(3-4 min read)

The need to safeguard supply chain against today’s world of uncertainties, be it political or natural has never been so significant. In such scenario, one of the most important decisions that most supply chains need to take is whether to prebuild the stock and carry excess inventory, or rather plan ahead of the time if there is any additional production required. Each of the options has its own pros and cons. However, what is important is to have the right visibility in terms of volumes and costs to enable effective decisions. In the following blog, let us take a view on how this information could be derived using the power of technology.

Current State of affairs

Digital transformation seems to be a buzzword at the moment in business. Everybody is talking about Digital transformation and planning for the big change that Industry 4.0 is bringing. Yet, very few seem to be actively transforming their business to be able to take advantage of the opportunities it brings.

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.

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.

Warehouse space is one of those things that nearly all supply chains can’t live without. We talk a lot about getting our inventory targets correct, but how often are our targets driven by other constraints such as warehouse space at certain nodes in our network? 

Should such a commodity such as warehouse space drive our inventory decisions, and potentially put customer service at risk? These are big, philosophical supply chains questions which S&OP processes are in place to address – but we can help this process by providing the best information we can. 

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 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.

For  those  businesses  wanting  to  improve  on their supply chain planning processes, implementing the APO SNP Optimiser can often appear to be a daunting task. End-to-end supply chains and resulting SNP Optimiser implementations are becoming increasingly complex. The decision to utilise the SNP optimiser  is  usually  aligned  with  the requirement to plan a realistic, constrained plan with  multiple alternative  solution  options within the constraints and across the whole supply chain.

Choosing which planning method to use the SNP optimiser is undoubtedly the most capable of modelling and dealing with complex planning scenarios. The prospect  of  determining  and  managing all of the dependent costs in the supply chain may lead an organisation to opt out of using the SNP Optimiser. Heuristics or CTM are clear rules based algorithms and therefore easier to use and understand, however, they also tend not to properly reflect a complex and constrained supply chain, and thus the value proposition of the APO implementation can be diminished.