support end-to-end visibility by measuring what matters to bridge the gap between finance and supply chain.

Introduction

Big organizations generally have very complex manufacturing scenarios and requirements. One of such requirement revolves around setting up their production process in ERP systems. They may have huge production premise which is further subdivided into various divisions and departments, forming small sublocations within same production facility e.g. automobile production units subdivided into various divisions like Body and Paint shop, engine assembly etc. Generally this requires quiet intensive master data maintenance in mapping the production process in such cases.

In this blog we will be talking on high level how SAP IBP TS Supply helps you in modelling such requirements with spending less efforts on maintaining master data.

Introduction

Demand Driven MRP (DDMRP) is a great trigger to buy/make/distribute in the short-term. But what occurs over the next 1 to 12 months? Supply chains need the visibility over this period of time as well. Can we still use DDMRP? In short, yes!

What is DDMRP used for?

DDMRP is the operational component of the Demand Driven Adaptive Enterprise (DDAE) to deliver inventory and customer service benefits. For further case studies and education, please read this article on Demand Driven MRP & The Demand Driven Adaptive Enterprise.

DDMRP is an unconstrained order release process based on inventory buffers within the Decoupled Leadtime (DLT). These buffers can be sized using historical demand and/or future demand. This will ensure the buffers are dynamically sized. However, DDMRP has no visibility of the capacity available to supply those buffers. Demand Driven S&OP (DDS&OP) is vital to allow us to carry out a supply and demand reconciliation to recommend buffer adjustments to reduce these risks.

The relevant range of Demand Driven S&OP is everything outside the DLT up to approx. 1-2 years. DDS&OP also reviews historical performance of the buffers and ensures the key inputs to buffer sizing (Average Daily Usage (ADU), Red zone, Green Zone) are optimal.

A SAP IBP solution

The Olivehorse DDS&OP solution is built within SAP IBP and therefore seamlessly integrates with SAP Demand Driven Replenishment (DDR). The flexibility of SAP IBP plus the Supply Optimiser allows us to consider capacity constraints, material lead-times across the End-to-End network and calculate buffer adjustments outside the DLT.

(2-3 MIN READ)

Interested in SAP IBP for Inventory but unsure whether your business will realise the benefit? Olivehorse can provide you with a 6 week Proof of Value, with your data to enable understanding of how the solution will deliver benefit to your organisation.

Together we can build your Supply Chain, to enable a fast and informed decision on the benefits of SAP IBP Inventory in your company. At Olivehorse we can provide calculations, analytics and planning views to evaluate the proposed plans versus your current Inventory Plans.

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.

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.

In this modern era of globalisation where geographical boundaries are no longer a limitation, with shrinking lead times and increasing speed of business, organisations have a greater need for efficient supply chains than ever before.

In this blog I will explore the collaboration features allowing organisations to plan their supply chains effectively and efficiently.

The planning capabilities of SAP Integrated Business Planning (IBP) for Response is always compared with a much matured and stable product, SAP Advanced Planning & Optimisation (APO). While SAP plans to discontinue the support for APO in the near future, the businesses have now started looking forward to IBP for Response as a potential replacement for planning in order series. This module is yet to reach the pinnacle of its maturity in terms of functionalities, but the progress has been rapid and the road map looks stronger. Further, it looks promising in terms of speed and flexibility which was not possible in APO.

In this blog I will explore features of IBP for Response & Supply. This includes integration of IBP for Response with external systems and the time series counterpart of it followed by the planning operators being offered

For some time now, SAP have been clear with their intention to develop forecast automation via machine learning capability and functionality. With the 1811 release of SAP Integrated Business Planning (IBP), SAP are really starting on their machine learning journey by leveraging content within their Predictive Analytics Library (PAL) and have introduced Gradient Boosting of Decision Trees (GBDT) to the available set of statistical forecasting algorithms within the IBP for demand license.

For many manufacturing organisations, Detailed Scheduling is mainly driven by the need to perform manufacturing by following a specific preferred sequence whilst generating an achievable finite plan. It is quite normal for companies to use Detailed Scheduling heuristics for this function, but there are scenarios where this does not quite fit the bill.