An In-Depth Guide for Supply Chain Leaders
Logistics is the nervous system of the modern economy, moving trillions of dollars of goods around the world each year. Yet it remains frustratingly opaque and inefficient at times. Process bottlenecks, unclear root causes, and demand forecasting errors lead supply chain breakdowns that ripple across global markets.
Process mining provides a data-driven lens into these logistics black boxes, unlocking operational efficiency, cost savings, and customer experience gains.
In this 3000+ word guide, we‘ll explore the past, present, and future of process mining in logistics to answer:
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What are the top applications of process mining in logistics and supply chain management?
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What benefits can companies expect from implementing process mining for logistics?
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What are key challenges with logistics process mining and how can they be overcome?
Equipped with these insights, supply chain leaders can chart a course to leverage process mining on their journey towards operational excellence.
An Optimizer‘s Dream: Mining Value from Logistics Data
Logistics generates vast streams of operational data each day – purchase orders, shipping notices, inventory adjustments, transport routes, weather disruptions, social media signals, and more.
Yet much of this data sits in silos, spread across legacy platforms and formats. Supply chain leaders have hazy visibility into questions like:
- Where and why do process bottlenecks occur? How can we fix them?
- What root causes drive late deliveries? How can we prevent them?
- How accurate are our demand forecasts? Can we improve them?
Enter process mining – a fast-emerging discipline that extracts end-to-end process intelligence from these data silos.
First, process mining reconstructs logistics processes from the digital breadcrumbs they leave behind – effectively producing a live "X-ray" view of supply chains. Leaders gain clarity into how processes actually operate, warts and all.
Next, built-in analytics spotlight process bottlenecks, deviations, and performance variance across sites, products, suppliers, and other dimensions. Drilling down shows the root events driving inefficiencies like late deliveries or inventory shortfalls.
Armed with this intelligence, process owners can continuously improve logistics operations – tweaking routes, reallocating stock, smoothing hand-offs between functions and partners.
Over time, companies condense decades of performance gains into months and weeks versus the typical trial-and-error slog. It‘s an optimizer‘s dream.
Let‘s explore leading applications showing this process mining flywheel accelerating across logistics.
Top Process Mining Use Cases in Logistics
Process mining lends itself to diverse applications across the supply chain – from informational to transformational.
1. As-Is Process Mapping
The first application is "as-is" process discovery – gaining an objective view of current processes by mining event data.
Consider the real-world example below from pharmaceutical giant GSK vaccines:
This annotated process map of an order-to-cash process was generated automatically from GSK‘s IT systems using process mining algorithms.
It reveals delays between certain steps and overprocessing waste. For instance, nearly all orders flow through a manual order verification step that provides little value. Removing it would both reduce costs and speed up order delivery times.
This x-ray visibility allows rapid benchmarking across locations or business units to compare performance and highlight improvement opportunities.
Algorithm Comparison
When mining logistics processes, analysts can choose between directed, heuristic, inductive, evolutionary, or hybrid process discovery techniques. Each have pros and cons regarding completeness, precision, scalability and different data constraints.
For example, inductive mining handles "spaghetti" processes better but requires setting parameters. Directed discovery builds simplest models yet needs a case notion. Selecting optimal algorithms improves map fidelity.
2. Root Cause Analysis
Process mining helps pinpoint the root causes of operational issues like late deliveries by tracing back through event sequences to the original failure points.
For example, analysis might reveal that a spike in late West Coast deliveries originated from a series of overloaded outbound flights from a Midwest logistics hub during a blizzard week.
Drilling down further highlights the initial weather event as root cause – not warehouse staffing issues for instance. With inputs validated, leaders can develop pointed solutions – proactive capacity planning for weather disruptions in this case.
Such reliable root cause analysis removes guesswork around problems and prevents wasteful scattershot responses that gloss over actual origins.
Hybrid Analysis Methods
While process mining provides the most automated path to root cause discovery, combining other signals often enriches insights. For instance, marrying mining-detected delays with employee survey feedback or predictive weather data better explains certain distribution snags.
Analytics is a team sport – multiple approaches make the complete picture clear.
3. Predictive Logistics
Process mining enables stronger predictivelogistics capabilities such as demand forecasting, capacity planning, and dynamic inventory allocation.
By analyzing historical process data, algorithms spot telltale demand patterns and upstream supply signals. The systems then accurately forecast near-term needs – transport, warehousing, inventory – empowering proactive planning versus reactive scrambling when new orders arrive.
Correlating Signals
A furniture company links web sales clicks (planning signal) to warehouse withdrawals (execution signal) to strengthen demand predictions. Other predictive process models connect marketing activity to inquiry volume forecasts to production orders based on historical correlations.
Cross-referencing signals from multiple process logs using hybrid predictive mining techniques yields valuable causal inferences.
4. Transport Route Optimization
Analyzing delivery routes in granular detail allows logistics teams to optimize networks for speed, reliability and cost.
For instance, by comparing GPS tracking data, transport modes, costs and delivery times across all routes over 12 months, redundant trips become visible.
Say drivers consistently backtrack on certain suburban delivery circuits or routinely face congestion on specific highway segments during morning commutes.
Route optimization algorithms within process mining platforms rapidly identify ways to streamline routes saving time, mileage costs and driver wages. Some optimize massive networks like Fedex‘s global delivery routes.
5. Automation Opportunity Analysis
By analyzing resource-intensive processes, teams can identify the highest ROI automation opportunities – whether RPA bots, workflows, or embedded intelligence.
Order verification steps adding little value are prime RPA bot deployment targets to take over repetitive human approval clicks. Workflow automation then further shrinks process lead times.
Down the road, AI-enhanced process mining can even recommend specific process tweaks most likely to drive desired outcomes based on deep pattern analysis.
Quantifying the Benefits
The use cases above illustrate opportunities, but what outcomes can companies actually expect from process mining in logistics?
Let‘s examine some statistics and examples showcasing efficiency and cost optimization possible:
- 55% of logistics processes still manually executed [9]
- 18% faster delivery process completion [10]
- 30-50% typical process efficiency gains [11]
- 57% improvement in spare parts order lead time index [12]
- 40%+ potential supply chain cost savings [13]
These represent substantial efficiency and cost optimization possible from mining logistics processes. Now let‘s examine two real-world examples in detail:
Optimizing Order Accuracy
A large European retail group struggled with order accuracy issues, leading to substantial manual work to resolve problems. By mapping 400,000 order process cases in granular detail, their process mining analysis revealed [14]:
- 55% of orders had accuracy issues
- Just 3 root causes drove 75% of all inaccuracies
- Resolution required manual checking across 12+ systems
Equipped with these insights, they tackled root processes to dramatically boost order accuracy – reducing associated labor costs over 40% while accelerating delivery speeds.
Slashing Lead Times
An automation manufacturer sought to streamline worldwide spare parts distribution from factories to cut lead times. Their process mining work provided transparency: [15]
- 65% of orders had deviations causing delays
- No clear root cause analysis available
- Limited visibility into global process health
Addressing issues spotlighted by mining data across 12 months improved process lead time index by 57% over 8 months – showcasing the rapid optimization flywheel effect.
5 Benefits of Logistics Process Mining
Let‘s outline key quantitative and qualitative benefits supply chain leaders realize:
1. Granular Visibility – By aggregating siloed data, process mining reconstructs end-to-end operational visibility – warehouses, vendors, routes, inventory, etc. Teams monitor flows, compare sites, drill into delays with reliable accuracy.
2. Accelerated Improvement – By objectively highlighting bottlenecks, waste, variations, and root causes, process mining accelerates continuous improvement efforts. Rapid Plan-Do-Check-Act cycles condense years of performance gains into months.
3. Delivery Speeds & Reliability – Optimized routes and networks translate into faster and more reliable shipments. For example, revealing reasons behind higher retailer order delays allows targeted resolution that boost reliability.
4. Lower Logistics Costs – By optimizing routes, sharpening demand signals, identifying automation opportunities, and eliminating waste, costs related to transport, warehousing, inventory and labor plunge.
5. Happier Customers – Faster, cheaper and more reliable deliveries mean happier B2B and B2C customers – feeding higher retention, lifetime values and referrals.
Key Challenges and Mitigations
Despite the benefits, several challenges can slow process mining adoption and outcomes:
Data Collection – The prerequisite of complete, quality data means piecing together feeds from fragmented legacy systems. While tedious initially, integration middleware helps automate ongoing data feeds into process analytics engines.
Model Complexity – Massive global supply chains have endless variables requiring robust analytics. Platforms like Signavio embed both process and predictive expertise to accurately interpret data. Still complexity rises for intricate production networks.
Real-Time Signals – Dynamically processing signals like bad weather or traffic jams to update models on the fly requires advanced integration. But agile operational responses justify the effort.
Loosely-Structured Processes – Unlike manufacturing, logistics involves flexible human coordination causing fidelity issues. Combining signals from multiple process logs provides a more robust picture.
Adoption Inertia – Despite proven value, change-averse supply chain teams move slowly on innovations. Leadership must articulate the burning platform for process excellence while handling skills building.
Hybrid Techniques – While process mining provides unmatched efficiency, blending other approaches like machine learning, RPA, or employee surveys enriches the insights further. A true analytics team sport.
What Does the Future Hold?
With supply chain analytics techniques estimated to reduce costs 15-25% [16] while boosting reliability, logistics process mining adoption will skyrocket.
Here are two predictions for the road ahead:
Integrating Predictive Modeling – By combining complex forecasting models and machine learning atop process data, future platforms will enable incredibly precise dynamic inventory balancing, capacity planning, and demand sensing.
Operationalizing Insights via Automation – Today process analysts manually interpret data for operations teams. Soon mining-detected signals will trigger automated interventions – opening routes, assigning shipments, or refilling stock. Closing the loop between intelligence and action.
These ambitious futures prove process mining‘s potential as a keystone innovation for supply chain leaders pursuing transformations towards precision and resilience.
From Darkness to Daylight
In logistics, opaque "black box" processes enable endless waste and delays that destroy budgets, service levels, and competitiveness. By reverse-engineering such darkness into transparent process x-rays, supply chain leaders can systematically eliminate performance nightmares.
Process mining sheds new light on distribution bottlenecks, forecast inaccuracies, customer pains and more – powering focused data-driven problem elimination. Compounding improvements then accelerate across transport modes, suppliers, routes and products over weeks rather than years.
But pragmatically addressing data, skills, and adoption challenges is prerequisite to maximize payoff. With sound execution, process mining guides logistics leaders from darkness to daylight – enabling the operational optimizer‘s dream. The future has arrived.
To discuss process mining opportunities for your supply chain, request a demo from my team here.
