When coming to the end of my full-time master's programme at Cranfield University, the focus began to shift back towards the next step of my career and preparing myself to enter the challenging world of the job market. Keeping one eye on possible career opportunities while trudging through thesis research seemed like a daunting task which caused widespread panic across the students in my course, nightmares of sieving through online applications for various companies in tandem with hours of daily thesis research. Sounds like a wonderful way to spend your spring and summer right?Read Article
In today’s fast moving world of growing customer expectations, shrinking lead times, reduced profit margins and inventories, Demand Planners have a big responsibility to act as the first gatekeeper to be able to foresee changes in Demand Signals and accurately make predictions for future. The margin for error is continuously decreasing, as any errors cause a major rippling effect on downstream processes. For example, a change of forecast bias by 5% may induce the following risks;
- Reduced customer service levels
- Increased lead times
- Improper mix of inventory levels leading to possibility of stock outs or over stocked DCs
- Added costs due to firefighting production builds and expediting fulfilment (air freight)
To cope up with this fast-paced supply chains, new metric must be adopted by demand planners all over the world. One of those is Forecast Value Add. I am going to take you thru details of Forecast Value add and explain how we can use this to improve the forecasting process. I would also be explaining examples using SAP Integrated Business Planning.Read Article
SAP Integrated Business Planning (IBP) is SAP’s next-generation planning application, which uses the computational power of SAP HANA to enable a truly integrated and collaborative planning process.
SAP have also recognised their past problems with somewhat un-intuitive user interfaces and have invested a great deal of thought into the underlying technology and implementation of the interfaces with which users interact with the system. Of course supply planners are not an easy group to please: they usually work in high pressure environments where time is limited and ease of use vital. To further complicate the issue, the user community for an IBP process is much wider than just Demand & Supply Planning and brings in the Finance and Sales departments among others.Read Article
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.Read Article
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.Read Article
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.Read Article
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.Read Article
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.Read Article
Demand Driven MRP & The Demand Driven Adaptive Enterprise
The greatest barriers to supply chain delivering the desired service level at the lowest possible cost are lead-time and variability. Our lead-times are longer than customer expectations, meaning we must rely on forecasting and inventory to deliver service. The issue with forecasting is that it is never correct – meaning we never sell what we forecast, and the longer the lead-time the less reliable the forecast becomes. The combination of inaccurate forecasts and long lead-times with a conventional MRP planning solution (which relies on perfect demand, lead-time adherence and dependency between all nodes) results in a bimodal inventory distribution. The result is an excess of some items, and shortages of others, with the inventory position oscillating between feast and famine leading to poor service, despite high inventory and high expediting fees.Read Article