More questions about organizational design, capability building, and how to take your organization and employees along the transformation journey? Reach out to our expert Bart Paridaen!

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Turn your challenge into an opportunity for future success. Our experts recommend the following steps to accelerate supply chain digitalization and end-to-end transformation of your supply chain:

Start supply chain digitalization from a solid planning foundation

To realize robust end-to-end transformations it is key to start from a solid supply chain foundation. Start by developing an optimal planning model that translates strategy into operational planning principles. Understand the value drivers in your supply chain and define well connected planning processes and a matching organizational setup to facilitate high value planning decisions. Your process re-design needs to be focused on E2E connections, linking the different layers of planning and control of your supply chain through smart alerts.

Focus on high-quality data and planning parameter automation

A holistic planning model and integrated planning process require robust and well-integrated data structures to drive execution. A well-defined data model and supportive governance processes across your IT landscape are vital to secure setup, maintenance, and right-sizing of your planning parameters. It all starts with having a complete, accurate, and well-aligned master data fundament. Building on this solid baseline you can have the data work for you to build a process for smart-touch automation of your critical planning parameters. As your supply chain develops you can real time update your vital parameters to improve overall planning accuracy.

The full value of advanced planning solutions

A new generation of planning systems promise embedded cognitive automation to make real-time recommendations, predict outcomes, and take timely supply chain decisions. It is crucial that your functional design supports the E2E planning process, connects well to your enterprise-wide data model and allows progressive digital innovations (such as AI and advanced analytics). To realize the full value of your APS (advanced planning and scheduling) system you need to build the right in-house capabilities. Your planners require the right data processing, analytical, and data science skills to operationalize and embed digitalization into the day-to-day planning routine.

Building a digital ecosystem

If you are ready for the next step, look beyond traditional planning systems and investigate how systems of innovation can help to boost planning capabilities in a digital ecosystem. Data science platforms like Dataiku can bring advanced analytics capabilities in-house quickly and can be plugged into the systems that are already in place, like ERPs (enterprise resource planning), APSs, or data lakes. These systems of innovation allow data scientists to make the most out of their expertise, allow for easy collaboration between them and the business, and enable you to go from prototype to production quickly but robustly. Building an ecosystem of digital capabilities will help your organization to accelerate the digital transformation through the implementation of focused innovations.

We are here to support you in your supply chain digitalization journey!

Are you looking for ways to accelerate the digitalization of your E2E supply chain planning process and are you in need of the right expertise and hands-on support to make it happen? We are here to help you in this journey. Together we transform, automate, and optimize. Learn more about E2E transformations or contact us now!

This is the time to build resilient, agile, and sustainable supply chains, maximizing the benefits of digitalization and advanced analytics.

The post Accelerate the digitalization of your supply chain appeared first on EyeOn.

This is the time to build resilient, agile, and sustainable supply chains, maximizing the benefits of the digitalization and advanced analytics.  

Our experts have identified five key trends for 2022.

Turn challenges into opportunities by clicking through our 5 key trends:

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Companies need to advance data collection by building a digital twin, implement new tools that allow for more advanced analytics, prepare your organisation and build a data driven culture.

Undeniably, analytics is changing forecasting and supply planning processes – but quite some water has to pass under the bridge before companies will get to full no-touch planning. Start with developing a vision, select a business process to work on and take it from there by running projects to explore benefits and get acquainted with data science tools that go beyond the existing planning tools, build capabilities, KPIs and data.

If you want to know more about supply chain planning and forecasting in the digital age, read our white paper! Or get in contact with Freek Aertsen. 

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Our experts have identified three key steps for a successful supply chain data science journey:

Supply chain data science – step 1: Data collection

The right dataset is not only trustworthy and relevant to the question but also timely, accurate, clean, and unbiased. Here are some important considerations when collecting data:

1. First, decide what details you want from the data. You’ll need to choose which topics the information will cover and which questions will be answered – who do you want to collect it from and how much data you need? Your goals – what you hope to accomplish using your data – will determine your answers to these questions.
2. In the early stages of your planning process, you should establish a time frame for your data collection and a schedule for when you’ll start and end your data collection.
3. You should base the choice of data collection method on the type of information you want to collect, the time frame over which you’ll obtain it, and the other aspects you determine.
4. Once your plan is finalized, start collecting data, and be sure to stick to your plan and check on its progress regularly. You may want to make updates to your plan as conditions change and you get new information.

Supply chain data science – step 2: Data cleaning

This step is vital to ensure that the answers you generate are accurate. When collecting data from several streams and with manual input from users, information can carry mistakes, be incorrectly inputted, or have gaps. Data cleaning is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information.

Supply chain data science – step 3: Data integration

This step refers to the technical and business processes used to combine data from multiple sources such as web data, social media, machine-generated data, and data from the internet of things (IoT), into a single framework to provide a unified, single view of the data. Remember, it’s one thing to have access to lots of data, it’s another to use it. Data is usable when it is accessible, in other words:

1. Joinable: Data must be in a form that can be joined to other enterprise data when necessary.
2. Shareable: You need a data-sharing culture within the organization so that data can be joined, such as combining customers’ clickstream with their transactional history.
3. Query-able: There must be appropriate tools to query, slice and dice the data. All reporting and analysis requires filtering, grouping, and aggregating data to reduce the large amounts of raw data into a smaller set of higher-level numbers. This helps our brains comprehend what is happening in a business. Retailers need to be able to see trends or understand differences among customer segments. Analysts require tools that allow them to compute those metrics relatively easily.

When are you going to start your supply chain data science journey and make data-driven decisions? Find our how we can support you!

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Read here the article (in Dutch)

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Transition to the data driven supply chain

For most larger companies, technology has greatly improved availability of data. This data holds the potential of better, faster, and more efficient decision making. Turning the available data into better insights for decision making is not straightforward and requires a completely different mindset and capabilities than continuous improvements within a more traditional planning process and toolkit.

A key driver for success is the centralization of analytical skills in a team where subject-matter experts, data ninjas, project managers, and operational specialist work together. The tools these teams use, should support continuous innovation, collaboration, and easy integration with data sources. These tools should have an open architecture and have the ability to tap into the latest open source technologies.

How digitalization will change your company

More companies want to go to a ‘no-touch S&OP process’. Within forecasting we already see a level of autonomy. But is this autonomy possible in every part of the planning process and do people want to have an autonomous decision making process? Moving towards no-touch S&OP requires actions in different fields. You need a number of building blocks to get there, such as the organizational readiness and excellent data.

Innovative data science applications in forecasting, inventory, and supply management

We saw some real case examples of how data science has made its entrance to the domain of forecasting and planning in the following topics:

• Forecast value add
• Supply chain optimization using apps
• Production wheel

Key insights from the day

1. Start collecting and storing data as of tomorrow
2. Build strong analytical skills – often centrally organized
3. Do not make analytics a stand-alone exercise – embed in process
4. Develop fact-based collaboration & communication – planner as orchestrator

Slides of presentations

Below you can find some of the materials that were shared during the day:

• Intro – Building the data driven supply chain by Luc van Wouwe
• Transition to the data driven supply chain my MIchiel Jansen
• Supply chain optimization using apps by Francesco Russo
• Production Wheel by Arthur Nazarian 
• Forecast Value Add by Koen Koolen
• No Touch S&OP by Freek Aertsen

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