{"id":4763,"date":"2026-09-14T16:29:57","date_gmt":"2026-09-14T14:29:57","guid":{"rendered":"https:\/\/www.makeitalia.com\/supply-chain-process-automation-where-to-start\/"},"modified":"2026-10-01T17:10:37","modified_gmt":"2026-10-01T15:10:37","slug":"supply-chain-process-automation-where-to-start","status":"publish","type":"post","link":"https:\/\/www.makeitalia.com\/en\/supply-chain-process-automation-where-to-start\/","title":{"rendered":"Supply Chain Process Automation: Where to Start"},"content":{"rendered":"\n

Automating Supply Chain processes does not mean starting immediately with software, an algorithm, or an artificial intelligence project. The first step is more concrete: understanding which process<\/strong>, among those currently managed with manual activities, Excel files, emails, ERPs, and external portals, can generate a measurable benefit if automated<\/strong>. <\/p>\n\n

Many companies reach this point with a seemingly simple question: where to start? The answer depends not only on the available technology but also on process maturity, data quality, stability of operating rules, and clarity of internal responsibilities. Automating a poorly governed process risks making an already fragile workflow faster without resolving the root causes of errors, delays, or rework. <\/p>\n\n

An effective project therefore starts with a selective choice. Not everything needs to be automated immediately. The first process to automate should be repetitive enough to justify the intervention, critical enough to produce a visible impact, and stable enough not to require continuous manual exceptions. <\/p>\n\n

Before Automating, Understand if the Process Is Ready<\/h2>\n\n

The starting point is not the question \u201cwhat technology can we adopt?\u201d but \u201cwhat operational problem do we want to reduce?<\/strong>\u201d In a complex Supply Chain, manual activities are not all the same. Some consume time because they are repetitive and standardizable, while others still require interpretation, cross-functional comparison, or decisions related to frequent exceptions. <\/p>\n\n

A process is a good candidate for automation when it follows sufficiently clear rules<\/strong>, uses available and reliable data<\/strong>, repeats with significant frequency<\/strong>, and produces measurable effects<\/strong> on times, errors, backlog, or decision quality. If, however, the process changes continuously, has no defined owners, or relies on incomplete data, automation may be premature. <\/p>\n\n

In these cases, before automating, it is often necessary to work on digitalization, standardization, or revision of the operational flow. Digitalizing means making data and information available in a structured way. Automating means reducing or eliminating recurring manual activities. Introducing AI, in some cases, means supporting analyses, forecasts, or decisions with more advanced models. Confusing these levels leads to projects that are too broad, difficult to adopt, and complicated to measure. <\/p>\n\n

Automation, Digitalization, and AI Are Not the Same Thing<\/h3>\n\n

Digitalization creates the conditions for information to be collected, updated, and accessed in a more organized way. Automation intervenes in repetitive operational steps, reducing manual activities such as data entry, checks, notifications, data transfers, or updates between systems. Artificial intelligence can come into play when the process requires predictive capabilities, classifications, suggestions, or scenario analyses. <\/p>\n\n

In the Supply Chain, this distinction is essential. A company may need to automate a document flow between purchasing and suppliers without introducing AI. Or it may need to improve the quality of warehouse data before automating planning activities. In other cases, AI may only be useful after rules, historical data, and KPIs have been consolidated. <\/p>\n\n

The most common risk is starting at the last level, i.e., with the most visible technology, without having verified if the underlying process is ready. An automation project works when technology, process, and people move forward together<\/strong>.<\/p>\n\n

When a Manual Process Is Not Yet Automatable<\/h3>\n\n

Not all manual processes are automatically ready for automation. An activity can be arduous, repetitive, and perceived as inefficient, but not necessarily mature for technological intervention. <\/p>\n\n

A sign of low maturity is the continuous presence of uncodified exceptions. If each case is handled differently, if rules change based on the person involved, or if information comes from uncontrolled sources, automation risks amplifying complexity. The same happens when data is unreliable: automating a flow based on incomplete master data, outdated lead times, or inconsistent stock can generate faster decisions, but not necessarily better ones. <\/p>\n\n

Before proceeding, it is therefore useful to distinguish between processes to automate immediately<\/strong>, processes to standardize, and processes to redesign<\/strong>. This distinction reduces the risk of technological investments that are not adopted by users or are unable to produce measurable benefits. <\/p>\n\n

How to Map Supply Chain Processes Before Choosing the Pilot<\/h2>\n\n

The AS-IS mapping<\/strong> is the step that transforms a generic perception of inefficiency into a concrete basis for decision-making. It is not about bureaucratically documenting every detail, but about reconstructing how the process works today<\/strong>: who does what, with what tools, what data is used, where delays, errors, or rework occur. <\/p>\n\n

In many companies, critical points emerge in the transitions between systems and functions. Data is exported from the ERP, reprocessed in Excel, sent via email, compared with a supplier portal, and then manually re-entered into another system. Each step adds time, potential for error, and dependence on individuals. <\/p>\n\n

Mapping also serves to distinguish truly decision-making work from administrative or repetitive work. A planner, buyer, or logistics manager should dedicate time to managing priorities, exceptions, and critical scenarios, not to manual data transfer or repeated verification of already available information. <\/p>\n\n

Where to Look for Repetitive Activities, Errors, and Low-Value Steps<\/h3>\n\n

The first candidates for automation are often found in activities that connect planning, purchasing, logistics, and operational control. They are not always the most obvious or \u201ctechnological\u201d processes; often, they are daily, fragmented, and underestimated steps. <\/p>\n\n

This can involve the manual updating of progress reports<\/strong>, the collection of confirmations from suppliers<\/strong>, the checking of discrepancies between orders and deliveries<\/strong>, the generation of alerts for delays<\/strong>, the reconciliation of warehouse data and planning<\/strong>, or the preparation of operational dashboards<\/strong>. These are activities that, if performed manually, consume time and slow down the information cycle. <\/p>\n\n

The question to ask is not just how much time a single activity requires, but how many times it is repeated, how many people it involves, and what decisions it slows down. An activity of a few minutes, if repeated many times a week by different roles, can hide a significant operational cost<\/strong> and a relevant impact on information quality<\/strong>.<\/p>\n\n

The Role of Data, Operating Rules, and Ownership<\/h3>\n\n

An automatable process needs three basic conditions: available data<\/strong>, clear operating rules<\/strong>, and defined responsibilities<\/strong>. If one of these elements is missing, the project first requires preparatory work. <\/p>\n\n

Data must be accessible, updated, and consistent across sources. Operating rules must be sufficiently explicit: when to generate an alert, when to block a flow, when to request validation, or when to update information. Ownership must be clear, because every automation changes how people work and make decisions. <\/p>\n\n

Without ownership, even a good technical solution can remain unused. If no one governs the rules, updates parameters, interprets KPIs, or manages exceptions, automation becomes an object separate from the real process<\/strong>.<\/p>\n\n

Which Process to Automate First: Impact and Feasibility Criteria<\/h2>\n\n

The choice of the first process to automate should arise from the intersection of impact and feasibility<\/strong>. Impact measures how much the process affects time, costs, data quality, service, or operational load. Feasibility measures how stable, standardized, integrable, and acceptable the process is for the users involved. <\/p>\n\n

A high-impact but very complex process may be unsuitable for a first pilot, as it requires too many preliminary interventions. Conversely, a very simple but low-impact process may be useful as a technical test, but risks not generating sufficient internal attention. The balance point is found in processes where the benefit is visible and the complexity is manageable<\/strong>. <\/p>\n\n

To evaluate priorities, it is useful to observe several criteria jointly: activity frequency, time absorbed, number of errors or reworks, quality of available data, clarity of operating rules, number of systems involved, impact on decisions, and user willingness to change their way of working. This approach helps avoid choices based solely on immediate pressure or technology availability. <\/p>\n\n

Processes Most Suitable for a First Automation Project<\/h3>\n\n

A first pilot should involve a circumscribed, measurable process that users recognize as a source of inefficiency. In the Supply Chain, typical examples can be the automation of checks on operational data<\/strong>, the sending of notifications for delays or anomalies<\/strong>, the structured collection of information from suppliers<\/strong>, the automatic updating of dashboards<\/strong>, or the management of repetitive document flows<\/strong>. <\/p>\n\n

Some planning activities can also be good candidates, provided they are well-defined. For example, an automatic check on discrepancies between demand, availability, and capacity can help the planner focus on the most relevant exceptions. The goal is not to replace the decision, but to reduce the manual work required to reach the decision<\/strong>. <\/p>\n\n

The same applies to purchasing and logistics. Automating the collection of confirmations, reporting delays, verifying deadlines, or producing recurring reports can free up operational time and improve the timeliness of information. The benefit becomes more solid when the automated process is linked to clear and shared KPIs. <\/p>\n\n

Processes to Postpone When Data and Rules Are Not Stable<\/h3>\n\n

Some processes should be postponed, at least as a first intervention. These are processes where rules are not shared, data is incomplete, exceptions outweigh standard cases, or the organizational impact is too broad compared to internal maturity. <\/p>\n\n

Postponing does not mean giving up. It means preparing the process before automating it. In some cases, the correct work involves cleaning master data, updating parameters, defining responsibilities, reducing unnecessary variations, or clarifying decision criteria. Only after this step can automation produce a stable improvement. <\/p>\n\n

This caution is particularly important in projects linked to AI & Automation<\/strong>. Artificial intelligence can be a powerful enabler, but it requires sufficiently robust data, objectives, and processes. Without these conditions, the risk is introducing technological complexity into a context that would first need operational order. <\/p>\n\n

KPIs to Measure a Supply Chain Automation Project<\/h2>\n\n

An automation project must be measurable even before it is implemented<\/strong>. Defining KPIs retrospectively makes it difficult to distinguish a real improvement from an initial positive perception. For this reason, it is useful to establish a baseline: how the process works today, how much time it consumes, what errors it produces, how much rework it generates, and how much it affects decision speed. <\/p>\n\n

KPIs<\/strong> can relate to efficiency<\/strong>, data quality<\/strong>, internal or external service<\/strong>, and user adoption<\/strong>. Cycle time measures how long an activity takes from start to finish. The number of errors or reworks indicates the quality of the flow. Backlog shows whether automation reduces overdue activities. Man-hours saved help understand how much operational work is freed up. Data accuracy and information lead time, on the other hand, measure how much the process produces more timely and reliable information. <\/p>\n\n

However, abstract promises or undocumented numbers must be avoided. Each company starts from different conditions: systems, data, processes, skills, and volumes influence the results. The value of the pilot lies precisely in the ability to measure, within a controlled scope, what can be scaled more broadly. <\/p>\n\n

Indicators Before and After the Pilot<\/h3>\n\n

Before the pilot, KPIs describe the starting situation. After the pilot, they allow evaluation of whether automation has reduced real friction. Measurement should cover both the operational result and the quality of the user experience<\/strong>. <\/p>\n\n

If a flow becomes faster but requires continuous manual correction, the benefit is partial. If it reduces data update time but is not used by teams, the problem is not just technical: it concerns process design, training, or trust in the information produced. <\/p>\n\n

For this reason, alongside quantitative KPIs, it is useful to observe user behavior. How many exceptions are managed outside the system? How often do users revert to using Excel as a parallel tool? How many decisions continue to depend on informal email exchanges? These signals help understand whether automation has truly integrated into the process or if it has remained an additional layer. <\/p>\n\n

Why Measuring Adoption Is as Important as Measuring Efficiency<\/h3>\n\n

The success of an automation project does not depend solely on the flow functioning technically. It depends on the organization’s ability to use it stably. Unadopted automation creates a dual track: on one side, the new tool, on the other, old operational habits. <\/p>\n\n

Measuring adoption means verifying whether people trust the new process, whether data is considered reliable, whether rules are understood, and whether exceptions are managed as intended. When these elements are not addressed, the project may appear correct on paper but produce little value in practice. <\/p>\n\n

Training, therefore, plays an operational, not an accessory, role. It serves not only to explain how to use a tool but also to clarify why the process changes, what decisions are supported, and what responsibilities remain with individuals. <\/p>\n\n

From Pilot Process to Scalability<\/h2>\n\n

The pilot should not be conceived as an isolated experiment, but as the first step in a scalable journey. For this reason, it must be circumscribed enough to be manageable, yet significant enough to generate useful learning for other processes. <\/p>\n\n

After the pilot, the company should ask what it has learned about its way of working. Which data proved reliable? Which rules required corrections? Which users adopted the new flow more easily? Which integrations with existing systems proved necessary? These answers help build a more robust roadmap. <\/p>\n\n

Scaling does not mean mechanically replicating the same solution in other areas. It means applying the same method<\/strong>: process selection, readiness verification, KPI definition, pilot design, measurement, training, and progressive extension. In this way, automation becomes an organizational capability<\/strong>, not a sum of disconnected initiatives. <\/p>\n\n

How to Avoid Isolated or Unadopted Automations<\/h3>\n\n

Isolated automations often arise from local needs, resolved quickly but without a process vision. They can generate immediate benefits, but over time they risk creating new fragmentation: unintegrated tools, different logic between functions, duplicated data, and unclear responsibilities. <\/p>\n\n

To avoid this, every project should be linked to an overall Supply Chain process map and data governance. Even a small intervention must be consistent with how the company wants to manage information, decisions, and operational flows<\/strong>.<\/p>\n\n

Scalability also requires clear communication with users. If automation is perceived as a technical imposition, resistance grows. If, however, it is presented as a way to reduce repetitive activities, improve information quality, and make operational priorities more visible, adoption becomes more natural. <\/p>\n\n

When to Involve Consulting, System Integration, and AI & Automation<\/h3>\n\n

The involvement of external expertise<\/strong> can be useful when the company has already identified a need but lacks the internal method, time, or skills to transform it into a structured project. A Supply Chain process assessment can help interpret the AS-IS situation<\/strong>, identify candidate processes<\/strong>, and build a priority matrix based on impact and feasibility<\/strong>.<\/p>\n\n

Process consulting is particularly useful when the problem is not just technological but concerns operating rules, responsibilities, data, and decision-making methods. System integration becomes central when flows cross ERPs, portals, planning tools, databases, or dashboards. AI & Automation solutions, on the other hand, come into play when the process is mature enough to benefit from more advanced automations, predictive analytics, or decision support. <\/p>\n\n

The point is not to add technology where there is no order, but to build the conditions for technology to produce value<\/strong>. A well-designed project starts with a clear scope, measures results, and prepares the organization to progressively extend automation to other processes. <\/p>\n\n

FAQ<\/h2>\n\n

Which Supply Chain Process Is Best to Start Automating?<\/h3>\n\n

It is best to start with a repetitive, measurable, and high-impact operational process that still has manageable complexity. A good candidate is a flow where people currently spend a lot of time on manual activities, checks, updates, or data transfers. Furthermore, it should be a flow where sufficiently clear rules exist to define what should happen. The choice should not depend solely on perceived criticality, but on the intersection of expected benefit, data quality, process stability, and users’ willingness to adopt the new way of working. <\/p>\n\n

What KPIs Should Be Used to Evaluate Supply Chain Process Automation?<\/h3>\n\n

The most useful KPIs depend on the process, but generally include cycle time, errors, reworks, backlog, man-hours absorbed, data accuracy, information lead time, and user adoption level. Measurement must begin before the pilot to compare the initial situation with subsequent results. It is important not to limit oneself to efficiency: the quality of information and the actual adoption of the automated process are also decisive indicators. <\/p>\n\n

Is It Better to Choose Software First or Analyze Processes?<\/h3>\n\n

In most cases, it is preferable to analyze processes first. Software selection should come after clarifying which activities to automate, what data is needed, what rules guide the process, and what KPIs will measure the result. Choosing a tool first can lead to forcing the process into an unsuitable solution or introducing functionalities that users will not utilize. AS-IS analysis and defining the pilot’s scope reduce this risk. <\/p>\n\n

Does Supply Chain Automation Always Require Artificial Intelligence?<\/h3>\n\n

No, Supply Chain automation does not always require artificial intelligence. Many benefits can arise from workflow automations, system integrations, operational dashboards, automatic alerts, or more structured data management. AI becomes relevant when the process requires more advanced analyses, predictive support, classifications, or decision-making suggestions. Before introducing it, it is useful to verify that data, rules, and objectives are sufficiently robust. <\/p>\n\n

Concrete Support for Automation Projects<\/h2>\n\n

Identifying the right starting point is often the most complex part of an automation journey. For this reason, many companies choose to partner with experts<\/a> who understand both the processes and the technologies that support the Supply Chain. <\/p>\n\n

At Makeitalia, we support companies in process analysis, defining intervention priorities, designing pilots, and evaluating the most suitable automation solutions for the company context<\/strong>.<\/p>\n\n

If you are considering how to automate certain processes in your Supply Chain or want to discuss a specific need, you can contact us through the Contact<\/a> page: we will be happy to delve into your objectives and evaluate improvement opportunities together.<\/p>\n","protected":false},"excerpt":{"rendered":"

Supply Chain Automation: Processes, KPIs, and Pilot.<\/p>\n","protected":false},"author":5,"featured_media":4764,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-4763","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-supply-chain-articles"],"acf":[],"yoast_head":"\nSupply Chain Process Automation: Where to Start | Makeitalia<\/title>\n<meta name=\"description\" content=\"How to choose the first Supply Chain process to automate: AS-IS map, priority criteria, pilot, KPIs, and waste-free scalability.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.makeitalia.com\/en\/supply-chain-process-automation-where-to-start\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta 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