Enable Better Decision Making Through Effective use
of Data
INSIGHTS|ANALYTICS|INNOVATIONS
Data Science & Big Data Practice
Intelligent Supply
Chain
Supply Chain is generating significantly large unstructured data…
•
Components, equipment and finished goods
travel through multiple channels, and
increasingly, across multiple time zones.
A foul-up by a truck driver in Zimbabwe can cost a corporation in New York millions of dollars if the situation isn’t handled correctly
•
The scale, scope and depth of data, supply
chains are generating today, is accelerating,
providing ample data sets to drive contextual
intelligence
•
It is clear that the majority of supply chain data
is generated outside an enterprise
•
Forward-thinking manufacturers are looking at
big data as a catalyst for greater collaboration
SCM Big Data Analytics is the process of
applying advanced analytics techniques
in combination with SCM theory to datasets whose
volume, velocity or variety require information technology tools from the Big Data technology stack; leveraging supply chain professionals
with the ability to continually sense and respond to SCM relevant problems by
providing accurate and timely business insights
.
Source: Conference Paper, Big Data Analytics in Supply Chain Management
SCM Data Volume and Velocity vs. Variety
Challenges in Supply Chain
..creating huge demand for Big Data Analytics among leaders
Analytics enables more complex supplier networks those focus on
knowledge sharing and collaboration as the value-add over just completing
transactions
Supply chains evolve into value webs
64%
of supply chain executives consider big data analytics a
disruptive and important technology, setting the foundation for
long-term change management in their organizations.
Disruptive Technologies for Supply Chain
Sources: Deloitte Research & SCM World Big Data Analytics
Digital Supply Chain Internet of Things Cloud Computing Advanced Robotics 3D Printing
Drone/Self-guided Vehicles
Sharing Economy (e.g. Uber, Airbnd, Instacart)
Disruptive & Important Interesting, but unclear
usefulness Irrelevant % of respondents N = 1057
Linear supply chain are evolving into…. …complex, dynamic and connected value webs
COMPETITION COMPETITION
Value is based on the production
Procurement
Operations
Wear housing &
Distribution
De-risk supply chain
Improve accuracy of raw
material price prediction
Rate vendors objectively
Reduce supply variability
Schedule production based on
demand
Increase production
with
available resources
Reduce machine downtime
Identify failure proactively
Improve channel efficiency
Reduce total distribution cost
Optimize route for material
distribution
Reduce cost of distribution
Product Development
Optimize product features
Optimize production level
Concept testing
How to manage
supply chain volatility
and demand uncertainty?
How to
integrate
sales and operations planning?
How to…
Operations Management
Machine Downtime reduction
ANALYTICS
•
Our Supply Chain Solution framework is developed around
complex mathematical models and IP’ed algorithms
•
These models take into account a wide range of variables,
such as
the additional costs due to variations in the speed with which different suppliers can deliver their goods, proximity of the delivery centres
Information about consumer needs and wants to develop new product and/or brand extension
internal/external data to build pricing models that maximize profit margins
Demand of product by location, by customer type, product availability by warehouse, etc.
•
Users can select options with the highest return and the
lowest investment to maximize profit
•
It helps users to make better purchase decisions, more
flexible capacity planning, accurate demand forecast and
identify optimum route of distribution.
Our Supply Chain Big Data framework is highly scalable and
our Data Scientists are currently working on sourcing data
from connected technologies and Internet-of-Things to
optimize the supply chain of the future
How do you gain
How Data Science helps Supply Chain Leaders
Embedding big data analytics in operations leads to a 4.25x
improvement in order-to-cycle delivery times, and a 2.6x
improvement in supply chain efficiency of 10% or greater
Companies that employ a team of data scientists are far more likely
to generate a range of important supply chain benefits from their
use of big data analytics
How Supply Chain Leaders gaining by deploying Data Scientist
Source: Accenture Research Improvement in customer service & demand
fulfilment of 10% or greater Faster and more effective reaction time to
supply chain issues Increase in supply chain efficiency of 10% or greater
Greater integration across the supply chain Optimization of inventory and asset productivity More effective S&OP process and decision making Improved cost to serve Better customer and supplier relationships Improvement in customer service and demand
fulfilment of less than 10% Increase in supply chain efficiency of less than 10% Improvement in demand driven operations Shortened order-to-delivery cycle times
Shortened order-to-delivery cycle times Improvement in demand driven operations Increase in supply chain efficiency of 10% or greater Better customer and supplier relationships Improve cost to serve More effective S&OP process and decision making Faster and more effective relation
time to supply chain issues Greater integration across the supply chain Improvement in customer services and demand fulfilment of 10% or greater Optimization of inventory and asset productivity
Team of data
Transforming your Data Chromosome
0
1
0
1
1
1
DSI Case Studies on Supply Chain
Spare Parts Inventory Optimization
The client is a global leader in the design, manufacture, installation, and maintenance of wind, driven power generation plants.. It had been consistently carrying a large inventory to ensure high service level as agreed with its customers. The client wanted to reduce its inventory without affecting the service level. It had been using an automated forecasting and inventory management tool to keep track of inventory. Order used to get generated automatically whenever existing stock fell below the required stock based on pre defined inventory parameters like safety stock, lot order quantity, etc. The objective of the project was to build up an inventory management model based on maximization of RoCE.
Impact
Solutions
• Focus was on high value, capital spares – both repairable and non-repairable
• Determined impact of lead time reduction in overall inventory reduction and reliability improvement and identified the critical items, where small improvement could lead to large gain
• Identified environment parameters affecting parts failure
• Inventory segmentation and optimization of service level for each different class of inventory
• What-if scenario analysis between service level and RoCE
• Which are the high cost critical spares contributing to large inventory cost.
• What is causing high inventory for these spares
• Which critical spares are more likely to fail
• What environmental conditions (wind speed, humidity, temperature) are more likely to cause frequent failure of parts
• How much additional spares do I need to increase service level from x% to y%
– Trade off between predicted spares stock (RoCE) and likely achievable service level
Business Questions
Optimize stock levels, reduce spares costs and increase service level;
0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19 0.5 0.8 1 1.5 2 2.5 3 3.5 Pr of it M ar gi n Asset Turnover
10% RoCE 15% RoCE 20% RoCE
• Reduction in inventory by approximately 20% with increased reliability
• Automatic alert before a component failure reduced the need for ongoing inspections
• Managed spares requirement based on weather forecast by location
• Better forecast of parts failure helped in pro-active spares management and higher reliability
Value Proposition
Current State -3 DII -7 DII -10 DII 8 0 .0 % 8 5 .0 % 9 0 .0 % 9 2 .0 % 9 4 .0 % 9 5 .0 % 9 6 .0 % 9 7 .0 % 9 8 .0 % 9 8 .5 % 9 9 .0 % 9 9 .5 % 9 9 .7 % In ve nt or y ReliabilityRoute and Fleet Optimization
The client is a third party logistics service provider supporting transportation requirement of customers from varied industries like mining, manufacturing, retail and courier services. The client provided transport solution from its 300+ branches across one of the largest geographies in Asia. 25-20% of transportation requirement is serviced by its own fleet while the rest is serviced using hired vehicles from the market. It wants to utilize its own fleet judiciously into those routes where return is maximum and where availability of outside vehicle is lower. Another business constraint for the client was preference of drivers to ply on the same route because local knowledge was critical to manage operations efficiently. The objective of the project is given below:
• Demand projection by route by product type (steel, cement, furniture, oil, etc.) by major customers for next 6 months
• Identify optimum combination of own fleet and third party vehicles by route for the next 6 months. The combination of fleet includes type of vehicles (6 wheelers, 10 wheelers, oil tankers, etc.).
• Provide optimum route by type of vehicle for long term contracts
Impact
Solutions
The scope of the project was made limited to those top routes which were providing 80% of the revenue to the client. Pareto rule was again applied to select only those top products which were generating 80% of the revenue on the selected routes. The type of services were segregated into a) contract type and b) on-spot depending upon the type of payment and frequency of repeat business. It was also discovered that the contract type business needed the deliveries to be made at the same locations in majority of the cases. Following
• Visualizing demand: A baseline model was developed to represent the existing route structure and flow. Each route was scored based on demand volatility over the months
• Shipment consolidation:Identified the opportunity in terms of consolidating multiple demand generating at the same time either by using bigger vehicles or by consolidating nearby routes.
• Freight spend: Integer programming and other meta heuristic algorithms were used to optimize LTL (less-than-truckload) and TL (truckload) spend. Scenarios were used to create a set of sensitivity analyses of TL/LTL utilization thresholds.
• What is the optimum combination of own fleet and market hired fleet by route?
• What combination of fleet types will manage demand variability with lowest cost?
• How to trade off between penalty for late delivery with cost of transportation?
• How to trade off between smaller capacity trucks to gain flexibility and higher capacity truck to gain cost benefit
Business Questions
Optimum combination of types of vehicles, number of vehicles to manage demand variability with higher service level at lower cost
• Distribution spend was reduced by 15 percent•
• 12% improvement in on-time delivery through load balancing
• Identified opportunities to
increase truckload
shipments and reduce LTL spend.
Demand Forecasting
The client is one of the largest engine manufacturing and servicing companies. It has been managing multiple large Maintenance, Repair, and Overhaul (MRO) facilities across the world. The client engages with its customers both on long term contracts and on the spot contracts. On the spot contracts have been always more profitable than long term contracts. The client wanted to predict demand for engine servicing from on the spot contracts for a period of 3 years. This was useful for the client to sell long term contracts based on spare capacity by region. The demand for on the spot contract has been very volatile over the years. Predicting the same at a facility level has always been prone to large error. Time to time engineering upgrade of machine parts has also made prediction more difficult. The objective of the project was to understand the reason for demand variability by MRO facility.
Impact
Solutions
• We deployed both time series analysis (Exponential smoothing, ARIMA, etc.) and causal based forecasting algorithms in the project.
• Macro economic factors like GDP growth, IIP growth etc. were considered to develop a demand forecasting model using both traditional multi parameter non linear regression techniques
• Machine algorithm techniques like Random Forest was also used along with bagging and boosting methodology
• Ensemble methodology was used to average out output from multiple forecasting models
• Expert’s comments sourced from different industry websites, blogs, annual reports, etc. were also analysed using Natural Language Processing (NLP) techniques to improve model accuracy
• Multiple What-if scenario analysis were created based on probabilistic demand forecasting model
• How to trade off between assured long term contracts and more profitable on the spot contracts?
• How to distribute overall capacity utilization in an uniform manner throughout the year by reducing frequent spark in servicing demand?
• How to control inventory by reducing demand volatility?
• How to improve forecasting accuracy at a micro (product sub group) level?
• How to price my service for a long term contract based on future demand potential?
• Do I need to increase capacity in next 3-5 years?
Business Questions
Improved forecasting accuracy helps in achieving higher margin and more uniform capacity utilization
• Forecasting accuracy improved from below 60% to 75%+ in the first 6 months of implementation
• Capacity utilization increased by 10% across all MRO facilities
• Safety stock reduced by more than 40% due to higher accuracy of forecast
• Overtime cost reduced significantly across many MRO facilities.
Value Proposition
0 0.5 1 1.5 2 2.5 3 3.5 4 30% 40% 50% 60% 70% 80% Mnth-0Mnth-1Mnth-2Mnth-3Mnth-4Mnth-5Mnth-6 Fcst Accuracy (LHS) Safety Stock (RHS)Machine Downtime Analysis
• A leading DI pipe manufacturer in India produces pipes of varying diameters ranging from 80-800mm, each of two different types (K7 and K9).
• The company management experienced a considerable amount of gap between the ideal number of pipes that could be produced and the ones actually were produced and delivered. The gap or the loss was attributed to three factors
• Downtime
• Rejection
• Performance
Impact
Solutions
• For each stretch of time that a machine spends in a state, all pertinent information was collated from different data sets and brought into a unified representation
• Events were recorded as Scheduled and Unscheduled, and further divided as Mechanical, Electrical and Operational events. Analysis was separately undertaken for each type of event and each machine.
• Metrics like frequency, mean time between failures, mean time to repair, frequency distribution of time to repair, etc were computed for all top events
• Which are the critical machine / components failing more frequently
• How much idle time is lost due to failure of the machines / components
• How can we predict failure based on operational parameters
• How to find relationship between failure of one component with failure of other components
• How much time is spent on repair of failed machines / components
• How can we reduce overall downtime of machines / components
• How machine / component failure is affecting product quality / rejection rate
Business Questions
The mandate of this project is to bring down overall loss by 15% and reduce rejection rate by more than 50%
• Overall machine downtime reduced by little over 16%
• Finished goods production increased by 11%
• Rejection rate came down by more than 50%
Dealer Segmentation MTBF (hrs) for CCM Mould Change Scheduled 1 0 0 .0 0 9 3 .3 5 9 8 .9 4 9 2 .9 0 1 1 0 .7 7 80.00 85.00 90.00 95.00 100.00 105.00 110.00 115.00 Q4 FY14 indexed to 100
Value Proposition
Vendor Rationalization
One of the leading electronics manufacturing companies having manufacturing and assembling facilities spread across the globe had distributed sourcing functions by product category. The sourcing team of each product category had overlapping vendors, SKUs, multiple contracts with the same vendor with numerous different pricing structures and payment terms. The client realized financial (higher inventory, non-optimized delivered cost, etc.) and non-financial (complex reconciliation process, etc.) impacts of the overwhelming complex vendor portfolio. In an endeavour to simplify the sourcing process and to reduce overall cost, the client embarked on the journey to centralize the material procurement department across product categories. Europe region was selected to kick start the centralization process because of higher per unit cost in that region.
Impact
Solutions
• SKU selection:Classical inventory analysis like ABC analysis and VED analysis was done to classify SKUs based on numerous parameters like profit, revenue, quantity, etc.
• SKU commonalization:All vendors contributing 90% of the overall supply of the selected SKUs are identified and a consolidated master list was created
• Vendor scorecard: All vendors in the master list were evaluated based on numerous KPIs like cost, quality, service level, etc. An weightage system was developed through monetisation (dollar value) of each parameter. Example: 1 day delay in delivery reduces profit of the company by xx USD
• Vendor optimization: Optimum number of vendors for each SKU was determined based on volume, value, criticality, etc. of the SKU in the overall business
• What is the right number of vendor for important SKUs?
• How to trade off risk of overdependence on few vendors with too many vendors proving less than optimum cost efficiency?
• Which SKUs can be grouped together based on product engineering?
Business Questions
Optimum number of vendors that reduce overall cost of procurement, improve material availability, reduce risk and improve quality of raw material
• Aggregate raw material cost delivered at all facilities in Europe reduced by 7% during the first six months of implementation. The annual potential cost saving was
determined at 12% after
stabilization.
• Average raw material inventory reduced by 8 days of sale. This was primarily achieved through commonalization of SKUs across product categories
Value Proposition
0 20 40 60 80 100 0 20 40 60 80 100 V al ue In de x Vendor Scorecard Critical VendorsDSI Distribution Channel
Efficiency Enhancement Tool
DSI channel member segmentation tool evaluates a dealer’s performance across multiple dimensions (
Volume, Credit, Stickiness,
Differentiation, Premium
). The Big data based machine learning algorithm identifies the key parameters driving dealer performance.
•
The DSI-CE business suite has a
customized interactive Big Data based Decision
Support System
(BDSS) that help to rate,
track, compare performance of distributors
,
dealers/retailers and B2B customers
•
This is supported by a detailed solution architectures, big-data integration
framework,
statistical and machine learning algorithms, data visualizations
and
decision support tools
Common, Unified, Dynamic Big Data Repository
Sales, Stocks
Data PlanningData Excel Reports
Other Structured Data Sources Market Data Dealer / ECA Scoring Tool (R-CAP)
Open Source, Customized Statistical Modelling & Machine Learning
Channel Management Tool (CMT) Open Source/ Unstructured Data Customer Feedback Analysis Tool (CFT)
1. How do I rate my dealer’s performance?
2. How do I utilize maximum information
from existing data sources for my
decision process?
3. How do I get actionable insight about
each segment of best performing and
worst performing dealers?
4. How my customers and dealers are
perceiving my competitors?
Some key Questions
DSI-CE Architecture
Benefits
Help you to decide the optimum business parameters to set right targets for channel members
Provide insight on the sentiments of your customers and dealers
Streamline the complete Distribution value chain
Insights
Product – DSI Channel Efficiency (DSI-CE) Tool
Smart Incentives Design Tool (SIDT) Sales Forecasting Tool (SFT) Payment Cycle Analysis Tool (PCAT)
Advanced statistical and machine learning algorithms
are used to establish relationship between dependent
variables (
dealer growth
) and hundreds of independent variables (
shop details, promotional activities, etc
.)
Product – DSI Channel Efficiency (DSI-CE) Tool
Neural Network for identifying important variables affecting performance Overall Tenure Associated prdts COV of sales # of BTL activities Project / Ops On-time delivery Credit period Store size Signage ………. ………. New biz ratio
0.35
0.45
0.20
Dealer Rating
Hierarchical clustering for segmentation of salesperson
Dealer1 Dealer2
…
…
Dealer segmentation provides detail insight about each segment of dealers and their characteristics. This helps in
taking the right action for each group of dealers under different business dynamics
Characteristics about each micro segment (
e.g., Volume High, Credit Low, Stickiness
Medium
) are populated to provide right business insight for future action
1. Likely market reach is above 70% 2. Keep outstanding above 60 DOS
3. 32% of the stores are likely to grow above 15% in the next 12 months
4. Sales is strongly correlated with outstanding provided to these dealers
5. SOB is below 25%.
6. Likely to switch allegiance to competitor brands 7. Adherence to company guidelines is below average
Segment Characteristics
Dealer Segmentation
Product – DSI Channel Efficiency (DSI-CE) Tool
Nos represent number of dealers in this segment
