The rapid evolution of advanced digital tools and data science techniques such as machine learning and digital twins have become a powerful means to driving planning parameter insights, predictions, and accuracy improvements. However, in practice, the typical tendency of a planner is to apply additional information or assumptions, not known to an algorithm, to come to a final implementation.

Smart automation of planning parameters – the best of both worlds

At EyeOn we believe that by creating smart-touch planning solutions you can combine the best of both worlds. We can automate the planning parameter review process for elements that are easy to predict, provide recommendations for the more challenging ones, and flag where human intervention is needed. We call this smart (planning parameter) automation.

As shown in the figure below smart automation means that:

• Your planners obtain a planning parameter alert/recommendation from a control tower solution
• They receive feedback on their enrichment behavior while interacting with the planning tool
• The algorithm picks up on the behavior trends to drive better proposals and facilitate integral process automation over time

The smart-touch connectivity between the system and planner results in the optimal and most accepted parameter setting.

How to get started

• Create awareness: The use of advanced data science techniques and digital tools requires that you understand the ins and outs and the dynamics of your process. Create awareness in the organization that smart-touch combines the best of both worlds. No planner likes to hear that the system will take over.
• Alert driven: An important starting point is to create full visibility and enable an alert-based way of working. A control tower solution will enable users to get into a flow of parameter review and focus their time where it adds value.
• Assess previously performed adjustments: Understand why planners deviate from parameter proposals. It will help to identify process exceptions, outliers, and human intelligence. Make that an integral part of your process and digital infrastructure to develop even better algorithms.
• Activate planners in providing effective enrichments: It is key to find a good balance in planner behavior. To ensure awareness, you must get the organization onboard to play an active role in executing the parameter review process. In addition, it is essential that there is organizational acceptance and support for applying a segmented approach.
• Automate predictable changes where possible: Apply automation – first in a defined scope – and learn from it before scaling too quickly. There inevitably will be flows, maybe even a considerable amount, where full automation is not the right fit. However, where possible, it will help drive efficiency and improve planner value add.

Want to know more on driving planning parameter insights?

Is your company experiencing significant deviations between plan and execution? Are you under the impression that you are keeping inefficient stock levels (either over or under-stocking)? Your planning parameters probably need review, and we can help you do this in an efficient, controlled, and automated way.

If you are interested in learning how to transform a tedious review that never happens, to a meaningful continuous automated process, please watch our on-demand webinar, or reach out to us directly!

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Business challenges in supply chain planning

Looking at the state of business and current capability levels in the area of planning parameter management, we clearly see that companies with a competitive edge are able to overcome the following business challenges:

• The lack of data quality awareness: Even though master data management is on a rise we still see that companies poorly maintain master data and constantly postpone its assessment. As a result, there is no visibility and awareness of underlying master data quality and the impact it has on overall planning performance.
• The lack of ownership: Planning parameters are not a simple siloed matter; value creation lies in connecting the different functional contributors to achieve cross-functional alignment regarding a representative planning parameter value. To govern this, it is key to have a clear governance and ownership structure in place.
• The lack of a standardized review process: Defining a parameter at initial creation is one thing. At the same time, it is essential to have a well-structured and standardized review process in place. Ensuring that the supply chain planning parameters used day-to-day reflect reality, are robust, and well aligned.
• The lack of automation: The biggest struggle in planning parameter management is that it requires time and manual effort invested to align different data sources, calculate review, approve, and enter master data into different planning systems.

Impact on bottom-line performance

What is the impact when a transportation lead time between two production locations is actually 4 days instead of the 7 days currently registered in your ERP system? In this case, you are most likely holding excessive inventory. Goods will therefore stay in stock for a longer period than necessary. At our customers, we continuously come across real-life examples where unreliable supply chain planning parameters have a negative impact on bottom-line performance. Discrepancies between actual performance and maintained planning standards mean that projected plans and inventory projections are inaccurate, leading to supply chain impacts such as overstocks or underutilization of production assets.

How to build a solid data foundation

To overcome the key challenges in supply chain planning parameter management and to build a solid data foundation, we are strong advocates of taking a step-by-step approach while at the same time keeping the end goal in mind. So, foundational capabilities first, followed by smart automation. Now, what do we exactly mean by that? Our approach for building foundational capabilities consists of the following blocks:

• Focuses initially on defining clear business rules and parameter definitions. Ensuring that there is cross-functional alignment.
• Relies on automatically retrieving all necessary transactional and master data, consolidating it, and applying calculations based on predefined business rules.
• Entails meaningful dashboards enabling the user to easily derive insights into the data.
• Creates alerts to directly inform users about major discrepancies.
• Outlines the review process per parameter: who is responsible for doing what, by when, and how.
• Deals with change management. Operationalizing the process is what matters most.

Want to know more?

Is your company experiencing significant deviations between plan and execution? Are you under the impression that you are keeping inefficient stock levels (either over or under-stocking)? Your planning parameters probably need review.

If you are interested in learning how to transform a tedious review that never happens, to a meaningful continuous automated process, please watch our on-demand webinar, or reach out to us directly!

Next steps

Once you have a solid supply chain planning parameter framework in place, you can start thinking about smart automation to improve efficiency in the data workflows and eliminate human bias in the process. For more on smart automation please stay tuned for our next blog post. Follow us on LinkedIn so you don’t miss it.

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Recent supply chain disruptions like the pandemic or the container ship blocking the Suez Canal showed the inability of many businesses to rapidly react to unforeseen changes in demand or supply. Today’s supply chains are global and complex. The current war with Ukraine has shown yet again how easily they are disrupted. The question is not if more disruptions are to be expected in the future, rather are you prepared for them?

Here is our checklist with the key steps to increase supply chain resilience:

Keep your supply chain in sync with the strategy

• Understand your value chain: It is very important that you truly understand your value chain and define a business strategy that matches your supply chain capabilities. A detailed supply chain network model and a target operating model will help you in achieving this.
• Keep track: Supply chains evolve organically by a sequence of decisions. Keeping the optimal track requires a periodical assessment of your performance.
• Think beyond cost cutting: Sustainability and global supply chain disruptions are there to stay. In the long term, cost-cutting leaves your supply chain vulnerable and inevitably leads to more costs. A resilient supply chain will mitigate these risks.

Embed supply chain resilience into strategic development

• Test strategies: Develop a digital model of your supply chain. A so-called ‘digital twin’ provides you the ‘sandbox’ to test any strategy to detect an unexpected impact or seize opportunities when they arise.
• Assess risks: Traditional supply chain network modeling methods focus on the probability and impact of potential incidents. The EyeOn approach measures risk exposure and supply chain resilience by understanding time-to-survive (TTS) and time-to-recover (TTR) aspects. Our approach allows you to assess the impact of risks regardless of their cause or probability.

Create a data-driven culture

• Create a data-driven culture: A data-driven culture is an essential part of your digitalization journey.
• Take advantage of data: Data is the new gold. Use it to make better decisions on all levels.
• Avoid analysis paralysis: Accept uncertainty – the reality will be different.
• Use a fit-for-purpose level of modelling; when details matter: cascade down in the planning hierarchy.

Let’s increase supply chain resilience together!

We are happy to discuss your company’s supply chain risk management one-on-one. Please feel free to contact us! EyeOn can support you in building a more resilient supply chain, explore our website!

The post A checklist for supply chain resilience appeared first on EyeOn.

Turn your challenge into an opportunity for positive change! For example by using advanced analytics on supply chain data. Our experts recommend the following considerations to get the most out of your data:

Where can advanced analytics in supply chain have the biggest impact?

The key opportunity that you can unlock by using your data better? Reducing latency. Quicker data collection, better dashboards, and suitable tools help you get the right information on the right person’s desk at the right time – dramatically reducing the delay from event to decision, and thereby dramatically increasing the impact of your decisions. Create a first project where you demonstrate the impact of exploiting supply chain data using advanced analytics, and your data journey will be off to a flying start.

Implement and use specialized apps

Previously, companies’ data systems were composed of monolithic systems that executed rigid ERP and APS processes. These days, ever more companies see the benefits of using niche tools to solve specific challenges. Such tools can be implemented quickly: typically they are web-based and require minimal effort from IT. They provide virtual immediate return on investment and help your company go from ‘updated in the weekly batch run’ to ‘the live status’.

Add systems of innovation to your landscape

If you’re ready for the next step, look into systems of innovation. Data science platforms like Dataiku can bring advanced analytics capabilities in-house quickly. Such systems can be added to your software landscape, connecting to the systems that are already in place, like ERPs, APSs, or data lakes. These allow those hard-to-find 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.

Start a center of excellence

How can you make your digital transformation sustainable? How do we take a quick once-off improvement project and turn it into a sustained mindset change towards the digital age? In all industries, companies are starting ‘centers of excellence’. A center of excellence brings together a team of data scientists, data analysts, and business experts with the purpose of using their shared knowledge to accelerate innovation initiatives throughout the broader organization.

We are here for you!

EyeOn’s team specializes in taking your data and turning it into insights and recommendations. We can help you at every step of your data science journey: whether it is an assessment of forecasting or inventory management, forecasting a volatile portfolio using machine learning, or optimizing your inventory across all echelons of your supply chain. Explore our data science offering!

EyeOn Planning Services combines years of industry experience and know-how with cutting-edge data science techniques, analyzing your data and converting it into actionable insights, providing you with the tools you need to steer your business. Or ask for our help in kick-starting your center of excellence with one of our experienced data scientists. Learn more about Planning Services!

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

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Marco van Alfen, IBP expert: “I know examples of companies that have purchased very expensive software for forecasting, but in the end do not dare to trust what the system prescribes. It doesn’t happen by itself. If you want to do this well, you have to invest in data, people, systems and processes.”

Learn the four steps to create a data-driven culture in Alex van Groningen’s article (in Dutch) with Marco van Alfen, Sr. Business Consultant and IBP expert at EyeOn.

Read more about forecasting and planning!

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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. 

The post Getting digital, go human! appeared first on EyeOn.

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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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

The post Transition to the data driven supply chain appeared first on EyeOn.

Transition to the data driven supply chain

Technology has greatly improved the availability of data in large companies. This data hold the potential of better, faster and more efficient decision making. Data science is a crossroads between business and IT. It requires completely different capabilities of tools and a different mindset than is common in traditional planning processes.

A key driver for success is the central organization of analytical skills in a team where subject-matter experts, data ninjas, project managers and operational specialist work together. Tools used by these teams 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 to kick start a center of excellence?

Teams that facilitate the transition to digital supply chains are often organized in centers of excellence. They develop prototypes which are built, tested and operationalized, almost as a continuous process. As a consequence, payback periods for investments are greatly reduced and risks are much smaller than with traditional systems implementations. At the same time, speed of adaptation required from processes and peoples is big.

Blockchain in the automotive industry

Providing end-to-end supply chain visibility requires high-quality data. To prevent company data to be turned into a commodity, it is important to capture the relevant digital space. In the automotive industry, storing data about finished cars from manufacturer to dealer in a decentral and open blockchain adds an unprecedented level of traceability. Collaborative “co-opetition” with other parties such as government agencies and insurance providers opens up new use cases in the regulatory sphere and in fraud detection.

Innovative data science applications in forecasting, inventory and supply management

Data science has made its entrance to the domain of planning and forecasting. Four concrete company cases and examples illustrated how successful transition to a data driven supply chain can be achieved.

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:

• Building data driven supply chain
• Transition to data driven supply chain
• Blockchain in the automotive finished vehicle logistics by Jon Kuiper

The post Building the data driven supply chain appeared first on EyeOn.