Developing an Internal Supply Chain Analytics Competency internal supply chain analytics competency

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Transcript of Developing an Internal Supply Chain Analytics Competency internal supply chain analytics competency

  • Developing an Internal Supply Chain Analytics Competency

    A Case Study

  • Preview

    • Topic Description

    In this session we will discuss the process of developing an internal supply chain analytics competency through the lens of a major retailer’s recent initiatives. – What elements need to be considered before and during

    development of an internal supply chain analytics competency?

    – What challenges are commonly experienced?

    – What conditions are necessary, and what practices lead to long-term retention of a competency in supply chain analytics?

  • Agenda

    • Introduction and Background

    • Elements of a Supply Chain Analytics Competency

    • A Framework for Development

    • Common Challenges Experienced

    • Necessary Conditions and Best Practices

  • Agenda

    • Introduction and Background

    • Elements of a Supply Chain Analytics Competency

    • A Framework for Development

    • Common Challenges Experienced

    • Necessary Conditions and Best Practices

  • • Timeline: Major Retailer

    Background

    Green light for enhancement

    of internal supply chain

    analytics competency

    Chainalytics engaged for

    technology RFP and Selection

    Services

    Chainalytics engaged for Competency Development

    Services

    Joint rollout of new Analytics,

    Processes, Tools, Data, and Team

    Organization

    Newly instantiated

    Competency is now beginning to embed and

    grow

    2009 2010 2011 2012

  • Motivation

    • Common Drivers – Margin growth; path forward to get to next level

    – Increased complexity; old approaches no longer sufficient

    – Budget pressure; external services spend too high

    – Agility desire; increase ability to react and respond faster

    – Risk mitigation; preparedness and contingency planning

    • For This Retailer – Primary motivation for enhancing and expanding an internal supply chain

    analytics competency was Increased Complexity, but all of the above were part of the decision to proceed

    – Use of external services to help develop the competency was not without careful consideration; balancing cost vs. value and likelihood of success

  • Agenda

    • Introduction and Background

    • Elements of a Supply Chain Analytics Competency

    • A Framework for Development

    • Common Challenges Experienced

    • Necessary Conditions and Best Practices

  • Team Data

    Elements

    Process Technology

    Analyses

  • Framework

    Define the specific supply

    chain questions to

    answer

    Determine the Analyses

    required to answer the questions

    Understand the inputs,

    outputs, owners, form, and frequency

    Choose an approach and Technology to support each

    analysis

    Learn the usage rules of the approach

    and technology set selected

    Establish the detailed

    Process and workflows to be executed

    Map the Data elements,

    definitions, sources, and specific uses

    Design, create, test, and

    implement supporting

    architecture

    Identify the skills needed to execute all elements of the process

    Align the Team staffing to the resulting mix of skills and roles

    required

  • List of Analyses to Support (Partial)

    Strategic Network Design: Open/Closed/Location; and Territory Assignment Decisions

    Velocity-Based Analysis: This process identifies vendors and vendor-ship points with fast-moving product from a company-wide perspective rather than per buying group.

    Inventory Safety Stock Analysis: This process determines the necessary safety stock required to be on hand at a node based on supply lean time, lead time variability, product service level, demand, and demand variability

    Forward Buy Analysis: This process is performed when a product’s price will be increased in the near future and there is the ability to purchase an increased amount of the product in advance. This process determines the lowest average cost per product unit given shelf life restrictions, max. quantity of product the vendor will allow to be purchased, and any OTB constraints.

    Evaluate Import Opportunity: The purpose of this analysis process is to evaluate the different landed costs associated with importing a product or sourcing domestically.

    Evaluate Inbound Consolidation Analysis: The purpose of this analysis is to determine if there are cost benefits with using a consolidation center prior to bring a truck to the DC.

    Prepaid or Collect Delivery Analysis: This process is performed when the Buying/Planning Team needs to determine if there is a cost advantage of taking responsibility of transporting product to the vendor. This process can be used to evaluate a proposed reduction in list prices and allowances or to determine the amount of reduction that would be necessary from the vendor in order to make Pickup economically advantageous.

    Plant-Direct Shipment: The purpose of this analysis process is the evaluate picking up a product at a vendor’s manufacturing plant. This is possible when the Vendor uses its own distribution center not co-located with the manufacturing plan and will allow picking-up products from the DC.

    Evaluate X-Dock, Warehouse, or Combo at RSC: The purpose of this analysis is to provide a method to evaluate the different DC and transportation costs associated with either cross-docking a product, combo’ing the product, or warehousing the product. This analysis does not have to be determined for every product, but can be used as a guide to understanding the associated costs.

    Analyses

  • Technology Network Design/Product Flow Capabilities Weight

    1 Model Structure 20%

    2 Sourcing Rules/Contraints 20%

    3 Cost Elements Included/supported 20%

    4 Historical (Baseline) Modeling 10%

    5 Data Development/Manipulation 15%

    6 Technical 15%

    Inventory Optimization Capabilities Weight 1 Product Segmentation 10%

    2 Life-cycle Parameter Maintenance 10%

    3 Inventory Planning 50%

    4 Service Level Targets 15%

    5 Exception based workflow 15%

    Transporation Modeling Solution Capabilities Weight

    1 Ease of Use 10%

    2 Rating Capabilities 20%

    3 Load Building Capabilities 20%

    4 Scheduling 20%

    5 Reporting 10%

    6 Scalability 20%

    Area Sub Area Must Haves Weight

    Mentioned

    in RFP

    Response

    Verified in

    Demo

    Mentioned in

    RFP

    Response

    Verified in

    Demo

    Multi echelon Ability to skip echelons

    Product Flow forward and reverse

    Multiperiod Models open/close decisions, and associated costs

    by period with ability to respect prior period

    decisions.

    Inventory modeling Carry inventory from period to period

    Cycle stock, safety stock, and in-transit

    inventory

    Safety stock varying in a non-linear manner

    as facility throughput changes

    Days on hand/weeks on hand requirements

    Capacity constraints Transporation, facility, process, and product

    levels

    Processes/

    Resources

    Service Level

    requirements

    Constraints across

    model entities

    20% 5-Excellent 1-Unacceptable 5-Excellent 1-Unacceptable

    Model Structure

    Vendor 1 Vendor 2

  • Process

  • Data

  • 6 Supply Planners 80% Merchant Support 20% Internal SC Analysis MS Excel (MS Access)

    3 Supply Planners 80% Merchant Support 10% Internal SC Analysis 10% Data Preparation MS Access MS Excel Network/Flow Path Tool Inventory Optimization Tool

    2 Supply Chain Analysts 20% Merchant Support 60% Internal SC Analysis 20% Data Preparation MS Access MS Excel Network/Flow Path Tool Inventory Optimization Tool

    1 Data Analyst 0% Merchant Support 0% Internal SC Analysis 100% Data Preparation MS Access MS Excel SQL Server/SQL Scripting Data Systems

    Current State

    Future State

    Product Category Focus Network/Capacity Focus

    Team

  • Agenda

    • Introduction and Background

    • Elements of a Supply Chain Analytics Competency

    • A Framework for Development

    • Common Challenges Experienced

    • Necessary Conditions and Best Practices

  • Common Challenges Experienced

    • Team

    – Specific mix of skills is rare (business + data + analytical) – hard to hire

    – Long-term retention vs. career growth is tough – hard to keep

    – Line between IT and supply chain ownership blurs – hard to manage

    • Data

    – Supply chain analytics require vast breadth of data – hard to gather

    – Efficiency requires repeatability and “refreshability” – hard to maintain

    • Analyses

    – Analyses can be new and complex – hard to communicate the value

    • Process

    – Processes can be very different – change management is significant