Designing and Implementing Hard Drive Inventory Policies for Enterprise Computing Solutions

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Designing and Implementing Hard Drive Inventory Policies for Enterprise

Computing Solutions

by

Ephraim D. Machtinger B.A.Sc. Mechanical Engineering

University of Toronto (2012)

Submitted to the MIT Sloan School of Management and the Department of Mechanical Engineering in Partial Fulfillment of the Requirements for the Degrees of

Master of Business Administration and Master of Science in Mechanical Engineering

In conjunction with the Leaders for Global Operations Program at the Massachusetts Institute of Technology

June 2019

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in

any medium now known or hereafter created

Signature of Author

Signature redacted

T Sloan School of Management and the Deparnent of Mechanical Engineering

Certified by

Accepted

by-Accepted by_

MASSA HUSEM INSTrrtf!TE OF TEGHNOL.OGY

JUN 0 4

2M

LIBRARIES

3ignature redacted____

Jung-Hoon Chun Professor of Mechanical Engineering

Signature redacted

Thesis Supervisor

Donald Kieffer Senior Lecturer in Operations Management

Thesis Supervisor

Signature redacted

en MIT Sloan MBA Program I School of Management

Signature redacted'

Nicolas Hadjiconstantinou Chairman of the Committee on Graduate Students Department of Mechanical Engineering

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Designing and Implementing Hard Drive Inventory Policies for Enterprise Computing Solutions

by

Ephraim D. Machtinger

Submitted to the MIT Sloan School of Management and the Department of Mechanical Engineering in partial fulfillment of the requirements for the degrees of Master of

Business Administration and Master of Science in Mechanical Engineering in conjunction with the Leaders for Global Operations Program at the Massachusetts

Institute of Technology

Abstract

Historically, the Storage business unit of the Dell-EMC Infrastructure Solutions Group (ISG) has maintained large inventory buffers to deal with high demand uncertainty and minimize part shortages. High product configurability and complex product structures continue to present challenges to effectively managing component inventory. In addition, many supply and demand planning decisions are contextual rather than process driven, making it difficult to understand precisely how inventory level is influenced by its independent variables.

The objective of this project is to develop a set of dynamic inventory policies to enable inventory reduction at ISG while maintaining or improving cycle service levels. Our approach is based on modeling the inventory behavior of the existing supply chain system, and generating inventory policies that more accurately reflect consumption within the system. Three parameterized inventory policies have been built and tested. We modeled inventory, forecast and actual demand data, used demand classification techniques to selectively adjust policy recommendations for certain drives and validated policy

performance by adjusting input parameters. Based on model training for three quarters from August, 2017 to May, 2018 and validation from May, 2018 to August, 2018 our final choice was an order-up-to policy developed by fitting empirical distributions to historical forecast

errors and using those distributions to recommend safety stock levels. The policy was applied to 111 CFGs representing 2,758 part numbers.

We used August, 2018 to November, 2018 as a test period and applied the policy to observe its performance. Results indicated a 96.40% service level and 36% mean inventory reduction as compared to the baseline, which had a 98.40% service level. The 3.60% loss of service represented 56 shortages. Of those, we identified 31 that could be eliminated through simple policy refinement, leading to a revised service level of 98.55%.

Overall, our results suggest that a mathematical inventory management approach can be used reliably to model the hard drive supply chain, recommend an inventory policy and realize significant inventory reduction opportunities without compromising service level.

This thesis concludes by proposing important supply chain system design changes, where several issues at the root of ISG's inventory management challenges reside. Thesis Supervisor: Donald Kieffer

Title: Senior Lecturer in Operations Management Thesis Supervisor: Jung-Hoon Chun

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The author wishes to acknowledge the Leaders of Global

Operations Programfor its support of this work

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Acknowledgements

I would like to express my sincere gratitude to the people within Dell and the Infrastructure Solutions Group (ISG) who have been part of this work. I would like to thank Juan Correa for his unwavering guidance, resourcefulness and flexibility throughout the internship. His help navigating the Dell organization was critical to successful project execution. I am very grateful to Brian Feller for having driven the Dell-LGO partnership and sponsoring this project. I would like to thank Santosh Stephen for his continuous feedback and advocacy. Thank you Santosh for engaging numerous stakeholders in ISG to make this project come to life. I would also like to thank Christian Schneider for his constant reassurance as a recent LGO alumni and for being a great sounding board for ideas. Finally, I would like to thank Francisco Erize for his technical guidance.

I could not have completed this thesis in its final form without the attention and support of my Sloan advisor, Don Kieffer, and my Mechanical Engineering advisor, Jung-Hoon Chun. Don's brilliant insights helped me understand and eventually communicate a powerful set of recommendations, and Professor Chun's attention to detail led me to a deeper appreciation

for the considerable task of constructing academic writings.

I cannot overstate how grateful I am to all of my LGO classmates. Undoubtedly, they have made these past two years the best in my life and made me feel part of a lifelong family. Your inspiration helped to transform me into a better leader and a better person, enriching my life in more ways than I can ever imagine. I sincerely hope that over the course of our lives, we continue to support one another as we have for the last two years.

Finally, I would like to thank my family and friends for their support and encouragement throughout this journey.

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Table of Abbreviations

HDD Hard Disk Drive

SSD Solid State Drive

ISG Infrastructure Solutions Group

SAN Storage Area Networks

NAS Networked Attached Storage

PCM Product Configured Material

BOM Bill of Materials

DAE Disk Array Enclosure

DPE Disk Processing Enclosure

SKU Stock Keeping Unit

CFG Configuration

SVT Supplier Visibility Tool

PG Part Group

TAA Trade Agreements Act

VMI Vendor Managed Inventory

GEM Global External Manufacturer

3PL Third Party Logistics

DSI Days Sales of Inventory

CoC Center of Competence

ASP Average Selling Price

UPP Unit Production Plan

MPP Master Production Plan

MRP Material Resource Plan

GCM Global Commodity Manager

GSM Global Supply Manager

GDS Global Demand and Supply

RFQ Request for Quotation

TAM Total Addressable Market

BTS Build to Stock

CSG Client Solutions Group

KPI Key Performance Indicator

CQ Calendar Quarter

FQ Fiscal Quarter

CTO Configure to Order

EOL End of Life

ATO Assemble to Order

WIP Work in Process

FGI Finished Goods Inventory

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BI Business Intelligence

BDL Business Data Lake

ERP Enterprise Resource Planning

CV Coefficient of Variation

PDF Probability Density Function

CDF Cumulative Distribution Function

MLE Maximum Likelihood Estimator

AIC Akaike Information Criterion

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List of Figures

FIGURE 1-1: SIDE-VIEW OF TYPICAL ENTERPRISE HARD DRIVE ASSEMBLY ... 18

FIGURE 1-2: FRONT VIEW OF A VNX SERIES RACK SOLUTION ... 18

FIGURE 1-3: HARD DRIVE SUPPLY CHAIN M AP ... 20

FIGURE 1-4: SIMPLIFIED DEMAND PLANNING PROCESS MAP ... 23

FIGURE 1-5: SIM PLIFIED PROCUREM ENT PROCESS MAP ... 24

FIGURE 1-6: SAMPLE SECTION OF GLOBAL DEMAND AND SUPPLY (GDS) DOCUMENT... 25

FIGURE 1-7: COMPARISON OF DELL FISCAL QUARTERS AND CALENDAR QUARTERS... 27

FIGURE 2-1: W ILLIAM S CATEGORIZATION SCHEM ME... E ... 36

FIGURE 4-1: INVENTORY CONTROL CHART SHOWING EXCESS INVENTORY ... 50

FIGURE 4-2: INVENTORY CONTROL CHART WITH CS AND EG AS PRIMARY DEMAND SOURCES ... 51

FIGURE 4-3: INVENTORY CONTROL CHART FOR DRIVES WITH LONG LEAD TIME OR TIGHT MARKET SUPPLY ... 5 1 FIGURE 4-4: INVENTORY CONTROL CHART REPRESENTING AGED OR OBSOLETE DRIVES... 52

FIGURE 4-5: DEM AND CLASSIFICATION SCHEM ME... E ... 53

FIGURE 4-6: DISTRIBUTION OF CFGS ACCORDING TO CLASSIFICATION SCHEME ME... 54...

FIGURE 4-7: GENERALIZED PROBABILITY DENSITY FUNCTION FOR THE UNIFORM DISTRIBUTION ... 55

FIGURE 4-8: SAMPLE PROBABILITY DENSITY FUNCTIONS FOR THE NORMAL DISTRIBUTION... 55

FIGURE 4-9: SAMPLE PROBABILITY DENSITY FUNCTIONS FOR THE GAMMA DISTRIBUTION... 55

FIGURE 4-10: INVENTORY POLICY SYSTEM ARCHITECTURE ... 56

FIGURE 4-11: HISTOGRAM OF OBSERVED CV AT A CFG LEVEL... 58

FIGURE 4-12: HISTOGRAM OF ANDERSON-DARLING NORMALITY STATISTICS AT A CFG LEVEL... 59

FIGURE 4-13: EXAMPLE BEST FIT DISTRIBUTION FOR CFG FORECAST ERRORS... 63

FIGURE 4-14: EXAMPLE BEST FIT DISTRIBUTION FOR CFG ACTUAL DEMAND ... 65

FIGURE 4-15: GRAPHICAL REPRESENTATION OF READY RATE (TYPE 3) SERVICE LEVEL... 66

FIGURE 5-1: MEAN INVENTORY VALUE VS. SERVICE LEVEL GRAPH FOR POLICY 3 DURING VALIDATION P E R IO D ... 7 0 FIGURE 5-2: IMPACT OF DEMAND CLASSIFICATION MULTIPLIER ON SERVICE LEVEL AND MEAN INVENTORY VALUE FOR POLICY 1 DURING VALIDATION PERIOD ... 71

FIGURE 5-3: IMPACT OF DEMAND CLASSIFICATION CUTOFF ON SERVICE LEVEL AND MEAN INVENTORY VALUE FOR POLICY 1 DURING VALIDATION PERIOD ... 71

FIGURE 5-4: IMPACT OF DEMAND CLASSIFICATION MULTIPLIER ON SERVICE LEVEL AND MEAN INVENTORY VALUE FOR POLICY 2 DURING VALIDATION PERIOD... 72

FIGURE 5-5: IMPACT OF DEMAND CLASSIFICATION CUTOFF ON SERVICE LEVEL AND MEAN INVENTORY VALUE FOR POLICY 2 DURING VALIDATION PERIOD... 72

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1. Introduction

This thesis, one product of a six-month internship at Dell Technologies, provides a

mathematical approach for managing inventory of mechanical (HDD) and solid-state (SSD) hard drives used in the Dell-EMC Infrastructure Solutions Group (ISG) Storage product portfolio. The approach addresses a significant increase in global inventory by developing a set of dynamic stocking policies to aid in optimizing inventory investment subject to specified service levels. The goal is to enable inventory reduction and control based on policy recommendations, as well as to identify system design improvement opportunities to create an adaptive supply chain.

This chapter presents an overview of the project. Section 1.1 provides background

information regarding Dell-EMC, discusses ISG Storage's products, presents an overview of the hard drive supply chain and highlights ISG Storage's inventory management challenges. Section 1.2 presents the problem the project addresses, the goal of the project and our research methodology. Last, Section 1.3 presents the thesis outline.

1.1 Background

In what follows, we provide background information regarding Dell-EMC, the ISG Storage product portfolio and hierarchy, the hard drive supply chain and sources of inventory management challenges facing the ISG Storage division.

1.1.1 Dell-EMC Overview

EMC, founded in 1979, introduced its first 64-kilobyte memory boards for the Prime Computer in 1981 and continued with the development of memory boards for other computer types. In the mid-1980s the company expanded beyond memory to other computer data storage types and networked storage platforms. [1]

By late 1995, EMC took the lead in the mainframe storage market. In 1990, when EMC entered the mainframe storage market with its Symmetrix product, IBM held 76% of the

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market while EMC held 0.2%. In 1995, EMC accounted for 41% of mainframe storage terabytes shipped while IBM's share fell to 35%. [2]

On October 12, 2015, Dell Inc. announced that it would acquire EMC in a cash-and-stock deal valued at $67 billion; the largest-ever acquisition in the technology industry. The merger closed on September 7, 2016. As a result of the merger, the combined company has been renamed to Dell Technologies and EMC has been renamed to Dell-EMC. EMC had over 70,000 employees and was the world's largest provider of data-storage systems by market share, competing against NetApp, IBM, Hewlett Packard Enterprise, and Hitachi Data Systems. [3] Storage solutions are now produced under the ISG division of Dell Technologies. Hereafter, we refer to ISG Storage as ISG.

1.1.2 ISG Product Portfolio and Hierarchy

The ISG group develops, delivers, and supports information infrastructure and virtual infrastructure technologies, solutions, and services. It offers enterprise storage systems and software deployed in storage area networks (SAN), networked attached storage (NAS), unified storage combining NAS and SAN, object storage, and direct attached storage

environments; a portfolio of backup products that support enterprise application workloads; and cloud software and infrastructure-as-a-service. Additionally, it offers virtualization infrastructure solutions, which include a suite of products and services to deliver a software-defined data center, and support a range of operating system and application environments, as well as networking and storage infrastructures. [4] Table 1-1 indicates the major product categories, and their associated product families.

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Table 1-1: Dell EMC ISG Products Overview

Product Category Product Families

Information Storage VMAX3 Family, VNX/VNXe Family,

Isilon, Atmos, XtremlO, ScaleIO, DSSD, Unity, ECS

Archiving, Backup and Recovery Avamar, DataDomain, NetWorker, Mozy, RecoverPoint, Centera, SourceOne

Cloud computing/Converged Infrastructure VxBlock, VxRack, VxRail, VSPEX, Virtustream

The product families in Table 1-1 can be disaggregated into product lines (PCM) and further into anchor models. In providing a solution to a customer, some combination of anchor models is selected to meet their unique requirements. For this reason, the PCM is the highest level representation of the build, and is the solution that gets transacted to the customer. At the anchor model level, a bill of materials (BOM) can be specified for nearly all constituent components; hard drives are further configured by manufacturer, quantity, technology (HDD/SSD) and storage capacity. The result is an infinite number of possible combinations of a specific hard drive and quantity that can be mapped to a customer order

Figure 1-1 shows a side view of a typical hard drive assembly, which includes three major components: the raw hard drive, a paddle card that connects to a bus, and a plastic carriage to enable installation or removal from the system. Although each of the products listed in Table 1-1 have different architectures, it is common for their hard drives to be installed in

disk array enclosures (DAE) and disk processor enclosures (DPE). These subassemblies are shown in Figure 1-2, a front view of a VNX series rack.

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Raw hard drive

Paddle card Plastic

carriage-Figure 1-1: Side-view of typical enterprise hard drive assembly

Disk-array enrlosure (DAE) DAE DAE Control Station 1 (optonal) Control Staton R Blade enclosure I (blade 4) Blade enclosure 0 (blades 2 and 3) Disk processor enclosure Standby power supply PDU A PDU B -e-e:::::: :::j::::

j:0:0

-RM:OOU see--" M O meeeom *ae .1 AL I Front view

Figure 1-2: Front view of a VNX series rack solution

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To manage the large number of hard drive stock keeping units (SKU) for inventory control and forecasting purposes, ISG opted to group SKUs based on varying degrees of

interchangeability. The first level of aggregation is the ConEguration (CFG/SVT), which includes hard drives of similar raw drive specifications. The second and lowest level of aggregation is the Part Group (PG), which also includes hard drives of similar form factor, paddle card interface, product line compatibility, product generation and revision. Hard drives in the same PG can often be substituted for one another without any reconfiguration. Most cases requiring reconfiguration occur because one or more components of the hard drive assembly are not manufactured in the United States, and must be because of Trade Agreements Act (TAA) compliance reasons or unique customer requirements. For hard drives at the CFG level, substitution requires reconfiguration more often because of the characteristic differences across PG's mentioned earlier.

The different levels of aggregation have important implications for demand planning and procurement decisions, both of which are discussed in Section 1.1.3.

1.1.3 Hard Drive Supply Chain

A map of the hard drive supply chain, including the location of ISG factories, Vendor Managed Inventory (VMI) hubs, HDD/SSD manufacturers and global external

manufacturers (GEMs) is shown in Figure 1-3. The various types of material flows are also indicated on the map and discussed further in this section.

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-Lewend

EMC Factories Return flow to plants VMI Hubs (3) Inter-organizational

"""* transfer flow

SSD Factories Flow from SSD

Customer

--- ---

9

HDD Factories (7) factories to EMC

Returns

9

Global External factories, VMI hubs

Manufacturers (GEMs) and GEMs

Figure 1-3: Hard drive supply chain map

A list of hard drive manufacturers supporting ISG, including manufacturing regions and

number of facilities is presented in Table 1-2.

Table 1-2: Overview of HDD/SSD manufacturers and their locations

HDD

SSD

Manufacturers Toshiba, Western Digital, Toshiba, Western Digital,

Seagate Seagate, Intel, Micron,

Samsung, Smart, STEC Manufacturing regions China (2), Thailand (3), Various

(Number of facilities) Malaysia (1), Philippines (1)

In mid-2018, Western Digital announced plans to shut down its HDD factory in Kuala Lumpur and expand SSD manufacturing in Penang. [5] Given that the rapid market

adoption of SSDs continues to displace demand for HDDs - where speed, power

consumption and durability are more important considerations than price and capacity - it is

expected that ISG's supplier network will continue to change in the near future. [6] In

general, drives have become commoditized over time, though supply and demand dynamics dictate the need for long range capacity planning. ISG builds complete solutions out of three

facilities located in: (1) Apex, North Carolina; (2) Franklin, Massachusetts; (3) Cork, Ireland. Additionally, ISG provides some subassembly work to two GEMs operating out of China

and Galway, Ireland. Drive manufacturers contract with third-party logistics (3PL)

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companies to provide distribution, warehousing and fulfillment services as part of VMI agreements. A list of VMI hubs and the ISG facilities they support is presented in Table 1-3. Company names are masked for confidentiality.

Table 1-3: Table of VMI companies, hub locations and ISG facilities supported

3PL Company

Hub Locations

ISG Facilities Supported

Company A Canton, MA Franklin, MA

Cork, Ireland Cork, Ireland

Company B Morrisville, NC Apex, NC

Franklin, MA Franklin, MA

Company C Galway, Ireland Cork, Ireland

Company D Galway, Ireland Cork, Ireland

Company E Morrisville, NC Apex, NC

Company F Cork, Ireland Cork, Ireland

The process for assembling drives differs based on the manufacturing plant. At Apex, raw drives, paddle cards and plastic carriages are ordered in separate pieces from a hub and then assembled at the plant. At Cork and Franklin, raw drives are first sent from hubs to GEM

sites where they are assembled to the final drive configuration. The assembled drives are then purchased from the GEMs as part of a complex financial arrangement.

Material Inflows

Hard drives exhibit five types of material inflows:

i) Supplier to VMI Hub: Drive manufacturers ship finished product to stock VMI

hubs owned and operated by their 3PL provider. Drive shipments occur at irregular intervals based on current inventory level, demand rate, procurement metrics (i.e. procurement pushing excess material into the hubs to meet an artificial inventory level or buffering target) and supplier agreement terms (i.e. suppliers pushing excess material into the hubs at the end of the month or quarter to take advantage of ownership transfer rules). In general, suppliers provide guidance on when they expect to fulfill demand as reported in the forecast, leading to variability in delivery quantities.

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ii) VMI Hub to Manufacturing Plant: Drive shipments from hubs to manufacturing

plants are triggered through Kanban systems at each plant. The reorder point varies based on a fixed day sales of inventory (DSI) target set for raw drives by procurement. Factors considered in setting the DSI target include reasonable protection for large orders, market intelligence and internal requirements for a

five-day average production lead time.

iii) Direct to Manufacturing Plant: Approximately 10% of drives procured are

shipped directly to manufacturing plants, as they come from suppliers that do not have hub agreements.

iv) Reverse Logistics: A significant number of drives are received at manufacturing

plants in the form of returns. There are five categories of returns, including customer/trade-in returns, exchange returns, field returns, engineering returns and customer service returns. Customer/trade-in returns involve sending back drives previously owned by the customer as part of an upgrade or after finishing with a trial. If any drives are dispositioned as scrap, the net usable quantity is received into stock. Exchange returns involve full swap returns (e.g., Erroneously

sending a VMAX3 solution instead of a VMAX2) or mechanical replacements (e.g., The DAE inside of a cab fails and is sent back; either a replacement is offered or a return for sale and credit is initiated). Field returns are similar to customer/trade-in returns except that all drives must first be received into stock prior to executing any scrap dispositions. Engineering returns are a result of

engineering initiated testing programs on customer installations to further study drive performance. Customer service returns relate to drives sent to the customer for assistance, and are also subject to dispositioning rules.

v) Inter-organizational Transfer: Drives are shipped between manufacturing plants

to address global inventory needs, particularly part shortages.

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Demand Forecasting Process

COC creates UPP Demand planning MPP translated to To Procurement (product kine view in P translates UPP to 10 MRP and net of

un~month)MPP (model mix in _{unit/moth)unftonth)} existing supply (part level in units/week) Figure 1-4: Simplified demand planning process map

Figure 1-4 is a simplified process map for the global drive demand planning process. The center of competence (CoC) is responsible for building the sales forecast, which is done one-year out. It has a centralized planning team that engages with local planners across ISG's

sales territories. Local planners provide revenue expectations to the CoC based on market research and business goals. This is communicated as a regional average selling price (ASP) and total number of units. The CoC does a roll-up of the regional revenue expectations and presents this global view to the demand planning team. The two work together to back out

software and services such as purchased product maintenance from the revenue calculation in order to obtain a true hardware representation. Finally, the financial model is converted into a product line forecast called the UPP.

The demand planning team translates the UPP at a more discrete level, called the MPP, to meet business requirements. The MPP contains the predicted model mix, which is derived using a moving average of historical attach rates, defined as the proportion of orders of a

specific model with respect to orders of all models in the corresponding product line. It also considers returns, new product introductions, internal demand, engineering requirements and other factors. At this point, demand planners introduce a hedge between 2% and 8%,

increasing demand to cushion against forecasting errors. The MPP is then translated into a component level forecast as part of the material requirements planning (MRP) process, although the mechanisms for forecasting drives differs from other components because of configuration uncertainty at the model level and commonality across products. That is, one cannot use attach rates again since the BOM is undefined. The MRP is run at the beginning of each fiscal quarter, and updated each month based on guidance from the CoC. Demand planners also build a returns forecast at the component level using historical returns data. Finally, they compile all demand and returns forecast data and send it to procurement.

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Procurement Process

Figure 1-5 is a simplified conceptual diagram for the current drive procurement process. There are two primary roles in the procurement organization: global commodity managers (GCMs) and global supply managers (GSMs). GCMs, one for HDDs and one for SSDs, are responsible for negotiating supplier contracts to support forecasted drive demand, and developing and maintaining strategic supplier relationships. GSMs manage more tactical issues such as inventory burn rate, shortages, supplier delivery commitments and short term drive supply mix.

Build mid-term forecast using MRP and returns forecast

Negotiate purchase quantities with

suppliers

0

E

GSMs compile new supply data vi; Excel e-rnail

Analytics team compiles existing supply data

* On-hand inventory, backlog, and pre-allocations (SVT Bot report, QTD PG repor/MRP Whitebox, external manufacturer reports) Initiated one (HDD) or two (SSD) months from CO start Procurement builds GDS

Weekly review with suppliers

)emand planning team compiles all demand sources and returns data

. CQ (QTD PG report/MRP)

" CQ+1, CQ+2 (Bex reports)

" Excel e-mails from external manufacturers

Figure 1-5: Simplified procurement process map

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Two major outputs of the procurement process include the mid-term forecast and the global demand and supply (GDS) document. The mid-term forecast is given to suppliers as a signal for negotiation. GCMs use this information to generate a request for proposal (RFQ) that is presented to suppliers. At the same time, procurement determines the total addressable market (TAM) for enterprise drives to aid in negotiating the order split among suppliers, price and minimum volume.

The GDS shown in Figure 1-6 compares forecast drive demand and returns with supply commitments, on-hand inventory, pre-allocations and backlog at a weekly interval for a 22-week outlook period. The MRP output is the primary data source for demand. Procurement

adds a buffer of 20% to 30% to the MRP output, in large part because of TAM

requirements, supplier incentives and rebate programs. GSMs manually compile supply data from individual suppliers, and the analytics team compiles on-hand inventory, pre-allocations and backlog data from a number of different reports. GSMs update and review the GDS weekly as part of the overall inventory management process.

22 week outook

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-Figure 1-6: Sample section of Global Demand and Supply (GDS) document

1.1.4 Inventory Challenges at Dell-EMC

At the time Dell acquired EMC in 2016, EMC possessed substantial amounts of raw and

finished goods inventory. Reasons for this include greater emphasis on high margin sales,

limited work processes, hockey-stick demand, supply control challenges, and use of a hybrid

build-to-stock (BTS) model for highly configurable products. Each of these issues is

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i) High margin sales: Dell-EMC ISG sells highly engineered, customizable storage

and networking systems that have high capital costs and low order volumes. Consequently, competitive barriers to entry are high and the current landscape has few competitors. This enables the business to command higher profit margins and invites product differentiation based on performance, reliability, security, ease of integration with customer IT infrastructure and other factors besides cost. The result is lower priority on inventory management and developing a low cost supply chain.

ii) Limited workprocesses: Unlike Dell's Client Solutions Group (CSG), the division responsible for desktops, PCs, notebooks, tablets and peripherals, ISG does not have standardized processes governing tactical demand planning, procurement and inventory management decisions. This results in suboptimal supply chain coordination, which can be attributed in part to inadequate key performance indicators (KPIs), and a lack of visibility of KPIs across departments.

iii) Hockey-stick demand Even though orders may not actually be placed for months after a customer commits to purchase, most orders end up being placed in the last month of Dell's fiscal quarter. This occurs because customers can negotiate better discounts nearer to the end of a quarter, as sales teams come under increasing pressure to meet their targets. The result is a demand profile that resembles a hockey stick. In a production model with short delivery lead times and relatively long component lead times, sufficient inventory must be on-hand at the time of order placement to avoid significant delays. Since it is difficult to accurately forecast for hockey-stick demand driven by a small number of large orders, more inventory is held to buffer against variability in forecast errors.

iv) Supply control challenges (HDD/SSD) Hard drive inventory is vendor managed, but supplier contracts mandate that inventory ownership be transferred to ISG at either the end of the calendar quarter or calendar month, depending on the particular supplier. This incentivizes suppliers to push more inventory into vendor hubs than is actually required. In addition, Dell's fiscal quarters are offset

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one month from drive suppliers, which use calendar quarters, as shown in Figure 1-7. This creates inventory risk as drive purchase commitments for an upcoming calendar quarter must be made one-month before the quarter start date, and therefore without actual demand data from the last month of the prior fiscal quarter. It is especially problematic since the majority of quarterly demand occurs in the last month, and a significant portion of that in the last week.

Calendar Quarters

CQ1

CQ2

CQ3

CQ4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

FQ4

FQ1

FQ2

FQ3

FQ4

Dell Fiscal Quarters

Figure 1-7: Comparison of Dell fiscal quarters and calendar quarters

v) Use ofhybrid BTS modet Until mid-2017, systems were partially assembled and stocked, with the remaining assembly taking place after placement of a customer order. Since ISG's products are highly customizable and have no pre-specified set of end products that limits the customer's choice, reconfiguration was often necessary to meet specific order requirements. Although this model enables faster order fulfillment, it relies on accurate demand forecasting because of the high cost of carrying excess finished goods inventory. Since it is difficult to accurately forecast demand for systems that can be mass customized, more finished goods inventory is required. This translates into increased component level inventory because resource pooling is prevented, resulting in decreased inventory utilization.

In response, Dell moved to drastically cut inventory at EMC by reducing purchases, changing sales incentive structures to encourage more uniform demand and transitioning from the hybrid BTS model to a configure-to-order (CTO) model in mid-2017. However, inventory levels rose significantly through 2018 in response to a large order backlog caused by part shortages. Dell's inventory management teams have engaged in a number of projects with ISG to improve inventory management. Project focus areas include component life cycle management, ageing inventory, forecast horizon, SKU proliferation and forecast

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accuracy for hard drive returns. Note that as of the end of the internship, all of these projects were still underway and potential benefits remain to be realized.

1.2 Project Overview

1.2.1 Problem Statement

Historically, ISG has maintained large inventory buffers to deal with high demand

uncertainty and minimize part shortages. High product configurability and complex product structures continue to present challenges to effectively managing component inventory. In addition, many supply and demand planning decisions are contextual rather than process driven, making it difficult to understand precisely how inventory level is influenced by its independent variables.

Though Dell reduced inventory at ISG through 2017, it did not incorporate a

comprehensive approach to ensure that cycle service levels would not be adversely impacted. The subsequent increase in inventory risk coupled with market tightness in FQ4 of 2018 led to part shortages and a large order backlog as described in Section 1.1.4. In addressing this problem, worldwide inventory levels rose by 6 2% in 2018, with hard drives accounting for

40% of that increase.

Several potential initiatives were identified which could be pursued to help address these challenges. Based upon the charge to develop an initiative which is orthogonal to existing Dell inventory management initiatives, this project was scoped to focus on addressing the challenge of inventory modeling and control. The objective of the research work

documented in this thesis was to develop a set of dynamic inventory policies to enable inventory reduction at ISG while maintaining or improving cycle service levels.

1.2.2 Scope

The scope of this project is circumscribed in several dimensions.

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As discussed in Chapter 1, this research focuses specifically on hard drives, although the concepts outlined in the thesis are applicable to other components. The rationale for this scope is three-fold. First, drive inventory composes almost half of total on-hand inventory value, more than any other component. Therefore, drives present the largest financial opportunity for inventory reduction. Second, drives account for most of ISG's product upgrade and customer service revenue, suggesting that effective drive inventory management has the added potential to improve product life cycle management and long term customer relationships. Finally, drives are managed by dedicated procurement and demand planning teams, making it easier to align stakeholder interests and obtain relevant data for analysis.

It is first important to recognize that inventory is a product of supply chain system design. Although this project focuses on inventory management through insights obtained from inventory modeling, a comprehensive approach must consider how to manage the work

processes and operating assumptions for the larger system. As such, the efficacy of a mathematically derived inventory policy depends on the degree to which existing inventory levels are managed based on the needs of the system, as opposed to a financial or other

target. While this project aims to provide a method for improving accuracy in predicting inventory requirements, the ability to further improve inventory performance is rooted in

system design changes.

This project considers all active and end-of-life (EOL) drives, except new drives undergoing engineering qualification testing. Active drives can be selected as an option during new solution configuration, product upgrades and for ongoing customer service requirements. In general, EOL drives are not used in new solution offerings. During qualification, engineering teams can procure tens of thousands of drives, and depending on results, many of them can either be released into inventory or returned to the manufacturer. The decision to exclude qualification drives from this research is based on the extreme uncertainty in predicting the timing of new product releases from manufacturers, time to plan and initiate a qualification program, quantity of drives required from engineering and outcome of qualification testing. Customer returns are also excluded from this research for two reasons:

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i) The volume and mix of drive returns each quarter can be considered a stochastic process in a manner similar to customer demand, albeit with different probability

distributions. In considering both material flows, the distributions for demand and returns must be convolved, a process normally approximated through numerical simulation. Since this must be done across a large number of CFGs, it is deemed to be too complex for a first-time inventory policy implementation.

ii) Incorporating customer returns invariably has the effect of reducing required on-hand inventory. Therefore, ignoring them creates an upper bound on inventory

and provides more confidence that desired service levels can be achieved. Even though more inventory is required, it is still expected that a significant reduction overall can be realized.

Drive inventory is analyzed at the CFG level and aggregated across all major holding sites to enable better performance of proposed inventory policies. This decision is predicated on the fact that predictive accuracy increases with higher levels of aggregation. Furthermore, it is consistent with the level of aggregation used in demand forecasting and procurement.

1.2.3 Technical Approach

Our approach is based on modeling the inventory behavior of the existing supply chain system, and generating inventory policies that more accurately reflect consumption within the system. In turn, these policies provide the potential to reduce inventory levels without significantly impacting service level. We emphasize the concept of service level throughout this thesis, as a quality measure related to missed shipments. One reason is that it enables a feedback loop when viewed together with inventory level. The other is that it can easily highlight the failure of financial targets to capture the needs and intrinsic variability of the system. If targets are set independently of system needs and inventory is subsequently adjusted based on the targets, one of the first indicators that the targets are below the needs of the system will be a rise in missed shipments.

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Our process for developing drive inventory policies is based on integrating as few data sources as possible and using data maintained in a centralized database to devise a practical and scalable solution. In all, four data feeds derived from three different sources are used, incorporating information on: (1) drive inventory, forecast demand and actual bookings for

all manufacturing plants, VMI hubs and GEMs; (2) sales orders involving drives for all customers; (3) master assignment of drives to PGs and CFGs; and (4) actual returns.

Three parameterized inventory policies have been built and tested in an iterative process. We modeled inventory, forecast and actual demand data at the CFG level, used demand

classification techniques to selectively adjust policy recommendations for certain drives and validated policy performance by adjusting input parameters. Our final choice was an order-up-to policy developed by fitting empirical distributions to historical forecast errors and using those distributions to recommend safety stock levels. The policy was applied to 111 CFGs representing 2,758 part numbers.

Note that high drive demand variability suggests the need for a dynamic approach to setting inventory policy. This means that any implemented policy should be evaluated at routine intervals and updated if required, as changes in ISG's supply chain and the enterprise drive market continue to impact demand profiles. Thus, consideration must be given to the length of time a policy is deployed before reevaluating its performance using more recent actuals data. We argue that inventory policies should be reevaluated on a quarterly basis because drive demand tends to follow a hockey-stick curve each quarter, and consecutive quarters have more highly correlated demand profiles than not.

The three aforementioned policies formed part of a software platform built to enable the simple development and testing of custom-designed policies, in alignment with our dynamic approach.

1.3Thesis Organization

The thesis is organized as follows. Chapter 2 provides a review of relevant studies in the existing literature. Chapter 3 presents the hypothesis underlying our decision to design and

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implement inventory policies. It includes an assessment of the current state of hard drive inventory management and the proposed future state. Chapter 4 presents the methodology used in developing the inventory policies. This includes identifying relevant data sources needed to build the policies (Section 4.1), conducting preliminary analysis to understand historical drive supply and demand behavior (Section 4.2) and detailing the policy design process (Section 4.3). The design process involves creating a software system architecture

(Section 4.3.1), formulating different policies (Section 4.3.2), and determining selection criteria (Section 4.3.3)

Chapter 5 evaluates inventory policy performance. The policies were evaluated using both training and validation sets (Section 5.1), and using strictly a training set, but with a wider range of data than in Section 5.1 (Section 5.2).

Chapter 6 discusses the suggested roadmap for implementing analytical inventory policies at ISG. It includes the process for conducting a pilot implementation (Section 6.1) and

considerations for developing a technically robust tool that relevant stakeholders can use and sustain (Section 6.2).

Chapter 7 presents research conclusions and recommendations to enable more efficient hard drive inventory management at ISG, including supply chain system design changes.

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2. Literature Review

2.1 Inventory Management in Configure-to-Order (CTO) Systems

As mentioned in Section 1.1.4, ISG moved to a CTO production system in mid-2017. A CTO system is a hybrid of make-to-stock and make-to-order operations: A set of

components (subassemblies) are built to stock, whereas the end products are assembled to order. This hybrid model is most suitable in an environment where the time it takes to assemble the end product is negligible, while the production/replenishment lead time for each component is much more substantial. By keeping inventory at the component level, customer orders can be filled quickly. On the other hand, postponing the final assembly until order arrival provides a high level of flexibility in terms of product variety, and also achieves resource pooling in terms of maximizing the usage of component inventory. Therefore, the CTO system is an ideal business process model that provides both mass customization and a quick response time to order fulfillment. Such a hybrid model is often referred to as an assemble- to-order (ATO) system in the research literature. In a standard ATO system, usually there is a small set of preconfigured end products from which customers must choose. Unlike an ATO system, a CTO system allows each customer to configure an end product by selecting a personalized subset of components which may be ordered in any arbitrary multiples [7].

There are many studies of ATO systems that differ quite substantially in the detailed modeling assumptions and approaches. For example, Hausman et al. (1998) and Zhang (1997) study periodic-review (discrete- time) models with multivariate normal demand and constant component replenishment lead times. [8,9] Song (forthcoming, 1998) studies continuous review models with multivariate compound Poisson demand and deterministic lead times. [10] Song et al. (1999), Glasserman and Wang (1998), and Wang (1988) also consider multivariate (compound) Poisson demand, but the supply process for each component is capacitated and modeled as a single-server queue. [11-13] Gallien and Wein (2001) consider un-capacitated lead times, focusing on a single demand stream and assuming order synchronization. [14] Cheung and Hausman (1995) also assume un-capacitated lead

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times in the context of a repair shop. They use a combination of order synchronization and disaggregation in their analysis. [7,15]

In terms of approaches, Song (forthcoming, 1998) and Song et al. (1999) focus on developing exact and approximate performance evaluation procedures that are

computationally efficient. [10,11] Xu (2001) explores the dependence structure in several ATO systems using stochastic comparison techniques, and develops bounds on key performance measures. [16] Glasserman and Wang (1998) study the inventory-lead time

tradeoffs using asymptotics of tail distributions, and derive a linear relationship between lead time and inventory in the limiting sense of high fill rates; see also Glasserman (1999). [12,17] Wang (1988) further applies this asymptotic result in an optimization problem to minimize

average inventory holding cost with a constraint on the order fill rate; also refer to Wang (2001). [13,18] Swaminathan and Tayur (1999) use stochastic programming models to study three different strategies at the assembly stage: utilizing component commonality,

postponement (the "vanilla box approach"), and integrating assembly task design and operations. [19,20]

ATO systems are complex to analyze, and most models in the literature are designed to deliver insights regarding the structure of optimal, typically stationary, inventory ordering policies or the performance of a given policy when the optimal policy is too difficult to

express analytically. Therefore, the literature typically considers problem sizes of no more than 10 products and 20 components. [20]

Only a small portion of the ATO literature explicitly models uncertainty in product configurations. In particular, Cheng et al. (2002) study the inventory-service tradeoff in a CTO setting with multiple demand classes. [7] Lu et al. (2003) study the impact of product structure, demand and lead time variability, and advance demand information on system performance. [21] However, both Cheng et al. (2002) and Lu et al. (2003) assume a

stationary base stock policy and stationary demand. Chen-Ritzo et al. (2010) use two, two-stage stochastic programs with recourse to determine an initial and adjusted component supply plan based on an initial demand plan and subsequent demand/supply review, respectively. [20]

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One major obstacle to incorporating prior research methodologies into this thesis relates to the presence of additional supply and demand streams, which are not discussed in the literature. This includes significant drive demand outside of new solution orders, i.e., customer service and product upgrades, and significant drive supply outside of new component purchases, i.e., customer returns. In theory, these streams can be analyzed separately and the results merged, but the inherent complexity in doing so alongside a multitude of implementation difficulties merits a simpler approach, such as the one described in Chapter 1.

2.2Demand Classification

In production and operations management, companies often have to deal with many different products, or SKUs. For the purposes of this research, an SKU is considered to be the same as a drive CFG. The production and inventory policies of these different SKUs are influenced by the characteristics of the product. Differences in annual sales volume,

predictability of demand, product value, or storage requirements might result in different production and inventory policies. As a consequence, companies that sell a wide variety of SKUs often struggle with the control of their production and inventory systems. Therefore, in real-life situations, it is generally seen as advantageous to distinguish a limited number of SKU classes based on the characteristics of these SKUs. This enables companies to make decisions on production strategy (e.g. make-to-stock or make-to-order), production and inventory management and customer service for entire SKU classes rather than for each product separately. [22] In this research, we use SKU classification to make inventory management decisions, which, in general set out to determine order/production quantities, reorder points and safety stock for different SKU classes.

In order to create a SKU classification, two simple questions need to be answered: how many classes are used and how are the borders between the classes determined. Various approaches and techniques exist to classify SKUs. [22]

Williams proposed a method of categorization of demand patterns based on an idea that is called variance partition, i.e., we split the variance of the demand during lead time into its

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constituent parts. [23] Categorization of the items takes place in accordance with the matrix shown in Figure 2-1, with the cutoff values being the result of managerial decision.

Demand size variability CV2

(X)

AL

Transaction

variability

_AL

Figure 2-1: Williams Categorization Scheme

In the matrix shown, A is the mean (Poisson) demand arrival rate, L the mean lead time duration and CV2 (x) the squared coefficient of variation of demand sizes. 1/A L indicates the number of lead times between successive demands, i.e., how often demand occurs or how intermittent demand is. The higher the ratio, the more intermittent demand is.

CV2 (x)/AL indicates how lumpy demand is. Lumpiness depends on both the intermittence and the variability of the demand size, when demand occurs. The higher the ratio the lumpier demand is: category Dl- sporadic (lumpy); category D2-highly sporadic (lumpy). In

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A C

D1

B

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that case we have few very irregular transactions accompanied by highly variable demand

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3. Hypothesis

The inventory policies proposed in this thesis are intended to provide some of the benefits of using an analytical approach to control inventory level, model tradeoffs between

inventory and service level, and guide purchasing and inventory management decisions. Specifically, it is hypothesized that the proposed inventory policies will do the following:

i) Enable a reduction of on-hand inventory sufficient to justify the use of a mathematical inventory management approach in ISG.

ii) Improve customer service through a reduction in part shortages.

iii) Indicate drive CFGs with significant aged inventory or rapid increases in inventory based on recommended inventory levels being far below existing levels; this might trigger the sale, reallocation or disposal of inventory if possible, or inform a decision to restrict future purchase quantities such that inventory bleeds down organically. iv) Provide procurement with an impulse to monitor inventory performance more

frequently and investigate root causes of inventory accumulation.

Furthermore, it is hypothesized that certain sensitivities will exist within the proposed inventory policies:

i) The performance of the proposed inventory policies will increase with a reduction in the number of active CFGs; spreading out demand over fewer CFGs - even though decreasing product customizability might reduce demand - usually has the effect of reducing demand variability.

ii) The relationship between inventory and service level for a given CFG can be more accurately modeled the closer its fitted empirical distribution approximates its true demand distribution.

iii) The performance of the proposed inventory policies is sensitive to the degree that forecast errors result from common-cause variation as opposed to forecast bias or inaccurately-predicted timing, magnitude and behavior of special causes. Better performance is achieved the greater the proportion of the error results from

common-cause variation.

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3.1 Current State Hard Drive Inventory Management

The relevant characteristics of the current state of hard drive inventory management at ISG can be characterized as follows:

i) The GDS is reviewed weekly to compare forecast demand to current inventory and incoming supply; if a low or negative inventory balance is projected in any period, procurement investigates whether changes to the CFG supply mix can be made, decreasing excess supply of certain CFGs in exchange for additional

supply of CFGs with a projected shortage.

ii) Demand can also be satisfied by reconfiguring assembled drives from one PG or CFG to another and retesting them; these decisions are made at the factory level as the GDS does not account for work-in-process (WIP) or finished goods inventory (FGI).

iii) ISG uses a fixed DSI metric for raw drives, which is based on a rolling four-week

- or 20 working days - demand period; the DSI number for a given week can be calculated as:

20 x Delta.

DSI. =

Eq. 1

i+4

JDeltai

i+1

where Deltag is the net available supply for week i as determined from the Delta field in Figure 1-6.

iv) A daily factory shortage report identifies component level shortages at each of the manufacturing sites; most shortages are not global so they can be alleviated through inter-organizational inventory transfers. Note that local shortages are not technically shortages, but can sometimes be documented as such.

v) The finance team sets quarterly inventory targets, but functional ownership of those inventory metrics recently changed:

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o Previously, the demand planning team had responsibility for all forms of inventory even though net on-hand inventory position is a function of the activities of several departments.

o Currently, procurement is accountable for overall inventory metrics at the same time that the finance team is leading an effort to segment the responsibilities for different activities in the procurement and demand planning process; for example, there will be separate groups responsible for raw, WIP and FGI, raw drive buffering and planning for certain types of returns.

vi) Short-to-backlog (STBL) reports are prepared weekly to indicate the resolution status of all components in a shortage state.

3.2 Proposed Future State Hard Drive Inventory Management

The proposed state for hard drive inventory management seeks to (1) generate insights from historical inventory data and use them to more accurately model inventory behavior, and (2) reduce and control inventory level while maintaining confidence that the impact on expected part shortages is well-understood. The relevant characteristics of the proposed system are the following:

i) Once SSD and HDD procurement completes their mid-term forecasts and before submitting them to suppliers for contract negotiation, the forecasts will be loaded into the inventory policy tool prior to netting out customer returns and current inventory; the recommended inventory levels output by the policy will be netted of customer returns and current inventory and then sent to suppliers. ii) The business intelligence (BI) team will work with procurement and planning teams

to maintain an optimal inventory policy for ISG, including tuning specific inventory policy parameters on a quarterly basis.

iii) Procurement and planning teams will continue to add demand buffers in the early implementation phase as inventory policy outputs are tested against actual inventory positions; as confidence grows in accurately predicting service level, buffering percentages will be reduced and eventually eliminated since the

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buffering process introduces a component of special cause forecast error variation.

iv) The GDS will continue to be reviewed on a weekly basis, except that current inventory levels will be compared to the values recommended by the selected inventory policy; in this sense, drive mix changes requested of suppliers will be based on achieving a specified service level rather than a DSI metric.

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4. Methodology

This chapter discusses the methodology used to develop the inventory policies.

Section 4.1 describes the types of data used in developing the inventory policies, including inventory and forecast data (Section 4.1.1), sales order data (Section 4.1.2) and master data

on drive part numbers and their PG/CFG assignments (Section 4.1.3).

Section 4.2 presents preliminary analysis conducted to provide inputs to the policy development process and visualize historical inventory behavior. This includes measuring actual demand (Section 4.2.1), constructing inventory control charts (Section 4.2.2) and grouping drives using demand classification techniques (Section 4.2.3).

Section 4.3 describes the policy design process. The design process involves creating a software system architecture (Section 4.3.1), formulating different policies (Section 4.3.2) and determining selection criteria (Section 4.3.3).

4.1 Data Overview

Our approach to developing drive inventory policies is based on integrating as few data sources as possible and using data maintained in a centralized database to devise a practical and scalable solution. In all, four data feeds derived from three different sources are used, incorporating information on: (1) drive inventory, forecast demand and actual bookings for all manufacturing plants, VMI hubs and GEMs; (2) sales orders involving drives for all customers; (3) master assignment of drives to PGs and CFGs; and (4) actual returns.

Inventory and demand forecast data are considered together since both come from the same data feed and are sourced from ISG's business data lake (BDL), a massive, easily accessible, centralized repository of large volumes of structured and unstructured data. [24] Information in the BDL is dynamically updated since it is tied to critical business operations. The specific inventory and demand forecast data used to construct the inventory policies are discussed in

Section 4.1.1.

Designing and Implementing Hard Drive Inventory Policies for Enterprise Computing Solutions