Next Gen production scheduling

Supply Chain disruption remains a major global challenge

Supply chain disruptions are persisting and are likely to become more complex and frequent. A failure to respond to this ‘new normal’ leaves an organisation in a strategically disadvantaged position in the market.

Introduction

Supply chain challenges are becoming more complex and volatile. The lingering impact of Covid and geopolitical uncertainties have exacerbated supply challenges. Labour shortages and transportation delays are persistent and lead times unpredictable.

According to a Deloitte study in 2022, 80% of 200 US-based manufacturing executives have experienced the impact of at least one serious disruption to their supply chain in the past 12-18 months. Ninety per cent of them agreed that the frequency of disruptive events has greatly increased in the last 10 years, suggesting that companies need to adapt to the new normal.

In response there has been a flurry of investments by companies to future proof their supply chains. Next Gen production scheduling has been identified as one of the key capabilities to improve the responsiveness to these unpredictable disruptions.

Scheduling challenges

The key challenge for companies is that these disruptions are becoming increasingly difficult or even impossible to predict. The challenge has been exacerbated by the sheer number of disruptions and the pace at which they occur, on a daily basis. In other words, it has become ‘humanly’ impossible to identify and make the best production scheduling decisions. With the traditional production scheduling capability companies have used manual efforts to identify disruptions, longer lead times to collate data from disparate systems and analysing associated impacts/scenarios, in order to rapidly determine the best production schedule. Often these actions are performed too late and miss the opportunity to execute the optimal production schedule. This has led to a deterioration in customer service levels and/or increase in cost to maintain the same service level.

Next Gen production scheduling

According to a recent Deloitte article in 2022 (The case for supply chain agility), one of the main options to improve supply chain responsiveness (as a proxy for agility) is by implementing the Next Gen production scheduling system, making it dynamic and intelligent.

The objective of traditional production scheduling has been to determine the best schedule considering the probability of daily disruptions within the supply chain. By contrast, Next Gen production scheduling, powered by optimisation models and digital twin of process, analyses and identifies potential solutions by predicting and considering upcoming disruptive events. Planners cannot be omniscient. Therefore, companies need a system to prepare a defence against unforeseen events, even during non-working hours. Each new plan needs to be characterised by a specific set of operational (e.g., order fulfilment %, changeover time), economic (e.g., costs) and sustainability (e.g., Scope 3 emissions) KPIs that can be easily assessed to know if they meet the desired business outcome.

What is involved?

A study conducted by Deloitte with the U.S. Manufacturers Alliance for Productivity and Innovation – MAPI study – shows that 23% of respondents have implemented factory synchronisation and dynamic scheduling to improve their responsiveness since the outbreak of the Covid-19 pandemic.

The benefits have been great. Companies that have made the investment have seen a 95% increase in on-time deliveries and the increased responsiveness they have obtained has enabled them to manage unforeseen events, such as border closures and plant shutdowns, thereby reducing extraordinary costs. In addition, they have achieved a major increase in asset efficiency through prevention of stockouts and targeted equipment maintenance.

Next Gen production scheduling is a tangible solution, improving both the top and bottom lines of a company’s business performance.

Leveraging information close to ‘reality’

Next Gen production scheduling leverages the exchange of information seamlessly between execution and planning and allows your organisation to make rapid decisions.

The framework below is a representation of what a good Next Gen production scheduling process looks like:

1. It starts with the inputting of data from different organisational areas: maintenance plans, real-time machine conditions, demand and supply information. If unexpected changes occur (e.g., customer order changes, machine breakdown), they are immediately reflected in the data model, so that it is possible to adapt the plans promptly. In particular, the triggers considered are external and internal variables (e.g., labour strike) that alert the system to the need to change the schedules.
2. The optimisation model runs different scenarios according to the current situation (inputs, parameters, constraints) and the defined objective functions (e.g., maximise resource usage, minimise changeover time).
3. The schedules are then simulated on the digital twin which is seamlessly connected to the optimisation model, where the whole process is tested, and the machines’ behaviour and health status are analysed. In this way, the robustness of schedules is verified, helping to prevent unplanned shutdowns and defective products as a result of a degraded machine status.
4. Then the information from the simulation is fed back to the optimisation model so that the best schedule can be identified and suggested to production planners.
5. Finally, planners review the results and analyse the KPIs to approve or modify the optimal schedule, considering the impacts on the plant and the overall supply chain.

Key underlying capabilities to enable Next Gen scheduling

To enable Next Gen Scheduling you should assess if the right capabilities are in place by answering the following questions: are planners using outdated or irrelevant data?; does the system implemented provide the required functionalities?; is your company relying on outdated manual techniques to determine the optimal plans?

Prepare, visualise, optimise, be future-proof and scale

To leverage techniques such as plant visualisation, advanced analytics and simulation, together with dynamic master data maintenance, companies need to follow specific steps and guiding principles.

Preparing the foundation involves acquiring, processing and modelling the relevant data to synchronise the digital twin. There are two types of data needed to create your Next Gen characteristics (on top of transactional and master data):

1. Machine data is collected through IoT sensors, processed and distributed more efficiently through the cloud and edge computing. It is critical to obtain inputs from subject matter experts to:
   1. analyse the most critical machinery
   2. understand which mechanism/movement to analyse
   3. interpret machine data (e.g., mechanical, and electrical machines have different degradation processes)
2. The data related to the plant layout is collected by scanning and mapping the site, and then through 3D modelling the plant is digitalised. By allocating machine data in the plant representation, the digital twin is active and synchronised real-time with the physical plant. With this, the business user can visualise the entire plant production to a high level of granularity and follow the alerts by drilling down to production lines to detect anomalies and bottlenecks. This means time is not wasted investigating potential root causes.

When it comes to dealing with disruptions to the production schedule, optimisation is needed to identify robust scenarios that meet the business objectives and mitigate unexpected constraints. It is fundamental to identify the most suitable model and settings based on the company’s characteristics and business needs (e.g., financial targets, sustainability policies) and calibrate its level of sophistication, i.e., the time required to provide solutions, based on the responsiveness required.

Once the optimisation model delivers schedules aligned with the company’s priorities, it is time to empower it to deal proactively with the impacts of disruptions by creating a significant degree of automation and intelligence. To this end, the optimisation model will constantly predict the occurrence of disruptive events and, if triggered, reschedule autonomously, preventing planners from being caught off guard and with outdated schedules. To accomplish this, it is essential to identify which events actually cause deviations and constantly monitor and analyse the relevant data points, such as supply delays and absenteeism.

Future-proof your design by strategically planning the system architecture for the production process, considering your aspirations for the end-state. It is critical from the very beginning for companies to envision the eventual goal and design their Next Gen production scheduling capability in order to decide on the details of the future system architecture and integration design (e.g., information flow and data model, etc.).

This makes possible an efficient, seamless transition from design to deployment and ensures that future business needs will be met by IT functionalities and a redesign will not be needed.

To finally unlock the potential of the Next Gen production scheduling and increase drastically the level of visibility and collaboration within the entire organisation, multiple country plants should be integrated for cross-supply visibility. This allows for greater scalability of the network.

How to get there at glance

Key takeaways for your Next Gen journey