A process manufacturer with a network of assets spread across Europe needed to respond more flexibly to changes in customer demand while maintaining high asset utilisation, low working capital and low transport costs.
The situation was complex. The assets were different and had their own characteristics. The outflow from the installations could not simply be stopped between production runs, and a change of material resulted in a massive production loss – although a product type change without a change of material was doable.
The producer had 25 production lines and served 1,000 customers with a total of 2,500 products. In short, the perfect complex planning issue for which our More Optimal platform was designed.
APPROACH
The planners had been working with a combination of SAP and Excel spreadsheets. They were handling a huge number of variables and attempting to incorporate increasingly shorter delivery times. The planners understood their trade, but the complexity of the puzzle was too great for the resources available. There was much to gain.
Our generic More Optimal platform makes it possible to create a customer-specific application in a short time, with all relevant planning rules built in. The platform is set up in close consultation with the user. First, the relevant Key Performance Indicators (KPIs) were defined. These included (1) demand fulfilment, (2) asset pull / productivity, (3) inventory, (4) transport costs and (5) planning effort.
In a number of joint work sessions, we established the planning process and drew up the rules for allocating products to the various production lines. In addition, the transport options relating to production locations and the rules for product changes were built in. By working closely with the planners at every step, we gradually developed the More Optimal platform, and this now shows in real-time the consequences of the decisions made by the planners and gives advice on how to improve the planning process.
The application is also used to evaluate what-if scenarios and their impact on the KPIs. The manufacturer uses this functionality as part of the annual planning and budgeting process and relies on it for concrete operational issues on a more regular basis.
CHALLENGE
Companies that pack fresh products face massive complexity and unpredictability. They process many different products, all of which have specific requirements in terms of quality, class and size. They deal with a multitude of packaging requirements and variability in price agreements for each customer. And they handle huge swings in supply and demand. But the time frame in which packers must match supply and demand is short.
How do you balance customer requirements with product and process complexity to achieve high customer satisfaction and high ‘valorisation’? And how do you deal with last minute changes in supply and demand – for example, if a batch is rejected because it does not meet the quality requirements?
APPROACH
The packer had been using Excel spreadsheets to allocate products on packaging lines and carry out detailed line planning. This had caused misunderstandings and mistakes – and a higher workload than necessary for the planners. They were losing time creating iterative plans, and there was uncertainty about which version of the plan was most up-to-date and about which numbers were correct.
We knew that the More Optimal platform would resolve these problems and explained the benefits to our client. The need was so great and the benefits so obvious that the packer did not even want a ‘proof of value’, but immediately decided to develop and implement a dedicated application based on the More Optimal platform.
The goals were (1) to arrive at a workable schedule faster, (2) more efficiency in the operation, (3) shorter lead times relating to product freshness, (4) better demand fulfilment and (5) increased flexibility.
The More Optimal platform makes it possible to build a customer-specific application in a short time with all relevant planning rules built in. The application is set up in close consultation with the user. First, the relevant Key Performance Indicators (KPIs) are defined to quantitatively determine the quality of the allocation plan. Two of these KPIs were demand fulfilment and lead time (related to product freshness).
In a number of joint work sessions, we drew up the allocation rules for products and determined how products from suppliers should be allocated to customers. By working intensively with the packer, we developed a dedicated application that shows the consequences of the decisions made by the planners and gives advice for better planning. This application was further expanded with support from the planners in order to optimise the detailed planning per packaging line to minimise changeover times on the lines and to increase the throughput capacity (OEE) of the lines. The application measures the operational performance based on the agreed KPIs.
CHALLENGE
A container terminal reached the limits of its capacity due to a further increase in the number of units to be processed. The terminal also had to become more attractive for ships to dock by faster loading and unloading for shorter waiting times. Furthermore, container ships are getting bigger, increasing complexity and time pressure at the terminal.
The assignment was to increase efficiency to make more inbound and outbound truck movements possible and to shorten ship waiting times.
APPROACH
Based on data from the ERP system regarding plan and actual over a representative period, the current working method of the terminal was reconstructed in our Planning Platform. The actual operation was visualised and animated, allowing the movements of each individual container to be tracked from position to position. The reconstruction was validated and further fine-tuned in a highly interactive process with the client.
Subsequently, with our Planning Platform, the current operational performance of the terminal was determined based on jointly identified Key Performance Indicators (KPIs), such as the mooring time per barge, the number of crane movements in/out and the number of truck movements in/out. Subsequently, a simulation of an optimised operation was performed using the exact same dataset and boundary conditions. The comparison of the KPIs of the current and optimised operations immediately gave a clear picture of the improvement potential.
In close collaboration with the client, the plan for a number of containers was then optimised step-by-step until the total was finally optimised. After each step, the improvement was measured against the identified KPIs.
CHALLENGE
A global insurance company was receiving 40-50 claims a day which needed to be evaluated and verified according to several factors before being approved for payment. Most of the claims were arriving as unstructured data, either as PDFs or scanned documents, making it difficult to pull information from them to be entered into various systems – in a timely manner. As a result, claims weren’t being processed fast enough. The company was concluding each year with millions of dollars in claims left open, impacting customer service.
SOLUTION
The company implemented RPA with ABBYY Flexicapture to streamline claims processing and payments. The software Robots took scanned claims sent through email and ran them through Flexicapture to turn the unstructured data into structured formats readable by robots. From there, the robots took the data, verified that all the information was correct and checked all exceptions. Claims that were accurate were approved for payment and sent back to the brokers. If any information was incorrect or there were exceptions, the claims were routed to an employee for further investigation.
CHALLENGE
A young chain of affordable luxury hotels, with a history of strong growth, had recently received an injection of fresh capital to encourage even faster expansion.
The challenge was to scale up the internal processes and grow the organisation. The IT Director had experience of Robotic Process Automation (RPA) with his previous employer and wanted to explore the opportunities here.
APPROACH
Ideally we wanted to compare 3 RPA platforms to test the best fit. However, because there was no opportunity to do so in a test environment, and conducting the tests in the real-time production environment was not practical, we looked for another solution.
As our client had previous experience with UiPath, we decided to test the applicability of the platform in this environment.
Working together with people throughout the organisation, we identified a total of 16 potential use cases, including applications in the contact centre, various financial processes, handling “no shows”, and supporting the IT crew that flew out to test all the IT systems in a newly built hotel.
CHALLENGE
A firm specialising in electrical and mechanical engineering had decided to introduce process mining in the organisation, and wanted to develop the skills “in-house”.
Before finalising the details, the firm wanted to gain more insight in three areas: the degree of suitability in its own business process environment, the actual possibilities of process mining and what a well-fitting package would be.
The firm decided to undertake a proof of concept (PoC) for this purpose, and asked Axisto to carry it out.
APPROACH
The initiative was driven by the ICT department. They provided a two-person team for us to work with, and a project sponsor was quickly found. With the project team in place we decided to run the proof of concept on the auxiliary equipment management (AEM) process.
There were three questions to be answered:
Are the existing IT systems, their current setup and the data quality suitable?
Can the crucial questions related to performance issues be answered?
When compared with the current analysis tools available in-house, does Process Mining add value?
The firm was also interested in additional dashboarding functionality, so for the PoC we chose a process mining package that would provide this extra information. We also demonstrated a package that was more geared around pure project-based process analyses. Each package was deployed very differently in the organisation
CHALLENGE
A high-tech industrial equipment manufacturer has a complex supply chain with many SKUs and long lead times. It was struggling to match supply with demand, which was fluctuating strongly. Multiple engineering changes.
APPROACH
Through data analytics we mapped the characteristics of the business and identified key levers for improvement. This led to the design and implementation of a dedicated S&OP application – a decision-support tool.
CHALLENGE
Global chemical compounds company with multiple factories with multiple production lines.
Medium term decisions on allocation of products to product lines.
Strong influence of batch size and product sequence on output of production lines.
Need to plan allocation of products to production lines at least one year up front.
Uncertain demand and strong influence of market fluctuations.
Complex global supply chain cost and turnover picture including tax regimes.
APPROACH
Build a mathematical optimisation model of the global supply chain.
Embed the model in a user-friendly software system to support decision making.
Involve future users and management to guarantee quality and acceptance of decision support system.
Transfer system to the organisation and remain available for support.
CHALLENGE
Design of optimal and feasible integral supply chain for various companies in High-tech, Consumer Goods, Food and Building Materials
APPROACH
Determine goal, scope, decision criteria and decision-making process.
Involve all stakeholders and experts during the process to get commitment and create trust.
Make sure all relevant data on actual situation and future trends are available.
Tailor and validate a mathematical optimisation model of the global supply chain.
Organise decision preparation workshops in which scenarios are developed and discussed.
Organise session to discuss recommended options as basis for a decision on the design of choice.
CHALLENGE
Maintenance contractor for Rail Infrastructure executes maintenance based on human inspection of rail track videos.
Introduce predictive maintenance technology to streamline and improve inspection.
APPROACH
Gather data on inspections executed to create a large test set of observations.
Develop a deep learning algorithm (based on neural networks) to automate the inspection.
Minimise false positives (judged okay but in fact not okay) and false negatives (judged not okay but in fact okay).
Test and validate the software in the live environment.
Transfer the software to the maintenance organisation.
