Selected Publications

We quantify the bullwhip effect (which measures how the variance of replenishment orders is amplified as the orders move up the supply chain) when both random demands and random lead times are estimated using the industrially popular moving average forecasting method. We assume that the lead times constitute a sequence of independent identically distributed random variables and the correlated demands are described by a first-order autoregressive process. We obtain an expression that reveals the impact of demand and lead time forecasting on the bullwhip effect. We draw a number of conclusions on the bullwhip behaviour with respect to the demand auto-correlation and the number of past lead times and demands used in the forecasts. We find the maxima and minima in the bullwhip measure as a function of the demand auto-correlation.
Accepted for publication in the Omega: The International Journal of Management Science, 2019

The direct-from-model and tool-less manufacturing process of 3D printing (3DP) embodies a general-purpose technology, facilitating capacity sharing and outsourcing. Starting from a case study of a 3DP company (Shapeways) and a new market entrant (Panalpina), we develop dynamic practices for partial outsourcing in build-to-model manufacturing. We propose a new outsourcing scheme, bidirectional partial outsourcing (BPO), where 3D printers share capacity by alternating between the role of outsourcer and subcontractor based on need. Coupled with order book smoothing (OBS), where orders are released gradually to production, this provides 3D printers with two distinct ways to manage demand variability. By combining demand and cost field data with an analytical model, we find that BPO improves 3DP cost efficiency and delivery performance as the number of 3DP firms in the network increases. OBS is sufficient for an established 3D printer, when alternatives to in-house manufacturing are few, or of limited capacity. Nevertheless, OBS comes at the cost of reduced responsiveness, whereas BPO shifts the cost and delivery performance frontier. Our analysis shows how BPO combined with OBS makes 3DP companies more resilient to downward movements in both demand and price levels.
Accepted for publication in the Journal of Operations Management, 2019

We investigate the dynamics of a closed-loop supply chain with first-order auto-regressive (AR(1)) demand and return processes. We assume these two processes are cross-correlated. The remanufacturing process is subject to a random triage yield. Remanufactured products are considered as-good-as-new and used to partially satisfy market demand; newly manufactured products make up the remainder. We derive the optimal linear policy in our closed-loop supply chain setting to minimise the manufacturer’s inventory costs. We show that the lead-time paradox can emerge in many cases. In particular, the auto- and cross-correlation parameters and variances of the error terms in the demand and the returns, as well as the remanufacturing lead time, all influence the existence of the lead-time paradox. Finally, we propose managerial recommendations for manufacturers.
European Journal of Operational Research, 269 (1), 313–326, 2018

To avoid inventory risks, manufacturers often place rush orders with suppliers only after they receive firm orders from their customers (retailers). Rush orders are costly to both parties because the supplier incurs higher production costs. We consider a situation where the supplier’s production cost is reduced if the manufacturer can place some of its order in advance. In addition to the rush order contract with a pre-established price, we examine whether the supplier should offer advance-order discounts to encourage the manufacturer to place a portion of its order in advance, even though the manufacturer incurs some inventory risk. While the advance-order discount contract is Pareto-improving, our analysis shows that the discount contract cannot coordinate the supply chain. However, if the supplier imposes a pre-specified minimum order quantity requirement as a qualifier for the manufacturer to receive the advance-order discount, then such a combined contract can coordinate the supply chain. Furthermore, the combined contract enables the supplier to attain the first-best solution. We also explore a delegation contract that either party could propose. Under this contract, the manufacturer delegates the ordering and salvaging activities to the supplier in return for a discounted price on all units procured. We find the delegation contract coordinates the supply chain and is Pareto-improving. We extend our analysis to a setting where the suppliers capacity is limited for advance production but unlimited for rush orders. Our structural results obtained for the one-supplier-one-manufacturer case continue to hold when we have two manufacturers.
Production and Operations Management, 26 (12), 2175–2186, 2017

We study the impact of stochastic lead times with order crossover on inventory costs and safety stocks in the order-up-to (OUT) policy. To motivate our research we present global logistics data which violates the traditional assumption that lead time demand is normally distributed. We also observe that order crossover is a common and important phenomenon in real supply chains. We present a new method for determining the distribution of the number of open orders. Using this method we identify the distribution of inventory levels when orders and the work-in-process are correlated. This correlation is present when demand is auto-correlated, demand forecasts are generated with non-optimal methods, or when certain ordering policies are present. Our method allows us to obtain exact safety stock requirements for the so-called proportional order-up-to (POUT) policy, a popular, implementable, linear generalization of the OUT policy. We highlight that the OUT replenishment policy is not cost optimal in global supply chains, as we are able to demonstrate the POUT policy always outperforms it under order cross-over. We show that unlike the constant lead-time case, minimum safety stocks and minimal inventory variance do not always lead to minimum costs under stochastic lead-times with order crossover. We also highlight an interesting side effect of minimizing inventory costs under stochastic lead times with order crossover with the POUT policy—an often significant reduction in the order variance.
European Journal of Operational Research, 248, 473–486, 2016

Recent Publications

More Publications

. When the bullwhip effect is an increasing function of the lead time. 30th Production and Operations Management Society Annual Conference, Washington, USA, 2nd-6th May, 2019.

. Production optimization for short shelf-life products. 30th Production and Operations Management Society Annual Conference, Washington, USA, 2nd-6th May, 2019.

. Yield paradox in closed loop supply chains with auto- and cross-correlated demand and return processes. 30th Production and Operations Management Society Annual Conference, Washington, USA, 2nd-6th May, 2019.

. The inventory performance of forecasting methods: Evidence from the M3 competition data. International Journal of Forecasting, 35 (1), 251 - 265, 2019.

Preprint PDF

. Production optimization for short shelf-life products. Logistics and Operations Management Section Annual Conference, Cardiff Business School, 11th January, Cardiff, UK., 2019.

. The trials and tribulations of running a research group. Logistics and Operations Management Section Annual Conference, Cardiff Business School, 11th January, Cardiff, UK., 2019.

. How SpeedFactories help companies adapt to capricious consumers. Kellogg Insight, Editorial written by S. Waikar, 2018.

PDF

. How SpeedFactories help companies adapt to capricious consumers. IndustryWeek.com, Editorial written by S. Waikar, 2018.

PDF

. Avoiding the capacity cost trap: Three means of smoothing under cyclical production planning. International Journal of Production Economics, 201, 149-162, 2018.

Preprint PDF

. Understanding your supply chain: dynamic value stream mapping for business improvement. 25th International EurOMA Conference, Budapest, Hungary, 24th-26th June, 2018.

Preprint

Recent & Upcoming Talks

If you would like me to give a talk, please get in touch.

Discrete Control Theory
Mar 22, 2019 2:00 PM
Dual Sourcing and Smoothing Under Non-stationary Demand Time Series: Re-shoring with Speed Factories
Nov 6, 2018 12:05 PM

Recent Posts

More Posts

Recently I have been asked by my School, and my Research Group, to give a talk about how to write a good paper. Here are the notes of my talk, but please do not take them too seriously. There are many ways to write a good paper, this is just my personal viewpoint. Abstract for the talk.I consider the ingredients of a good paper. a) problem inspired by a practical setting b) a clear contribution c) a strong methodical background d) co-authors that contribute fully to the design of the research and its execution e) a complete analysis of the problem, even if it means discarding work or starting again f) incorporating feedback via lectures, seminars, and conference presentations g) a comprehensive review process, where all of the referees comments are fully explored and dealt with h) a considered approach where ideas are left to mature so a fresh perspective can be gained.

CONTINUE READING

Being a member of the Scientific Committee for Academic Conferences is an honor. I get to see some of the worlds best research before it is presented publically. However, this honor bestows upon me the responsibility to help promote the conferences. To this end please find below some information on some upcoming conferences: 26th European Operations Management Association Annual Conference, in Helsinki, Finland, 17th - 19th June 2019.International Society for Inventory Research, PhD Summer School in Leuven, Belgium, August 26th-30th, 2019.

CONTINUE READING

The SpeedFactory App, under development and hosted on this website, makes the news at Vlerick Business School in a nice article written by Professor Robert Boute.

CONTINUE READING

My current work-in-progress with Robert Boute (KULeuven) and Jan Van Mieghem (NorthWestern) on Speed Factories has been summarised into a nice article written by Sachin Waikar appearing in Kellogg Insight. This article has also been republished by IndustryWeek.com.

Postscript: Our Speed Factory article was voted one of the top 15 articles in Kellogg Insight for 2018! It was the only Operations Management article in the list.

CONTINUE READING

My current work-in-progress with Robert Boute (KULeuven) and Jan Van Mieghem (NorthWestern) on Speed Factories has been summarised into a nice article written by Sachin Waikar appearing in Kellogg Insight. This article has also been republished by IndustryWeek.com.

Postscript: Our Speed Factory article was voted one of the top 15 articles in Kellogg Insight for 2018! It was the only Operations Management article in the list.

CONTINUE READING

Projects

Forecasting and production planning at Yeo Valley

I am currently in the middle of a 2-year project aimed at improving the forcasting and production planning processes within Yeo Valley, one of the largest organic dairy producers in the UK.

Setting the cadence of your pacemaker: A lean workbook for reducing mura

This visual workbook shows the practical lean manager how to solve the bullwhip problem.

Teaching and Exec-Ed

I am currently teaching the following Masters courses at Cardiff University:

  • Operations Management
  • Project Management

In the past I have taught the following courses at Cardiff University:

  • Operations Analtyics (Masters)
  • Logistics and Transport Modeling (Undergraduate and Masters)
  • Supply Chain Modeling (Masters, service teaching for the Mathematics Department)
  • Operations Analysis (MBA, Exec MBA, and Part-time MBA)
  • Lean Operations (Exec MBA, and Part-time MBA)

I have also taught the following courses at Boston University, USA:

  • Project Management (Undergraduate)
  • Global Services and Supply Chain Management (Masters)
  • Quantitative and Qualitative Decision Making (Online Masters)

I also deliver exec-ed training, including:

  • Lexmark (Supply Chain Dynamics)
  • Yeo Valley (Dynamic Value Stream Mapping)
  • UK Intellectual Property Office (Operations Management)
  • ACME Automotive Industry of India (Dynamic Value Stream Mapping)

I have recently developed a 1-2 day course for Exec-Ed delivery entitled “Setting the cadence of your pacemaker”. The course shows you how to use dynamic value stream mapping to solve the bullwhip problem. Topics covered include: replenishment strategy selection, forecasting, designing replenishment decisions, detailed scheduling, and supplier MRP. If you are interested in this type of training please contact me.

Apps & Add-ins

Shiny Apps

Click here to be re-directed to a web-based Shiny App designed to support the SpeedFactory paper that I am currently writing. Please note this app is presently work-in-progress.

Excel Add-ins

Many of the mathematical functions required in operations management (OM) scenarios are not available in Microsoft Excel. To address this issue, I have created an Add-in that adds some OM functionality to Excel. The Add-in can be downloaded here.

To install this Add-in:

  1. Save the file onto your computer.
  2. Open Excel.
  3. Select Files/Options/Add-in/Manage Add-ins.
  4. Click ‘Go’.
  5. Browse to the folder where you saved the Add-in.
  6. Select the .xla file.
  7. Confirm the Operations Analysis Add-in is ticked in list of available Add-ins.

When you have done this, the following functions should now be available in Excel:

=InvLossFun(x)

Gives the inverse of the standard normal distribution function evaluated at x. This is a useful function when determining safety stock levels when net stock levels are normally distributed.

=LambertW(mode,z)

Gives the real solutions to the Lambert W function on the principle branch (when mode = 1) and the alternative branch (when mode = -1), evaluated at z. This function is useful for identifying stability boundaries (see Warburton et al. (2004)) and bullwhip expressions (see Warburton and Disney (2007)) in continuous time systems and also for identifying the Net Present Value of the cash flows in the EOQ problem (see Disney and Warburton (2013).

=CBk(phi_range,theta_range,k)

Calculates a critical bullwhip condition for ARMA(p,q) demand in the Order-Up-To policy with a lead-time of k periods. If CBk is positive bullwhip is generated. If CBk is negative bullwhip is avoided. This criteria even works with non-stationary demand. The maximum allowable k is set to 100 as otherwise it slows up the computer. More information can be found in Gaalman et al., (2018).

  • phi_range is an ordered list of the auto-regressive components of demand process.
  • theta_range is an ordered list of the moving components of the demand process.
  • k is the lead-time (without the sequence of events delay).

=DampedTrend(range, alpha, beta, phi, Tp, WIPQuery)

Calculates the Damped Trend forecast with a smoothing constant for the level of alpha, a smoothing constant for the trend of beta, a damping parameter of phi. More information can be found in Li and Disney, (2015).

  • range is the demand data that is to be forecasted.
  • alpha is the smothing constant used to predict the level.
  • beta is the smoothing constant used to predict the trend.
  • phi is the is the damping co-efficient that shapes the future demand projections.
  • WIPQuery. If WIPQuery is False then the Tp+1 period ahead forecast is calculated. If WIPQuery is True then the forecast calculated the sum of the forecasts over the next Tp periods.
  • Tp the forecast horizon over which you forecast. In the order-up-to policy Tp is the lead-time (without the sequence of events delay)

Note: When phi = 1, Holts forecasts are generated. When beta = 0, exponential smoothing forecasts are generated.

=Fillrate(mu1,sigma1,mu2,sigma2,rho)

Calculates the fill rate when demand and inventory is normally distributed. Both demand and inventory can be correlated, cross-correlated, and possibly negative in some periods which makes this calculation much more robust than many other other fillrate formulas. More information can be found in Disney et al., (2015).

  • mu1 is the mean of the opening inventory (opening inventory = previous closing inventory + demand),
  • sigma1 is the standard deviation of the opening inventory,
  • mu2 is the mean demand,
  • sigma2 is the standard deviation of demand and
  • rho is the Pearson Correlation Coefficient between the opening inventory and demand.

=FillrateInv(mu1,sigma1,mu2,sigma2,rho,FR,openTNS)

Calculates the safety stock required to achive a target fill rate when demand and inventory is normally distributed. Both demand and inventory can be correlated, cross-correlated, and possibly negative in some periods which makes this calculation much more robust than many other other fill rate formulas. More information can be found in Disney et al., (2015).

  • mu1 is the mean of the opening inventory (opening inventory = previous closing inventory + demand).
  • sigma1 is the standard deviation of the opening inventory.
  • mu2 is the mean demand.
  • sigma2 is the standard deviation of demand.
  • rho is the Pearson Correlation Coefficient between the opening inventory and demand.
  • FR is the targt fill rate that you wish to receive.
  • openTNS is the current safety stock (target net stock) used in the opening inventory data.

I am keen to add more functionality to my Operations Analysis Add-in. If you have an idea of something useful to add, please contact me.

CV

Please click here for a summary one page CV. My complete CV and full publication list is available here.

Contact

  • DisneySM@cardiff.ac.uk
  • +44 2920 876310
  • S07, Aberconway Building, Colum Drive, Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom.
  • My regular office hours are 9:30 to 11:30 on Wednesdays. Alternatively, you may email for an appointment.