Harnessing supply chain reliability by reducing lead time variability White Paper

Transcription

1 Harnessing supply chain reliability by reducing lead time variability White Paper

2 Table of contents 01. Executive summary 02. Background 03. Increasing the value of the supply chain by managing variances 04. Metrics for Lead Time Variability in Supply Chains 05. Financial implications of Lead Time Variability 06. The end goal creating consistent lead times 07. Conclusion 08. Authors 09. References

3 01. Executive summary Basic forms of statistics have been used since the beginning of civilization. While their original scope was limited primarily to data for taxation or military purposes, the early 18th century brought with it new probability theory that positioned statistics as a way of predicting chance and certainty. By the late 20th century, GE and Motorola had implemented Six Sigma management techniques to improve quality by minimizing variability in manufacturing and business processes. This has raised many questions for our industry. Namely, can these empirical and statistical methods be used to enhance supply chain reliability? Can the probability of shipment arrival times be improved? And, how would such statistical parameters impact overall supply chain costs? Today s global companies operate within complex supply chains that require a coordinated flow of information, services, goods and payments within and across international boundaries. Companies are dependent on receiving their order on a set date and time. Any alteration to these expectations can negatively impact the supplier. Cargo that arrives too late or too early can lead to financial repercussions such as increased costs. As a result, On Time Shipments are key. because decisions are often made on forecasts alone. This makes the entire supply chain vulnerable to unexpected events. The challenge here is supply chain lead time variability. Most studies on lead time management focus on the manufacturing firm s internal processes - with very little or no reference to the inbound logistics (Garg, Vishwanandham and Narahari 2006). The purpose of this paper is to examine the effects of lead time variability on inbound logistics performance. Inbound Logistics Performance can be measured by looking at the production lead time, shipping lead time, customs brokerage turnaround timelines as well as the speed of receipt and inspection of goods. However, the ideal just in time arrival is nearly impossible to achieve due to these multiple factors. Only through root-cause analysis and metrics of measure can organizations truly understand the sources of lead time variability across their supply chain. 03. Increasing the value of the supply chain by managing variances Lead time is of great importance to the delivery of products. It is a core parameter that varies and influences the supply chain and the respective partners within it. Most supply chains are inconsistent and often excessively long. For many companies, On Time Shipments is defined as the ability to fulfill the shipments by the due date. The American Production and Control Society (APICS) defines it as delivery made on the designated due date(s). A 2016 Warehouse Education Research Council (WERC) Survey found that On Time Shipments is the number one metric that many companies look for, and that these companies expect On Time Shipments of 98.3% i.e. fulfillment of their shipment orders within the period promised by their suppliers. An equally important measure is supply chain reliability. Supply chain reliability means predicting the outcome of a process. But this cannot be measured without addressing variance. As with missed delivery dates, variability in lead time performance can also result in excess inventories and/or inventory shortages; both of which can impact the bottom line. These outcomes in turn may cause reputational damage. Getting to grips with lead-time variability is therefore also of critical importance. This paper discusses the tangible and intangible influences of lead time variability on supply chains and demonstrates how statistical methodologies can help. 02. Background From a lead time perspective, organizations seek just-in-time capabilities from their suppliers to maintain their minimum inventory. But supply chain planners struggle to manage lead time The right approach is to better manage the inconsistencies by understanding variability as the norm rather than the exception. Variability cannot be eliminated entirely from supply chains but actively targeting the sources of variability and ways to address them are realistic goals. The role of supply chain management is to reduce variability while at the same time establishing synchronized and responsive processes for managing variability. Companies that do this effectively can gain significant advantage over their competitors. Just as a production manager aims to reduce process variation to improve quality, supply chain managers can use a lead time variance framework to: improve delivery performance, reduce early and late shipments, properly manage safety stock levels and ultimately reduce cost. Furthermore, by reducing lead time variability across multiple activities, overall responsiveness to customer requirements

4 can be improved while total supply chain costs are reduced (Heydri, Kazemzadeh and Chaharsooghi, 2008) 04. Metrics for Lead Time Variability in Supply Chains The Six Sigma route to quality control has emerged as a robust approach to driving variability out of the manufacturing processes and thus guaranteeing the reliability of products. Unlike manufacturing processes, supply chains are often measured in time i.e. on-time deliveries as opposed to physical dimension or quantity. In this context, (and as illustrated in the Graphs 1 and 2 below) the higher the sigma value, the less likely it is that delivery will take place within the required shipment window. The second metric to monitor is the delivery sharpness (DS). DS measures how close a customer order is delivered to the target date (Garg, Vishwanandham and Narahari 2006). In any setting, achieving superior delivery performance requires highly accurate deliveries. The Taguchi Capability Matrix (Boyles, 1991) emphasizes the importance of focusing on the target value. If the deliveries made are not in line with the target value, the costs to the company can increase. The aim of delivery performance is to achieve the target while reducing the variation in transit times. 05. Financial implications of Lead Time Variability For a holistic view of the total Supply Chain Cost for a given product, both cost and variance must be taken into consideration. A range of costs influence and contribute to the final cost of an item. These include: Purchase: buying cost per unit Ordering: a fixed cost to place, receive and process the goods Holding: cost to hold inventory, insurance cost, service cost or capital cost. Graph 1: Transit Time of Carrier 1 from Shanghai to Los Angeles in 2017 Cycle stock: the excess holding cost of the goods between the cycles of orders Pipeline inventory: the additional holding cost of goods in transit Safety stock: the cost of buffer stock needed to ensure the right service level The total cost equation is the sum of these cost factors: Graph 2: Transit Time of Carrier 2 from Shanghai to Los Angeles in 2017 Supply chain managers are tasked with anticipating, planning for, and reacting to all possible supply and demand scenarios. Lead time variability is therefore of key importance in all organizations. For every business process that forms part of the supply chain, delivery time of the product or service is an important characteristic. Variability in lead time affects almost all supply chain activities, so it is important to measure the delivery time capability and delivery time quality. Delivery Probability (DP) is one such metric that can help. DP is defined as the probability that a typical order will be delivered during a customer specified window (Garg, Vishwanandham and Narahari 2006). From a Six Sigma perspective, early and late deliveries are considered process defects. The probability of ontime delivery therefore demonstrates the benefit of the supply chain delivery process. Adopted from Dr. Chris Caplice s course at MITx- Supply Chain Fundamentals Careful examination of this equation reveals that safety stock contributes to the total cost equation and that safety stock itself is a product of 3 elements: excess holding cost (ce), safety factor (k) and standard deviation of the demand and lead time (σdl). In other words;

5 standard deviation (or variance) directly impacts the total cost. The example below further illustrates the impact of variance on the total cost: Consider the following 4 carriers: A, B, C and D along with their respective rates as per table 1. If cost is the only criteria being taken into account, then Carrier C is the best choice as it is the least expensive. Carrier Cost/TEU ($) Transit time (days) (miul) A B C D Table 4: demand and cost assumed numbers. Applying these numbers in the total cost equation reveals that the most cost-efficient carrier per unit of demand is in fact carrier A and not carrier C or B as we first thought (table 5). Carrier Cost/TEU ($) Table 1: Carrier cost per TEU A 2235 B 2155 C 2030 D 2380 Table 2 introduces the additional element of Transit Time. Factoring in both Cost and Transit time, Carrier B now appears to be the best option as it is only slightly more expensive than Carrier C and has the fastest transit time. Carrier Cost/TEU ($) Transit time (days) (miul) A B C D Table 2: Carrier cost (USD) and transit time (days) Table 3 introduces yet another key element, the standard deviation. Referring to the total cost equation, standard deviation (or variance) has an influence on total cost as well, so we should examine its influence on the cost. Carrier Cost/TEU ($) Transit time (days) (miul) Service accuracy (+- days from transit time) (sigmal) A B C D Table 3: Carrier cost (USD), transit time (days) and standard deviation of the transit time To determine which carrier is the best choice while taking into consideration cost, transit time, and lead time variance we apply the total cost equation with the data assumed from table 4: Carrier Logistic cost per item ($) A B C D Table 5: cost per item to move the goods on each carrier 06. The end goal creating consistent lead times There are a number of benefits that can be derived from analyzing and managing lead time variability. These include: better stock positioning, better cargo utilization, and a reduction in the total cost of inventory. To deliver these benefits, companies need to track, document and constantly consult with their vendors and carriers on their lead time variability (Tanai, Guiffrida, 2015). This is a data driven process, thus reaching out to the supply chain partners and vendors to act around the data is vital for success. Companies could adopt a scorecard approach to monthly reviews, by identifying outliers and holding vendors and partners accountable. Lead time variability can be reduced by monitoring lead time distribution charts and conducting lead time profiling i.e. understanding the probability of a shipment delivery arriving in a specific time window, and understanding the conformance level of the lead time with a given target. 07. Conclusion Supply chain managers are often tasked with the seemingly difficult challenge of managing uncertainty. The difficulty of this challenge can significantly affect a company s financial performance and its reputation. While empirical methods such as Set Dates, and Set Shipment Windows such as specifying Expected Delivery Dates or Expected Delivery Windows can aid in monitoring lead time variability, statistical methods can be used to identify the probability of a shipment arriving at the set date. In other words, statistics can drive better discipline in predicting the possible outcomes in the supply chain.

6 08. Authors Dr. Erez Agmoni is Head of Supply Chain Development for Damco Americas and is now based in New Jersey, USA. Erez has more than 20 years of international experience in supply chain, logistics, engineering and digital innovation which he applies in developing complex solutions that improve endto-end supply chains. 09. References Manav Jain is a Supply Chain Development consultant at Damco and is based in Florham Park, New Jersey. Manav has almost 10 years of experience in supply chain and logistic analytics and has delivered solutions to customers across the retail, lifestyle, FMCG and technology industries. Garg, D., Narahari, Y., & Viswanadham, N. (2006). Achieving sharp deliveries in supply chains through variance pool allocation. European Journal of Operational Research Boyles, R. (1991). The Taguchi Capability Index. Journal of Quality Technology Heydari, J., Baradaran Kazemzadeh, R., & Chaharsooghi, S. K. (2008). A study of lead time variation impact on supply chain performance. The International Journal of Advanced Manufacturing Technology Tanai, Y., & Guiffrida, A. L. (2015). Reducing the cost of untimely supply chain delivery performance for asymmetric Laplace distributed delivery. Applied Mathematical Modelling Damco is at the forefront of developing innovative supply chain solutions. We fuse our global network and depth of expertise with pioneering digital innovations to enable our customers to stay ahead. Our vision is to connect and simplify supply chains across the globe. We are experts in the field of complex, rapidly changing markets such as Fashion, Retail, Chemical, FMCG and Technology. With a presence in over 100 countries, employing more than 10,000 people worldwide, we combine global reach with depth of local understanding. In 2017 we reached a turnover of 2.7 billion US dollars, managed 664 thousand TEUs (twenty-foot equivalent units) of ocean freight and 206 thousand tons of air freight. We are proud to be a part of A.P. Moller Maersk. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior written permission of Damco International BV.